diff --git a/Chat_Bot Week4.ipynb b/Chat_Bot Week4.ipynb new file mode 100644 index 000000000..1573a27cc --- /dev/null +++ b/Chat_Bot Week4.ipynb @@ -0,0 +1,4365 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "P_QTQHHq4342" + }, + "outputs": [], + "source": [ + "import numpy\n", + "\n", + "import tensorflow\n", + "import random" + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "RFoJ82Lf5K4j" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import nltk\n", + "nltk.download('punkt')\n", + "from nltk.stem.lancaster import LancasterStemmer\n", + "stemmer = LancasterStemmer()" + ], + "metadata": { + "id": "FO6ClwGd46Ht", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "575b971d-f3f3-43c2-ecc5-d077eddde95d" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "[nltk_data] Downloading package punkt to /root/nltk_data...\n", + "[nltk_data] Unzipping tokenizers/punkt.zip.\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import json\n", + "\n", + "\n", + "json_file_path = \"/content/intents.json\"\n", + "\n", + "with open(json_file_path) as json_file:\n", + " data = json.load(json_file)\n" + ], + "metadata": { + "id": "Dp6FwiiO4-dq" + }, + "execution_count": 10, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "words = []\n", + "labels = []\n", + "docs_x = []\n", + "docs_y = []" + ], + "metadata": { + "id": "RIYdjslF5Qcx" + }, + "execution_count": 11, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "for intent in data['intents']:\n", + " for pattern in intent['patterns']:\n", + " wrds = nltk.word_tokenize(pattern)\n", + " words.extend(wrds)\n", + " docs_x.append(wrds)\n", + " docs_y.append(intent[\"tag\"])\n", + "\n", + " if intent['tag'] not in labels:\n", + " labels.append(intent['tag'])" + ], + "metadata": { + "id": "aetDZr5l7n9T" + }, + "execution_count": 12, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "words = [stemmer.stem(w.lower()) for w in words if w != \"?\"]\n", + "words = sorted(list(set(words)))\n", + "\n", + "labels = sorted(labels)" + ], + "metadata": { + "id": "nIXu0XZc8DfO" + }, + "execution_count": 13, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "training = []\n", + "output = []\n", + "\n", + "out_empty = [0 for _ in range(len(labels))]\n", + "\n", + "for x, doc in enumerate(docs_x):\n", + " bag = []\n", + "\n", + " wrds = [stemmer.stem(w.lower()) for w in doc]\n", + "\n", + " for w in words:\n", + " if w in wrds:\n", + " bag.append(1)\n", + " else:\n", + " bag.append(0)\n", + "\n", + " output_row = out_empty[:]\n", + " output_row[labels.index(docs_y[x])] = 1\n", + "\n", + " training.append(bag)\n", + " output.append(output_row)\n", + "\n", + "\n", + "training = numpy.array(training)\n", + "output = numpy.array(output)" + ], + "metadata": { + "id": "mCZmk41W8k-5" + }, + "execution_count": 14, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "!pip install tflearn" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "U7wwe4hZ8pJu", + "outputId": "1e1b9419-d937-4250-c654-cb8e83cf4dd0" + }, + "execution_count": 16, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting tflearn\n", + " Downloading tflearn-0.5.0.tar.gz (107 kB)\n", + "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/107.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m107.3/107.3 kB\u001b[0m \u001b[31m2.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from tflearn) (1.22.4)\n", + "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from tflearn) (1.16.0)\n", + "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from tflearn) (8.4.0)\n", + "Building wheels for collected packages: tflearn\n", + " Building wheel for tflearn (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for tflearn: filename=tflearn-0.5.0-py3-none-any.whl size=127283 sha256=a6f381c5e8399b87389ed4ee68a1595b8e5a28699b1ac506fe7833af19ae0709\n", + " Stored in directory: /root/.cache/pip/wheels/55/fb/7b/e06204a0ceefa45443930b9a250cb5ebe31def0e4e8245a465\n", + "Successfully built tflearn\n", + "Installing collected packages: tflearn\n", + "Successfully installed tflearn-0.5.0\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import tflearn" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IDIwBP4w9QzO", + "outputId": "23a5ccdf-2ec4-461e-fb52-889277544b6d" + }, + "execution_count": 17, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "WARNING:tensorflow:From /usr/local/lib/python3.10/dist-packages/tensorflow/python/compat/v2_compat.py:107: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "non-resource variables are not supported in the long term\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import tensorflow as tf\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense\n", + "\n", + "\n", + "model = Sequential([\n", + " Dense(8, input_shape=(len(training[0]),)),\n", + " Dense(8),\n", + " Dense(len(output[0]), activation=\"softmax\")\n", + "])\n", + "\n", + "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "model.fit(training, output, epochs=1000, batch_size=8)\n", + "\n", + "model.save('model.tflearn')\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "834kf2sG9oAs", + "outputId": "0587720f-cab1-42be-a961-5abc13494436" + }, + "execution_count": 25, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Train on 26 samples\n", + "Epoch 1/1000\n", + "26/26 [==============================] - 0s 9ms/sample - loss: 1.7451 - acc: 0.3077\n", + "Epoch 2/1000\n", + "26/26 [==============================] - 0s 547us/sample - loss: 1.7246 - acc: 0.3462\n", + "Epoch 3/1000\n", + "26/26 [==============================] - 0s 701us/sample - loss: 1.7090 - acc: 0.3462\n", + "Epoch 4/1000\n", + "26/26 [==============================] - 0s 455us/sample - loss: 1.6939 - acc: 0.3462\n", + "Epoch 5/1000\n", + "26/26 [==============================] - 0s 455us/sample - loss: 1.6792 - acc: 0.3462\n", + "Epoch 6/1000\n", + "26/26 [==============================] - 0s 431us/sample - loss: 1.6643 - acc: 0.3846\n", + "Epoch 7/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 1.6498 - acc: 0.3846\n", + "Epoch 8/1000\n", + "26/26 [==============================] - 0s 338us/sample - loss: 1.6356 - acc: 0.3846\n", + "Epoch 9/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 1.6215 - acc: 0.3846\n", + "Epoch 10/1000\n", + "26/26 [==============================] - 0s 413us/sample - loss: 1.6080 - acc: 0.4231\n", + "Epoch 11/1000\n", + "26/26 [==============================] - 0s 406us/sample - loss: 1.5936 - acc: 0.4231\n", + "Epoch 12/1000\n", + "26/26 [==============================] - 0s 536us/sample - loss: 1.5813 - acc: 0.4615\n", + "Epoch 13/1000\n", + "26/26 [==============================] - 0s 475us/sample - loss: 1.5654 - acc: 0.5000\n", + "Epoch 14/1000\n", + "26/26 [==============================] - 0s 521us/sample - loss: 1.5518 - acc: 0.5000\n", + "Epoch 15/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 1.5381 - acc: 0.5000\n", + "Epoch 16/1000\n", + "26/26 [==============================] - 0s 515us/sample - loss: 1.5239 - acc: 0.5000\n", + "Epoch 17/1000\n", + "26/26 [==============================] - 0s 395us/sample - loss: 1.5087 - acc: 0.5000\n", + "Epoch 18/1000\n", + "26/26 [==============================] - 0s 409us/sample - loss: 1.4943 - acc: 0.5000\n", + "Epoch 19/1000\n", + "26/26 [==============================] - 0s 453us/sample - loss: 1.4796 - acc: 0.5000\n", + "Epoch 20/1000\n", + "26/26 [==============================] - 0s 412us/sample - loss: 1.4645 - acc: 0.5769\n", + "Epoch 21/1000\n", + "26/26 [==============================] - 0s 416us/sample - loss: 1.4497 - acc: 0.5769\n", + "Epoch 22/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 1.4356 - acc: 0.5385\n", + "Epoch 23/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 1.4206 - acc: 0.5385\n", + "Epoch 24/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 1.4054 - acc: 0.5385\n", + "Epoch 25/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 1.3914 - acc: 0.5385\n", + "Epoch 26/1000\n", + "26/26 [==============================] - 0s 384us/sample - loss: 1.3770 - acc: 0.5385\n", + "Epoch 27/1000\n", + "26/26 [==============================] - 0s 417us/sample - loss: 1.3612 - acc: 0.5385\n", + "Epoch 28/1000\n", + "26/26 [==============================] - 0s 430us/sample - loss: 1.3468 - acc: 0.5385\n", + "Epoch 29/1000\n", + "26/26 [==============================] - 0s 399us/sample - loss: 1.3319 - acc: 0.5385\n", + "Epoch 30/1000\n", + "26/26 [==============================] - 0s 428us/sample - loss: 1.3163 - acc: 0.5385\n", + "Epoch 31/1000\n", + "26/26 [==============================] - 0s 402us/sample - loss: 1.3021 - acc: 0.5769\n", + "Epoch 32/1000\n", + "26/26 [==============================] - 0s 445us/sample - loss: 1.2863 - acc: 0.5769\n", + "Epoch 33/1000\n", + "26/26 [==============================] - 0s 360us/sample - loss: 1.2712 - acc: 0.5769\n", + "Epoch 34/1000\n", + "26/26 [==============================] - 0s 412us/sample - loss: 1.2558 - acc: 0.5769\n", + "Epoch 35/1000\n", + "26/26 [==============================] - 0s 410us/sample - loss: 1.2400 - acc: 0.5769\n", + "Epoch 36/1000\n", + "26/26 [==============================] - 0s 431us/sample - loss: 1.2245 - acc: 0.5769\n", + "Epoch 37/1000\n", + "26/26 [==============================] - 0s 429us/sample - loss: 1.2090 - acc: 0.5769\n", + "Epoch 38/1000\n", + "26/26 [==============================] - 0s 393us/sample - loss: 1.1923 - acc: 0.6154\n", + "Epoch 39/1000\n", + "26/26 [==============================] - 0s 514us/sample - loss: 1.1767 - acc: 0.6154\n", + "Epoch 40/1000\n", + "26/26 [==============================] - 0s 679us/sample - loss: 1.1609 - acc: 0.6538\n", + "Epoch 41/1000\n", + "26/26 [==============================] - 0s 505us/sample - loss: 1.1455 - acc: 0.6538\n", + "Epoch 42/1000\n", + "26/26 [==============================] - 0s 583us/sample - loss: 1.1291 - acc: 0.6538\n", + "Epoch 43/1000\n", + "26/26 [==============================] - 0s 428us/sample - loss: 1.1143 - acc: 0.6923\n", + "Epoch 44/1000\n", + "26/26 [==============================] - 0s 445us/sample - loss: 1.0985 - acc: 0.6923\n", + "Epoch 45/1000\n", + "26/26 [==============================] - 0s 398us/sample - loss: 1.0829 - acc: 0.6923\n", + "Epoch 46/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 1.0676 - acc: 0.6923\n", + "Epoch 47/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 1.0519 - acc: 0.6923\n", + "Epoch 48/1000\n", + "26/26 [==============================] - 0s 393us/sample - loss: 1.0366 - acc: 0.7308\n", + "Epoch 49/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 1.0210 - acc: 0.7308\n", + "Epoch 50/1000\n", + "26/26 [==============================] - 0s 425us/sample - loss: 1.0059 - acc: 0.7308\n", + "Epoch 51/1000\n", + "26/26 [==============================] - 0s 413us/sample - loss: 0.9903 - acc: 0.7308\n", + "Epoch 52/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 0.9756 - acc: 0.7308\n", + "Epoch 53/1000\n", + "26/26 [==============================] - 0s 554us/sample - loss: 0.9597 - acc: 0.7692\n", + "Epoch 54/1000\n", + "26/26 [==============================] - 0s 420us/sample - loss: 0.9455 - acc: 0.7692\n", + "Epoch 55/1000\n", + "26/26 [==============================] - 0s 413us/sample - loss: 0.9300 - acc: 0.7692\n", + "Epoch 56/1000\n", + "26/26 [==============================] - 0s 469us/sample - loss: 0.9167 - acc: 0.7692\n", + "Epoch 57/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 0.9017 - acc: 0.7692\n", + "Epoch 58/1000\n", + "26/26 [==============================] - 0s 378us/sample - loss: 0.8889 - acc: 0.7692\n", + "Epoch 59/1000\n", + "26/26 [==============================] - 0s 383us/sample - loss: 0.8739 - acc: 0.7692\n", + "Epoch 60/1000\n", + "26/26 [==============================] - 0s 375us/sample - loss: 0.8605 - acc: 0.7692\n", + "Epoch 61/1000\n", + "26/26 [==============================] - 0s 444us/sample - loss: 0.8466 - acc: 0.8077\n", + "Epoch 62/1000\n", + "26/26 [==============================] - 0s 449us/sample - loss: 0.8330 - acc: 0.8462\n", + "Epoch 63/1000\n", + "26/26 [==============================] - 0s 428us/sample - loss: 0.8188 - acc: 0.8462\n", + "Epoch 64/1000\n", + "26/26 [==============================] - 0s 417us/sample - loss: 0.8053 - acc: 0.8462\n", + "Epoch 65/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 0.7920 - acc: 0.8846\n", + "Epoch 66/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 0.7772 - acc: 0.8846\n", + "Epoch 67/1000\n", + "26/26 [==============================] - 0s 415us/sample - loss: 0.7643 - acc: 0.8846\n", + "Epoch 68/1000\n", + "26/26 [==============================] - 0s 393us/sample - loss: 0.7514 - acc: 0.8462\n", + "Epoch 69/1000\n", + "26/26 [==============================] - 0s 389us/sample - loss: 0.7376 - acc: 0.8846\n", + "Epoch 70/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 0.7254 - acc: 0.8846\n", + "Epoch 71/1000\n", + "26/26 [==============================] - 0s 415us/sample - loss: 0.7126 - acc: 0.8846\n", + "Epoch 72/1000\n", + "26/26 [==============================] - 0s 421us/sample - loss: 0.7013 - acc: 0.8846\n", + "Epoch 73/1000\n", + "26/26 [==============================] - 0s 554us/sample - loss: 0.6887 - acc: 0.8846\n", + "Epoch 74/1000\n", + "26/26 [==============================] - 0s 387us/sample - loss: 0.6770 - acc: 0.8846\n", + "Epoch 75/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 0.6653 - acc: 0.8846\n", + "Epoch 76/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 0.6537 - acc: 0.8846\n", + "Epoch 77/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 0.6424 - acc: 0.8846\n", + "Epoch 78/1000\n", + "26/26 [==============================] - 0s 370us/sample - loss: 0.6306 - acc: 0.8846\n", + "Epoch 79/1000\n", + "26/26 [==============================] - 0s 372us/sample - loss: 0.6197 - acc: 0.8846\n", + "Epoch 80/1000\n", + "26/26 [==============================] - 0s 416us/sample - loss: 0.6079 - acc: 0.8846\n", + "Epoch 81/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 0.5966 - acc: 0.8846\n", + "Epoch 82/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 0.5860 - acc: 0.8846\n", + "Epoch 83/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 0.5753 - acc: 0.8846\n", + "Epoch 84/1000\n", + "26/26 [==============================] - 0s 408us/sample - loss: 0.5640 - acc: 0.8846\n", + "Epoch 85/1000\n", + "26/26 [==============================] - 0s 408us/sample - loss: 0.5542 - acc: 0.8846\n", + "Epoch 86/1000\n", + "26/26 [==============================] - 0s 416us/sample - loss: 0.5434 - acc: 0.8846\n", + "Epoch 87/1000\n", + "26/26 [==============================] - 0s 464us/sample - loss: 0.5332 - acc: 0.9231\n", + "Epoch 88/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 0.5229 - acc: 0.9231\n", + "Epoch 89/1000\n", + "26/26 [==============================] - 0s 410us/sample - loss: 0.5125 - acc: 0.9231\n", + "Epoch 90/1000\n", + "26/26 [==============================] - 0s 455us/sample - loss: 0.5027 - acc: 0.9231\n", + "Epoch 91/1000\n", + "26/26 [==============================] - 0s 430us/sample - loss: 0.4932 - acc: 0.9231\n", + "Epoch 92/1000\n", + "26/26 [==============================] - 0s 476us/sample - loss: 0.4835 - acc: 0.9231\n", + "Epoch 93/1000\n", + "26/26 [==============================] - 0s 533us/sample - loss: 0.4742 - acc: 0.9231\n", + "Epoch 94/1000\n", + "26/26 [==============================] - 0s 601us/sample - loss: 0.4656 - acc: 0.9231\n", + "Epoch 95/1000\n", + "26/26 [==============================] - 0s 517us/sample - loss: 0.4566 - acc: 0.9231\n", + "Epoch 96/1000\n", + "26/26 [==============================] - 0s 468us/sample - loss: 0.4478 - acc: 0.9615\n", + "Epoch 97/1000\n", + "26/26 [==============================] - 0s 434us/sample - loss: 0.4399 - acc: 0.9615\n", + "Epoch 98/1000\n", + "26/26 [==============================] - 0s 423us/sample - loss: 0.4313 - acc: 0.9615\n", + "Epoch 99/1000\n", + "26/26 [==============================] - 0s 560us/sample - loss: 0.4220 - acc: 0.9615\n", + "Epoch 100/1000\n", + "26/26 [==============================] - 0s 445us/sample - loss: 0.4149 - acc: 0.9615\n", + "Epoch 101/1000\n", + "26/26 [==============================] - 0s 466us/sample - loss: 0.4072 - acc: 0.9615\n", + "Epoch 102/1000\n", + "26/26 [==============================] - 0s 579us/sample - loss: 0.3992 - acc: 0.9615\n", + "Epoch 103/1000\n", + "26/26 [==============================] - 0s 368us/sample - loss: 0.3911 - acc: 0.9615\n", + "Epoch 104/1000\n", + "26/26 [==============================] - 0s 375us/sample - loss: 0.3836 - acc: 1.0000\n", + "Epoch 105/1000\n", + "26/26 [==============================] - 0s 384us/sample - loss: 0.3763 - acc: 1.0000\n", + "Epoch 106/1000\n", + "26/26 [==============================] - 0s 409us/sample - loss: 0.3682 - acc: 1.0000\n", + "Epoch 107/1000\n", + "26/26 [==============================] - 0s 425us/sample - loss: 0.3611 - acc: 1.0000\n", + "Epoch 108/1000\n", + "26/26 [==============================] - 0s 423us/sample - loss: 0.3541 - acc: 1.0000\n", + "Epoch 109/1000\n", + "26/26 [==============================] - 0s 428us/sample - loss: 0.3475 - acc: 1.0000\n", + "Epoch 110/1000\n", + "26/26 [==============================] - 0s 450us/sample - loss: 0.3410 - acc: 1.0000\n", + "Epoch 111/1000\n", + "26/26 [==============================] - 0s 468us/sample - loss: 0.3349 - acc: 1.0000\n", + "Epoch 112/1000\n", + "26/26 [==============================] - 0s 438us/sample - loss: 0.3283 - acc: 1.0000\n", + "Epoch 113/1000\n", + "26/26 [==============================] - 0s 394us/sample - loss: 0.3219 - acc: 1.0000\n", + "Epoch 114/1000\n", + "26/26 [==============================] - 0s 425us/sample - loss: 0.3165 - acc: 1.0000\n", + "Epoch 115/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 0.3108 - acc: 1.0000\n", + "Epoch 116/1000\n", + "26/26 [==============================] - 0s 449us/sample - loss: 0.3047 - acc: 1.0000\n", + "Epoch 117/1000\n", + "26/26 [==============================] - 0s 509us/sample - loss: 0.2989 - acc: 1.0000\n", + "Epoch 118/1000\n", + "26/26 [==============================] - 0s 578us/sample - loss: 0.2931 - acc: 1.0000\n", + "Epoch 119/1000\n", + "26/26 [==============================] - 0s 563us/sample - loss: 0.2876 - acc: 1.0000\n", + "Epoch 120/1000\n", + "26/26 [==============================] - 0s 384us/sample - loss: 0.2825 - acc: 1.0000\n", + "Epoch 121/1000\n", + "26/26 [==============================] - 0s 408us/sample - loss: 0.2770 - acc: 1.0000\n", + "Epoch 122/1000\n", + "26/26 [==============================] - 0s 370us/sample - loss: 0.2715 - acc: 1.0000\n", + "Epoch 123/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 0.2664 - acc: 1.0000\n", + "Epoch 124/1000\n", + "26/26 [==============================] - 0s 398us/sample - loss: 0.2606 - acc: 1.0000\n", + "Epoch 125/1000\n", + "26/26 [==============================] - 0s 423us/sample - loss: 0.2553 - acc: 1.0000\n", + "Epoch 126/1000\n", + "26/26 [==============================] - 0s 427us/sample - loss: 0.2507 - acc: 1.0000\n", + "Epoch 127/1000\n", + "26/26 [==============================] - 0s 396us/sample - loss: 0.2459 - acc: 1.0000\n", + "Epoch 128/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 0.2411 - acc: 1.0000\n", + "Epoch 129/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 0.2368 - acc: 1.0000\n", + "Epoch 130/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 0.2325 - acc: 1.0000\n", + "Epoch 131/1000\n", + "26/26 [==============================] - 0s 368us/sample - loss: 0.2274 - acc: 1.0000\n", + "Epoch 132/1000\n", + "26/26 [==============================] - 0s 385us/sample - loss: 0.2232 - acc: 1.0000\n", + "Epoch 133/1000\n", + "26/26 [==============================] - 0s 395us/sample - loss: 0.2189 - acc: 1.0000\n", + "Epoch 134/1000\n", + "26/26 [==============================] - 0s 420us/sample - loss: 0.2147 - acc: 1.0000\n", + "Epoch 135/1000\n", + "26/26 [==============================] - 0s 378us/sample - loss: 0.2105 - acc: 1.0000\n", + "Epoch 136/1000\n", + "26/26 [==============================] - 0s 439us/sample - loss: 0.2063 - acc: 1.0000\n", + "Epoch 137/1000\n", + "26/26 [==============================] - 0s 442us/sample - loss: 0.2024 - acc: 1.0000\n", + "Epoch 138/1000\n", + "26/26 [==============================] - 0s 416us/sample - loss: 0.1987 - acc: 1.0000\n", + "Epoch 139/1000\n", + "26/26 [==============================] - 0s 611us/sample - loss: 0.1946 - acc: 1.0000\n", + "Epoch 140/1000\n", + "26/26 [==============================] - 0s 428us/sample - loss: 0.1906 - acc: 1.0000\n", + "Epoch 141/1000\n", + "26/26 [==============================] - 0s 401us/sample - loss: 0.1871 - acc: 1.0000\n", + "Epoch 142/1000\n", + "26/26 [==============================] - 0s 540us/sample - loss: 0.1836 - acc: 1.0000\n", + "Epoch 143/1000\n", + "26/26 [==============================] - 0s 450us/sample - loss: 0.1796 - acc: 1.0000\n", + "Epoch 144/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 0.1764 - acc: 1.0000\n", + "Epoch 145/1000\n", + "26/26 [==============================] - 0s 584us/sample - loss: 0.1726 - acc: 1.0000\n", + "Epoch 146/1000\n", + "26/26 [==============================] - 0s 411us/sample - loss: 0.1693 - acc: 1.0000\n", + "Epoch 147/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 0.1661 - acc: 1.0000\n", + "Epoch 148/1000\n", + "26/26 [==============================] - 0s 412us/sample - loss: 0.1629 - acc: 1.0000\n", + "Epoch 149/1000\n", + "26/26 [==============================] - 0s 391us/sample - loss: 0.1598 - acc: 1.0000\n", + "Epoch 150/1000\n", + "26/26 [==============================] - 0s 385us/sample - loss: 0.1565 - acc: 1.0000\n", + "Epoch 151/1000\n", + "26/26 [==============================] - 0s 395us/sample - loss: 0.1535 - acc: 1.0000\n", + "Epoch 152/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 0.1508 - acc: 1.0000\n", + "Epoch 153/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 0.1478 - acc: 1.0000\n", + "Epoch 154/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 0.1449 - acc: 1.0000\n", + "Epoch 155/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 0.1417 - acc: 1.0000\n", + "Epoch 156/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 0.1391 - acc: 1.0000\n", + "Epoch 157/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 0.1363 - acc: 1.0000\n", + "Epoch 158/1000\n", + "26/26 [==============================] - 0s 393us/sample - loss: 0.1339 - acc: 1.0000\n", + "Epoch 159/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 0.1314 - acc: 1.0000\n", + "Epoch 160/1000\n", + "26/26 [==============================] - 0s 551us/sample - loss: 0.1291 - acc: 1.0000\n", + "Epoch 161/1000\n", + "26/26 [==============================] - 0s 419us/sample - loss: 0.1268 - acc: 1.0000\n", + "Epoch 162/1000\n", + "26/26 [==============================] - 0s 376us/sample - loss: 0.1247 - acc: 1.0000\n", + "Epoch 163/1000\n", + "26/26 [==============================] - 0s 502us/sample - loss: 0.1226 - acc: 1.0000\n", + "Epoch 164/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 0.1205 - acc: 1.0000\n", + "Epoch 165/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 0.1184 - acc: 1.0000\n", + "Epoch 166/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 0.1164 - acc: 1.0000\n", + "Epoch 167/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 0.1144 - acc: 1.0000\n", + "Epoch 168/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 0.1126 - acc: 1.0000\n", + "Epoch 169/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 0.1105 - acc: 1.0000\n", + "Epoch 170/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 0.1087 - acc: 1.0000\n", + "Epoch 171/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 0.1070 - acc: 1.0000\n", + "Epoch 172/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 0.1052 - acc: 1.0000\n", + "Epoch 173/1000\n", + "26/26 [==============================] - 0s 375us/sample - loss: 0.1035 - acc: 1.0000\n", + "Epoch 174/1000\n", + "26/26 [==============================] - 0s 375us/sample - loss: 0.1019 - acc: 1.0000\n", + "Epoch 175/1000\n", + "26/26 [==============================] - 0s 454us/sample - loss: 0.0999 - acc: 1.0000\n", + "Epoch 176/1000\n", + "26/26 [==============================] - 0s 519us/sample - loss: 0.0983 - acc: 1.0000\n", + "Epoch 177/1000\n", + "26/26 [==============================] - 0s 498us/sample - loss: 0.0964 - acc: 1.0000\n", + "Epoch 178/1000\n", + "26/26 [==============================] - 0s 445us/sample - loss: 0.0948 - acc: 1.0000\n", + "Epoch 179/1000\n", + "26/26 [==============================] - 0s 428us/sample - loss: 0.0930 - acc: 1.0000\n", + "Epoch 180/1000\n", + "26/26 [==============================] - 0s 424us/sample - loss: 0.0915 - acc: 1.0000\n", + "Epoch 181/1000\n", + "26/26 [==============================] - 0s 415us/sample - loss: 0.0901 - acc: 1.0000\n", + "Epoch 182/1000\n", + "26/26 [==============================] - 0s 391us/sample - loss: 0.0883 - acc: 1.0000\n", + "Epoch 183/1000\n", + "26/26 [==============================] - 0s 401us/sample - loss: 0.0869 - acc: 1.0000\n", + "Epoch 184/1000\n", + "26/26 [==============================] - 0s 372us/sample - loss: 0.0855 - acc: 1.0000\n", + "Epoch 185/1000\n", + "26/26 [==============================] - 0s 387us/sample - loss: 0.0840 - acc: 1.0000\n", + "Epoch 186/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 0.0826 - acc: 1.0000\n", + "Epoch 187/1000\n", + "26/26 [==============================] - 0s 410us/sample - loss: 0.0814 - acc: 1.0000\n", + "Epoch 188/1000\n", + "26/26 [==============================] - 0s 593us/sample - loss: 0.0801 - acc: 1.0000\n", + "Epoch 189/1000\n", + "26/26 [==============================] - 0s 448us/sample - loss: 0.0789 - acc: 1.0000\n", + "Epoch 190/1000\n", + "26/26 [==============================] - 0s 431us/sample - loss: 0.0777 - acc: 1.0000\n", + "Epoch 191/1000\n", + "26/26 [==============================] - 0s 395us/sample - loss: 0.0764 - acc: 1.0000\n", + "Epoch 192/1000\n", + "26/26 [==============================] - 0s 395us/sample - loss: 0.0753 - acc: 1.0000\n", + "Epoch 193/1000\n", + "26/26 [==============================] - 0s 383us/sample - loss: 0.0739 - acc: 1.0000\n", + "Epoch 194/1000\n", + "26/26 [==============================] - 0s 402us/sample - loss: 0.0728 - acc: 1.0000\n", + "Epoch 195/1000\n", + "26/26 [==============================] - 0s 419us/sample - loss: 0.0717 - acc: 1.0000\n", + "Epoch 196/1000\n", + "26/26 [==============================] - 0s 375us/sample - loss: 0.0704 - acc: 1.0000\n", + "Epoch 197/1000\n", + "26/26 [==============================] - 0s 360us/sample - loss: 0.0694 - acc: 1.0000\n", + "Epoch 198/1000\n", + "26/26 [==============================] - 0s 360us/sample - loss: 0.0684 - acc: 1.0000\n", + "Epoch 199/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 0.0673 - acc: 1.0000\n", + "Epoch 200/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 0.0662 - acc: 1.0000\n", + "Epoch 201/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 0.0652 - acc: 1.0000\n", + "Epoch 202/1000\n", + "26/26 [==============================] - 0s 386us/sample - loss: 0.0641 - acc: 1.0000\n", + "Epoch 203/1000\n", + "26/26 [==============================] - 0s 449us/sample - loss: 0.0632 - acc: 1.0000\n", + "Epoch 204/1000\n", + "26/26 [==============================] - 0s 395us/sample - loss: 0.0623 - acc: 1.0000\n", + "Epoch 205/1000\n", + "26/26 [==============================] - 0s 386us/sample - loss: 0.0613 - acc: 1.0000\n", + "Epoch 206/1000\n", + "26/26 [==============================] - 0s 386us/sample - loss: 0.0604 - acc: 1.0000\n", + "Epoch 207/1000\n", + "26/26 [==============================] - 0s 442us/sample - loss: 0.0594 - acc: 1.0000\n", + "Epoch 208/1000\n", + "26/26 [==============================] - 0s 428us/sample - loss: 0.0586 - acc: 1.0000\n", + "Epoch 209/1000\n", + "26/26 [==============================] - 0s 378us/sample - loss: 0.0577 - acc: 1.0000\n", + "Epoch 210/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 0.0567 - acc: 1.0000\n", + "Epoch 211/1000\n", + "26/26 [==============================] - 0s 579us/sample - loss: 0.0559 - acc: 1.0000\n", + "Epoch 212/1000\n", + "26/26 [==============================] - 0s 490us/sample - loss: 0.0551 - acc: 1.0000\n", + "Epoch 213/1000\n", + "26/26 [==============================] - 0s 594us/sample - loss: 0.0544 - acc: 1.0000\n", + "Epoch 214/1000\n", + "26/26 [==============================] - 0s 432us/sample - loss: 0.0537 - acc: 1.0000\n", + "Epoch 215/1000\n", + "26/26 [==============================] - 0s 417us/sample - loss: 0.0529 - acc: 1.0000\n", + "Epoch 216/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 0.0522 - acc: 1.0000\n", + "Epoch 217/1000\n", + "26/26 [==============================] - 0s 450us/sample - loss: 0.0516 - acc: 1.0000\n", + "Epoch 218/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 0.0508 - acc: 1.0000\n", + "Epoch 219/1000\n", + "26/26 [==============================] - 0s 373us/sample - loss: 0.0500 - acc: 1.0000\n", + "Epoch 220/1000\n", + "26/26 [==============================] - 0s 572us/sample - loss: 0.0493 - acc: 1.0000\n", + "Epoch 221/1000\n", + "26/26 [==============================] - 0s 427us/sample - loss: 0.0486 - acc: 1.0000\n", + "Epoch 222/1000\n", + "26/26 [==============================] - 0s 372us/sample - loss: 0.0479 - acc: 1.0000\n", + "Epoch 223/1000\n", + "26/26 [==============================] - 0s 383us/sample - loss: 0.0473 - acc: 1.0000\n", + "Epoch 224/1000\n", + "26/26 [==============================] - 0s 626us/sample - loss: 0.0467 - acc: 1.0000\n", + "Epoch 225/1000\n", + "26/26 [==============================] - 0s 424us/sample - loss: 0.0460 - acc: 1.0000\n", + "Epoch 226/1000\n", + "26/26 [==============================] - 0s 331us/sample - loss: 0.0454 - acc: 1.0000\n", + "Epoch 227/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 0.0449 - acc: 1.0000\n", + "Epoch 228/1000\n", + "26/26 [==============================] - 0s 340us/sample - loss: 0.0443 - acc: 1.0000\n", + "Epoch 229/1000\n", + "26/26 [==============================] - 0s 387us/sample - loss: 0.0436 - acc: 1.0000\n", + "Epoch 230/1000\n", + "26/26 [==============================] - 0s 383us/sample - loss: 0.0430 - acc: 1.0000\n", + "Epoch 231/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 0.0424 - acc: 1.0000\n", + "Epoch 232/1000\n", + "26/26 [==============================] - 0s 403us/sample - loss: 0.0419 - acc: 1.0000\n", + "Epoch 233/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 0.0414 - acc: 1.0000\n", + "Epoch 234/1000\n", + "26/26 [==============================] - 0s 370us/sample - loss: 0.0408 - acc: 1.0000\n", + "Epoch 235/1000\n", + "26/26 [==============================] - 0s 368us/sample - loss: 0.0403 - acc: 1.0000\n", + "Epoch 236/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 0.0397 - acc: 1.0000\n", + "Epoch 237/1000\n", + "26/26 [==============================] - 0s 394us/sample - loss: 0.0392 - acc: 1.0000\n", + "Epoch 238/1000\n", + "26/26 [==============================] - 0s 353us/sample - loss: 0.0388 - acc: 1.0000\n", + "Epoch 239/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 0.0382 - acc: 1.0000\n", + "Epoch 240/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 0.0377 - acc: 1.0000\n", + "Epoch 241/1000\n", + "26/26 [==============================] - 0s 338us/sample - loss: 0.0373 - acc: 1.0000\n", + "Epoch 242/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 0.0368 - acc: 1.0000\n", + "Epoch 243/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 0.0364 - acc: 1.0000\n", + "Epoch 244/1000\n", + "26/26 [==============================] - 0s 353us/sample - loss: 0.0360 - acc: 1.0000\n", + "Epoch 245/1000\n", + "26/26 [==============================] - 0s 523us/sample - loss: 0.0355 - acc: 1.0000\n", + "Epoch 246/1000\n", + "26/26 [==============================] - 0s 424us/sample - loss: 0.0351 - acc: 1.0000\n", + "Epoch 247/1000\n", + "26/26 [==============================] - 0s 410us/sample - loss: 0.0347 - acc: 1.0000\n", + "Epoch 248/1000\n", + "26/26 [==============================] - 0s 472us/sample - loss: 0.0343 - acc: 1.0000\n", + "Epoch 249/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 0.0339 - acc: 1.0000\n", + "Epoch 250/1000\n", + "26/26 [==============================] - 0s 458us/sample - loss: 0.0334 - acc: 1.0000\n", + "Epoch 251/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 0.0331 - acc: 1.0000\n", + "Epoch 252/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 0.0327 - acc: 1.0000\n", + "Epoch 253/1000\n", + "26/26 [==============================] - 0s 375us/sample - loss: 0.0323 - acc: 1.0000\n", + "Epoch 254/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 0.0319 - acc: 1.0000\n", + "Epoch 255/1000\n", + "26/26 [==============================] - 0s 387us/sample - loss: 0.0315 - acc: 1.0000\n", + "Epoch 256/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 0.0311 - acc: 1.0000\n", + "Epoch 257/1000\n", + "26/26 [==============================] - 0s 375us/sample - loss: 0.0308 - acc: 1.0000\n", + "Epoch 258/1000\n", + "26/26 [==============================] - 0s 492us/sample - loss: 0.0304 - acc: 1.0000\n", + "Epoch 259/1000\n", + "26/26 [==============================] - 0s 459us/sample - loss: 0.0301 - acc: 1.0000\n", + "Epoch 260/1000\n", + "26/26 [==============================] - 0s 408us/sample - loss: 0.0297 - acc: 1.0000\n", + "Epoch 261/1000\n", + "26/26 [==============================] - 0s 383us/sample - loss: 0.0294 - acc: 1.0000\n", + "Epoch 262/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 0.0291 - acc: 1.0000\n", + "Epoch 263/1000\n", + "26/26 [==============================] - 0s 406us/sample - loss: 0.0287 - acc: 1.0000\n", + "Epoch 264/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 0.0284 - acc: 1.0000\n", + "Epoch 265/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 0.0281 - acc: 1.0000\n", + "Epoch 266/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 0.0278 - acc: 1.0000\n", + "Epoch 267/1000\n", + "26/26 [==============================] - 0s 404us/sample - loss: 0.0275 - acc: 1.0000\n", + "Epoch 268/1000\n", + "26/26 [==============================] - 0s 387us/sample - loss: 0.0272 - acc: 1.0000\n", + "Epoch 269/1000\n", + "26/26 [==============================] - 0s 385us/sample - loss: 0.0269 - acc: 1.0000\n", + "Epoch 270/1000\n", + "26/26 [==============================] - 0s 420us/sample - loss: 0.0266 - acc: 1.0000\n", + "Epoch 271/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 0.0263 - acc: 1.0000\n", + "Epoch 272/1000\n", + "26/26 [==============================] - 0s 419us/sample - loss: 0.0260 - acc: 1.0000\n", + "Epoch 273/1000\n", + "26/26 [==============================] - 0s 427us/sample - loss: 0.0257 - acc: 1.0000\n", + "Epoch 274/1000\n", + "26/26 [==============================] - 0s 385us/sample - loss: 0.0255 - acc: 1.0000\n", + "Epoch 275/1000\n", + "26/26 [==============================] - 0s 421us/sample - loss: 0.0252 - acc: 1.0000\n", + "Epoch 276/1000\n", + "26/26 [==============================] - 0s 568us/sample - loss: 0.0249 - acc: 1.0000\n", + "Epoch 277/1000\n", + "26/26 [==============================] - 0s 550us/sample - loss: 0.0246 - acc: 1.0000\n", + "Epoch 278/1000\n", + "26/26 [==============================] - 0s 503us/sample - loss: 0.0244 - acc: 1.0000\n", + "Epoch 279/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 0.0241 - acc: 1.0000\n", + "Epoch 280/1000\n", + "26/26 [==============================] - 0s 384us/sample - loss: 0.0239 - acc: 1.0000\n", + "Epoch 281/1000\n", + "26/26 [==============================] - 0s 370us/sample - loss: 0.0236 - acc: 1.0000\n", + "Epoch 282/1000\n", + "26/26 [==============================] - 0s 377us/sample - loss: 0.0234 - acc: 1.0000\n", + "Epoch 283/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 0.0231 - acc: 1.0000\n", + "Epoch 284/1000\n", + "26/26 [==============================] - 0s 412us/sample - loss: 0.0229 - acc: 1.0000\n", + "Epoch 285/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 0.0227 - acc: 1.0000\n", + "Epoch 286/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 0.0224 - acc: 1.0000\n", + "Epoch 287/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 0.0222 - acc: 1.0000\n", + "Epoch 288/1000\n", + "26/26 [==============================] - 0s 671us/sample - loss: 0.0220 - acc: 1.0000\n", + "Epoch 289/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 0.0217 - acc: 1.0000\n", + "Epoch 290/1000\n", + "26/26 [==============================] - 0s 389us/sample - loss: 0.0215 - acc: 1.0000\n", + "Epoch 291/1000\n", + "26/26 [==============================] - 0s 512us/sample - loss: 0.0213 - acc: 1.0000\n", + "Epoch 292/1000\n", + "26/26 [==============================] - 0s 490us/sample - loss: 0.0211 - acc: 1.0000\n", + "Epoch 293/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 0.0209 - acc: 1.0000\n", + "Epoch 294/1000\n", + "26/26 [==============================] - 0s 483us/sample - loss: 0.0206 - acc: 1.0000\n", + "Epoch 295/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 0.0204 - acc: 1.0000\n", + "Epoch 296/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 0.0202 - acc: 1.0000\n", + "Epoch 297/1000\n", + "26/26 [==============================] - 0s 421us/sample - loss: 0.0200 - acc: 1.0000\n", + "Epoch 298/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 0.0198 - acc: 1.0000\n", + "Epoch 299/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 0.0196 - acc: 1.0000\n", + "Epoch 300/1000\n", + "26/26 [==============================] - 0s 394us/sample - loss: 0.0194 - acc: 1.0000\n", + "Epoch 301/1000\n", + "26/26 [==============================] - 0s 350us/sample - loss: 0.0192 - acc: 1.0000\n", + "Epoch 302/1000\n", + "26/26 [==============================] - 0s 420us/sample - loss: 0.0190 - acc: 1.0000\n", + "Epoch 303/1000\n", + "26/26 [==============================] - 0s 418us/sample - loss: 0.0189 - acc: 1.0000\n", + "Epoch 304/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 0.0187 - acc: 1.0000\n", + "Epoch 305/1000\n", + "26/26 [==============================] - 0s 350us/sample - loss: 0.0185 - acc: 1.0000\n", + "Epoch 306/1000\n", + "26/26 [==============================] - 0s 349us/sample - loss: 0.0183 - acc: 1.0000\n", + "Epoch 307/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 0.0182 - acc: 1.0000\n", + "Epoch 308/1000\n", + "26/26 [==============================] - 0s 356us/sample - loss: 0.0180 - acc: 1.0000\n", + "Epoch 309/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 0.0178 - acc: 1.0000\n", + "Epoch 310/1000\n", + "26/26 [==============================] - 0s 326us/sample - loss: 0.0177 - acc: 1.0000\n", + "Epoch 311/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 0.0175 - acc: 1.0000\n", + "Epoch 312/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 0.0173 - acc: 1.0000\n", + "Epoch 313/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 0.0172 - acc: 1.0000\n", + "Epoch 314/1000\n", + "26/26 [==============================] - 0s 387us/sample - loss: 0.0170 - acc: 1.0000\n", + "Epoch 315/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 0.0169 - acc: 1.0000\n", + "Epoch 316/1000\n", + "26/26 [==============================] - 0s 378us/sample - loss: 0.0167 - acc: 1.0000\n", + "Epoch 317/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 0.0166 - acc: 1.0000\n", + "Epoch 318/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 0.0164 - acc: 1.0000\n", + "Epoch 319/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 0.0163 - acc: 1.0000\n", + "Epoch 320/1000\n", + "26/26 [==============================] - 0s 329us/sample - loss: 0.0161 - acc: 1.0000\n", + "Epoch 321/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 0.0160 - acc: 1.0000\n", + "Epoch 322/1000\n", + "26/26 [==============================] - 0s 348us/sample - loss: 0.0158 - acc: 1.0000\n", + "Epoch 323/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 0.0157 - acc: 1.0000\n", + "Epoch 324/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 0.0155 - acc: 1.0000\n", + "Epoch 325/1000\n", + "26/26 [==============================] - 0s 372us/sample - loss: 0.0154 - acc: 1.0000\n", + "Epoch 326/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 0.0152 - acc: 1.0000\n", + "Epoch 327/1000\n", + "26/26 [==============================] - 0s 421us/sample - loss: 0.0151 - acc: 1.0000\n", + "Epoch 328/1000\n", + "26/26 [==============================] - 0s 445us/sample - loss: 0.0150 - acc: 1.0000\n", + "Epoch 329/1000\n", + "26/26 [==============================] - 0s 425us/sample - loss: 0.0148 - acc: 1.0000\n", + "Epoch 330/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 0.0147 - acc: 1.0000\n", + "Epoch 331/1000\n", + "26/26 [==============================] - 0s 355us/sample - loss: 0.0146 - acc: 1.0000\n", + "Epoch 332/1000\n", + "26/26 [==============================] - 0s 447us/sample - loss: 0.0145 - acc: 1.0000\n", + "Epoch 333/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 0.0143 - acc: 1.0000\n", + "Epoch 334/1000\n", + "26/26 [==============================] - 0s 368us/sample - loss: 0.0142 - acc: 1.0000\n", + "Epoch 335/1000\n", + "26/26 [==============================] - 0s 466us/sample - loss: 0.0141 - acc: 1.0000\n", + "Epoch 336/1000\n", + "26/26 [==============================] - 0s 370us/sample - loss: 0.0140 - acc: 1.0000\n", + "Epoch 337/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 0.0138 - acc: 1.0000\n", + "Epoch 338/1000\n", + "26/26 [==============================] - 0s 384us/sample - loss: 0.0137 - acc: 1.0000\n", + "Epoch 339/1000\n", + "26/26 [==============================] - 0s 386us/sample - loss: 0.0136 - acc: 1.0000\n", + "Epoch 340/1000\n", + "26/26 [==============================] - 0s 393us/sample - loss: 0.0135 - acc: 1.0000\n", + "Epoch 341/1000\n", + "26/26 [==============================] - 0s 391us/sample - loss: 0.0134 - acc: 1.0000\n", + "Epoch 342/1000\n", + "26/26 [==============================] - 0s 401us/sample - loss: 0.0133 - acc: 1.0000\n", + "Epoch 343/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 0.0131 - acc: 1.0000\n", + "Epoch 344/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 0.0130 - acc: 1.0000\n", + "Epoch 345/1000\n", + "26/26 [==============================] - 0s 389us/sample - loss: 0.0129 - acc: 1.0000\n", + "Epoch 346/1000\n", + "26/26 [==============================] - 0s 370us/sample - loss: 0.0128 - acc: 1.0000\n", + "Epoch 347/1000\n", + "26/26 [==============================] - 0s 424us/sample - loss: 0.0127 - acc: 1.0000\n", + "Epoch 348/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 0.0126 - acc: 1.0000\n", + "Epoch 349/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 0.0125 - acc: 1.0000\n", + "Epoch 350/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 0.0124 - acc: 1.0000\n", + "Epoch 351/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 0.0123 - acc: 1.0000\n", + "Epoch 352/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 0.0123 - acc: 1.0000\n", + "Epoch 353/1000\n", + "26/26 [==============================] - 0s 360us/sample - loss: 0.0122 - acc: 1.0000\n", + "Epoch 354/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 0.0121 - acc: 1.0000\n", + "Epoch 355/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 0.0120 - acc: 1.0000\n", + "Epoch 356/1000\n", + "26/26 [==============================] - 0s 349us/sample - loss: 0.0118 - acc: 1.0000\n", + "Epoch 357/1000\n", + "26/26 [==============================] - 0s 393us/sample - loss: 0.0118 - acc: 1.0000\n", + "Epoch 358/1000\n", + "26/26 [==============================] - 0s 395us/sample - loss: 0.0117 - acc: 1.0000\n", + "Epoch 359/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 0.0116 - acc: 1.0000\n", + "Epoch 360/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 0.0115 - acc: 1.0000\n", + "Epoch 361/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 0.0114 - acc: 1.0000\n", + "Epoch 362/1000\n", + "26/26 [==============================] - 0s 398us/sample - loss: 0.0113 - acc: 1.0000\n", + "Epoch 363/1000\n", + "26/26 [==============================] - 0s 611us/sample - loss: 0.0112 - acc: 1.0000\n", + "Epoch 364/1000\n", + "26/26 [==============================] - 0s 522us/sample - loss: 0.0111 - acc: 1.0000\n", + "Epoch 365/1000\n", + "26/26 [==============================] - 0s 637us/sample - loss: 0.0111 - acc: 1.0000\n", + "Epoch 366/1000\n", + "26/26 [==============================] - 0s 716us/sample - loss: 0.0110 - acc: 1.0000\n", + "Epoch 367/1000\n", + "26/26 [==============================] - 0s 605us/sample - loss: 0.0109 - acc: 1.0000\n", + "Epoch 368/1000\n", + "26/26 [==============================] - 0s 495us/sample - loss: 0.0108 - acc: 1.0000\n", + "Epoch 369/1000\n", + "26/26 [==============================] - 0s 521us/sample - loss: 0.0107 - acc: 1.0000\n", + "Epoch 370/1000\n", + "26/26 [==============================] - 0s 620us/sample - loss: 0.0106 - acc: 1.0000\n", + "Epoch 371/1000\n", + "26/26 [==============================] - 0s 686us/sample - loss: 0.0105 - acc: 1.0000\n", + "Epoch 372/1000\n", + "26/26 [==============================] - 0s 676us/sample - loss: 0.0104 - acc: 1.0000\n", + "Epoch 373/1000\n", + "26/26 [==============================] - 0s 675us/sample - loss: 0.0104 - acc: 1.0000\n", + "Epoch 374/1000\n", + "26/26 [==============================] - 0s 453us/sample - loss: 0.0103 - acc: 1.0000\n", + "Epoch 375/1000\n", + "26/26 [==============================] - 0s 469us/sample - loss: 0.0102 - acc: 1.0000\n", + "Epoch 376/1000\n", + "26/26 [==============================] - 0s 456us/sample - loss: 0.0101 - acc: 1.0000\n", + "Epoch 377/1000\n", + "26/26 [==============================] - 0s 459us/sample - loss: 0.0101 - acc: 1.0000\n", + "Epoch 378/1000\n", + "26/26 [==============================] - 0s 395us/sample - loss: 0.0100 - acc: 1.0000\n", + "Epoch 379/1000\n", + "26/26 [==============================] - 0s 499us/sample - loss: 0.0099 - acc: 1.0000\n", + "Epoch 380/1000\n", + "26/26 [==============================] - 0s 446us/sample - loss: 0.0098 - acc: 1.0000\n", + "Epoch 381/1000\n", + "26/26 [==============================] - 0s 438us/sample - loss: 0.0098 - acc: 1.0000\n", + "Epoch 382/1000\n", + "26/26 [==============================] - 0s 554us/sample - loss: 0.0097 - acc: 1.0000\n", + "Epoch 383/1000\n", + "26/26 [==============================] - 0s 538us/sample - loss: 0.0096 - acc: 1.0000\n", + "Epoch 384/1000\n", + "26/26 [==============================] - 0s 517us/sample - loss: 0.0096 - acc: 1.0000\n", + "Epoch 385/1000\n", + "26/26 [==============================] - 0s 605us/sample - loss: 0.0095 - acc: 1.0000\n", + "Epoch 386/1000\n", + "26/26 [==============================] - 0s 394us/sample - loss: 0.0094 - acc: 1.0000\n", + "Epoch 387/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 0.0093 - acc: 1.0000\n", + "Epoch 388/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 0.0093 - acc: 1.0000\n", + "Epoch 389/1000\n", + "26/26 [==============================] - 0s 396us/sample - loss: 0.0092 - acc: 1.0000\n", + "Epoch 390/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 0.0091 - acc: 1.0000\n", + "Epoch 391/1000\n", + "26/26 [==============================] - 0s 396us/sample - loss: 0.0091 - acc: 1.0000\n", + "Epoch 392/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 0.0090 - acc: 1.0000\n", + "Epoch 393/1000\n", + "26/26 [==============================] - 0s 581us/sample - loss: 0.0090 - acc: 1.0000\n", + "Epoch 394/1000\n", + "26/26 [==============================] - 0s 433us/sample - loss: 0.0089 - acc: 1.0000\n", + "Epoch 395/1000\n", + "26/26 [==============================] - 0s 443us/sample - loss: 0.0088 - acc: 1.0000\n", + "Epoch 396/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 0.0088 - acc: 1.0000\n", + "Epoch 397/1000\n", + "26/26 [==============================] - 0s 423us/sample - loss: 0.0087 - acc: 1.0000\n", + "Epoch 398/1000\n", + "26/26 [==============================] - 0s 348us/sample - loss: 0.0087 - acc: 1.0000\n", + "Epoch 399/1000\n", + "26/26 [==============================] - 0s 322us/sample - loss: 0.0086 - acc: 1.0000\n", + "Epoch 400/1000\n", + "26/26 [==============================] - 0s 417us/sample - loss: 0.0085 - acc: 1.0000\n", + "Epoch 401/1000\n", + "26/26 [==============================] - 0s 403us/sample - loss: 0.0085 - acc: 1.0000\n", + "Epoch 402/1000\n", + "26/26 [==============================] - 0s 404us/sample - loss: 0.0084 - acc: 1.0000\n", + "Epoch 403/1000\n", + "26/26 [==============================] - 0s 394us/sample - loss: 0.0084 - acc: 1.0000\n", + "Epoch 404/1000\n", + "26/26 [==============================] - 0s 319us/sample - loss: 0.0083 - acc: 1.0000\n", + "Epoch 405/1000\n", + "26/26 [==============================] - 0s 489us/sample - loss: 0.0082 - acc: 1.0000\n", + "Epoch 406/1000\n", + "26/26 [==============================] - 0s 548us/sample - loss: 0.0082 - acc: 1.0000\n", + "Epoch 407/1000\n", + "26/26 [==============================] - 0s 535us/sample - loss: 0.0081 - acc: 1.0000\n", + "Epoch 408/1000\n", + "26/26 [==============================] - 0s 491us/sample - loss: 0.0081 - acc: 1.0000\n", + "Epoch 409/1000\n", + "26/26 [==============================] - 0s 425us/sample - loss: 0.0080 - acc: 1.0000\n", + "Epoch 410/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 0.0080 - acc: 1.0000\n", + "Epoch 411/1000\n", + "26/26 [==============================] - 0s 464us/sample - loss: 0.0079 - acc: 1.0000\n", + "Epoch 412/1000\n", + "26/26 [==============================] - 0s 557us/sample - loss: 0.0079 - acc: 1.0000\n", + "Epoch 413/1000\n", + "26/26 [==============================] - 0s 597us/sample - loss: 0.0078 - acc: 1.0000\n", + "Epoch 414/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 0.0078 - acc: 1.0000\n", + "Epoch 415/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 0.0077 - acc: 1.0000\n", + "Epoch 416/1000\n", + "26/26 [==============================] - 0s 350us/sample - loss: 0.0077 - acc: 1.0000\n", + "Epoch 417/1000\n", + "26/26 [==============================] - 0s 353us/sample - loss: 0.0076 - acc: 1.0000\n", + "Epoch 418/1000\n", + "26/26 [==============================] - 0s 340us/sample - loss: 0.0076 - acc: 1.0000\n", + "Epoch 419/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 0.0075 - acc: 1.0000\n", + "Epoch 420/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 0.0075 - acc: 1.0000\n", + "Epoch 421/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 0.0074 - acc: 1.0000\n", + "Epoch 422/1000\n", + "26/26 [==============================] - 0s 448us/sample - loss: 0.0074 - acc: 1.0000\n", + "Epoch 423/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 0.0073 - acc: 1.0000\n", + "Epoch 424/1000\n", + "26/26 [==============================] - 0s 450us/sample - loss: 0.0073 - acc: 1.0000\n", + "Epoch 425/1000\n", + "26/26 [==============================] - 0s 589us/sample - loss: 0.0073 - acc: 1.0000\n", + "Epoch 426/1000\n", + "26/26 [==============================] - 0s 702us/sample - loss: 0.0072 - acc: 1.0000\n", + "Epoch 427/1000\n", + "26/26 [==============================] - 0s 765us/sample - loss: 0.0072 - acc: 1.0000\n", + "Epoch 428/1000\n", + "26/26 [==============================] - 0s 722us/sample - loss: 0.0071 - acc: 1.0000\n", + "Epoch 429/1000\n", + "26/26 [==============================] - 0s 429us/sample - loss: 0.0071 - acc: 1.0000\n", + "Epoch 430/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 0.0070 - acc: 1.0000\n", + "Epoch 431/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 0.0070 - acc: 1.0000\n", + "Epoch 432/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 0.0070 - acc: 1.0000\n", + "Epoch 433/1000\n", + "26/26 [==============================] - 0s 499us/sample - loss: 0.0069 - acc: 1.0000\n", + "Epoch 434/1000\n", + "26/26 [==============================] - 0s 594us/sample - loss: 0.0069 - acc: 1.0000\n", + "Epoch 435/1000\n", + "26/26 [==============================] - 0s 470us/sample - loss: 0.0068 - acc: 1.0000\n", + "Epoch 436/1000\n", + "26/26 [==============================] - 0s 571us/sample - loss: 0.0068 - acc: 1.0000\n", + "Epoch 437/1000\n", + "26/26 [==============================] - 0s 491us/sample - loss: 0.0068 - acc: 1.0000\n", + "Epoch 438/1000\n", + "26/26 [==============================] - 0s 383us/sample - loss: 0.0067 - acc: 1.0000\n", + "Epoch 439/1000\n", + "26/26 [==============================] - 0s 550us/sample - loss: 0.0067 - acc: 1.0000\n", + "Epoch 440/1000\n", + "26/26 [==============================] - 0s 439us/sample - loss: 0.0066 - acc: 1.0000\n", + "Epoch 441/1000\n", + "26/26 [==============================] - 0s 356us/sample - loss: 0.0066 - acc: 1.0000\n", + "Epoch 442/1000\n", + "26/26 [==============================] - 0s 383us/sample - loss: 0.0066 - acc: 1.0000\n", + "Epoch 443/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 0.0065 - acc: 1.0000\n", + "Epoch 444/1000\n", + "26/26 [==============================] - 0s 429us/sample - loss: 0.0065 - acc: 1.0000\n", + "Epoch 445/1000\n", + "26/26 [==============================] - 0s 409us/sample - loss: 0.0064 - acc: 1.0000\n", + "Epoch 446/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 0.0064 - acc: 1.0000\n", + "Epoch 447/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 0.0064 - acc: 1.0000\n", + "Epoch 448/1000\n", + "26/26 [==============================] - 0s 334us/sample - loss: 0.0063 - acc: 1.0000\n", + "Epoch 449/1000\n", + "26/26 [==============================] - 0s 326us/sample - loss: 0.0063 - acc: 1.0000\n", + "Epoch 450/1000\n", + "26/26 [==============================] - 0s 328us/sample - loss: 0.0062 - acc: 1.0000\n", + "Epoch 451/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 0.0062 - acc: 1.0000\n", + "Epoch 452/1000\n", + "26/26 [==============================] - 0s 350us/sample - loss: 0.0062 - acc: 1.0000\n", + "Epoch 453/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 0.0061 - acc: 1.0000\n", + "Epoch 454/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 0.0061 - acc: 1.0000\n", + "Epoch 455/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 0.0061 - acc: 1.0000\n", + "Epoch 456/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 0.0060 - acc: 1.0000\n", + "Epoch 457/1000\n", + "26/26 [==============================] - 0s 434us/sample - loss: 0.0060 - acc: 1.0000\n", + "Epoch 458/1000\n", + "26/26 [==============================] - 0s 463us/sample - loss: 0.0060 - acc: 1.0000\n", + "Epoch 459/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 0.0059 - acc: 1.0000\n", + "Epoch 460/1000\n", + "26/26 [==============================] - 0s 340us/sample - loss: 0.0059 - acc: 1.0000\n", + "Epoch 461/1000\n", + "26/26 [==============================] - 0s 337us/sample - loss: 0.0058 - acc: 1.0000\n", + "Epoch 462/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 0.0058 - acc: 1.0000\n", + "Epoch 463/1000\n", + "26/26 [==============================] - 0s 360us/sample - loss: 0.0058 - acc: 1.0000\n", + "Epoch 464/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 0.0057 - acc: 1.0000\n", + "Epoch 465/1000\n", + "26/26 [==============================] - 0s 378us/sample - loss: 0.0057 - acc: 1.0000\n", + "Epoch 466/1000\n", + "26/26 [==============================] - 0s 350us/sample - loss: 0.0057 - acc: 1.0000\n", + "Epoch 467/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 0.0056 - acc: 1.0000\n", + "Epoch 468/1000\n", + "26/26 [==============================] - 0s 386us/sample - loss: 0.0056 - acc: 1.0000\n", + "Epoch 469/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 0.0056 - acc: 1.0000\n", + "Epoch 470/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 0.0055 - acc: 1.0000\n", + "Epoch 471/1000\n", + "26/26 [==============================] - 0s 402us/sample - loss: 0.0055 - acc: 1.0000\n", + "Epoch 472/1000\n", + "26/26 [==============================] - 0s 396us/sample - loss: 0.0055 - acc: 1.0000\n", + "Epoch 473/1000\n", + "26/26 [==============================] - 0s 438us/sample - loss: 0.0054 - acc: 1.0000\n", + "Epoch 474/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 0.0054 - acc: 1.0000\n", + "Epoch 475/1000\n", + "26/26 [==============================] - 0s 385us/sample - loss: 0.0054 - acc: 1.0000\n", + "Epoch 476/1000\n", + "26/26 [==============================] - 0s 563us/sample - loss: 0.0053 - acc: 1.0000\n", + "Epoch 477/1000\n", + "26/26 [==============================] - 0s 461us/sample - loss: 0.0053 - acc: 1.0000\n", + "Epoch 478/1000\n", + "26/26 [==============================] - 0s 476us/sample - loss: 0.0053 - acc: 1.0000\n", + "Epoch 479/1000\n", + "26/26 [==============================] - 0s 454us/sample - loss: 0.0052 - acc: 1.0000\n", + "Epoch 480/1000\n", + "26/26 [==============================] - 0s 372us/sample - loss: 0.0052 - acc: 1.0000\n", + "Epoch 481/1000\n", + "26/26 [==============================] - 0s 472us/sample - loss: 0.0052 - acc: 1.0000\n", + "Epoch 482/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 0.0052 - acc: 1.0000\n", + "Epoch 483/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 0.0051 - acc: 1.0000\n", + "Epoch 484/1000\n", + "26/26 [==============================] - 0s 395us/sample - loss: 0.0051 - acc: 1.0000\n", + "Epoch 485/1000\n", + "26/26 [==============================] - 0s 456us/sample - loss: 0.0051 - acc: 1.0000\n", + "Epoch 486/1000\n", + "26/26 [==============================] - 0s 476us/sample - loss: 0.0050 - acc: 1.0000\n", + "Epoch 487/1000\n", + "26/26 [==============================] - 0s 419us/sample - loss: 0.0050 - acc: 1.0000\n", + "Epoch 488/1000\n", + "26/26 [==============================] - 0s 420us/sample - loss: 0.0050 - acc: 1.0000\n", + "Epoch 489/1000\n", + "26/26 [==============================] - 0s 435us/sample - loss: 0.0049 - acc: 1.0000\n", + "Epoch 490/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 0.0049 - acc: 1.0000\n", + "Epoch 491/1000\n", + "26/26 [==============================] - 0s 394us/sample - loss: 0.0049 - acc: 1.0000\n", + "Epoch 492/1000\n", + "26/26 [==============================] - 0s 418us/sample - loss: 0.0049 - acc: 1.0000\n", + "Epoch 493/1000\n", + "26/26 [==============================] - 0s 418us/sample - loss: 0.0048 - acc: 1.0000\n", + "Epoch 494/1000\n", + "26/26 [==============================] - 0s 405us/sample - loss: 0.0048 - acc: 1.0000\n", + "Epoch 495/1000\n", + "26/26 [==============================] - 0s 348us/sample - loss: 0.0048 - acc: 1.0000\n", + "Epoch 496/1000\n", + "26/26 [==============================] - 0s 335us/sample - loss: 0.0048 - acc: 1.0000\n", + "Epoch 497/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 0.0047 - acc: 1.0000\n", + "Epoch 498/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 0.0047 - acc: 1.0000\n", + "Epoch 499/1000\n", + "26/26 [==============================] - 0s 350us/sample - loss: 0.0047 - acc: 1.0000\n", + "Epoch 500/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 0.0046 - acc: 1.0000\n", + "Epoch 501/1000\n", + "26/26 [==============================] - 0s 376us/sample - loss: 0.0046 - acc: 1.0000\n", + "Epoch 502/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 0.0046 - acc: 1.0000\n", + "Epoch 503/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 0.0046 - acc: 1.0000\n", + "Epoch 504/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 0.0045 - acc: 1.0000\n", + "Epoch 505/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 0.0045 - acc: 1.0000\n", + "Epoch 506/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 0.0045 - acc: 1.0000\n", + "Epoch 507/1000\n", + "26/26 [==============================] - 0s 326us/sample - loss: 0.0045 - acc: 1.0000\n", + "Epoch 508/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 0.0044 - acc: 1.0000\n", + "Epoch 509/1000\n", + "26/26 [==============================] - 0s 340us/sample - loss: 0.0044 - acc: 1.0000\n", + "Epoch 510/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 0.0044 - acc: 1.0000\n", + "Epoch 511/1000\n", + "26/26 [==============================] - 0s 421us/sample - loss: 0.0044 - acc: 1.0000\n", + "Epoch 512/1000\n", + "26/26 [==============================] - 0s 450us/sample - loss: 0.0043 - acc: 1.0000\n", + "Epoch 513/1000\n", + "26/26 [==============================] - 0s 487us/sample - loss: 0.0043 - acc: 1.0000\n", + "Epoch 514/1000\n", + "26/26 [==============================] - 0s 482us/sample - loss: 0.0043 - acc: 1.0000\n", + "Epoch 515/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 0.0043 - acc: 1.0000\n", + "Epoch 516/1000\n", + "26/26 [==============================] - 0s 604us/sample - loss: 0.0043 - acc: 1.0000\n", + "Epoch 517/1000\n", + "26/26 [==============================] - 0s 441us/sample - loss: 0.0042 - acc: 1.0000\n", + "Epoch 518/1000\n", + "26/26 [==============================] - 0s 573us/sample - loss: 0.0042 - acc: 1.0000\n", + "Epoch 519/1000\n", + "26/26 [==============================] - 0s 482us/sample - loss: 0.0042 - acc: 1.0000\n", + "Epoch 520/1000\n", + "26/26 [==============================] - 0s 376us/sample - loss: 0.0042 - acc: 1.0000\n", + "Epoch 521/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 0.0041 - acc: 1.0000\n", + "Epoch 522/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 0.0041 - acc: 1.0000\n", + "Epoch 523/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 0.0041 - acc: 1.0000\n", + "Epoch 524/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 0.0041 - acc: 1.0000\n", + "Epoch 525/1000\n", + "26/26 [==============================] - 0s 360us/sample - loss: 0.0041 - acc: 1.0000\n", + "Epoch 526/1000\n", + "26/26 [==============================] - 0s 343us/sample - loss: 0.0040 - acc: 1.0000\n", + "Epoch 527/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 0.0040 - acc: 1.0000\n", + "Epoch 528/1000\n", + "26/26 [==============================] - 0s 380us/sample - loss: 0.0040 - acc: 1.0000\n", + "Epoch 529/1000\n", + "26/26 [==============================] - 0s 690us/sample - loss: 0.0040 - acc: 1.0000\n", + "Epoch 530/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 0.0040 - acc: 1.0000\n", + "Epoch 531/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 0.0039 - acc: 1.0000\n", + "Epoch 532/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 0.0039 - acc: 1.0000\n", + "Epoch 533/1000\n", + "26/26 [==============================] - 0s 348us/sample - loss: 0.0039 - acc: 1.0000\n", + "Epoch 534/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 0.0039 - acc: 1.0000\n", + "Epoch 535/1000\n", + "26/26 [==============================] - 0s 482us/sample - loss: 0.0039 - acc: 1.0000\n", + "Epoch 536/1000\n", + "26/26 [==============================] - 0s 477us/sample - loss: 0.0038 - acc: 1.0000\n", + "Epoch 537/1000\n", + "26/26 [==============================] - 0s 519us/sample - loss: 0.0038 - acc: 1.0000\n", + "Epoch 538/1000\n", + "26/26 [==============================] - 0s 429us/sample - loss: 0.0038 - acc: 1.0000\n", + "Epoch 539/1000\n", + "26/26 [==============================] - 0s 377us/sample - loss: 0.0038 - acc: 1.0000\n", + "Epoch 540/1000\n", + "26/26 [==============================] - 0s 452us/sample - loss: 0.0038 - acc: 1.0000\n", + "Epoch 541/1000\n", + "26/26 [==============================] - 0s 391us/sample - loss: 0.0037 - acc: 1.0000\n", + "Epoch 542/1000\n", + "26/26 [==============================] - 0s 502us/sample - loss: 0.0037 - acc: 1.0000\n", + "Epoch 543/1000\n", + "26/26 [==============================] - 0s 508us/sample - loss: 0.0037 - acc: 1.0000\n", + "Epoch 544/1000\n", + "26/26 [==============================] - 0s 573us/sample - loss: 0.0037 - acc: 1.0000\n", + "Epoch 545/1000\n", + "26/26 [==============================] - 0s 477us/sample - loss: 0.0037 - acc: 1.0000\n", + "Epoch 546/1000\n", + "26/26 [==============================] - 0s 436us/sample - loss: 0.0036 - acc: 1.0000\n", + "Epoch 547/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 0.0036 - acc: 1.0000\n", + "Epoch 548/1000\n", + "26/26 [==============================] - 0s 396us/sample - loss: 0.0036 - acc: 1.0000\n", + "Epoch 549/1000\n", + "26/26 [==============================] - 0s 425us/sample - loss: 0.0036 - acc: 1.0000\n", + "Epoch 550/1000\n", + "26/26 [==============================] - 0s 457us/sample - loss: 0.0036 - acc: 1.0000\n", + "Epoch 551/1000\n", + "26/26 [==============================] - 0s 402us/sample - loss: 0.0036 - acc: 1.0000\n", + "Epoch 552/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 0.0035 - acc: 1.0000\n", + "Epoch 553/1000\n", + "26/26 [==============================] - 0s 410us/sample - loss: 0.0035 - acc: 1.0000\n", + "Epoch 554/1000\n", + "26/26 [==============================] - 0s 398us/sample - loss: 0.0035 - acc: 1.0000\n", + "Epoch 555/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 0.0035 - acc: 1.0000\n", + "Epoch 556/1000\n", + "26/26 [==============================] - 0s 348us/sample - loss: 0.0035 - acc: 1.0000\n", + "Epoch 557/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 0.0034 - acc: 1.0000\n", + "Epoch 558/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 0.0034 - acc: 1.0000\n", + "Epoch 559/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 0.0034 - acc: 1.0000\n", + "Epoch 560/1000\n", + "26/26 [==============================] - 0s 396us/sample - loss: 0.0034 - acc: 1.0000\n", + "Epoch 561/1000\n", + "26/26 [==============================] - 0s 436us/sample - loss: 0.0034 - acc: 1.0000\n", + "Epoch 562/1000\n", + "26/26 [==============================] - 0s 536us/sample - loss: 0.0034 - acc: 1.0000\n", + "Epoch 563/1000\n", + "26/26 [==============================] - 0s 442us/sample - loss: 0.0033 - acc: 1.0000\n", + "Epoch 564/1000\n", + "26/26 [==============================] - 0s 448us/sample - loss: 0.0033 - acc: 1.0000\n", + "Epoch 565/1000\n", + "26/26 [==============================] - 0s 428us/sample - loss: 0.0033 - acc: 1.0000\n", + "Epoch 566/1000\n", + "26/26 [==============================] - 0s 399us/sample - loss: 0.0033 - acc: 1.0000\n", + "Epoch 567/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 0.0033 - acc: 1.0000\n", + "Epoch 568/1000\n", + "26/26 [==============================] - 0s 389us/sample - loss: 0.0033 - acc: 1.0000\n", + "Epoch 569/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 0.0032 - acc: 1.0000\n", + "Epoch 570/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 0.0032 - acc: 1.0000\n", + "Epoch 571/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 0.0032 - acc: 1.0000\n", + "Epoch 572/1000\n", + "26/26 [==============================] - 0s 462us/sample - loss: 0.0032 - acc: 1.0000\n", + "Epoch 573/1000\n", + "26/26 [==============================] - 0s 506us/sample - loss: 0.0032 - acc: 1.0000\n", + "Epoch 574/1000\n", + "26/26 [==============================] - 0s 487us/sample - loss: 0.0032 - acc: 1.0000\n", + "Epoch 575/1000\n", + "26/26 [==============================] - 0s 444us/sample - loss: 0.0032 - acc: 1.0000\n", + "Epoch 576/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 0.0031 - acc: 1.0000\n", + "Epoch 577/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 0.0031 - acc: 1.0000\n", + "Epoch 578/1000\n", + "26/26 [==============================] - 0s 370us/sample - loss: 0.0031 - acc: 1.0000\n", + "Epoch 579/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 0.0031 - acc: 1.0000\n", + "Epoch 580/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 0.0031 - acc: 1.0000\n", + "Epoch 581/1000\n", + "26/26 [==============================] - 0s 384us/sample - loss: 0.0031 - acc: 1.0000\n", + "Epoch 582/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 0.0030 - acc: 1.0000\n", + "Epoch 583/1000\n", + "26/26 [==============================] - 0s 355us/sample - loss: 0.0030 - acc: 1.0000\n", + "Epoch 584/1000\n", + "26/26 [==============================] - 0s 424us/sample - loss: 0.0030 - acc: 1.0000\n", + "Epoch 585/1000\n", + "26/26 [==============================] - 0s 436us/sample - loss: 0.0030 - acc: 1.0000\n", + "Epoch 586/1000\n", + "26/26 [==============================] - 0s 660us/sample - loss: 0.0030 - acc: 1.0000\n", + "Epoch 587/1000\n", + "26/26 [==============================] - 0s 561us/sample - loss: 0.0030 - acc: 1.0000\n", + "Epoch 588/1000\n", + "26/26 [==============================] - 0s 600us/sample - loss: 0.0030 - acc: 1.0000\n", + "Epoch 589/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 0.0029 - acc: 1.0000\n", + "Epoch 590/1000\n", + "26/26 [==============================] - 0s 460us/sample - loss: 0.0029 - acc: 1.0000\n", + "Epoch 591/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 0.0029 - acc: 1.0000\n", + "Epoch 592/1000\n", + "26/26 [==============================] - 0s 474us/sample - loss: 0.0029 - acc: 1.0000\n", + "Epoch 593/1000\n", + "26/26 [==============================] - 0s 485us/sample - loss: 0.0029 - acc: 1.0000\n", + "Epoch 594/1000\n", + "26/26 [==============================] - 0s 594us/sample - loss: 0.0029 - acc: 1.0000\n", + "Epoch 595/1000\n", + "26/26 [==============================] - 0s 633us/sample - loss: 0.0029 - acc: 1.0000\n", + "Epoch 596/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 0.0029 - acc: 1.0000\n", + "Epoch 597/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 0.0028 - acc: 1.0000\n", + "Epoch 598/1000\n", + "26/26 [==============================] - 0s 372us/sample - loss: 0.0028 - acc: 1.0000\n", + "Epoch 599/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 0.0028 - acc: 1.0000\n", + "Epoch 600/1000\n", + "26/26 [==============================] - 0s 386us/sample - loss: 0.0028 - acc: 1.0000\n", + "Epoch 601/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 0.0028 - acc: 1.0000\n", + "Epoch 602/1000\n", + "26/26 [==============================] - 0s 391us/sample - loss: 0.0028 - acc: 1.0000\n", + "Epoch 603/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 0.0028 - acc: 1.0000\n", + "Epoch 604/1000\n", + "26/26 [==============================] - 0s 408us/sample - loss: 0.0028 - acc: 1.0000\n", + "Epoch 605/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 0.0027 - acc: 1.0000\n", + "Epoch 606/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 0.0027 - acc: 1.0000\n", + "Epoch 607/1000\n", + "26/26 [==============================] - 0s 447us/sample - loss: 0.0027 - acc: 1.0000\n", + "Epoch 608/1000\n", + "26/26 [==============================] - 0s 410us/sample - loss: 0.0027 - acc: 1.0000\n", + "Epoch 609/1000\n", + "26/26 [==============================] - 0s 398us/sample - loss: 0.0027 - acc: 1.0000\n", + "Epoch 610/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 0.0027 - acc: 1.0000\n", + "Epoch 611/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 0.0027 - acc: 1.0000\n", + "Epoch 612/1000\n", + "26/26 [==============================] - 0s 402us/sample - loss: 0.0027 - acc: 1.0000\n", + "Epoch 613/1000\n", + "26/26 [==============================] - 0s 349us/sample - loss: 0.0026 - acc: 1.0000\n", + "Epoch 614/1000\n", + "26/26 [==============================] - 0s 330us/sample - loss: 0.0026 - acc: 1.0000\n", + "Epoch 615/1000\n", + "26/26 [==============================] - 0s 340us/sample - loss: 0.0026 - acc: 1.0000\n", + "Epoch 616/1000\n", + "26/26 [==============================] - 0s 368us/sample - loss: 0.0026 - acc: 1.0000\n", + "Epoch 617/1000\n", + "26/26 [==============================] - 0s 404us/sample - loss: 0.0026 - acc: 1.0000\n", + "Epoch 618/1000\n", + "26/26 [==============================] - 0s 323us/sample - loss: 0.0026 - acc: 1.0000\n", + "Epoch 619/1000\n", + "26/26 [==============================] - 0s 396us/sample - loss: 0.0026 - acc: 1.0000\n", + "Epoch 620/1000\n", + "26/26 [==============================] - 0s 407us/sample - loss: 0.0026 - acc: 1.0000\n", + "Epoch 621/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 0.0026 - acc: 1.0000\n", + "Epoch 622/1000\n", + "26/26 [==============================] - 0s 420us/sample - loss: 0.0025 - acc: 1.0000\n", + "Epoch 623/1000\n", + "26/26 [==============================] - 0s 401us/sample - loss: 0.0025 - acc: 1.0000\n", + "Epoch 624/1000\n", + "26/26 [==============================] - 0s 471us/sample - loss: 0.0025 - acc: 1.0000\n", + "Epoch 625/1000\n", + "26/26 [==============================] - 0s 426us/sample - loss: 0.0025 - acc: 1.0000\n", + "Epoch 626/1000\n", + "26/26 [==============================] - 0s 498us/sample - loss: 0.0025 - acc: 1.0000\n", + "Epoch 627/1000\n", + "26/26 [==============================] - 0s 490us/sample - loss: 0.0025 - acc: 1.0000\n", + "Epoch 628/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 0.0025 - acc: 1.0000\n", + "Epoch 629/1000\n", + "26/26 [==============================] - 0s 540us/sample - loss: 0.0025 - acc: 1.0000\n", + "Epoch 630/1000\n", + "26/26 [==============================] - 0s 548us/sample - loss: 0.0025 - acc: 1.0000\n", + "Epoch 631/1000\n", + "26/26 [==============================] - 0s 444us/sample - loss: 0.0024 - acc: 1.0000\n", + "Epoch 632/1000\n", + "26/26 [==============================] - 0s 435us/sample - loss: 0.0024 - acc: 1.0000\n", + "Epoch 633/1000\n", + "26/26 [==============================] - 0s 410us/sample - loss: 0.0024 - acc: 1.0000\n", + "Epoch 634/1000\n", + "26/26 [==============================] - 0s 803us/sample - loss: 0.0024 - acc: 1.0000\n", + "Epoch 635/1000\n", + "26/26 [==============================] - 0s 785us/sample - loss: 0.0024 - acc: 1.0000\n", + "Epoch 636/1000\n", + "26/26 [==============================] - 0s 756us/sample - loss: 0.0024 - acc: 1.0000\n", + "Epoch 637/1000\n", + "26/26 [==============================] - 0s 421us/sample - loss: 0.0024 - acc: 1.0000\n", + "Epoch 638/1000\n", + "26/26 [==============================] - 0s 521us/sample - loss: 0.0024 - acc: 1.0000\n", + "Epoch 639/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 0.0023 - acc: 1.0000\n", + "Epoch 640/1000\n", + "26/26 [==============================] - 0s 505us/sample - loss: 0.0023 - acc: 1.0000\n", + "Epoch 641/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 0.0023 - acc: 1.0000\n", + "Epoch 642/1000\n", + "26/26 [==============================] - 0s 458us/sample - loss: 0.0023 - acc: 1.0000\n", + "Epoch 643/1000\n", + "26/26 [==============================] - 0s 515us/sample - loss: 0.0023 - acc: 1.0000\n", + "Epoch 644/1000\n", + "26/26 [==============================] - 0s 538us/sample - loss: 0.0023 - acc: 1.0000\n", + "Epoch 645/1000\n", + "26/26 [==============================] - 0s 416us/sample - loss: 0.0023 - acc: 1.0000\n", + "Epoch 646/1000\n", + "26/26 [==============================] - 0s 356us/sample - loss: 0.0023 - acc: 1.0000\n", + "Epoch 647/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 0.0023 - acc: 1.0000\n", + "Epoch 648/1000\n", + "26/26 [==============================] - 0s 387us/sample - loss: 0.0023 - acc: 1.0000\n", + "Epoch 649/1000\n", + "26/26 [==============================] - 0s 394us/sample - loss: 0.0022 - acc: 1.0000\n", + "Epoch 650/1000\n", + "26/26 [==============================] - 0s 433us/sample - loss: 0.0022 - acc: 1.0000\n", + "Epoch 651/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 0.0022 - acc: 1.0000\n", + "Epoch 652/1000\n", + "26/26 [==============================] - 0s 441us/sample - loss: 0.0022 - acc: 1.0000\n", + "Epoch 653/1000\n", + "26/26 [==============================] - 0s 455us/sample - loss: 0.0022 - acc: 1.0000\n", + "Epoch 654/1000\n", + "26/26 [==============================] - 0s 466us/sample - loss: 0.0022 - acc: 1.0000\n", + "Epoch 655/1000\n", + "26/26 [==============================] - 0s 440us/sample - loss: 0.0022 - acc: 1.0000\n", + "Epoch 656/1000\n", + "26/26 [==============================] - 0s 479us/sample - loss: 0.0022 - acc: 1.0000\n", + "Epoch 657/1000\n", + "26/26 [==============================] - 0s 604us/sample - loss: 0.0022 - acc: 1.0000\n", + "Epoch 658/1000\n", + "26/26 [==============================] - 0s 479us/sample - loss: 0.0022 - acc: 1.0000\n", + "Epoch 659/1000\n", + "26/26 [==============================] - 0s 531us/sample - loss: 0.0021 - acc: 1.0000\n", + "Epoch 660/1000\n", + "26/26 [==============================] - 0s 426us/sample - loss: 0.0021 - acc: 1.0000\n", + "Epoch 661/1000\n", + "26/26 [==============================] - 0s 457us/sample - loss: 0.0021 - acc: 1.0000\n", + "Epoch 662/1000\n", + "26/26 [==============================] - 0s 404us/sample - loss: 0.0021 - acc: 1.0000\n", + "Epoch 663/1000\n", + "26/26 [==============================] - 0s 449us/sample - loss: 0.0021 - acc: 1.0000\n", + "Epoch 664/1000\n", + "26/26 [==============================] - 0s 391us/sample - loss: 0.0021 - acc: 1.0000\n", + "Epoch 665/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 0.0021 - acc: 1.0000\n", + "Epoch 666/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 0.0021 - acc: 1.0000\n", + "Epoch 667/1000\n", + "26/26 [==============================] - 0s 391us/sample - loss: 0.0021 - acc: 1.0000\n", + "Epoch 668/1000\n", + "26/26 [==============================] - 0s 505us/sample - loss: 0.0021 - acc: 1.0000\n", + "Epoch 669/1000\n", + "26/26 [==============================] - 0s 376us/sample - loss: 0.0021 - acc: 1.0000\n", + "Epoch 670/1000\n", + "26/26 [==============================] - 0s 419us/sample - loss: 0.0020 - acc: 1.0000\n", + "Epoch 671/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 0.0020 - acc: 1.0000\n", + "Epoch 672/1000\n", + "26/26 [==============================] - 0s 420us/sample - loss: 0.0020 - acc: 1.0000\n", + "Epoch 673/1000\n", + "26/26 [==============================] - 0s 399us/sample - loss: 0.0020 - acc: 1.0000\n", + "Epoch 674/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 0.0020 - acc: 1.0000\n", + "Epoch 675/1000\n", + "26/26 [==============================] - 0s 349us/sample - loss: 0.0020 - acc: 1.0000\n", + "Epoch 676/1000\n", + "26/26 [==============================] - 0s 603us/sample - loss: 0.0020 - acc: 1.0000\n", + "Epoch 677/1000\n", + "26/26 [==============================] - 0s 509us/sample - loss: 0.0020 - acc: 1.0000\n", + "Epoch 678/1000\n", + "26/26 [==============================] - 0s 386us/sample - loss: 0.0020 - acc: 1.0000\n", + "Epoch 679/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 0.0020 - acc: 1.0000\n", + "Epoch 680/1000\n", + "26/26 [==============================] - 0s 427us/sample - loss: 0.0020 - acc: 1.0000\n", + "Epoch 681/1000\n", + "26/26 [==============================] - 0s 467us/sample - loss: 0.0020 - acc: 1.0000\n", + "Epoch 682/1000\n", + "26/26 [==============================] - 0s 412us/sample - loss: 0.0019 - acc: 1.0000\n", + "Epoch 683/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 0.0019 - acc: 1.0000\n", + "Epoch 684/1000\n", + "26/26 [==============================] - 0s 368us/sample - loss: 0.0019 - acc: 1.0000\n", + "Epoch 685/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 0.0019 - acc: 1.0000\n", + "Epoch 686/1000\n", + "26/26 [==============================] - 0s 483us/sample - loss: 0.0019 - acc: 1.0000\n", + "Epoch 687/1000\n", + "26/26 [==============================] - 0s 433us/sample - loss: 0.0019 - acc: 1.0000\n", + "Epoch 688/1000\n", + "26/26 [==============================] - 0s 438us/sample - loss: 0.0019 - acc: 1.0000\n", + "Epoch 689/1000\n", + "26/26 [==============================] - 0s 464us/sample - loss: 0.0019 - acc: 1.0000\n", + "Epoch 690/1000\n", + "26/26 [==============================] - 0s 630us/sample - loss: 0.0019 - acc: 1.0000\n", + "Epoch 691/1000\n", + "26/26 [==============================] - 0s 573us/sample - loss: 0.0019 - acc: 1.0000\n", + "Epoch 692/1000\n", + "26/26 [==============================] - 0s 546us/sample - loss: 0.0019 - acc: 1.0000\n", + "Epoch 693/1000\n", + "26/26 [==============================] - 0s 505us/sample - loss: 0.0019 - acc: 1.0000\n", + "Epoch 694/1000\n", + "26/26 [==============================] - 0s 378us/sample - loss: 0.0018 - acc: 1.0000\n", + "Epoch 695/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 0.0018 - acc: 1.0000\n", + "Epoch 696/1000\n", + "26/26 [==============================] - 0s 349us/sample - loss: 0.0018 - acc: 1.0000\n", + "Epoch 697/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 0.0018 - acc: 1.0000\n", + "Epoch 698/1000\n", + "26/26 [==============================] - 0s 349us/sample - loss: 0.0018 - acc: 1.0000\n", + "Epoch 699/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 0.0018 - acc: 1.0000\n", + "Epoch 700/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 0.0018 - acc: 1.0000\n", + "Epoch 701/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 0.0018 - acc: 1.0000\n", + "Epoch 702/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 0.0018 - acc: 1.0000\n", + "Epoch 703/1000\n", + "26/26 [==============================] - 0s 522us/sample - loss: 0.0018 - acc: 1.0000\n", + "Epoch 704/1000\n", + "26/26 [==============================] - 0s 576us/sample - loss: 0.0018 - acc: 1.0000\n", + "Epoch 705/1000\n", + "26/26 [==============================] - 0s 383us/sample - loss: 0.0018 - acc: 1.0000\n", + "Epoch 706/1000\n", + "26/26 [==============================] - 0s 355us/sample - loss: 0.0018 - acc: 1.0000\n", + "Epoch 707/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 708/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 709/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 710/1000\n", + "26/26 [==============================] - 0s 410us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 711/1000\n", + "26/26 [==============================] - 0s 583us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 712/1000\n", + "26/26 [==============================] - 0s 562us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 713/1000\n", + "26/26 [==============================] - 0s 586us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 714/1000\n", + "26/26 [==============================] - 0s 665us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 715/1000\n", + "26/26 [==============================] - 0s 827us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 716/1000\n", + "26/26 [==============================] - 0s 499us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 717/1000\n", + "26/26 [==============================] - 0s 478us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 718/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 719/1000\n", + "26/26 [==============================] - 0s 377us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 720/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 721/1000\n", + "26/26 [==============================] - 0s 281us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 722/1000\n", + "26/26 [==============================] - 0s 531us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 723/1000\n", + "26/26 [==============================] - 0s 461us/sample - loss: 0.0016 - acc: 1.0000\n", + "Epoch 724/1000\n", + "26/26 [==============================] - 0s 467us/sample - loss: 0.0016 - acc: 1.0000\n", + "Epoch 725/1000\n", + "26/26 [==============================] - 0s 481us/sample - loss: 0.0016 - acc: 1.0000\n", + "Epoch 726/1000\n", + "26/26 [==============================] - 0s 421us/sample - loss: 0.0016 - acc: 1.0000\n", + "Epoch 727/1000\n", + "26/26 [==============================] - 0s 480us/sample - loss: 0.0016 - acc: 1.0000\n", + "Epoch 728/1000\n", + "26/26 [==============================] - 0s 456us/sample - loss: 0.0016 - acc: 1.0000\n", + "Epoch 729/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 0.0016 - acc: 1.0000\n", + "Epoch 730/1000\n", + "26/26 [==============================] - 0s 478us/sample - loss: 0.0016 - acc: 1.0000\n", + "Epoch 731/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 0.0016 - acc: 1.0000\n", + "Epoch 732/1000\n", + "26/26 [==============================] - 0s 459us/sample - loss: 0.0016 - acc: 1.0000\n", + "Epoch 733/1000\n", + "26/26 [==============================] - 0s 550us/sample - loss: 0.0016 - acc: 1.0000\n", + "Epoch 734/1000\n", + "26/26 [==============================] - 0s 470us/sample - loss: 0.0016 - acc: 1.0000\n", + "Epoch 735/1000\n", + "26/26 [==============================] - 0s 376us/sample - loss: 0.0016 - acc: 1.0000\n", + "Epoch 736/1000\n", + "26/26 [==============================] - 0s 343us/sample - loss: 0.0016 - acc: 1.0000\n", + "Epoch 737/1000\n", + "26/26 [==============================] - 0s 398us/sample - loss: 0.0016 - acc: 1.0000\n", + "Epoch 738/1000\n", + "26/26 [==============================] - 0s 469us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 739/1000\n", + "26/26 [==============================] - 0s 582us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 740/1000\n", + "26/26 [==============================] - 0s 471us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 741/1000\n", + "26/26 [==============================] - 0s 461us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 742/1000\n", + "26/26 [==============================] - 0s 441us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 743/1000\n", + "26/26 [==============================] - 0s 425us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 744/1000\n", + "26/26 [==============================] - 0s 477us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 745/1000\n", + "26/26 [==============================] - 0s 435us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 746/1000\n", + "26/26 [==============================] - 0s 529us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 747/1000\n", + "26/26 [==============================] - 0s 553us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 748/1000\n", + "26/26 [==============================] - 0s 570us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 749/1000\n", + "26/26 [==============================] - 0s 308us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 750/1000\n", + "26/26 [==============================] - 0s 391us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 751/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 752/1000\n", + "26/26 [==============================] - 0s 368us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 753/1000\n", + "26/26 [==============================] - 0s 340us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 754/1000\n", + "26/26 [==============================] - 0s 372us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 755/1000\n", + "26/26 [==============================] - 0s 319us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 756/1000\n", + "26/26 [==============================] - 0s 322us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 757/1000\n", + "26/26 [==============================] - 0s 331us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 758/1000\n", + "26/26 [==============================] - 0s 335us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 759/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 760/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 761/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 762/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 763/1000\n", + "26/26 [==============================] - 0s 380us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 764/1000\n", + "26/26 [==============================] - 0s 401us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 765/1000\n", + "26/26 [==============================] - 0s 399us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 766/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 767/1000\n", + "26/26 [==============================] - 0s 451us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 768/1000\n", + "26/26 [==============================] - 0s 393us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 769/1000\n", + "26/26 [==============================] - 0s 404us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 770/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 771/1000\n", + "26/26 [==============================] - 0s 376us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 772/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 773/1000\n", + "26/26 [==============================] - 0s 370us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 774/1000\n", + "26/26 [==============================] - 0s 370us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 775/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 776/1000\n", + "26/26 [==============================] - 0s 343us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 777/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 778/1000\n", + "26/26 [==============================] - 0s 337us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 779/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 780/1000\n", + "26/26 [==============================] - 0s 373us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 781/1000\n", + "26/26 [==============================] - 0s 316us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 782/1000\n", + "26/26 [==============================] - 0s 329us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 783/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 784/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 785/1000\n", + "26/26 [==============================] - 0s 360us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 786/1000\n", + "26/26 [==============================] - 0s 389us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 787/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 788/1000\n", + "26/26 [==============================] - 0s 325us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 789/1000\n", + "26/26 [==============================] - 0s 334us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 790/1000\n", + "26/26 [==============================] - 0s 355us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 791/1000\n", + "26/26 [==============================] - 0s 505us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 792/1000\n", + "26/26 [==============================] - 0s 573us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 793/1000\n", + "26/26 [==============================] - 0s 511us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 794/1000\n", + "26/26 [==============================] - 0s 511us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 795/1000\n", + "26/26 [==============================] - 0s 506us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 796/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 797/1000\n", + "26/26 [==============================] - 0s 596us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 798/1000\n", + "26/26 [==============================] - 0s 646us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 799/1000\n", + "26/26 [==============================] - 0s 411us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 800/1000\n", + "26/26 [==============================] - 0s 375us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 801/1000\n", + "26/26 [==============================] - 0s 337us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 802/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 803/1000\n", + "26/26 [==============================] - 0s 407us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 804/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 805/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 806/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 807/1000\n", + "26/26 [==============================] - 0s 353us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 808/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 809/1000\n", + "26/26 [==============================] - 0s 355us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 810/1000\n", + "26/26 [==============================] - 0s 459us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 811/1000\n", + "26/26 [==============================] - 0s 577us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 812/1000\n", + "26/26 [==============================] - 0s 549us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 813/1000\n", + "26/26 [==============================] - 0s 391us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 814/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 815/1000\n", + "26/26 [==============================] - 0s 324us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 816/1000\n", + "26/26 [==============================] - 0s 321us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 817/1000\n", + "26/26 [==============================] - 0s 335us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 818/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 819/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 820/1000\n", + "26/26 [==============================] - 0s 348us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 821/1000\n", + "26/26 [==============================] - 0s 333us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 822/1000\n", + "26/26 [==============================] - 0s 335us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 823/1000\n", + "26/26 [==============================] - 0s 335us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 824/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 825/1000\n", + "26/26 [==============================] - 0s 353us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 826/1000\n", + "26/26 [==============================] - 0s 329us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 827/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 828/1000\n", + "26/26 [==============================] - 0s 320us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 829/1000\n", + "26/26 [==============================] - 0s 334us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 830/1000\n", + "26/26 [==============================] - 0s 334us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 831/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 832/1000\n", + "26/26 [==============================] - 0s 333us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 833/1000\n", + "26/26 [==============================] - 0s 425us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 834/1000\n", + "26/26 [==============================] - 0s 330us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 835/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 836/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 837/1000\n", + "26/26 [==============================] - 0s 356us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 838/1000\n", + "26/26 [==============================] - 0s 329us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 839/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 840/1000\n", + "26/26 [==============================] - 0s 315us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 841/1000\n", + "26/26 [==============================] - 0s 328us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 842/1000\n", + "26/26 [==============================] - 0s 313us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 843/1000\n", + "26/26 [==============================] - 0s 334us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 844/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 0.0010 - acc: 1.0000\n", + "Epoch 845/1000\n", + "26/26 [==============================] - 0s 308us/sample - loss: 0.0010 - acc: 1.0000\n", + "Epoch 846/1000\n", + "26/26 [==============================] - 0s 334us/sample - loss: 0.0010 - acc: 1.0000\n", + "Epoch 847/1000\n", + "26/26 [==============================] - 0s 313us/sample - loss: 0.0010 - acc: 1.0000\n", + "Epoch 848/1000\n", + "26/26 [==============================] - 0s 325us/sample - loss: 0.0010 - acc: 1.0000\n", + "Epoch 849/1000\n", + "26/26 [==============================] - 0s 302us/sample - loss: 0.0010 - acc: 1.0000\n", + "Epoch 850/1000\n", + "26/26 [==============================] - 0s 311us/sample - loss: 0.0010 - acc: 1.0000\n", + "Epoch 851/1000\n", + "26/26 [==============================] - 0s 322us/sample - loss: 0.0010 - acc: 1.0000\n", + "Epoch 852/1000\n", + "26/26 [==============================] - 0s 320us/sample - loss: 0.0010 - acc: 1.0000\n", + "Epoch 853/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 0.0010 - acc: 1.0000\n", + "Epoch 854/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 0.0010 - acc: 1.0000\n", + "Epoch 855/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 0.0010 - acc: 1.0000\n", + "Epoch 856/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 0.0010 - acc: 1.0000\n", + "Epoch 857/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 0.0010 - acc: 1.0000\n", + "Epoch 858/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 0.0010 - acc: 1.0000\n", + "Epoch 859/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 9.9701e-04 - acc: 1.0000\n", + "Epoch 860/1000\n", + "26/26 [==============================] - 0s 343us/sample - loss: 9.9342e-04 - acc: 1.0000\n", + "Epoch 861/1000\n", + "26/26 [==============================] - 0s 331us/sample - loss: 9.9023e-04 - acc: 1.0000\n", + "Epoch 862/1000\n", + "26/26 [==============================] - 0s 320us/sample - loss: 9.8631e-04 - acc: 1.0000\n", + "Epoch 863/1000\n", + "26/26 [==============================] - 0s 349us/sample - loss: 9.8267e-04 - acc: 1.0000\n", + "Epoch 864/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 9.7901e-04 - acc: 1.0000\n", + "Epoch 865/1000\n", + "26/26 [==============================] - 0s 328us/sample - loss: 9.7601e-04 - acc: 1.0000\n", + "Epoch 866/1000\n", + "26/26 [==============================] - 0s 396us/sample - loss: 9.7201e-04 - acc: 1.0000\n", + "Epoch 867/1000\n", + "26/26 [==============================] - 0s 409us/sample - loss: 9.6872e-04 - acc: 1.0000\n", + "Epoch 868/1000\n", + "26/26 [==============================] - 0s 348us/sample - loss: 9.6529e-04 - acc: 1.0000\n", + "Epoch 869/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 9.6207e-04 - acc: 1.0000\n", + "Epoch 870/1000\n", + "26/26 [==============================] - 0s 417us/sample - loss: 9.5898e-04 - acc: 1.0000\n", + "Epoch 871/1000\n", + "26/26 [==============================] - 0s 384us/sample - loss: 9.5517e-04 - acc: 1.0000\n", + "Epoch 872/1000\n", + "26/26 [==============================] - 0s 335us/sample - loss: 9.5133e-04 - acc: 1.0000\n", + "Epoch 873/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 9.4806e-04 - acc: 1.0000\n", + "Epoch 874/1000\n", + "26/26 [==============================] - 0s 348us/sample - loss: 9.4486e-04 - acc: 1.0000\n", + "Epoch 875/1000\n", + "26/26 [==============================] - 0s 310us/sample - loss: 9.4108e-04 - acc: 1.0000\n", + "Epoch 876/1000\n", + "26/26 [==============================] - 0s 327us/sample - loss: 9.3826e-04 - acc: 1.0000\n", + "Epoch 877/1000\n", + "26/26 [==============================] - 0s 375us/sample - loss: 9.3489e-04 - acc: 1.0000\n", + "Epoch 878/1000\n", + "26/26 [==============================] - 0s 531us/sample - loss: 9.3196e-04 - acc: 1.0000\n", + "Epoch 879/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 9.2895e-04 - acc: 1.0000\n", + "Epoch 880/1000\n", + "26/26 [==============================] - 0s 601us/sample - loss: 9.2584e-04 - acc: 1.0000\n", + "Epoch 881/1000\n", + "26/26 [==============================] - 0s 360us/sample - loss: 9.2291e-04 - acc: 1.0000\n", + "Epoch 882/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 9.2006e-04 - acc: 1.0000\n", + "Epoch 883/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 9.1699e-04 - acc: 1.0000\n", + "Epoch 884/1000\n", + "26/26 [==============================] - 0s 776us/sample - loss: 9.1408e-04 - acc: 1.0000\n", + "Epoch 885/1000\n", + "26/26 [==============================] - 0s 463us/sample - loss: 9.1137e-04 - acc: 1.0000\n", + "Epoch 886/1000\n", + "26/26 [==============================] - 0s 309us/sample - loss: 9.0805e-04 - acc: 1.0000\n", + "Epoch 887/1000\n", + "26/26 [==============================] - 0s 329us/sample - loss: 9.0477e-04 - acc: 1.0000\n", + "Epoch 888/1000\n", + "26/26 [==============================] - 0s 350us/sample - loss: 9.0106e-04 - acc: 1.0000\n", + "Epoch 889/1000\n", + "26/26 [==============================] - 0s 333us/sample - loss: 8.9827e-04 - acc: 1.0000\n", + "Epoch 890/1000\n", + "26/26 [==============================] - 0s 395us/sample - loss: 8.9475e-04 - acc: 1.0000\n", + "Epoch 891/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 8.9123e-04 - acc: 1.0000\n", + "Epoch 892/1000\n", + "26/26 [==============================] - 0s 355us/sample - loss: 8.8832e-04 - acc: 1.0000\n", + "Epoch 893/1000\n", + "26/26 [==============================] - 0s 310us/sample - loss: 8.8509e-04 - acc: 1.0000\n", + "Epoch 894/1000\n", + "26/26 [==============================] - 0s 312us/sample - loss: 8.8189e-04 - acc: 1.0000\n", + "Epoch 895/1000\n", + "26/26 [==============================] - 0s 309us/sample - loss: 8.7851e-04 - acc: 1.0000\n", + "Epoch 896/1000\n", + "26/26 [==============================] - 0s 329us/sample - loss: 8.7573e-04 - acc: 1.0000\n", + "Epoch 897/1000\n", + "26/26 [==============================] - 0s 572us/sample - loss: 8.7279e-04 - acc: 1.0000\n", + "Epoch 898/1000\n", + "26/26 [==============================] - 0s 416us/sample - loss: 8.7003e-04 - acc: 1.0000\n", + "Epoch 899/1000\n", + "26/26 [==============================] - 0s 389us/sample - loss: 8.6728e-04 - acc: 1.0000\n", + "Epoch 900/1000\n", + "26/26 [==============================] - 0s 510us/sample - loss: 8.6435e-04 - acc: 1.0000\n", + "Epoch 901/1000\n", + "26/26 [==============================] - 0s 378us/sample - loss: 8.6132e-04 - acc: 1.0000\n", + "Epoch 902/1000\n", + "26/26 [==============================] - 0s 375us/sample - loss: 8.5855e-04 - acc: 1.0000\n", + "Epoch 903/1000\n", + "26/26 [==============================] - 0s 551us/sample - loss: 8.5554e-04 - acc: 1.0000\n", + "Epoch 904/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 8.5273e-04 - acc: 1.0000\n", + "Epoch 905/1000\n", + "26/26 [==============================] - 0s 321us/sample - loss: 8.4990e-04 - acc: 1.0000\n", + "Epoch 906/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 8.4710e-04 - acc: 1.0000\n", + "Epoch 907/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 8.4430e-04 - acc: 1.0000\n", + "Epoch 908/1000\n", + "26/26 [==============================] - 0s 404us/sample - loss: 8.4181e-04 - acc: 1.0000\n", + "Epoch 909/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 8.3837e-04 - acc: 1.0000\n", + "Epoch 910/1000\n", + "26/26 [==============================] - 0s 340us/sample - loss: 8.3549e-04 - acc: 1.0000\n", + "Epoch 911/1000\n", + "26/26 [==============================] - 0s 297us/sample - loss: 8.3257e-04 - acc: 1.0000\n", + "Epoch 912/1000\n", + "26/26 [==============================] - 0s 491us/sample - loss: 8.2977e-04 - acc: 1.0000\n", + "Epoch 913/1000\n", + "26/26 [==============================] - 0s 326us/sample - loss: 8.2689e-04 - acc: 1.0000\n", + "Epoch 914/1000\n", + "26/26 [==============================] - 0s 376us/sample - loss: 8.2446e-04 - acc: 1.0000\n", + "Epoch 915/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 8.2171e-04 - acc: 1.0000\n", + "Epoch 916/1000\n", + "26/26 [==============================] - 0s 373us/sample - loss: 8.1915e-04 - acc: 1.0000\n", + "Epoch 917/1000\n", + "26/26 [==============================] - 0s 304us/sample - loss: 8.1657e-04 - acc: 1.0000\n", + "Epoch 918/1000\n", + "26/26 [==============================] - 0s 340us/sample - loss: 8.1368e-04 - acc: 1.0000\n", + "Epoch 919/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 8.1075e-04 - acc: 1.0000\n", + "Epoch 920/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 8.0821e-04 - acc: 1.0000\n", + "Epoch 921/1000\n", + "26/26 [==============================] - 0s 330us/sample - loss: 8.0514e-04 - acc: 1.0000\n", + "Epoch 922/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 8.0250e-04 - acc: 1.0000\n", + "Epoch 923/1000\n", + "26/26 [==============================] - 0s 496us/sample - loss: 7.9946e-04 - acc: 1.0000\n", + "Epoch 924/1000\n", + "26/26 [==============================] - 0s 429us/sample - loss: 7.9721e-04 - acc: 1.0000\n", + "Epoch 925/1000\n", + "26/26 [==============================] - 0s 349us/sample - loss: 7.9391e-04 - acc: 1.0000\n", + "Epoch 926/1000\n", + "26/26 [==============================] - 0s 560us/sample - loss: 7.9110e-04 - acc: 1.0000\n", + "Epoch 927/1000\n", + "26/26 [==============================] - 0s 336us/sample - loss: 7.8843e-04 - acc: 1.0000\n", + "Epoch 928/1000\n", + "26/26 [==============================] - 0s 465us/sample - loss: 7.8576e-04 - acc: 1.0000\n", + "Epoch 929/1000\n", + "26/26 [==============================] - 0s 427us/sample - loss: 7.8293e-04 - acc: 1.0000\n", + "Epoch 930/1000\n", + "26/26 [==============================] - 0s 337us/sample - loss: 7.8071e-04 - acc: 1.0000\n", + "Epoch 931/1000\n", + "26/26 [==============================] - 0s 444us/sample - loss: 7.7809e-04 - acc: 1.0000\n", + "Epoch 932/1000\n", + "26/26 [==============================] - 0s 299us/sample - loss: 7.7553e-04 - acc: 1.0000\n", + "Epoch 933/1000\n", + "26/26 [==============================] - 0s 325us/sample - loss: 7.7312e-04 - acc: 1.0000\n", + "Epoch 934/1000\n", + "26/26 [==============================] - 0s 318us/sample - loss: 7.7052e-04 - acc: 1.0000\n", + "Epoch 935/1000\n", + "26/26 [==============================] - 0s 442us/sample - loss: 7.6810e-04 - acc: 1.0000\n", + "Epoch 936/1000\n", + "26/26 [==============================] - 0s 463us/sample - loss: 7.6561e-04 - acc: 1.0000\n", + "Epoch 937/1000\n", + "26/26 [==============================] - 0s 350us/sample - loss: 7.6323e-04 - acc: 1.0000\n", + "Epoch 938/1000\n", + "26/26 [==============================] - 0s 430us/sample - loss: 7.6079e-04 - acc: 1.0000\n", + "Epoch 939/1000\n", + "26/26 [==============================] - 0s 515us/sample - loss: 7.5830e-04 - acc: 1.0000\n", + "Epoch 940/1000\n", + "26/26 [==============================] - 0s 440us/sample - loss: 7.5604e-04 - acc: 1.0000\n", + "Epoch 941/1000\n", + "26/26 [==============================] - 0s 318us/sample - loss: 7.5326e-04 - acc: 1.0000\n", + "Epoch 942/1000\n", + "26/26 [==============================] - 0s 491us/sample - loss: 7.5096e-04 - acc: 1.0000\n", + "Epoch 943/1000\n", + "26/26 [==============================] - 0s 440us/sample - loss: 7.4852e-04 - acc: 1.0000\n", + "Epoch 944/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 7.4616e-04 - acc: 1.0000\n", + "Epoch 945/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 7.4382e-04 - acc: 1.0000\n", + "Epoch 946/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 7.4155e-04 - acc: 1.0000\n", + "Epoch 947/1000\n", + "26/26 [==============================] - 0s 448us/sample - loss: 7.3885e-04 - acc: 1.0000\n", + "Epoch 948/1000\n", + "26/26 [==============================] - 0s 441us/sample - loss: 7.3667e-04 - acc: 1.0000\n", + "Epoch 949/1000\n", + "26/26 [==============================] - 0s 525us/sample - loss: 7.3447e-04 - acc: 1.0000\n", + "Epoch 950/1000\n", + "26/26 [==============================] - 0s 405us/sample - loss: 7.3181e-04 - acc: 1.0000\n", + "Epoch 951/1000\n", + "26/26 [==============================] - 0s 421us/sample - loss: 7.2932e-04 - acc: 1.0000\n", + "Epoch 952/1000\n", + "26/26 [==============================] - 0s 368us/sample - loss: 7.2682e-04 - acc: 1.0000\n", + "Epoch 953/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 7.2428e-04 - acc: 1.0000\n", + "Epoch 954/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 7.2192e-04 - acc: 1.0000\n", + "Epoch 955/1000\n", + "26/26 [==============================] - 0s 332us/sample - loss: 7.1955e-04 - acc: 1.0000\n", + "Epoch 956/1000\n", + "26/26 [==============================] - 0s 303us/sample - loss: 7.1718e-04 - acc: 1.0000\n", + "Epoch 957/1000\n", + "26/26 [==============================] - 0s 325us/sample - loss: 7.1498e-04 - acc: 1.0000\n", + "Epoch 958/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 7.1282e-04 - acc: 1.0000\n", + "Epoch 959/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 7.1044e-04 - acc: 1.0000\n", + "Epoch 960/1000\n", + "26/26 [==============================] - 0s 332us/sample - loss: 7.0836e-04 - acc: 1.0000\n", + "Epoch 961/1000\n", + "26/26 [==============================] - 0s 300us/sample - loss: 7.0611e-04 - acc: 1.0000\n", + "Epoch 962/1000\n", + "26/26 [==============================] - 0s 385us/sample - loss: 7.0399e-04 - acc: 1.0000\n", + "Epoch 963/1000\n", + "26/26 [==============================] - 0s 294us/sample - loss: 7.0178e-04 - acc: 1.0000\n", + "Epoch 964/1000\n", + "26/26 [==============================] - 0s 281us/sample - loss: 6.9945e-04 - acc: 1.0000\n", + "Epoch 965/1000\n", + "26/26 [==============================] - 0s 311us/sample - loss: 6.9712e-04 - acc: 1.0000\n", + "Epoch 966/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 6.9452e-04 - acc: 1.0000\n", + "Epoch 967/1000\n", + "26/26 [==============================] - 0s 306us/sample - loss: 6.9264e-04 - acc: 1.0000\n", + "Epoch 968/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 6.9005e-04 - acc: 1.0000\n", + "Epoch 969/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 6.8808e-04 - acc: 1.0000\n", + "Epoch 970/1000\n", + "26/26 [==============================] - 0s 326us/sample - loss: 6.8596e-04 - acc: 1.0000\n", + "Epoch 971/1000\n", + "26/26 [==============================] - 0s 462us/sample - loss: 6.8387e-04 - acc: 1.0000\n", + "Epoch 972/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 6.8177e-04 - acc: 1.0000\n", + "Epoch 973/1000\n", + "26/26 [==============================] - 0s 327us/sample - loss: 6.7971e-04 - acc: 1.0000\n", + "Epoch 974/1000\n", + "26/26 [==============================] - 0s 405us/sample - loss: 6.7773e-04 - acc: 1.0000\n", + "Epoch 975/1000\n", + "26/26 [==============================] - 0s 426us/sample - loss: 6.7538e-04 - acc: 1.0000\n", + "Epoch 976/1000\n", + "26/26 [==============================] - 0s 333us/sample - loss: 6.7323e-04 - acc: 1.0000\n", + "Epoch 977/1000\n", + "26/26 [==============================] - 0s 315us/sample - loss: 6.7110e-04 - acc: 1.0000\n", + "Epoch 978/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 6.6902e-04 - acc: 1.0000\n", + "Epoch 979/1000\n", + "26/26 [==============================] - 0s 420us/sample - loss: 6.6703e-04 - acc: 1.0000\n", + "Epoch 980/1000\n", + "26/26 [==============================] - 0s 419us/sample - loss: 6.6497e-04 - acc: 1.0000\n", + "Epoch 981/1000\n", + "26/26 [==============================] - 0s 452us/sample - loss: 6.6300e-04 - acc: 1.0000\n", + "Epoch 982/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 6.6105e-04 - acc: 1.0000\n", + "Epoch 983/1000\n", + "26/26 [==============================] - 0s 507us/sample - loss: 6.5898e-04 - acc: 1.0000\n", + "Epoch 984/1000\n", + "26/26 [==============================] - 0s 373us/sample - loss: 6.5700e-04 - acc: 1.0000\n", + "Epoch 985/1000\n", + "26/26 [==============================] - 0s 393us/sample - loss: 6.5506e-04 - acc: 1.0000\n", + "Epoch 986/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 6.5288e-04 - acc: 1.0000\n", + "Epoch 987/1000\n", + "26/26 [==============================] - 0s 435us/sample - loss: 6.5099e-04 - acc: 1.0000\n", + "Epoch 988/1000\n", + "26/26 [==============================] - 0s 423us/sample - loss: 6.4897e-04 - acc: 1.0000\n", + "Epoch 989/1000\n", + "26/26 [==============================] - 0s 314us/sample - loss: 6.4694e-04 - acc: 1.0000\n", + "Epoch 990/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 6.4497e-04 - acc: 1.0000\n", + "Epoch 991/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 6.4279e-04 - acc: 1.0000\n", + "Epoch 992/1000\n", + "26/26 [==============================] - 0s 443us/sample - loss: 6.4098e-04 - acc: 1.0000\n", + "Epoch 993/1000\n", + "26/26 [==============================] - 0s 423us/sample - loss: 6.3900e-04 - acc: 1.0000\n", + "Epoch 994/1000\n", + "26/26 [==============================] - 0s 479us/sample - loss: 6.3675e-04 - acc: 1.0000\n", + "Epoch 995/1000\n", + "26/26 [==============================] - 0s 428us/sample - loss: 6.3473e-04 - acc: 1.0000\n", + "Epoch 996/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 6.3240e-04 - acc: 1.0000\n", + "Epoch 997/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 6.3059e-04 - acc: 1.0000\n", + "Epoch 998/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 6.2870e-04 - acc: 1.0000\n", + "Epoch 999/1000\n", + "26/26 [==============================] - 0s 297us/sample - loss: 6.2625e-04 - acc: 1.0000\n", + "Epoch 1000/1000\n", + "26/26 [==============================] - 0s 344us/sample - loss: 6.2455e-04 - acc: 1.0000\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "\n", + "try:\n", + " model.load(\"model.tflearn\")\n", + "except:\n", + " model.fit(training, output, epochs=1000, batch_size=8)\n", + " model.save(\"model.tflearn\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "yMA7TxSt8tuR", + "outputId": "4fe84cc9-a2b1-4a00-a6db-9d577a663bc9" + }, + "execution_count": 28, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Train on 26 samples\n", + "Epoch 1/1000\n", + "26/26 [==============================] - 0s 489us/sample - loss: 6.2248e-04 - acc: 1.0000\n", + "Epoch 2/1000\n", + "26/26 [==============================] - 0s 399us/sample - loss: 6.2066e-04 - acc: 1.0000\n", + "Epoch 3/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 6.1863e-04 - acc: 1.0000\n", + "Epoch 4/1000\n", + "26/26 [==============================] - 0s 493us/sample - loss: 6.1703e-04 - acc: 1.0000\n", + "Epoch 5/1000\n", + "26/26 [==============================] - 0s 429us/sample - loss: 6.1497e-04 - acc: 1.0000\n", + "Epoch 6/1000\n", + "26/26 [==============================] - 0s 447us/sample - loss: 6.1282e-04 - acc: 1.0000\n", + "Epoch 7/1000\n", + "26/26 [==============================] - 0s 419us/sample - loss: 6.1096e-04 - acc: 1.0000\n", + "Epoch 8/1000\n", + "26/26 [==============================] - 0s 419us/sample - loss: 6.0934e-04 - acc: 1.0000\n", + "Epoch 9/1000\n", + "26/26 [==============================] - 0s 412us/sample - loss: 6.0735e-04 - acc: 1.0000\n", + "Epoch 10/1000\n", + "26/26 [==============================] - 0s 435us/sample - loss: 6.0566e-04 - acc: 1.0000\n", + "Epoch 11/1000\n", + "26/26 [==============================] - 0s 423us/sample - loss: 6.0332e-04 - acc: 1.0000\n", + "Epoch 12/1000\n", + "26/26 [==============================] - 0s 438us/sample - loss: 6.0162e-04 - acc: 1.0000\n", + "Epoch 13/1000\n", + "26/26 [==============================] - 0s 421us/sample - loss: 5.9995e-04 - acc: 1.0000\n", + "Epoch 14/1000\n", + "26/26 [==============================] - 0s 418us/sample - loss: 5.9787e-04 - acc: 1.0000\n", + "Epoch 15/1000\n", + "26/26 [==============================] - 0s 524us/sample - loss: 5.9599e-04 - acc: 1.0000\n", + "Epoch 16/1000\n", + "26/26 [==============================] - 0s 412us/sample - loss: 5.9400e-04 - acc: 1.0000\n", + "Epoch 17/1000\n", + "26/26 [==============================] - 0s 403us/sample - loss: 5.9228e-04 - acc: 1.0000\n", + "Epoch 18/1000\n", + "26/26 [==============================] - 0s 432us/sample - loss: 5.9041e-04 - acc: 1.0000\n", + "Epoch 19/1000\n", + "26/26 [==============================] - 0s 427us/sample - loss: 5.8872e-04 - acc: 1.0000\n", + "Epoch 20/1000\n", + "26/26 [==============================] - 0s 537us/sample - loss: 5.8697e-04 - acc: 1.0000\n", + "Epoch 21/1000\n", + "26/26 [==============================] - 0s 609us/sample - loss: 5.8517e-04 - acc: 1.0000\n", + "Epoch 22/1000\n", + "26/26 [==============================] - 0s 427us/sample - loss: 5.8335e-04 - acc: 1.0000\n", + "Epoch 23/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 5.8174e-04 - acc: 1.0000\n", + "Epoch 24/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 5.7976e-04 - acc: 1.0000\n", + "Epoch 25/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 5.7799e-04 - acc: 1.0000\n", + "Epoch 26/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 5.7619e-04 - acc: 1.0000\n", + "Epoch 27/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 5.7449e-04 - acc: 1.0000\n", + "Epoch 28/1000\n", + "26/26 [==============================] - 0s 383us/sample - loss: 5.7280e-04 - acc: 1.0000\n", + "Epoch 29/1000\n", + "26/26 [==============================] - 0s 416us/sample - loss: 5.7085e-04 - acc: 1.0000\n", + "Epoch 30/1000\n", + "26/26 [==============================] - 0s 384us/sample - loss: 5.6909e-04 - acc: 1.0000\n", + "Epoch 31/1000\n", + "26/26 [==============================] - 0s 411us/sample - loss: 5.6732e-04 - acc: 1.0000\n", + "Epoch 32/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 5.6559e-04 - acc: 1.0000\n", + "Epoch 33/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 5.6381e-04 - acc: 1.0000\n", + "Epoch 34/1000\n", + "26/26 [==============================] - 0s 426us/sample - loss: 5.6217e-04 - acc: 1.0000\n", + "Epoch 35/1000\n", + "26/26 [==============================] - 0s 391us/sample - loss: 5.6038e-04 - acc: 1.0000\n", + "Epoch 36/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 5.5837e-04 - acc: 1.0000\n", + "Epoch 37/1000\n", + "26/26 [==============================] - 0s 380us/sample - loss: 5.5687e-04 - acc: 1.0000\n", + "Epoch 38/1000\n", + "26/26 [==============================] - 0s 378us/sample - loss: 5.5503e-04 - acc: 1.0000\n", + "Epoch 39/1000\n", + "26/26 [==============================] - 0s 427us/sample - loss: 5.5335e-04 - acc: 1.0000\n", + "Epoch 40/1000\n", + "26/26 [==============================] - 0s 380us/sample - loss: 5.5153e-04 - acc: 1.0000\n", + "Epoch 41/1000\n", + "26/26 [==============================] - 0s 389us/sample - loss: 5.4990e-04 - acc: 1.0000\n", + "Epoch 42/1000\n", + "26/26 [==============================] - 0s 380us/sample - loss: 5.4792e-04 - acc: 1.0000\n", + "Epoch 43/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 5.4625e-04 - acc: 1.0000\n", + "Epoch 44/1000\n", + "26/26 [==============================] - 0s 401us/sample - loss: 5.4460e-04 - acc: 1.0000\n", + "Epoch 45/1000\n", + "26/26 [==============================] - 0s 398us/sample - loss: 5.4289e-04 - acc: 1.0000\n", + "Epoch 46/1000\n", + "26/26 [==============================] - 0s 401us/sample - loss: 5.4130e-04 - acc: 1.0000\n", + "Epoch 47/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 5.3942e-04 - acc: 1.0000\n", + "Epoch 48/1000\n", + "26/26 [==============================] - 0s 407us/sample - loss: 5.3757e-04 - acc: 1.0000\n", + "Epoch 49/1000\n", + "26/26 [==============================] - 0s 326us/sample - loss: 5.3597e-04 - acc: 1.0000\n", + "Epoch 50/1000\n", + "26/26 [==============================] - 0s 360us/sample - loss: 5.3446e-04 - acc: 1.0000\n", + "Epoch 51/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 5.3264e-04 - acc: 1.0000\n", + "Epoch 52/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 5.3109e-04 - acc: 1.0000\n", + "Epoch 53/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 5.2951e-04 - acc: 1.0000\n", + "Epoch 54/1000\n", + "26/26 [==============================] - 0s 458us/sample - loss: 5.2789e-04 - acc: 1.0000\n", + "Epoch 55/1000\n", + "26/26 [==============================] - 0s 407us/sample - loss: 5.2624e-04 - acc: 1.0000\n", + "Epoch 56/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 5.2468e-04 - acc: 1.0000\n", + "Epoch 57/1000\n", + "26/26 [==============================] - 0s 454us/sample - loss: 5.2304e-04 - acc: 1.0000\n", + "Epoch 58/1000\n", + "26/26 [==============================] - 0s 425us/sample - loss: 5.2157e-04 - acc: 1.0000\n", + "Epoch 59/1000\n", + "26/26 [==============================] - 0s 376us/sample - loss: 5.1991e-04 - acc: 1.0000\n", + "Epoch 60/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 5.1817e-04 - acc: 1.0000\n", + "Epoch 61/1000\n", + "26/26 [==============================] - 0s 416us/sample - loss: 5.1680e-04 - acc: 1.0000\n", + "Epoch 62/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 5.1523e-04 - acc: 1.0000\n", + "Epoch 63/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 5.1350e-04 - acc: 1.0000\n", + "Epoch 64/1000\n", + "26/26 [==============================] - 0s 372us/sample - loss: 5.1207e-04 - acc: 1.0000\n", + "Epoch 65/1000\n", + "26/26 [==============================] - 0s 343us/sample - loss: 5.1046e-04 - acc: 1.0000\n", + "Epoch 66/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 5.0885e-04 - acc: 1.0000\n", + "Epoch 67/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 5.0737e-04 - acc: 1.0000\n", + "Epoch 68/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 5.0597e-04 - acc: 1.0000\n", + "Epoch 69/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 5.0452e-04 - acc: 1.0000\n", + "Epoch 70/1000\n", + "26/26 [==============================] - 0s 344us/sample - loss: 5.0289e-04 - acc: 1.0000\n", + "Epoch 71/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 5.0147e-04 - acc: 1.0000\n", + "Epoch 72/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 5.0005e-04 - acc: 1.0000\n", + "Epoch 73/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 4.9858e-04 - acc: 1.0000\n", + "Epoch 74/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 4.9726e-04 - acc: 1.0000\n", + "Epoch 75/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 4.9565e-04 - acc: 1.0000\n", + "Epoch 76/1000\n", + "26/26 [==============================] - 0s 336us/sample - loss: 4.9422e-04 - acc: 1.0000\n", + "Epoch 77/1000\n", + "26/26 [==============================] - 0s 322us/sample - loss: 4.9277e-04 - acc: 1.0000\n", + "Epoch 78/1000\n", + "26/26 [==============================] - 0s 326us/sample - loss: 4.9121e-04 - acc: 1.0000\n", + "Epoch 79/1000\n", + "26/26 [==============================] - 0s 494us/sample - loss: 4.8976e-04 - acc: 1.0000\n", + "Epoch 80/1000\n", + "26/26 [==============================] - 0s 520us/sample - loss: 4.8833e-04 - acc: 1.0000\n", + "Epoch 81/1000\n", + "26/26 [==============================] - 0s 334us/sample - loss: 4.8673e-04 - acc: 1.0000\n", + "Epoch 82/1000\n", + "26/26 [==============================] - 0s 328us/sample - loss: 4.8530e-04 - acc: 1.0000\n", + "Epoch 83/1000\n", + "26/26 [==============================] - 0s 330us/sample - loss: 4.8383e-04 - acc: 1.0000\n", + "Epoch 84/1000\n", + "26/26 [==============================] - 0s 496us/sample - loss: 4.8240e-04 - acc: 1.0000\n", + "Epoch 85/1000\n", + "26/26 [==============================] - 0s 370us/sample - loss: 4.8099e-04 - acc: 1.0000\n", + "Epoch 86/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 4.7955e-04 - acc: 1.0000\n", + "Epoch 87/1000\n", + "26/26 [==============================] - 0s 411us/sample - loss: 4.7813e-04 - acc: 1.0000\n", + "Epoch 88/1000\n", + "26/26 [==============================] - 0s 405us/sample - loss: 4.7681e-04 - acc: 1.0000\n", + "Epoch 89/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 4.7548e-04 - acc: 1.0000\n", + "Epoch 90/1000\n", + "26/26 [==============================] - 0s 391us/sample - loss: 4.7394e-04 - acc: 1.0000\n", + "Epoch 91/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 4.7261e-04 - acc: 1.0000\n", + "Epoch 92/1000\n", + "26/26 [==============================] - 0s 474us/sample - loss: 4.7114e-04 - acc: 1.0000\n", + "Epoch 93/1000\n", + "26/26 [==============================] - 0s 402us/sample - loss: 4.6956e-04 - acc: 1.0000\n", + "Epoch 94/1000\n", + "26/26 [==============================] - 0s 393us/sample - loss: 4.6812e-04 - acc: 1.0000\n", + "Epoch 95/1000\n", + "26/26 [==============================] - 0s 387us/sample - loss: 4.6673e-04 - acc: 1.0000\n", + "Epoch 96/1000\n", + "26/26 [==============================] - 0s 327us/sample - loss: 4.6538e-04 - acc: 1.0000\n", + "Epoch 97/1000\n", + "26/26 [==============================] - 0s 333us/sample - loss: 4.6409e-04 - acc: 1.0000\n", + "Epoch 98/1000\n", + "26/26 [==============================] - 0s 434us/sample - loss: 4.6280e-04 - acc: 1.0000\n", + "Epoch 99/1000\n", + "26/26 [==============================] - 0s 350us/sample - loss: 4.6143e-04 - acc: 1.0000\n", + "Epoch 100/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 4.6023e-04 - acc: 1.0000\n", + "Epoch 101/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 4.5888e-04 - acc: 1.0000\n", + "Epoch 102/1000\n", + "26/26 [==============================] - 0s 393us/sample - loss: 4.5768e-04 - acc: 1.0000\n", + "Epoch 103/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 4.5639e-04 - acc: 1.0000\n", + "Epoch 104/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 4.5495e-04 - acc: 1.0000\n", + "Epoch 105/1000\n", + "26/26 [==============================] - 0s 380us/sample - loss: 4.5361e-04 - acc: 1.0000\n", + "Epoch 106/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 4.5236e-04 - acc: 1.0000\n", + "Epoch 107/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 4.5087e-04 - acc: 1.0000\n", + "Epoch 108/1000\n", + "26/26 [==============================] - 0s 387us/sample - loss: 4.4956e-04 - acc: 1.0000\n", + "Epoch 109/1000\n", + "26/26 [==============================] - 0s 423us/sample - loss: 4.4813e-04 - acc: 1.0000\n", + "Epoch 110/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 4.4693e-04 - acc: 1.0000\n", + "Epoch 111/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 4.4548e-04 - acc: 1.0000\n", + "Epoch 112/1000\n", + "26/26 [==============================] - 0s 425us/sample - loss: 4.4421e-04 - acc: 1.0000\n", + "Epoch 113/1000\n", + "26/26 [==============================] - 0s 325us/sample - loss: 4.4296e-04 - acc: 1.0000\n", + "Epoch 114/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 4.4168e-04 - acc: 1.0000\n", + "Epoch 115/1000\n", + "26/26 [==============================] - 0s 384us/sample - loss: 4.4049e-04 - acc: 1.0000\n", + "Epoch 116/1000\n", + "26/26 [==============================] - 0s 385us/sample - loss: 4.3917e-04 - acc: 1.0000\n", + "Epoch 117/1000\n", + "26/26 [==============================] - 0s 394us/sample - loss: 4.3786e-04 - acc: 1.0000\n", + "Epoch 118/1000\n", + "26/26 [==============================] - 0s 406us/sample - loss: 4.3642e-04 - acc: 1.0000\n", + "Epoch 119/1000\n", + "26/26 [==============================] - 0s 437us/sample - loss: 4.3526e-04 - acc: 1.0000\n", + "Epoch 120/1000\n", + "26/26 [==============================] - 0s 409us/sample - loss: 4.3361e-04 - acc: 1.0000\n", + "Epoch 121/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 4.3237e-04 - acc: 1.0000\n", + "Epoch 122/1000\n", + "26/26 [==============================] - 0s 389us/sample - loss: 4.3097e-04 - acc: 1.0000\n", + "Epoch 123/1000\n", + "26/26 [==============================] - 0s 375us/sample - loss: 4.2969e-04 - acc: 1.0000\n", + "Epoch 124/1000\n", + "26/26 [==============================] - 0s 373us/sample - loss: 4.2855e-04 - acc: 1.0000\n", + "Epoch 125/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 4.2705e-04 - acc: 1.0000\n", + "Epoch 126/1000\n", + "26/26 [==============================] - 0s 386us/sample - loss: 4.2577e-04 - acc: 1.0000\n", + "Epoch 127/1000\n", + "26/26 [==============================] - 0s 387us/sample - loss: 4.2442e-04 - acc: 1.0000\n", + "Epoch 128/1000\n", + "26/26 [==============================] - 0s 391us/sample - loss: 4.2311e-04 - acc: 1.0000\n", + "Epoch 129/1000\n", + "26/26 [==============================] - 0s 404us/sample - loss: 4.2183e-04 - acc: 1.0000\n", + "Epoch 130/1000\n", + "26/26 [==============================] - 0s 428us/sample - loss: 4.2044e-04 - acc: 1.0000\n", + "Epoch 131/1000\n", + "26/26 [==============================] - 0s 404us/sample - loss: 4.1890e-04 - acc: 1.0000\n", + "Epoch 132/1000\n", + "26/26 [==============================] - 0s 454us/sample - loss: 4.1774e-04 - acc: 1.0000\n", + "Epoch 133/1000\n", + "26/26 [==============================] - 0s 393us/sample - loss: 4.1641e-04 - acc: 1.0000\n", + "Epoch 134/1000\n", + "26/26 [==============================] - 0s 580us/sample - loss: 4.1510e-04 - acc: 1.0000\n", + "Epoch 135/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 4.1385e-04 - acc: 1.0000\n", + "Epoch 136/1000\n", + "26/26 [==============================] - 0s 368us/sample - loss: 4.1266e-04 - acc: 1.0000\n", + "Epoch 137/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 4.1138e-04 - acc: 1.0000\n", + "Epoch 138/1000\n", + "26/26 [==============================] - 0s 386us/sample - loss: 4.1019e-04 - acc: 1.0000\n", + "Epoch 139/1000\n", + "26/26 [==============================] - 0s 375us/sample - loss: 4.0880e-04 - acc: 1.0000\n", + "Epoch 140/1000\n", + "26/26 [==============================] - 0s 372us/sample - loss: 4.0776e-04 - acc: 1.0000\n", + "Epoch 141/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 4.0666e-04 - acc: 1.0000\n", + "Epoch 142/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 4.0545e-04 - acc: 1.0000\n", + "Epoch 143/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 4.0426e-04 - acc: 1.0000\n", + "Epoch 144/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 4.0318e-04 - acc: 1.0000\n", + "Epoch 145/1000\n", + "26/26 [==============================] - 0s 387us/sample - loss: 4.0199e-04 - acc: 1.0000\n", + "Epoch 146/1000\n", + "26/26 [==============================] - 0s 547us/sample - loss: 4.0076e-04 - acc: 1.0000\n", + "Epoch 147/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 3.9951e-04 - acc: 1.0000\n", + "Epoch 148/1000\n", + "26/26 [==============================] - 0s 405us/sample - loss: 3.9826e-04 - acc: 1.0000\n", + "Epoch 149/1000\n", + "26/26 [==============================] - 0s 597us/sample - loss: 3.9715e-04 - acc: 1.0000\n", + "Epoch 150/1000\n", + "26/26 [==============================] - 0s 534us/sample - loss: 3.9590e-04 - acc: 1.0000\n", + "Epoch 151/1000\n", + "26/26 [==============================] - 0s 645us/sample - loss: 3.9481e-04 - acc: 1.0000\n", + "Epoch 152/1000\n", + "26/26 [==============================] - 0s 496us/sample - loss: 3.9368e-04 - acc: 1.0000\n", + "Epoch 153/1000\n", + "26/26 [==============================] - 0s 398us/sample - loss: 3.9251e-04 - acc: 1.0000\n", + "Epoch 154/1000\n", + "26/26 [==============================] - 0s 441us/sample - loss: 3.9124e-04 - acc: 1.0000\n", + "Epoch 155/1000\n", + "26/26 [==============================] - 0s 408us/sample - loss: 3.9008e-04 - acc: 1.0000\n", + "Epoch 156/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 3.8893e-04 - acc: 1.0000\n", + "Epoch 157/1000\n", + "26/26 [==============================] - 0s 387us/sample - loss: 3.8780e-04 - acc: 1.0000\n", + "Epoch 158/1000\n", + "26/26 [==============================] - 0s 355us/sample - loss: 3.8666e-04 - acc: 1.0000\n", + "Epoch 159/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 3.8555e-04 - acc: 1.0000\n", + "Epoch 160/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 3.8424e-04 - acc: 1.0000\n", + "Epoch 161/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 3.8309e-04 - acc: 1.0000\n", + "Epoch 162/1000\n", + "26/26 [==============================] - 0s 404us/sample - loss: 3.8202e-04 - acc: 1.0000\n", + "Epoch 163/1000\n", + "26/26 [==============================] - 0s 356us/sample - loss: 3.8078e-04 - acc: 1.0000\n", + "Epoch 164/1000\n", + "26/26 [==============================] - 0s 376us/sample - loss: 3.7967e-04 - acc: 1.0000\n", + "Epoch 165/1000\n", + "26/26 [==============================] - 0s 464us/sample - loss: 3.7846e-04 - acc: 1.0000\n", + "Epoch 166/1000\n", + "26/26 [==============================] - 0s 387us/sample - loss: 3.7740e-04 - acc: 1.0000\n", + "Epoch 167/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 3.7616e-04 - acc: 1.0000\n", + "Epoch 168/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 3.7519e-04 - acc: 1.0000\n", + "Epoch 169/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 3.7416e-04 - acc: 1.0000\n", + "Epoch 170/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 3.7314e-04 - acc: 1.0000\n", + "Epoch 171/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 3.7219e-04 - acc: 1.0000\n", + "Epoch 172/1000\n", + "26/26 [==============================] - 0s 377us/sample - loss: 3.7108e-04 - acc: 1.0000\n", + "Epoch 173/1000\n", + "26/26 [==============================] - 0s 421us/sample - loss: 3.7013e-04 - acc: 1.0000\n", + "Epoch 174/1000\n", + "26/26 [==============================] - 0s 408us/sample - loss: 3.6919e-04 - acc: 1.0000\n", + "Epoch 175/1000\n", + "26/26 [==============================] - 0s 353us/sample - loss: 3.6812e-04 - acc: 1.0000\n", + "Epoch 176/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 3.6692e-04 - acc: 1.0000\n", + "Epoch 177/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 3.6580e-04 - acc: 1.0000\n", + "Epoch 178/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 3.6477e-04 - acc: 1.0000\n", + "Epoch 179/1000\n", + "26/26 [==============================] - 0s 356us/sample - loss: 3.6375e-04 - acc: 1.0000\n", + "Epoch 180/1000\n", + "26/26 [==============================] - 0s 370us/sample - loss: 3.6264e-04 - acc: 1.0000\n", + "Epoch 181/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 3.6147e-04 - acc: 1.0000\n", + "Epoch 182/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 3.6052e-04 - acc: 1.0000\n", + "Epoch 183/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 3.5941e-04 - acc: 1.0000\n", + "Epoch 184/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 3.5835e-04 - acc: 1.0000\n", + "Epoch 185/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 3.5740e-04 - acc: 1.0000\n", + "Epoch 186/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 3.5625e-04 - acc: 1.0000\n", + "Epoch 187/1000\n", + "26/26 [==============================] - 0s 406us/sample - loss: 3.5537e-04 - acc: 1.0000\n", + "Epoch 188/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 3.5412e-04 - acc: 1.0000\n", + "Epoch 189/1000\n", + "26/26 [==============================] - 0s 383us/sample - loss: 3.5301e-04 - acc: 1.0000\n", + "Epoch 190/1000\n", + "26/26 [==============================] - 0s 353us/sample - loss: 3.5195e-04 - acc: 1.0000\n", + "Epoch 191/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 3.5090e-04 - acc: 1.0000\n", + "Epoch 192/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 3.4978e-04 - acc: 1.0000\n", + "Epoch 193/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 3.4898e-04 - acc: 1.0000\n", + "Epoch 194/1000\n", + "26/26 [==============================] - 0s 331us/sample - loss: 3.4778e-04 - acc: 1.0000\n", + "Epoch 195/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 3.4682e-04 - acc: 1.0000\n", + "Epoch 196/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 3.4591e-04 - acc: 1.0000\n", + "Epoch 197/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 3.4486e-04 - acc: 1.0000\n", + "Epoch 198/1000\n", + "26/26 [==============================] - 0s 435us/sample - loss: 3.4399e-04 - acc: 1.0000\n", + "Epoch 199/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 3.4315e-04 - acc: 1.0000\n", + "Epoch 200/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 3.4212e-04 - acc: 1.0000\n", + "Epoch 201/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 3.4122e-04 - acc: 1.0000\n", + "Epoch 202/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 3.4026e-04 - acc: 1.0000\n", + "Epoch 203/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 3.3921e-04 - acc: 1.0000\n", + "Epoch 204/1000\n", + "26/26 [==============================] - 0s 368us/sample - loss: 3.3838e-04 - acc: 1.0000\n", + "Epoch 205/1000\n", + "26/26 [==============================] - 0s 370us/sample - loss: 3.3737e-04 - acc: 1.0000\n", + "Epoch 206/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 3.3644e-04 - acc: 1.0000\n", + "Epoch 207/1000\n", + "26/26 [==============================] - 0s 356us/sample - loss: 3.3533e-04 - acc: 1.0000\n", + "Epoch 208/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 3.3433e-04 - acc: 1.0000\n", + "Epoch 209/1000\n", + "26/26 [==============================] - 0s 398us/sample - loss: 3.3337e-04 - acc: 1.0000\n", + "Epoch 210/1000\n", + "26/26 [==============================] - 0s 445us/sample - loss: 3.3237e-04 - acc: 1.0000\n", + "Epoch 211/1000\n", + "26/26 [==============================] - 0s 598us/sample - loss: 3.3145e-04 - acc: 1.0000\n", + "Epoch 212/1000\n", + "26/26 [==============================] - 0s 427us/sample - loss: 3.3054e-04 - acc: 1.0000\n", + "Epoch 213/1000\n", + "26/26 [==============================] - 0s 427us/sample - loss: 3.2956e-04 - acc: 1.0000\n", + "Epoch 214/1000\n", + "26/26 [==============================] - 0s 454us/sample - loss: 3.2869e-04 - acc: 1.0000\n", + "Epoch 215/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 3.2783e-04 - acc: 1.0000\n", + "Epoch 216/1000\n", + "26/26 [==============================] - 0s 380us/sample - loss: 3.2686e-04 - acc: 1.0000\n", + "Epoch 217/1000\n", + "26/26 [==============================] - 0s 399us/sample - loss: 3.2591e-04 - acc: 1.0000\n", + "Epoch 218/1000\n", + "26/26 [==============================] - 0s 376us/sample - loss: 3.2486e-04 - acc: 1.0000\n", + "Epoch 219/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 3.2388e-04 - acc: 1.0000\n", + "Epoch 220/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 3.2296e-04 - acc: 1.0000\n", + "Epoch 221/1000\n", + "26/26 [==============================] - 0s 333us/sample - loss: 3.2212e-04 - acc: 1.0000\n", + "Epoch 222/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 3.2120e-04 - acc: 1.0000\n", + "Epoch 223/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 3.2033e-04 - acc: 1.0000\n", + "Epoch 224/1000\n", + "26/26 [==============================] - 0s 353us/sample - loss: 3.1947e-04 - acc: 1.0000\n", + "Epoch 225/1000\n", + "26/26 [==============================] - 0s 356us/sample - loss: 3.1854e-04 - acc: 1.0000\n", + "Epoch 226/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 3.1776e-04 - acc: 1.0000\n", + "Epoch 227/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 3.1695e-04 - acc: 1.0000\n", + "Epoch 228/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 3.1607e-04 - acc: 1.0000\n", + "Epoch 229/1000\n", + "26/26 [==============================] - 0s 385us/sample - loss: 3.1524e-04 - acc: 1.0000\n", + "Epoch 230/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 3.1406e-04 - acc: 1.0000\n", + "Epoch 231/1000\n", + "26/26 [==============================] - 0s 408us/sample - loss: 3.1306e-04 - acc: 1.0000\n", + "Epoch 232/1000\n", + "26/26 [==============================] - 0s 373us/sample - loss: 3.1218e-04 - acc: 1.0000\n", + "Epoch 233/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 3.1125e-04 - acc: 1.0000\n", + "Epoch 234/1000\n", + "26/26 [==============================] - 0s 376us/sample - loss: 3.1037e-04 - acc: 1.0000\n", + "Epoch 235/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 3.0947e-04 - acc: 1.0000\n", + "Epoch 236/1000\n", + "26/26 [==============================] - 0s 375us/sample - loss: 3.0859e-04 - acc: 1.0000\n", + "Epoch 237/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 3.0773e-04 - acc: 1.0000\n", + "Epoch 238/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 3.0669e-04 - acc: 1.0000\n", + "Epoch 239/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 3.0594e-04 - acc: 1.0000\n", + "Epoch 240/1000\n", + "26/26 [==============================] - 0s 330us/sample - loss: 3.0485e-04 - acc: 1.0000\n", + "Epoch 241/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 3.0411e-04 - acc: 1.0000\n", + "Epoch 242/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 3.0312e-04 - acc: 1.0000\n", + "Epoch 243/1000\n", + "26/26 [==============================] - 0s 329us/sample - loss: 3.0236e-04 - acc: 1.0000\n", + "Epoch 244/1000\n", + "26/26 [==============================] - 0s 326us/sample - loss: 3.0151e-04 - acc: 1.0000\n", + "Epoch 245/1000\n", + "26/26 [==============================] - 0s 348us/sample - loss: 3.0056e-04 - acc: 1.0000\n", + "Epoch 246/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 2.9972e-04 - acc: 1.0000\n", + "Epoch 247/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 2.9881e-04 - acc: 1.0000\n", + "Epoch 248/1000\n", + "26/26 [==============================] - 0s 356us/sample - loss: 2.9802e-04 - acc: 1.0000\n", + "Epoch 249/1000\n", + "26/26 [==============================] - 0s 337us/sample - loss: 2.9726e-04 - acc: 1.0000\n", + "Epoch 250/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 2.9638e-04 - acc: 1.0000\n", + "Epoch 251/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 2.9573e-04 - acc: 1.0000\n", + "Epoch 252/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 2.9484e-04 - acc: 1.0000\n", + "Epoch 253/1000\n", + "26/26 [==============================] - 0s 380us/sample - loss: 2.9405e-04 - acc: 1.0000\n", + "Epoch 254/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 2.9316e-04 - acc: 1.0000\n", + "Epoch 255/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 2.9238e-04 - acc: 1.0000\n", + "Epoch 256/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 2.9157e-04 - acc: 1.0000\n", + "Epoch 257/1000\n", + "26/26 [==============================] - 0s 589us/sample - loss: 2.9080e-04 - acc: 1.0000\n", + "Epoch 258/1000\n", + "26/26 [==============================] - 0s 455us/sample - loss: 2.9004e-04 - acc: 1.0000\n", + "Epoch 259/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 2.8929e-04 - acc: 1.0000\n", + "Epoch 260/1000\n", + "26/26 [==============================] - 0s 452us/sample - loss: 2.8852e-04 - acc: 1.0000\n", + "Epoch 261/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 2.8779e-04 - acc: 1.0000\n", + "Epoch 262/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 2.8697e-04 - acc: 1.0000\n", + "Epoch 263/1000\n", + "26/26 [==============================] - 0s 449us/sample - loss: 2.8615e-04 - acc: 1.0000\n", + "Epoch 264/1000\n", + "26/26 [==============================] - 0s 372us/sample - loss: 2.8541e-04 - acc: 1.0000\n", + "Epoch 265/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 2.8456e-04 - acc: 1.0000\n", + "Epoch 266/1000\n", + "26/26 [==============================] - 0s 419us/sample - loss: 2.8375e-04 - acc: 1.0000\n", + "Epoch 267/1000\n", + "26/26 [==============================] - 0s 368us/sample - loss: 2.8294e-04 - acc: 1.0000\n", + "Epoch 268/1000\n", + "26/26 [==============================] - 0s 380us/sample - loss: 2.8213e-04 - acc: 1.0000\n", + "Epoch 269/1000\n", + "26/26 [==============================] - 0s 405us/sample - loss: 2.8135e-04 - acc: 1.0000\n", + "Epoch 270/1000\n", + "26/26 [==============================] - 0s 417us/sample - loss: 2.8050e-04 - acc: 1.0000\n", + "Epoch 271/1000\n", + "26/26 [==============================] - 0s 370us/sample - loss: 2.7965e-04 - acc: 1.0000\n", + "Epoch 272/1000\n", + "26/26 [==============================] - 0s 370us/sample - loss: 2.7887e-04 - acc: 1.0000\n", + "Epoch 273/1000\n", + "26/26 [==============================] - 0s 331us/sample - loss: 2.7803e-04 - acc: 1.0000\n", + "Epoch 274/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 2.7721e-04 - acc: 1.0000\n", + "Epoch 275/1000\n", + "26/26 [==============================] - 0s 404us/sample - loss: 2.7646e-04 - acc: 1.0000\n", + "Epoch 276/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 2.7566e-04 - acc: 1.0000\n", + "Epoch 277/1000\n", + "26/26 [==============================] - 0s 442us/sample - loss: 2.7489e-04 - acc: 1.0000\n", + "Epoch 278/1000\n", + "26/26 [==============================] - 0s 494us/sample - loss: 2.7412e-04 - acc: 1.0000\n", + "Epoch 279/1000\n", + "26/26 [==============================] - 0s 494us/sample - loss: 2.7339e-04 - acc: 1.0000\n", + "Epoch 280/1000\n", + "26/26 [==============================] - 0s 542us/sample - loss: 2.7262e-04 - acc: 1.0000\n", + "Epoch 281/1000\n", + "26/26 [==============================] - 0s 413us/sample - loss: 2.7190e-04 - acc: 1.0000\n", + "Epoch 282/1000\n", + "26/26 [==============================] - 0s 409us/sample - loss: 2.7113e-04 - acc: 1.0000\n", + "Epoch 283/1000\n", + "26/26 [==============================] - 0s 636us/sample - loss: 2.7037e-04 - acc: 1.0000\n", + "Epoch 284/1000\n", + "26/26 [==============================] - 0s 504us/sample - loss: 2.6961e-04 - acc: 1.0000\n", + "Epoch 285/1000\n", + "26/26 [==============================] - 0s 594us/sample - loss: 2.6897e-04 - acc: 1.0000\n", + "Epoch 286/1000\n", + "26/26 [==============================] - 0s 410us/sample - loss: 2.6817e-04 - acc: 1.0000\n", + "Epoch 287/1000\n", + "26/26 [==============================] - 0s 470us/sample - loss: 2.6757e-04 - acc: 1.0000\n", + "Epoch 288/1000\n", + "26/26 [==============================] - 0s 378us/sample - loss: 2.6677e-04 - acc: 1.0000\n", + "Epoch 289/1000\n", + "26/26 [==============================] - 0s 344us/sample - loss: 2.6610e-04 - acc: 1.0000\n", + "Epoch 290/1000\n", + "26/26 [==============================] - 0s 638us/sample - loss: 2.6534e-04 - acc: 1.0000\n", + "Epoch 291/1000\n", + "26/26 [==============================] - 0s 378us/sample - loss: 2.6457e-04 - acc: 1.0000\n", + "Epoch 292/1000\n", + "26/26 [==============================] - 0s 332us/sample - loss: 2.6381e-04 - acc: 1.0000\n", + "Epoch 293/1000\n", + "26/26 [==============================] - 0s 329us/sample - loss: 2.6301e-04 - acc: 1.0000\n", + "Epoch 294/1000\n", + "26/26 [==============================] - 0s 430us/sample - loss: 2.6217e-04 - acc: 1.0000\n", + "Epoch 295/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 2.6147e-04 - acc: 1.0000\n", + "Epoch 296/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 2.6074e-04 - acc: 1.0000\n", + "Epoch 297/1000\n", + "26/26 [==============================] - 0s 543us/sample - loss: 2.6007e-04 - acc: 1.0000\n", + "Epoch 298/1000\n", + "26/26 [==============================] - 0s 324us/sample - loss: 2.5931e-04 - acc: 1.0000\n", + "Epoch 299/1000\n", + "26/26 [==============================] - 0s 323us/sample - loss: 2.5868e-04 - acc: 1.0000\n", + "Epoch 300/1000\n", + "26/26 [==============================] - 0s 303us/sample - loss: 2.5799e-04 - acc: 1.0000\n", + "Epoch 301/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 2.5726e-04 - acc: 1.0000\n", + "Epoch 302/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 2.5665e-04 - acc: 1.0000\n", + "Epoch 303/1000\n", + "26/26 [==============================] - 0s 322us/sample - loss: 2.5590e-04 - acc: 1.0000\n", + "Epoch 304/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 2.5522e-04 - acc: 1.0000\n", + "Epoch 305/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 2.5457e-04 - acc: 1.0000\n", + "Epoch 306/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 2.5383e-04 - acc: 1.0000\n", + "Epoch 307/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 2.5306e-04 - acc: 1.0000\n", + "Epoch 308/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 2.5241e-04 - acc: 1.0000\n", + "Epoch 309/1000\n", + "26/26 [==============================] - 0s 407us/sample - loss: 2.5162e-04 - acc: 1.0000\n", + "Epoch 310/1000\n", + "26/26 [==============================] - 0s 432us/sample - loss: 2.5088e-04 - acc: 1.0000\n", + "Epoch 311/1000\n", + "26/26 [==============================] - 0s 368us/sample - loss: 2.5025e-04 - acc: 1.0000\n", + "Epoch 312/1000\n", + "26/26 [==============================] - 0s 331us/sample - loss: 2.4951e-04 - acc: 1.0000\n", + "Epoch 313/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 2.4893e-04 - acc: 1.0000\n", + "Epoch 314/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 2.4823e-04 - acc: 1.0000\n", + "Epoch 315/1000\n", + "26/26 [==============================] - 0s 325us/sample - loss: 2.4755e-04 - acc: 1.0000\n", + "Epoch 316/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 2.4690e-04 - acc: 1.0000\n", + "Epoch 317/1000\n", + "26/26 [==============================] - 0s 336us/sample - loss: 2.4608e-04 - acc: 1.0000\n", + "Epoch 318/1000\n", + "26/26 [==============================] - 0s 511us/sample - loss: 2.4566e-04 - acc: 1.0000\n", + "Epoch 319/1000\n", + "26/26 [==============================] - 0s 391us/sample - loss: 2.4475e-04 - acc: 1.0000\n", + "Epoch 320/1000\n", + "26/26 [==============================] - 0s 350us/sample - loss: 2.4399e-04 - acc: 1.0000\n", + "Epoch 321/1000\n", + "26/26 [==============================] - 0s 325us/sample - loss: 2.4329e-04 - acc: 1.0000\n", + "Epoch 322/1000\n", + "26/26 [==============================] - 0s 373us/sample - loss: 2.4260e-04 - acc: 1.0000\n", + "Epoch 323/1000\n", + "26/26 [==============================] - 0s 328us/sample - loss: 2.4190e-04 - acc: 1.0000\n", + "Epoch 324/1000\n", + "26/26 [==============================] - 0s 340us/sample - loss: 2.4122e-04 - acc: 1.0000\n", + "Epoch 325/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 2.4058e-04 - acc: 1.0000\n", + "Epoch 326/1000\n", + "26/26 [==============================] - 0s 356us/sample - loss: 2.3995e-04 - acc: 1.0000\n", + "Epoch 327/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 2.3926e-04 - acc: 1.0000\n", + "Epoch 328/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 2.3859e-04 - acc: 1.0000\n", + "Epoch 329/1000\n", + "26/26 [==============================] - 0s 323us/sample - loss: 2.3790e-04 - acc: 1.0000\n", + "Epoch 330/1000\n", + "26/26 [==============================] - 0s 309us/sample - loss: 2.3726e-04 - acc: 1.0000\n", + "Epoch 331/1000\n", + "26/26 [==============================] - 0s 315us/sample - loss: 2.3659e-04 - acc: 1.0000\n", + "Epoch 332/1000\n", + "26/26 [==============================] - 0s 298us/sample - loss: 2.3590e-04 - acc: 1.0000\n", + "Epoch 333/1000\n", + "26/26 [==============================] - 0s 310us/sample - loss: 2.3523e-04 - acc: 1.0000\n", + "Epoch 334/1000\n", + "26/26 [==============================] - 0s 293us/sample - loss: 2.3464e-04 - acc: 1.0000\n", + "Epoch 335/1000\n", + "26/26 [==============================] - 0s 286us/sample - loss: 2.3400e-04 - acc: 1.0000\n", + "Epoch 336/1000\n", + "26/26 [==============================] - 0s 344us/sample - loss: 2.3331e-04 - acc: 1.0000\n", + "Epoch 337/1000\n", + "26/26 [==============================] - 0s 317us/sample - loss: 2.3267e-04 - acc: 1.0000\n", + "Epoch 338/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 2.3206e-04 - acc: 1.0000\n", + "Epoch 339/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 2.3138e-04 - acc: 1.0000\n", + "Epoch 340/1000\n", + "26/26 [==============================] - 0s 325us/sample - loss: 2.3071e-04 - acc: 1.0000\n", + "Epoch 341/1000\n", + "26/26 [==============================] - 0s 320us/sample - loss: 2.3019e-04 - acc: 1.0000\n", + "Epoch 342/1000\n", + "26/26 [==============================] - 0s 520us/sample - loss: 2.2946e-04 - acc: 1.0000\n", + "Epoch 343/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 2.2875e-04 - acc: 1.0000\n", + "Epoch 344/1000\n", + "26/26 [==============================] - 0s 344us/sample - loss: 2.2818e-04 - acc: 1.0000\n", + "Epoch 345/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 2.2755e-04 - acc: 1.0000\n", + "Epoch 346/1000\n", + "26/26 [==============================] - 0s 461us/sample - loss: 2.2696e-04 - acc: 1.0000\n", + "Epoch 347/1000\n", + "26/26 [==============================] - 0s 368us/sample - loss: 2.2631e-04 - acc: 1.0000\n", + "Epoch 348/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 2.2572e-04 - acc: 1.0000\n", + "Epoch 349/1000\n", + "26/26 [==============================] - 0s 433us/sample - loss: 2.2508e-04 - acc: 1.0000\n", + "Epoch 350/1000\n", + "26/26 [==============================] - 0s 431us/sample - loss: 2.2452e-04 - acc: 1.0000\n", + "Epoch 351/1000\n", + "26/26 [==============================] - 0s 396us/sample - loss: 2.2401e-04 - acc: 1.0000\n", + "Epoch 352/1000\n", + "26/26 [==============================] - 0s 385us/sample - loss: 2.2339e-04 - acc: 1.0000\n", + "Epoch 353/1000\n", + "26/26 [==============================] - 0s 429us/sample - loss: 2.2286e-04 - acc: 1.0000\n", + "Epoch 354/1000\n", + "26/26 [==============================] - 0s 333us/sample - loss: 2.2235e-04 - acc: 1.0000\n", + "Epoch 355/1000\n", + "26/26 [==============================] - 0s 373us/sample - loss: 2.2176e-04 - acc: 1.0000\n", + "Epoch 356/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 2.2122e-04 - acc: 1.0000\n", + "Epoch 357/1000\n", + "26/26 [==============================] - 0s 410us/sample - loss: 2.2063e-04 - acc: 1.0000\n", + "Epoch 358/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 2.1996e-04 - acc: 1.0000\n", + "Epoch 359/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 2.1935e-04 - acc: 1.0000\n", + "Epoch 360/1000\n", + "26/26 [==============================] - 0s 406us/sample - loss: 2.1873e-04 - acc: 1.0000\n", + "Epoch 361/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 2.1814e-04 - acc: 1.0000\n", + "Epoch 362/1000\n", + "26/26 [==============================] - 0s 470us/sample - loss: 2.1755e-04 - acc: 1.0000\n", + "Epoch 363/1000\n", + "26/26 [==============================] - 0s 432us/sample - loss: 2.1690e-04 - acc: 1.0000\n", + "Epoch 364/1000\n", + "26/26 [==============================] - 0s 494us/sample - loss: 2.1640e-04 - acc: 1.0000\n", + "Epoch 365/1000\n", + "26/26 [==============================] - 0s 463us/sample - loss: 2.1583e-04 - acc: 1.0000\n", + "Epoch 366/1000\n", + "26/26 [==============================] - 0s 417us/sample - loss: 2.1532e-04 - acc: 1.0000\n", + "Epoch 367/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 2.1482e-04 - acc: 1.0000\n", + "Epoch 368/1000\n", + "26/26 [==============================] - 0s 386us/sample - loss: 2.1423e-04 - acc: 1.0000\n", + "Epoch 369/1000\n", + "26/26 [==============================] - 0s 407us/sample - loss: 2.1367e-04 - acc: 1.0000\n", + "Epoch 370/1000\n", + "26/26 [==============================] - 0s 469us/sample - loss: 2.1316e-04 - acc: 1.0000\n", + "Epoch 371/1000\n", + "26/26 [==============================] - 0s 545us/sample - loss: 2.1259e-04 - acc: 1.0000\n", + "Epoch 372/1000\n", + "26/26 [==============================] - 0s 411us/sample - loss: 2.1207e-04 - acc: 1.0000\n", + "Epoch 373/1000\n", + "26/26 [==============================] - 0s 380us/sample - loss: 2.1153e-04 - acc: 1.0000\n", + "Epoch 374/1000\n", + "26/26 [==============================] - 0s 355us/sample - loss: 2.1096e-04 - acc: 1.0000\n", + "Epoch 375/1000\n", + "26/26 [==============================] - 0s 327us/sample - loss: 2.1042e-04 - acc: 1.0000\n", + "Epoch 376/1000\n", + "26/26 [==============================] - 0s 375us/sample - loss: 2.0986e-04 - acc: 1.0000\n", + "Epoch 377/1000\n", + "26/26 [==============================] - 0s 372us/sample - loss: 2.0933e-04 - acc: 1.0000\n", + "Epoch 378/1000\n", + "26/26 [==============================] - 0s 336us/sample - loss: 2.0871e-04 - acc: 1.0000\n", + "Epoch 379/1000\n", + "26/26 [==============================] - 0s 353us/sample - loss: 2.0823e-04 - acc: 1.0000\n", + "Epoch 380/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 2.0757e-04 - acc: 1.0000\n", + "Epoch 381/1000\n", + "26/26 [==============================] - 0s 320us/sample - loss: 2.0711e-04 - acc: 1.0000\n", + "Epoch 382/1000\n", + "26/26 [==============================] - 0s 334us/sample - loss: 2.0659e-04 - acc: 1.0000\n", + "Epoch 383/1000\n", + "26/26 [==============================] - 0s 360us/sample - loss: 2.0599e-04 - acc: 1.0000\n", + "Epoch 384/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 2.0552e-04 - acc: 1.0000\n", + "Epoch 385/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 2.0501e-04 - acc: 1.0000\n", + "Epoch 386/1000\n", + "26/26 [==============================] - 0s 333us/sample - loss: 2.0444e-04 - acc: 1.0000\n", + "Epoch 387/1000\n", + "26/26 [==============================] - 0s 315us/sample - loss: 2.0394e-04 - acc: 1.0000\n", + "Epoch 388/1000\n", + "26/26 [==============================] - 0s 340us/sample - loss: 2.0331e-04 - acc: 1.0000\n", + "Epoch 389/1000\n", + "26/26 [==============================] - 0s 356us/sample - loss: 2.0276e-04 - acc: 1.0000\n", + "Epoch 390/1000\n", + "26/26 [==============================] - 0s 334us/sample - loss: 2.0224e-04 - acc: 1.0000\n", + "Epoch 391/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 2.0169e-04 - acc: 1.0000\n", + "Epoch 392/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 2.0109e-04 - acc: 1.0000\n", + "Epoch 393/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 2.0056e-04 - acc: 1.0000\n", + "Epoch 394/1000\n", + "26/26 [==============================] - 0s 389us/sample - loss: 1.9998e-04 - acc: 1.0000\n", + "Epoch 395/1000\n", + "26/26 [==============================] - 0s 353us/sample - loss: 1.9946e-04 - acc: 1.0000\n", + "Epoch 396/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 1.9895e-04 - acc: 1.0000\n", + "Epoch 397/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 1.9837e-04 - acc: 1.0000\n", + "Epoch 398/1000\n", + "26/26 [==============================] - 0s 335us/sample - loss: 1.9782e-04 - acc: 1.0000\n", + "Epoch 399/1000\n", + "26/26 [==============================] - 0s 337us/sample - loss: 1.9733e-04 - acc: 1.0000\n", + "Epoch 400/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 1.9670e-04 - acc: 1.0000\n", + "Epoch 401/1000\n", + "26/26 [==============================] - 0s 353us/sample - loss: 1.9611e-04 - acc: 1.0000\n", + "Epoch 402/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 1.9560e-04 - acc: 1.0000\n", + "Epoch 403/1000\n", + "26/26 [==============================] - 0s 376us/sample - loss: 1.9508e-04 - acc: 1.0000\n", + "Epoch 404/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 1.9454e-04 - acc: 1.0000\n", + "Epoch 405/1000\n", + "26/26 [==============================] - 0s 333us/sample - loss: 1.9402e-04 - acc: 1.0000\n", + "Epoch 406/1000\n", + "26/26 [==============================] - 0s 323us/sample - loss: 1.9357e-04 - acc: 1.0000\n", + "Epoch 407/1000\n", + "26/26 [==============================] - 0s 329us/sample - loss: 1.9299e-04 - acc: 1.0000\n", + "Epoch 408/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 1.9245e-04 - acc: 1.0000\n", + "Epoch 409/1000\n", + "26/26 [==============================] - 0s 386us/sample - loss: 1.9186e-04 - acc: 1.0000\n", + "Epoch 410/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 1.9137e-04 - acc: 1.0000\n", + "Epoch 411/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 1.9081e-04 - acc: 1.0000\n", + "Epoch 412/1000\n", + "26/26 [==============================] - 0s 445us/sample - loss: 1.9030e-04 - acc: 1.0000\n", + "Epoch 413/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 1.8973e-04 - acc: 1.0000\n", + "Epoch 414/1000\n", + "26/26 [==============================] - 0s 378us/sample - loss: 1.8921e-04 - acc: 1.0000\n", + "Epoch 415/1000\n", + "26/26 [==============================] - 0s 335us/sample - loss: 1.8865e-04 - acc: 1.0000\n", + "Epoch 416/1000\n", + "26/26 [==============================] - 0s 336us/sample - loss: 1.8814e-04 - acc: 1.0000\n", + "Epoch 417/1000\n", + "26/26 [==============================] - 0s 348us/sample - loss: 1.8760e-04 - acc: 1.0000\n", + "Epoch 418/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 1.8708e-04 - acc: 1.0000\n", + "Epoch 419/1000\n", + "26/26 [==============================] - 0s 340us/sample - loss: 1.8656e-04 - acc: 1.0000\n", + "Epoch 420/1000\n", + "26/26 [==============================] - 0s 349us/sample - loss: 1.8609e-04 - acc: 1.0000\n", + "Epoch 421/1000\n", + "26/26 [==============================] - 0s 325us/sample - loss: 1.8557e-04 - acc: 1.0000\n", + "Epoch 422/1000\n", + "26/26 [==============================] - 0s 349us/sample - loss: 1.8506e-04 - acc: 1.0000\n", + "Epoch 423/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 1.8460e-04 - acc: 1.0000\n", + "Epoch 424/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 1.8412e-04 - acc: 1.0000\n", + "Epoch 425/1000\n", + "26/26 [==============================] - 0s 337us/sample - loss: 1.8361e-04 - acc: 1.0000\n", + "Epoch 426/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 1.8308e-04 - acc: 1.0000\n", + "Epoch 427/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 1.8252e-04 - acc: 1.0000\n", + "Epoch 428/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 1.8204e-04 - acc: 1.0000\n", + "Epoch 429/1000\n", + "26/26 [==============================] - 0s 360us/sample - loss: 1.8153e-04 - acc: 1.0000\n", + "Epoch 430/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 1.8104e-04 - acc: 1.0000\n", + "Epoch 431/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 1.8052e-04 - acc: 1.0000\n", + "Epoch 432/1000\n", + "26/26 [==============================] - 0s 306us/sample - loss: 1.8003e-04 - acc: 1.0000\n", + "Epoch 433/1000\n", + "26/26 [==============================] - 0s 330us/sample - loss: 1.7958e-04 - acc: 1.0000\n", + "Epoch 434/1000\n", + "26/26 [==============================] - 0s 325us/sample - loss: 1.7909e-04 - acc: 1.0000\n", + "Epoch 435/1000\n", + "26/26 [==============================] - 0s 329us/sample - loss: 1.7865e-04 - acc: 1.0000\n", + "Epoch 436/1000\n", + "26/26 [==============================] - 0s 430us/sample - loss: 1.7820e-04 - acc: 1.0000\n", + "Epoch 437/1000\n", + "26/26 [==============================] - 0s 494us/sample - loss: 1.7773e-04 - acc: 1.0000\n", + "Epoch 438/1000\n", + "26/26 [==============================] - 0s 355us/sample - loss: 1.7730e-04 - acc: 1.0000\n", + "Epoch 439/1000\n", + "26/26 [==============================] - 0s 498us/sample - loss: 1.7684e-04 - acc: 1.0000\n", + "Epoch 440/1000\n", + "26/26 [==============================] - 0s 472us/sample - loss: 1.7636e-04 - acc: 1.0000\n", + "Epoch 441/1000\n", + "26/26 [==============================] - 0s 566us/sample - loss: 1.7584e-04 - acc: 1.0000\n", + "Epoch 442/1000\n", + "26/26 [==============================] - 0s 498us/sample - loss: 1.7542e-04 - acc: 1.0000\n", + "Epoch 443/1000\n", + "26/26 [==============================] - 0s 608us/sample - loss: 1.7497e-04 - acc: 1.0000\n", + "Epoch 444/1000\n", + "26/26 [==============================] - 0s 445us/sample - loss: 1.7453e-04 - acc: 1.0000\n", + "Epoch 445/1000\n", + "26/26 [==============================] - 0s 348us/sample - loss: 1.7408e-04 - acc: 1.0000\n", + "Epoch 446/1000\n", + "26/26 [==============================] - 0s 507us/sample - loss: 1.7364e-04 - acc: 1.0000\n", + "Epoch 447/1000\n", + "26/26 [==============================] - 0s 407us/sample - loss: 1.7321e-04 - acc: 1.0000\n", + "Epoch 448/1000\n", + "26/26 [==============================] - 0s 542us/sample - loss: 1.7278e-04 - acc: 1.0000\n", + "Epoch 449/1000\n", + "26/26 [==============================] - 0s 468us/sample - loss: 1.7233e-04 - acc: 1.0000\n", + "Epoch 450/1000\n", + "26/26 [==============================] - 0s 380us/sample - loss: 1.7177e-04 - acc: 1.0000\n", + "Epoch 451/1000\n", + "26/26 [==============================] - 0s 545us/sample - loss: 1.7130e-04 - acc: 1.0000\n", + "Epoch 452/1000\n", + "26/26 [==============================] - 0s 430us/sample - loss: 1.7095e-04 - acc: 1.0000\n", + "Epoch 453/1000\n", + "26/26 [==============================] - 0s 555us/sample - loss: 1.7051e-04 - acc: 1.0000\n", + "Epoch 454/1000\n", + "26/26 [==============================] - 0s 610us/sample - loss: 1.7007e-04 - acc: 1.0000\n", + "Epoch 455/1000\n", + "26/26 [==============================] - 0s 449us/sample - loss: 1.6958e-04 - acc: 1.0000\n", + "Epoch 456/1000\n", + "26/26 [==============================] - 0s 431us/sample - loss: 1.6918e-04 - acc: 1.0000\n", + "Epoch 457/1000\n", + "26/26 [==============================] - 0s 513us/sample - loss: 1.6872e-04 - acc: 1.0000\n", + "Epoch 458/1000\n", + "26/26 [==============================] - 0s 437us/sample - loss: 1.6825e-04 - acc: 1.0000\n", + "Epoch 459/1000\n", + "26/26 [==============================] - 0s 446us/sample - loss: 1.6780e-04 - acc: 1.0000\n", + "Epoch 460/1000\n", + "26/26 [==============================] - 0s 372us/sample - loss: 1.6739e-04 - acc: 1.0000\n", + "Epoch 461/1000\n", + "26/26 [==============================] - 0s 384us/sample - loss: 1.6693e-04 - acc: 1.0000\n", + "Epoch 462/1000\n", + "26/26 [==============================] - 0s 621us/sample - loss: 1.6645e-04 - acc: 1.0000\n", + "Epoch 463/1000\n", + "26/26 [==============================] - 0s 484us/sample - loss: 1.6606e-04 - acc: 1.0000\n", + "Epoch 464/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 1.6560e-04 - acc: 1.0000\n", + "Epoch 465/1000\n", + "26/26 [==============================] - 0s 525us/sample - loss: 1.6518e-04 - acc: 1.0000\n", + "Epoch 466/1000\n", + "26/26 [==============================] - 0s 504us/sample - loss: 1.6475e-04 - acc: 1.0000\n", + "Epoch 467/1000\n", + "26/26 [==============================] - 0s 398us/sample - loss: 1.6428e-04 - acc: 1.0000\n", + "Epoch 468/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 1.6377e-04 - acc: 1.0000\n", + "Epoch 469/1000\n", + "26/26 [==============================] - 0s 424us/sample - loss: 1.6341e-04 - acc: 1.0000\n", + "Epoch 470/1000\n", + "26/26 [==============================] - 0s 618us/sample - loss: 1.6292e-04 - acc: 1.0000\n", + "Epoch 471/1000\n", + "26/26 [==============================] - 0s 482us/sample - loss: 1.6249e-04 - acc: 1.0000\n", + "Epoch 472/1000\n", + "26/26 [==============================] - 0s 444us/sample - loss: 1.6202e-04 - acc: 1.0000\n", + "Epoch 473/1000\n", + "26/26 [==============================] - 0s 408us/sample - loss: 1.6154e-04 - acc: 1.0000\n", + "Epoch 474/1000\n", + "26/26 [==============================] - 0s 376us/sample - loss: 1.6110e-04 - acc: 1.0000\n", + "Epoch 475/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 1.6077e-04 - acc: 1.0000\n", + "Epoch 476/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 1.6025e-04 - acc: 1.0000\n", + "Epoch 477/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 1.5984e-04 - acc: 1.0000\n", + "Epoch 478/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 1.5945e-04 - acc: 1.0000\n", + "Epoch 479/1000\n", + "26/26 [==============================] - 0s 429us/sample - loss: 1.5904e-04 - acc: 1.0000\n", + "Epoch 480/1000\n", + "26/26 [==============================] - 0s 476us/sample - loss: 1.5862e-04 - acc: 1.0000\n", + "Epoch 481/1000\n", + "26/26 [==============================] - 0s 385us/sample - loss: 1.5816e-04 - acc: 1.0000\n", + "Epoch 482/1000\n", + "26/26 [==============================] - 0s 344us/sample - loss: 1.5771e-04 - acc: 1.0000\n", + "Epoch 483/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 1.5731e-04 - acc: 1.0000\n", + "Epoch 484/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 1.5691e-04 - acc: 1.0000\n", + "Epoch 485/1000\n", + "26/26 [==============================] - 0s 335us/sample - loss: 1.5646e-04 - acc: 1.0000\n", + "Epoch 486/1000\n", + "26/26 [==============================] - 0s 336us/sample - loss: 1.5604e-04 - acc: 1.0000\n", + "Epoch 487/1000\n", + "26/26 [==============================] - 0s 323us/sample - loss: 1.5558e-04 - acc: 1.0000\n", + "Epoch 488/1000\n", + "26/26 [==============================] - 0s 343us/sample - loss: 1.5520e-04 - acc: 1.0000\n", + "Epoch 489/1000\n", + "26/26 [==============================] - 0s 398us/sample - loss: 1.5482e-04 - acc: 1.0000\n", + "Epoch 490/1000\n", + "26/26 [==============================] - 0s 437us/sample - loss: 1.5439e-04 - acc: 1.0000\n", + "Epoch 491/1000\n", + "26/26 [==============================] - 0s 408us/sample - loss: 1.5402e-04 - acc: 1.0000\n", + "Epoch 492/1000\n", + "26/26 [==============================] - 0s 609us/sample - loss: 1.5363e-04 - acc: 1.0000\n", + "Epoch 493/1000\n", + "26/26 [==============================] - 0s 498us/sample - loss: 1.5326e-04 - acc: 1.0000\n", + "Epoch 494/1000\n", + "26/26 [==============================] - 0s 542us/sample - loss: 1.5288e-04 - acc: 1.0000\n", + "Epoch 495/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 1.5252e-04 - acc: 1.0000\n", + "Epoch 496/1000\n", + "26/26 [==============================] - 0s 373us/sample - loss: 1.5210e-04 - acc: 1.0000\n", + "Epoch 497/1000\n", + "26/26 [==============================] - 0s 403us/sample - loss: 1.5170e-04 - acc: 1.0000\n", + "Epoch 498/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 1.5132e-04 - acc: 1.0000\n", + "Epoch 499/1000\n", + "26/26 [==============================] - 0s 577us/sample - loss: 1.5093e-04 - acc: 1.0000\n", + "Epoch 500/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 1.5052e-04 - acc: 1.0000\n", + "Epoch 501/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 1.5010e-04 - acc: 1.0000\n", + "Epoch 502/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 1.4964e-04 - acc: 1.0000\n", + "Epoch 503/1000\n", + "26/26 [==============================] - 0s 418us/sample - loss: 1.4927e-04 - acc: 1.0000\n", + "Epoch 504/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 1.4883e-04 - acc: 1.0000\n", + "Epoch 505/1000\n", + "26/26 [==============================] - 0s 470us/sample - loss: 1.4845e-04 - acc: 1.0000\n", + "Epoch 506/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 1.4800e-04 - acc: 1.0000\n", + "Epoch 507/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 1.4762e-04 - acc: 1.0000\n", + "Epoch 508/1000\n", + "26/26 [==============================] - 0s 447us/sample - loss: 1.4727e-04 - acc: 1.0000\n", + "Epoch 509/1000\n", + "26/26 [==============================] - 0s 446us/sample - loss: 1.4687e-04 - acc: 1.0000\n", + "Epoch 510/1000\n", + "26/26 [==============================] - 0s 453us/sample - loss: 1.4644e-04 - acc: 1.0000\n", + "Epoch 511/1000\n", + "26/26 [==============================] - 0s 577us/sample - loss: 1.4611e-04 - acc: 1.0000\n", + "Epoch 512/1000\n", + "26/26 [==============================] - 0s 527us/sample - loss: 1.4567e-04 - acc: 1.0000\n", + "Epoch 513/1000\n", + "26/26 [==============================] - 0s 493us/sample - loss: 1.4531e-04 - acc: 1.0000\n", + "Epoch 514/1000\n", + "26/26 [==============================] - 0s 484us/sample - loss: 1.4491e-04 - acc: 1.0000\n", + "Epoch 515/1000\n", + "26/26 [==============================] - 0s 404us/sample - loss: 1.4457e-04 - acc: 1.0000\n", + "Epoch 516/1000\n", + "26/26 [==============================] - 0s 391us/sample - loss: 1.4421e-04 - acc: 1.0000\n", + "Epoch 517/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 1.4387e-04 - acc: 1.0000\n", + "Epoch 518/1000\n", + "26/26 [==============================] - 0s 474us/sample - loss: 1.4346e-04 - acc: 1.0000\n", + "Epoch 519/1000\n", + "26/26 [==============================] - 0s 485us/sample - loss: 1.4308e-04 - acc: 1.0000\n", + "Epoch 520/1000\n", + "26/26 [==============================] - 0s 433us/sample - loss: 1.4269e-04 - acc: 1.0000\n", + "Epoch 521/1000\n", + "26/26 [==============================] - 0s 409us/sample - loss: 1.4230e-04 - acc: 1.0000\n", + "Epoch 522/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 1.4188e-04 - acc: 1.0000\n", + "Epoch 523/1000\n", + "26/26 [==============================] - 0s 416us/sample - loss: 1.4151e-04 - acc: 1.0000\n", + "Epoch 524/1000\n", + "26/26 [==============================] - 0s 408us/sample - loss: 1.4117e-04 - acc: 1.0000\n", + "Epoch 525/1000\n", + "26/26 [==============================] - 0s 439us/sample - loss: 1.4076e-04 - acc: 1.0000\n", + "Epoch 526/1000\n", + "26/26 [==============================] - 0s 496us/sample - loss: 1.4040e-04 - acc: 1.0000\n", + "Epoch 527/1000\n", + "26/26 [==============================] - 0s 583us/sample - loss: 1.4004e-04 - acc: 1.0000\n", + "Epoch 528/1000\n", + "26/26 [==============================] - 0s 403us/sample - loss: 1.3971e-04 - acc: 1.0000\n", + "Epoch 529/1000\n", + "26/26 [==============================] - 0s 539us/sample - loss: 1.3935e-04 - acc: 1.0000\n", + "Epoch 530/1000\n", + "26/26 [==============================] - 0s 766us/sample - loss: 1.3896e-04 - acc: 1.0000\n", + "Epoch 531/1000\n", + "26/26 [==============================] - 0s 596us/sample - loss: 1.3864e-04 - acc: 1.0000\n", + "Epoch 532/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 1.3830e-04 - acc: 1.0000\n", + "Epoch 533/1000\n", + "26/26 [==============================] - 0s 377us/sample - loss: 1.3795e-04 - acc: 1.0000\n", + "Epoch 534/1000\n", + "26/26 [==============================] - 0s 355us/sample - loss: 1.3762e-04 - acc: 1.0000\n", + "Epoch 535/1000\n", + "26/26 [==============================] - 0s 344us/sample - loss: 1.3729e-04 - acc: 1.0000\n", + "Epoch 536/1000\n", + "26/26 [==============================] - 0s 335us/sample - loss: 1.3692e-04 - acc: 1.0000\n", + "Epoch 537/1000\n", + "26/26 [==============================] - 0s 325us/sample - loss: 1.3657e-04 - acc: 1.0000\n", + "Epoch 538/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 1.3626e-04 - acc: 1.0000\n", + "Epoch 539/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 1.3589e-04 - acc: 1.0000\n", + "Epoch 540/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 1.3551e-04 - acc: 1.0000\n", + "Epoch 541/1000\n", + "26/26 [==============================] - 0s 349us/sample - loss: 1.3522e-04 - acc: 1.0000\n", + "Epoch 542/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 1.3488e-04 - acc: 1.0000\n", + "Epoch 543/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 1.3453e-04 - acc: 1.0000\n", + "Epoch 544/1000\n", + "26/26 [==============================] - 0s 372us/sample - loss: 1.3423e-04 - acc: 1.0000\n", + "Epoch 545/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 1.3395e-04 - acc: 1.0000\n", + "Epoch 546/1000\n", + "26/26 [==============================] - 0s 316us/sample - loss: 1.3360e-04 - acc: 1.0000\n", + "Epoch 547/1000\n", + "26/26 [==============================] - 0s 332us/sample - loss: 1.3329e-04 - acc: 1.0000\n", + "Epoch 548/1000\n", + "26/26 [==============================] - 0s 334us/sample - loss: 1.3296e-04 - acc: 1.0000\n", + "Epoch 549/1000\n", + "26/26 [==============================] - 0s 343us/sample - loss: 1.3260e-04 - acc: 1.0000\n", + "Epoch 550/1000\n", + "26/26 [==============================] - 0s 389us/sample - loss: 1.3228e-04 - acc: 1.0000\n", + "Epoch 551/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 1.3184e-04 - acc: 1.0000\n", + "Epoch 552/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 1.3157e-04 - acc: 1.0000\n", + "Epoch 553/1000\n", + "26/26 [==============================] - 0s 437us/sample - loss: 1.3115e-04 - acc: 1.0000\n", + "Epoch 554/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 1.3081e-04 - acc: 1.0000\n", + "Epoch 555/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 1.3044e-04 - acc: 1.0000\n", + "Epoch 556/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 1.3007e-04 - acc: 1.0000\n", + "Epoch 557/1000\n", + "26/26 [==============================] - 0s 411us/sample - loss: 1.2974e-04 - acc: 1.0000\n", + "Epoch 558/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 1.2944e-04 - acc: 1.0000\n", + "Epoch 559/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 1.2912e-04 - acc: 1.0000\n", + "Epoch 560/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 1.2880e-04 - acc: 1.0000\n", + "Epoch 561/1000\n", + "26/26 [==============================] - 0s 373us/sample - loss: 1.2849e-04 - acc: 1.0000\n", + "Epoch 562/1000\n", + "26/26 [==============================] - 0s 343us/sample - loss: 1.2816e-04 - acc: 1.0000\n", + "Epoch 563/1000\n", + "26/26 [==============================] - 0s 404us/sample - loss: 1.2785e-04 - acc: 1.0000\n", + "Epoch 564/1000\n", + "26/26 [==============================] - 0s 403us/sample - loss: 1.2754e-04 - acc: 1.0000\n", + "Epoch 565/1000\n", + "26/26 [==============================] - 0s 410us/sample - loss: 1.2720e-04 - acc: 1.0000\n", + "Epoch 566/1000\n", + "26/26 [==============================] - 0s 404us/sample - loss: 1.2689e-04 - acc: 1.0000\n", + "Epoch 567/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 1.2657e-04 - acc: 1.0000\n", + "Epoch 568/1000\n", + "26/26 [==============================] - 0s 355us/sample - loss: 1.2629e-04 - acc: 1.0000\n", + "Epoch 569/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 1.2596e-04 - acc: 1.0000\n", + "Epoch 570/1000\n", + "26/26 [==============================] - 0s 377us/sample - loss: 1.2563e-04 - acc: 1.0000\n", + "Epoch 571/1000\n", + "26/26 [==============================] - 0s 332us/sample - loss: 1.2531e-04 - acc: 1.0000\n", + "Epoch 572/1000\n", + "26/26 [==============================] - 0s 472us/sample - loss: 1.2496e-04 - acc: 1.0000\n", + "Epoch 573/1000\n", + "26/26 [==============================] - 0s 384us/sample - loss: 1.2461e-04 - acc: 1.0000\n", + "Epoch 574/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 1.2428e-04 - acc: 1.0000\n", + "Epoch 575/1000\n", + "26/26 [==============================] - 0s 343us/sample - loss: 1.2394e-04 - acc: 1.0000\n", + "Epoch 576/1000\n", + "26/26 [==============================] - 0s 395us/sample - loss: 1.2363e-04 - acc: 1.0000\n", + "Epoch 577/1000\n", + "26/26 [==============================] - 0s 370us/sample - loss: 1.2325e-04 - acc: 1.0000\n", + "Epoch 578/1000\n", + "26/26 [==============================] - 0s 403us/sample - loss: 1.2295e-04 - acc: 1.0000\n", + "Epoch 579/1000\n", + "26/26 [==============================] - 0s 592us/sample - loss: 1.2262e-04 - acc: 1.0000\n", + "Epoch 580/1000\n", + "26/26 [==============================] - 0s 653us/sample - loss: 1.2233e-04 - acc: 1.0000\n", + "Epoch 581/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 1.2201e-04 - acc: 1.0000\n", + "Epoch 582/1000\n", + "26/26 [==============================] - 0s 557us/sample - loss: 1.2173e-04 - acc: 1.0000\n", + "Epoch 583/1000\n", + "26/26 [==============================] - 0s 492us/sample - loss: 1.2142e-04 - acc: 1.0000\n", + "Epoch 584/1000\n", + "26/26 [==============================] - 0s 469us/sample - loss: 1.2113e-04 - acc: 1.0000\n", + "Epoch 585/1000\n", + "26/26 [==============================] - 0s 477us/sample - loss: 1.2083e-04 - acc: 1.0000\n", + "Epoch 586/1000\n", + "26/26 [==============================] - 0s 626us/sample - loss: 1.2053e-04 - acc: 1.0000\n", + "Epoch 587/1000\n", + "26/26 [==============================] - 0s 502us/sample - loss: 1.2026e-04 - acc: 1.0000\n", + "Epoch 588/1000\n", + "26/26 [==============================] - 0s 550us/sample - loss: 1.1996e-04 - acc: 1.0000\n", + "Epoch 589/1000\n", + "26/26 [==============================] - 0s 419us/sample - loss: 1.1971e-04 - acc: 1.0000\n", + "Epoch 590/1000\n", + "26/26 [==============================] - 0s 401us/sample - loss: 1.1942e-04 - acc: 1.0000\n", + "Epoch 591/1000\n", + "26/26 [==============================] - 0s 428us/sample - loss: 1.1911e-04 - acc: 1.0000\n", + "Epoch 592/1000\n", + "26/26 [==============================] - 0s 597us/sample - loss: 1.1884e-04 - acc: 1.0000\n", + "Epoch 593/1000\n", + "26/26 [==============================] - 0s 491us/sample - loss: 1.1854e-04 - acc: 1.0000\n", + "Epoch 594/1000\n", + "26/26 [==============================] - 0s 551us/sample - loss: 1.1822e-04 - acc: 1.0000\n", + "Epoch 595/1000\n", + "26/26 [==============================] - 0s 513us/sample - loss: 1.1798e-04 - acc: 1.0000\n", + "Epoch 596/1000\n", + "26/26 [==============================] - 0s 455us/sample - loss: 1.1766e-04 - acc: 1.0000\n", + "Epoch 597/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 1.1740e-04 - acc: 1.0000\n", + "Epoch 598/1000\n", + "26/26 [==============================] - 0s 465us/sample - loss: 1.1711e-04 - acc: 1.0000\n", + "Epoch 599/1000\n", + "26/26 [==============================] - 0s 452us/sample - loss: 1.1685e-04 - acc: 1.0000\n", + "Epoch 600/1000\n", + "26/26 [==============================] - 0s 492us/sample - loss: 1.1655e-04 - acc: 1.0000\n", + "Epoch 601/1000\n", + "26/26 [==============================] - 0s 480us/sample - loss: 1.1628e-04 - acc: 1.0000\n", + "Epoch 602/1000\n", + "26/26 [==============================] - 0s 420us/sample - loss: 1.1595e-04 - acc: 1.0000\n", + "Epoch 603/1000\n", + "26/26 [==============================] - 0s 385us/sample - loss: 1.1568e-04 - acc: 1.0000\n", + "Epoch 604/1000\n", + "26/26 [==============================] - 0s 343us/sample - loss: 1.1539e-04 - acc: 1.0000\n", + "Epoch 605/1000\n", + "26/26 [==============================] - 0s 321us/sample - loss: 1.1510e-04 - acc: 1.0000\n", + "Epoch 606/1000\n", + "26/26 [==============================] - 0s 314us/sample - loss: 1.1475e-04 - acc: 1.0000\n", + "Epoch 607/1000\n", + "26/26 [==============================] - 0s 333us/sample - loss: 1.1447e-04 - acc: 1.0000\n", + "Epoch 608/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 1.1417e-04 - acc: 1.0000\n", + "Epoch 609/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 1.1387e-04 - acc: 1.0000\n", + "Epoch 610/1000\n", + "26/26 [==============================] - 0s 337us/sample - loss: 1.1356e-04 - acc: 1.0000\n", + "Epoch 611/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 1.1324e-04 - acc: 1.0000\n", + "Epoch 612/1000\n", + "26/26 [==============================] - 0s 377us/sample - loss: 1.1295e-04 - acc: 1.0000\n", + "Epoch 613/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 1.1267e-04 - acc: 1.0000\n", + "Epoch 614/1000\n", + "26/26 [==============================] - 0s 334us/sample - loss: 1.1238e-04 - acc: 1.0000\n", + "Epoch 615/1000\n", + "26/26 [==============================] - 0s 326us/sample - loss: 1.1208e-04 - acc: 1.0000\n", + "Epoch 616/1000\n", + "26/26 [==============================] - 0s 325us/sample - loss: 1.1180e-04 - acc: 1.0000\n", + "Epoch 617/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 1.1152e-04 - acc: 1.0000\n", + "Epoch 618/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 1.1125e-04 - acc: 1.0000\n", + "Epoch 619/1000\n", + "26/26 [==============================] - 0s 515us/sample - loss: 1.1094e-04 - acc: 1.0000\n", + "Epoch 620/1000\n", + "26/26 [==============================] - 0s 389us/sample - loss: 1.1066e-04 - acc: 1.0000\n", + "Epoch 621/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 1.1039e-04 - acc: 1.0000\n", + "Epoch 622/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 1.1013e-04 - acc: 1.0000\n", + "Epoch 623/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 1.0985e-04 - acc: 1.0000\n", + "Epoch 624/1000\n", + "26/26 [==============================] - 0s 384us/sample - loss: 1.0956e-04 - acc: 1.0000\n", + "Epoch 625/1000\n", + "26/26 [==============================] - 0s 423us/sample - loss: 1.0925e-04 - acc: 1.0000\n", + "Epoch 626/1000\n", + "26/26 [==============================] - 0s 720us/sample - loss: 1.0895e-04 - acc: 1.0000\n", + "Epoch 627/1000\n", + "26/26 [==============================] - 0s 488us/sample - loss: 1.0867e-04 - acc: 1.0000\n", + "Epoch 628/1000\n", + "26/26 [==============================] - 0s 489us/sample - loss: 1.0834e-04 - acc: 1.0000\n", + "Epoch 629/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 1.0807e-04 - acc: 1.0000\n", + "Epoch 630/1000\n", + "26/26 [==============================] - 0s 451us/sample - loss: 1.0780e-04 - acc: 1.0000\n", + "Epoch 631/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 1.0754e-04 - acc: 1.0000\n", + "Epoch 632/1000\n", + "26/26 [==============================] - 0s 405us/sample - loss: 1.0725e-04 - acc: 1.0000\n", + "Epoch 633/1000\n", + "26/26 [==============================] - 0s 389us/sample - loss: 1.0699e-04 - acc: 1.0000\n", + "Epoch 634/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 1.0674e-04 - acc: 1.0000\n", + "Epoch 635/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 1.0650e-04 - acc: 1.0000\n", + "Epoch 636/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 1.0620e-04 - acc: 1.0000\n", + "Epoch 637/1000\n", + "26/26 [==============================] - 0s 599us/sample - loss: 1.0597e-04 - acc: 1.0000\n", + "Epoch 638/1000\n", + "26/26 [==============================] - 0s 447us/sample - loss: 1.0574e-04 - acc: 1.0000\n", + "Epoch 639/1000\n", + "26/26 [==============================] - 0s 413us/sample - loss: 1.0541e-04 - acc: 1.0000\n", + "Epoch 640/1000\n", + "26/26 [==============================] - 0s 409us/sample - loss: 1.0511e-04 - acc: 1.0000\n", + "Epoch 641/1000\n", + "26/26 [==============================] - 0s 467us/sample - loss: 1.0485e-04 - acc: 1.0000\n", + "Epoch 642/1000\n", + "26/26 [==============================] - 0s 447us/sample - loss: 1.0459e-04 - acc: 1.0000\n", + "Epoch 643/1000\n", + "26/26 [==============================] - 0s 486us/sample - loss: 1.0434e-04 - acc: 1.0000\n", + "Epoch 644/1000\n", + "26/26 [==============================] - 0s 453us/sample - loss: 1.0408e-04 - acc: 1.0000\n", + "Epoch 645/1000\n", + "26/26 [==============================] - 0s 487us/sample - loss: 1.0381e-04 - acc: 1.0000\n", + "Epoch 646/1000\n", + "26/26 [==============================] - 0s 410us/sample - loss: 1.0355e-04 - acc: 1.0000\n", + "Epoch 647/1000\n", + "26/26 [==============================] - 0s 438us/sample - loss: 1.0326e-04 - acc: 1.0000\n", + "Epoch 648/1000\n", + "26/26 [==============================] - 0s 477us/sample - loss: 1.0304e-04 - acc: 1.0000\n", + "Epoch 649/1000\n", + "26/26 [==============================] - 0s 398us/sample - loss: 1.0280e-04 - acc: 1.0000\n", + "Epoch 650/1000\n", + "26/26 [==============================] - 0s 540us/sample - loss: 1.0254e-04 - acc: 1.0000\n", + "Epoch 651/1000\n", + "26/26 [==============================] - 0s 409us/sample - loss: 1.0230e-04 - acc: 1.0000\n", + "Epoch 652/1000\n", + "26/26 [==============================] - 0s 494us/sample - loss: 1.0203e-04 - acc: 1.0000\n", + "Epoch 653/1000\n", + "26/26 [==============================] - 0s 469us/sample - loss: 1.0174e-04 - acc: 1.0000\n", + "Epoch 654/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 1.0150e-04 - acc: 1.0000\n", + "Epoch 655/1000\n", + "26/26 [==============================] - 0s 499us/sample - loss: 1.0121e-04 - acc: 1.0000\n", + "Epoch 656/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 1.0091e-04 - acc: 1.0000\n", + "Epoch 657/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 1.0067e-04 - acc: 1.0000\n", + "Epoch 658/1000\n", + "26/26 [==============================] - 0s 478us/sample - loss: 1.0036e-04 - acc: 1.0000\n", + "Epoch 659/1000\n", + "26/26 [==============================] - 0s 401us/sample - loss: 1.0011e-04 - acc: 1.0000\n", + "Epoch 660/1000\n", + "26/26 [==============================] - 0s 416us/sample - loss: 9.9874e-05 - acc: 1.0000\n", + "Epoch 661/1000\n", + "26/26 [==============================] - 0s 444us/sample - loss: 9.9617e-05 - acc: 1.0000\n", + "Epoch 662/1000\n", + "26/26 [==============================] - 0s 463us/sample - loss: 9.9379e-05 - acc: 1.0000\n", + "Epoch 663/1000\n", + "26/26 [==============================] - 0s 399us/sample - loss: 9.9113e-05 - acc: 1.0000\n", + "Epoch 664/1000\n", + "26/26 [==============================] - 0s 393us/sample - loss: 9.8879e-05 - acc: 1.0000\n", + "Epoch 665/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 9.8627e-05 - acc: 1.0000\n", + "Epoch 666/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 9.8348e-05 - acc: 1.0000\n", + "Epoch 667/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 9.8082e-05 - acc: 1.0000\n", + "Epoch 668/1000\n", + "26/26 [==============================] - 0s 350us/sample - loss: 9.7843e-05 - acc: 1.0000\n", + "Epoch 669/1000\n", + "26/26 [==============================] - 0s 332us/sample - loss: 9.7550e-05 - acc: 1.0000\n", + "Epoch 670/1000\n", + "26/26 [==============================] - 0s 360us/sample - loss: 9.7321e-05 - acc: 1.0000\n", + "Epoch 671/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 9.7087e-05 - acc: 1.0000\n", + "Epoch 672/1000\n", + "26/26 [==============================] - 0s 377us/sample - loss: 9.6844e-05 - acc: 1.0000\n", + "Epoch 673/1000\n", + "26/26 [==============================] - 0s 350us/sample - loss: 9.6619e-05 - acc: 1.0000\n", + "Epoch 674/1000\n", + "26/26 [==============================] - 0s 360us/sample - loss: 9.6395e-05 - acc: 1.0000\n", + "Epoch 675/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 9.6138e-05 - acc: 1.0000\n", + "Epoch 676/1000\n", + "26/26 [==============================] - 0s 348us/sample - loss: 9.5927e-05 - acc: 1.0000\n", + "Epoch 677/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 9.5652e-05 - acc: 1.0000\n", + "Epoch 678/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 9.5405e-05 - acc: 1.0000\n", + "Epoch 679/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 9.5153e-05 - acc: 1.0000\n", + "Epoch 680/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 9.4914e-05 - acc: 1.0000\n", + "Epoch 681/1000\n", + "26/26 [==============================] - 0s 335us/sample - loss: 9.4667e-05 - acc: 1.0000\n", + "Epoch 682/1000\n", + "26/26 [==============================] - 0s 373us/sample - loss: 9.4447e-05 - acc: 1.0000\n", + "Epoch 683/1000\n", + "26/26 [==============================] - 0s 328us/sample - loss: 9.4217e-05 - acc: 1.0000\n", + "Epoch 684/1000\n", + "26/26 [==============================] - 0s 328us/sample - loss: 9.3984e-05 - acc: 1.0000\n", + "Epoch 685/1000\n", + "26/26 [==============================] - 0s 329us/sample - loss: 9.3759e-05 - acc: 1.0000\n", + "Epoch 686/1000\n", + "26/26 [==============================] - 0s 321us/sample - loss: 9.3488e-05 - acc: 1.0000\n", + "Epoch 687/1000\n", + "26/26 [==============================] - 0s 317us/sample - loss: 9.3287e-05 - acc: 1.0000\n", + "Epoch 688/1000\n", + "26/26 [==============================] - 0s 312us/sample - loss: 9.3016e-05 - acc: 1.0000\n", + "Epoch 689/1000\n", + "26/26 [==============================] - 0s 325us/sample - loss: 9.2783e-05 - acc: 1.0000\n", + "Epoch 690/1000\n", + "26/26 [==============================] - 0s 319us/sample - loss: 9.2549e-05 - acc: 1.0000\n", + "Epoch 691/1000\n", + "26/26 [==============================] - 0s 328us/sample - loss: 9.2315e-05 - acc: 1.0000\n", + "Epoch 692/1000\n", + "26/26 [==============================] - 0s 325us/sample - loss: 9.2081e-05 - acc: 1.0000\n", + "Epoch 693/1000\n", + "26/26 [==============================] - 0s 389us/sample - loss: 9.1866e-05 - acc: 1.0000\n", + "Epoch 694/1000\n", + "26/26 [==============================] - 0s 355us/sample - loss: 9.1636e-05 - acc: 1.0000\n", + "Epoch 695/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 9.1416e-05 - acc: 1.0000\n", + "Epoch 696/1000\n", + "26/26 [==============================] - 0s 337us/sample - loss: 9.1187e-05 - acc: 1.0000\n", + "Epoch 697/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 9.0976e-05 - acc: 1.0000\n", + "Epoch 698/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 9.0752e-05 - acc: 1.0000\n", + "Epoch 699/1000\n", + "26/26 [==============================] - 0s 335us/sample - loss: 9.0518e-05 - acc: 1.0000\n", + "Epoch 700/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 9.0280e-05 - acc: 1.0000\n", + "Epoch 701/1000\n", + "26/26 [==============================] - 0s 428us/sample - loss: 9.0096e-05 - acc: 1.0000\n", + "Epoch 702/1000\n", + "26/26 [==============================] - 0s 386us/sample - loss: 8.9817e-05 - acc: 1.0000\n", + "Epoch 703/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 8.9592e-05 - acc: 1.0000\n", + "Epoch 704/1000\n", + "26/26 [==============================] - 0s 329us/sample - loss: 8.9372e-05 - acc: 1.0000\n", + "Epoch 705/1000\n", + "26/26 [==============================] - 0s 1ms/sample - loss: 8.9111e-05 - acc: 1.0000\n", + "Epoch 706/1000\n", + "26/26 [==============================] - 0s 766us/sample - loss: 8.8840e-05 - acc: 1.0000\n", + "Epoch 707/1000\n", + "26/26 [==============================] - 0s 564us/sample - loss: 8.8643e-05 - acc: 1.0000\n", + "Epoch 708/1000\n", + "26/26 [==============================] - 0s 475us/sample - loss: 8.8428e-05 - acc: 1.0000\n", + "Epoch 709/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 8.8189e-05 - acc: 1.0000\n", + "Epoch 710/1000\n", + "26/26 [==============================] - 0s 530us/sample - loss: 8.7983e-05 - acc: 1.0000\n", + "Epoch 711/1000\n", + "26/26 [==============================] - 0s 449us/sample - loss: 8.7754e-05 - acc: 1.0000\n", + "Epoch 712/1000\n", + "26/26 [==============================] - 0s 449us/sample - loss: 8.7529e-05 - acc: 1.0000\n", + "Epoch 713/1000\n", + "26/26 [==============================] - 0s 754us/sample - loss: 8.7300e-05 - acc: 1.0000\n", + "Epoch 714/1000\n", + "26/26 [==============================] - 0s 425us/sample - loss: 8.7098e-05 - acc: 1.0000\n", + "Epoch 715/1000\n", + "26/26 [==============================] - 0s 396us/sample - loss: 8.6873e-05 - acc: 1.0000\n", + "Epoch 716/1000\n", + "26/26 [==============================] - 0s 403us/sample - loss: 8.6658e-05 - acc: 1.0000\n", + "Epoch 717/1000\n", + "26/26 [==============================] - 0s 401us/sample - loss: 8.6461e-05 - acc: 1.0000\n", + "Epoch 718/1000\n", + "26/26 [==============================] - 0s 336us/sample - loss: 8.6250e-05 - acc: 1.0000\n", + "Epoch 719/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 8.6012e-05 - acc: 1.0000\n", + "Epoch 720/1000\n", + "26/26 [==============================] - 0s 383us/sample - loss: 8.5778e-05 - acc: 1.0000\n", + "Epoch 721/1000\n", + "26/26 [==============================] - 0s 329us/sample - loss: 8.5562e-05 - acc: 1.0000\n", + "Epoch 722/1000\n", + "26/26 [==============================] - 0s 320us/sample - loss: 8.5319e-05 - acc: 1.0000\n", + "Epoch 723/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 8.5063e-05 - acc: 1.0000\n", + "Epoch 724/1000\n", + "26/26 [==============================] - 0s 355us/sample - loss: 8.4861e-05 - acc: 1.0000\n", + "Epoch 725/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 8.4632e-05 - acc: 1.0000\n", + "Epoch 726/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 8.4398e-05 - acc: 1.0000\n", + "Epoch 727/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 8.4183e-05 - acc: 1.0000\n", + "Epoch 728/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 8.3995e-05 - acc: 1.0000\n", + "Epoch 729/1000\n", + "26/26 [==============================] - 0s 372us/sample - loss: 8.3752e-05 - acc: 1.0000\n", + "Epoch 730/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 8.3573e-05 - acc: 1.0000\n", + "Epoch 731/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 8.3348e-05 - acc: 1.0000\n", + "Epoch 732/1000\n", + "26/26 [==============================] - 0s 423us/sample - loss: 8.3119e-05 - acc: 1.0000\n", + "Epoch 733/1000\n", + "26/26 [==============================] - 0s 469us/sample - loss: 8.2913e-05 - acc: 1.0000\n", + "Epoch 734/1000\n", + "26/26 [==============================] - 0s 451us/sample - loss: 8.2693e-05 - acc: 1.0000\n", + "Epoch 735/1000\n", + "26/26 [==============================] - 0s 438us/sample - loss: 8.2473e-05 - acc: 1.0000\n", + "Epoch 736/1000\n", + "26/26 [==============================] - 0s 502us/sample - loss: 8.2262e-05 - acc: 1.0000\n", + "Epoch 737/1000\n", + "26/26 [==============================] - 0s 446us/sample - loss: 8.2065e-05 - acc: 1.0000\n", + "Epoch 738/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 8.1886e-05 - acc: 1.0000\n", + "Epoch 739/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 8.1675e-05 - acc: 1.0000\n", + "Epoch 740/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 8.1492e-05 - acc: 1.0000\n", + "Epoch 741/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 8.1290e-05 - acc: 1.0000\n", + "Epoch 742/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 8.1107e-05 - acc: 1.0000\n", + "Epoch 743/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 8.0900e-05 - acc: 1.0000\n", + "Epoch 744/1000\n", + "26/26 [==============================] - 0s 468us/sample - loss: 8.0671e-05 - acc: 1.0000\n", + "Epoch 745/1000\n", + "26/26 [==============================] - 0s 420us/sample - loss: 8.0465e-05 - acc: 1.0000\n", + "Epoch 746/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 8.0281e-05 - acc: 1.0000\n", + "Epoch 747/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 8.0103e-05 - acc: 1.0000\n", + "Epoch 748/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 7.9896e-05 - acc: 1.0000\n", + "Epoch 749/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 7.9704e-05 - acc: 1.0000\n", + "Epoch 750/1000\n", + "26/26 [==============================] - 0s 335us/sample - loss: 7.9497e-05 - acc: 1.0000\n", + "Epoch 751/1000\n", + "26/26 [==============================] - 0s 321us/sample - loss: 7.9273e-05 - acc: 1.0000\n", + "Epoch 752/1000\n", + "26/26 [==============================] - 0s 296us/sample - loss: 7.9089e-05 - acc: 1.0000\n", + "Epoch 753/1000\n", + "26/26 [==============================] - 0s 287us/sample - loss: 7.8874e-05 - acc: 1.0000\n", + "Epoch 754/1000\n", + "26/26 [==============================] - 0s 280us/sample - loss: 7.8681e-05 - acc: 1.0000\n", + "Epoch 755/1000\n", + "26/26 [==============================] - 0s 277us/sample - loss: 7.8493e-05 - acc: 1.0000\n", + "Epoch 756/1000\n", + "26/26 [==============================] - 0s 296us/sample - loss: 7.8301e-05 - acc: 1.0000\n", + "Epoch 757/1000\n", + "26/26 [==============================] - 0s 304us/sample - loss: 7.8145e-05 - acc: 1.0000\n", + "Epoch 758/1000\n", + "26/26 [==============================] - 0s 310us/sample - loss: 7.7971e-05 - acc: 1.0000\n", + "Epoch 759/1000\n", + "26/26 [==============================] - 0s 327us/sample - loss: 7.7742e-05 - acc: 1.0000\n", + "Epoch 760/1000\n", + "26/26 [==============================] - 0s 243us/sample - loss: 7.7586e-05 - acc: 1.0000\n", + "Epoch 761/1000\n", + "26/26 [==============================] - 0s 336us/sample - loss: 7.7384e-05 - acc: 1.0000\n", + "Epoch 762/1000\n", + "26/26 [==============================] - 0s 287us/sample - loss: 7.7210e-05 - acc: 1.0000\n", + "Epoch 763/1000\n", + "26/26 [==============================] - 0s 288us/sample - loss: 7.7017e-05 - acc: 1.0000\n", + "Epoch 764/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 7.6843e-05 - acc: 1.0000\n", + "Epoch 765/1000\n", + "26/26 [==============================] - 0s 329us/sample - loss: 7.6600e-05 - acc: 1.0000\n", + "Epoch 766/1000\n", + "26/26 [==============================] - 0s 326us/sample - loss: 7.6412e-05 - acc: 1.0000\n", + "Epoch 767/1000\n", + "26/26 [==============================] - 0s 296us/sample - loss: 7.6210e-05 - acc: 1.0000\n", + "Epoch 768/1000\n", + "26/26 [==============================] - 0s 335us/sample - loss: 7.6036e-05 - acc: 1.0000\n", + "Epoch 769/1000\n", + "26/26 [==============================] - 0s 292us/sample - loss: 7.5853e-05 - acc: 1.0000\n", + "Epoch 770/1000\n", + "26/26 [==============================] - 0s 300us/sample - loss: 7.5670e-05 - acc: 1.0000\n", + "Epoch 771/1000\n", + "26/26 [==============================] - 0s 291us/sample - loss: 7.5472e-05 - acc: 1.0000\n", + "Epoch 772/1000\n", + "26/26 [==============================] - 0s 283us/sample - loss: 7.5307e-05 - acc: 1.0000\n", + "Epoch 773/1000\n", + "26/26 [==============================] - 0s 286us/sample - loss: 7.5087e-05 - acc: 1.0000\n", + "Epoch 774/1000\n", + "26/26 [==============================] - 0s 287us/sample - loss: 7.4890e-05 - acc: 1.0000\n", + "Epoch 775/1000\n", + "26/26 [==============================] - 0s 327us/sample - loss: 7.4743e-05 - acc: 1.0000\n", + "Epoch 776/1000\n", + "26/26 [==============================] - 0s 432us/sample - loss: 7.4533e-05 - acc: 1.0000\n", + "Epoch 777/1000\n", + "26/26 [==============================] - 0s 373us/sample - loss: 7.4354e-05 - acc: 1.0000\n", + "Epoch 778/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 7.4166e-05 - acc: 1.0000\n", + "Epoch 779/1000\n", + "26/26 [==============================] - 0s 317us/sample - loss: 7.3992e-05 - acc: 1.0000\n", + "Epoch 780/1000\n", + "26/26 [==============================] - 0s 430us/sample - loss: 7.3822e-05 - acc: 1.0000\n", + "Epoch 781/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 7.3625e-05 - acc: 1.0000\n", + "Epoch 782/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 7.3446e-05 - acc: 1.0000\n", + "Epoch 783/1000\n", + "26/26 [==============================] - 0s 384us/sample - loss: 7.3272e-05 - acc: 1.0000\n", + "Epoch 784/1000\n", + "26/26 [==============================] - 0s 324us/sample - loss: 7.3066e-05 - acc: 1.0000\n", + "Epoch 785/1000\n", + "26/26 [==============================] - 0s 340us/sample - loss: 7.2882e-05 - acc: 1.0000\n", + "Epoch 786/1000\n", + "26/26 [==============================] - 0s 404us/sample - loss: 7.2722e-05 - acc: 1.0000\n", + "Epoch 787/1000\n", + "26/26 [==============================] - 0s 407us/sample - loss: 7.2529e-05 - acc: 1.0000\n", + "Epoch 788/1000\n", + "26/26 [==============================] - 0s 489us/sample - loss: 7.2346e-05 - acc: 1.0000\n", + "Epoch 789/1000\n", + "26/26 [==============================] - 0s 456us/sample - loss: 7.2153e-05 - acc: 1.0000\n", + "Epoch 790/1000\n", + "26/26 [==============================] - 0s 458us/sample - loss: 7.2002e-05 - acc: 1.0000\n", + "Epoch 791/1000\n", + "26/26 [==============================] - 0s 493us/sample - loss: 7.1814e-05 - acc: 1.0000\n", + "Epoch 792/1000\n", + "26/26 [==============================] - 0s 520us/sample - loss: 7.1649e-05 - acc: 1.0000\n", + "Epoch 793/1000\n", + "26/26 [==============================] - 0s 471us/sample - loss: 7.1429e-05 - acc: 1.0000\n", + "Epoch 794/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 7.1246e-05 - acc: 1.0000\n", + "Epoch 795/1000\n", + "26/26 [==============================] - 0s 424us/sample - loss: 7.1094e-05 - acc: 1.0000\n", + "Epoch 796/1000\n", + "26/26 [==============================] - 0s 443us/sample - loss: 7.0920e-05 - acc: 1.0000\n", + "Epoch 797/1000\n", + "26/26 [==============================] - 0s 432us/sample - loss: 7.0741e-05 - acc: 1.0000\n", + "Epoch 798/1000\n", + "26/26 [==============================] - 0s 443us/sample - loss: 7.0576e-05 - acc: 1.0000\n", + "Epoch 799/1000\n", + "26/26 [==============================] - 0s 417us/sample - loss: 7.0430e-05 - acc: 1.0000\n", + "Epoch 800/1000\n", + "26/26 [==============================] - 0s 310us/sample - loss: 7.0210e-05 - acc: 1.0000\n", + "Epoch 801/1000\n", + "26/26 [==============================] - 0s 444us/sample - loss: 7.0045e-05 - acc: 1.0000\n", + "Epoch 802/1000\n", + "26/26 [==============================] - 0s 385us/sample - loss: 6.9884e-05 - acc: 1.0000\n", + "Epoch 803/1000\n", + "26/26 [==============================] - 0s 467us/sample - loss: 6.9728e-05 - acc: 1.0000\n", + "Epoch 804/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 6.9527e-05 - acc: 1.0000\n", + "Epoch 805/1000\n", + "26/26 [==============================] - 0s 452us/sample - loss: 6.9357e-05 - acc: 1.0000\n", + "Epoch 806/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 6.9206e-05 - acc: 1.0000\n", + "Epoch 807/1000\n", + "26/26 [==============================] - 0s 442us/sample - loss: 6.9018e-05 - acc: 1.0000\n", + "Epoch 808/1000\n", + "26/26 [==============================] - 0s 424us/sample - loss: 6.8848e-05 - acc: 1.0000\n", + "Epoch 809/1000\n", + "26/26 [==============================] - 0s 435us/sample - loss: 6.8692e-05 - acc: 1.0000\n", + "Epoch 810/1000\n", + "26/26 [==============================] - 0s 348us/sample - loss: 6.8513e-05 - acc: 1.0000\n", + "Epoch 811/1000\n", + "26/26 [==============================] - 0s 343us/sample - loss: 6.8344e-05 - acc: 1.0000\n", + "Epoch 812/1000\n", + "26/26 [==============================] - 0s 353us/sample - loss: 6.8192e-05 - acc: 1.0000\n", + "Epoch 813/1000\n", + "26/26 [==============================] - 0s 457us/sample - loss: 6.8000e-05 - acc: 1.0000\n", + "Epoch 814/1000\n", + "26/26 [==============================] - 0s 417us/sample - loss: 6.7817e-05 - acc: 1.0000\n", + "Epoch 815/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 6.7670e-05 - acc: 1.0000\n", + "Epoch 816/1000\n", + "26/26 [==============================] - 0s 492us/sample - loss: 6.7519e-05 - acc: 1.0000\n", + "Epoch 817/1000\n", + "26/26 [==============================] - 0s 429us/sample - loss: 6.7344e-05 - acc: 1.0000\n", + "Epoch 818/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 6.7161e-05 - acc: 1.0000\n", + "Epoch 819/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 6.6991e-05 - acc: 1.0000\n", + "Epoch 820/1000\n", + "26/26 [==============================] - 0s 340us/sample - loss: 6.6817e-05 - acc: 1.0000\n", + "Epoch 821/1000\n", + "26/26 [==============================] - 0s 329us/sample - loss: 6.6657e-05 - acc: 1.0000\n", + "Epoch 822/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 6.6473e-05 - acc: 1.0000\n", + "Epoch 823/1000\n", + "26/26 [==============================] - 0s 396us/sample - loss: 6.6322e-05 - acc: 1.0000\n", + "Epoch 824/1000\n", + "26/26 [==============================] - 0s 429us/sample - loss: 6.6175e-05 - acc: 1.0000\n", + "Epoch 825/1000\n", + "26/26 [==============================] - 0s 396us/sample - loss: 6.6024e-05 - acc: 1.0000\n", + "Epoch 826/1000\n", + "26/26 [==============================] - 0s 434us/sample - loss: 6.5873e-05 - acc: 1.0000\n", + "Epoch 827/1000\n", + "26/26 [==============================] - 0s 432us/sample - loss: 6.5703e-05 - acc: 1.0000\n", + "Epoch 828/1000\n", + "26/26 [==============================] - 0s 421us/sample - loss: 6.5529e-05 - acc: 1.0000\n", + "Epoch 829/1000\n", + "26/26 [==============================] - 0s 440us/sample - loss: 6.5378e-05 - acc: 1.0000\n", + "Epoch 830/1000\n", + "26/26 [==============================] - 0s 405us/sample - loss: 6.5190e-05 - acc: 1.0000\n", + "Epoch 831/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 6.5034e-05 - acc: 1.0000\n", + "Epoch 832/1000\n", + "26/26 [==============================] - 0s 474us/sample - loss: 6.4869e-05 - acc: 1.0000\n", + "Epoch 833/1000\n", + "26/26 [==============================] - 0s 415us/sample - loss: 6.4722e-05 - acc: 1.0000\n", + "Epoch 834/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 6.4575e-05 - acc: 1.0000\n", + "Epoch 835/1000\n", + "26/26 [==============================] - 0s 433us/sample - loss: 6.4429e-05 - acc: 1.0000\n", + "Epoch 836/1000\n", + "26/26 [==============================] - 0s 332us/sample - loss: 6.4241e-05 - acc: 1.0000\n", + "Epoch 837/1000\n", + "26/26 [==============================] - 0s 323us/sample - loss: 6.4103e-05 - acc: 1.0000\n", + "Epoch 838/1000\n", + "26/26 [==============================] - 0s 377us/sample - loss: 6.3961e-05 - acc: 1.0000\n", + "Epoch 839/1000\n", + "26/26 [==============================] - 0s 373us/sample - loss: 6.3814e-05 - acc: 1.0000\n", + "Epoch 840/1000\n", + "26/26 [==============================] - 0s 445us/sample - loss: 6.3672e-05 - acc: 1.0000\n", + "Epoch 841/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 6.3507e-05 - acc: 1.0000\n", + "Epoch 842/1000\n", + "26/26 [==============================] - 0s 328us/sample - loss: 6.3361e-05 - acc: 1.0000\n", + "Epoch 843/1000\n", + "26/26 [==============================] - 0s 431us/sample - loss: 6.3205e-05 - acc: 1.0000\n", + "Epoch 844/1000\n", + "26/26 [==============================] - 0s 478us/sample - loss: 6.3035e-05 - acc: 1.0000\n", + "Epoch 845/1000\n", + "26/26 [==============================] - 0s 356us/sample - loss: 6.2911e-05 - acc: 1.0000\n", + "Epoch 846/1000\n", + "26/26 [==============================] - 0s 395us/sample - loss: 6.2751e-05 - acc: 1.0000\n", + "Epoch 847/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 6.2595e-05 - acc: 1.0000\n", + "Epoch 848/1000\n", + "26/26 [==============================] - 0s 510us/sample - loss: 6.2448e-05 - acc: 1.0000\n", + "Epoch 849/1000\n", + "26/26 [==============================] - 0s 432us/sample - loss: 6.2302e-05 - acc: 1.0000\n", + "Epoch 850/1000\n", + "26/26 [==============================] - 0s 376us/sample - loss: 6.2159e-05 - acc: 1.0000\n", + "Epoch 851/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 6.1985e-05 - acc: 1.0000\n", + "Epoch 852/1000\n", + "26/26 [==============================] - 0s 439us/sample - loss: 6.1861e-05 - acc: 1.0000\n", + "Epoch 853/1000\n", + "26/26 [==============================] - 0s 496us/sample - loss: 6.1715e-05 - acc: 1.0000\n", + "Epoch 854/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 6.1568e-05 - acc: 1.0000\n", + "Epoch 855/1000\n", + "26/26 [==============================] - 0s 464us/sample - loss: 6.1403e-05 - acc: 1.0000\n", + "Epoch 856/1000\n", + "26/26 [==============================] - 0s 398us/sample - loss: 6.1252e-05 - acc: 1.0000\n", + "Epoch 857/1000\n", + "26/26 [==============================] - 0s 446us/sample - loss: 6.1110e-05 - acc: 1.0000\n", + "Epoch 858/1000\n", + "26/26 [==============================] - 0s 454us/sample - loss: 6.0972e-05 - acc: 1.0000\n", + "Epoch 859/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 6.0825e-05 - acc: 1.0000\n", + "Epoch 860/1000\n", + "26/26 [==============================] - 0s 415us/sample - loss: 6.0669e-05 - acc: 1.0000\n", + "Epoch 861/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 6.0532e-05 - acc: 1.0000\n", + "Epoch 862/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 6.0404e-05 - acc: 1.0000\n", + "Epoch 863/1000\n", + "26/26 [==============================] - 0s 394us/sample - loss: 6.0261e-05 - acc: 1.0000\n", + "Epoch 864/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 6.0106e-05 - acc: 1.0000\n", + "Epoch 865/1000\n", + "26/26 [==============================] - 0s 355us/sample - loss: 5.9977e-05 - acc: 1.0000\n", + "Epoch 866/1000\n", + "26/26 [==============================] - 0s 321us/sample - loss: 5.9817e-05 - acc: 1.0000\n", + "Epoch 867/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 5.9670e-05 - acc: 1.0000\n", + "Epoch 868/1000\n", + "26/26 [==============================] - 0s 376us/sample - loss: 5.9537e-05 - acc: 1.0000\n", + "Epoch 869/1000\n", + "26/26 [==============================] - 0s 410us/sample - loss: 5.9418e-05 - acc: 1.0000\n", + "Epoch 870/1000\n", + "26/26 [==============================] - 0s 528us/sample - loss: 5.9262e-05 - acc: 1.0000\n", + "Epoch 871/1000\n", + "26/26 [==============================] - 0s 588us/sample - loss: 5.9102e-05 - acc: 1.0000\n", + "Epoch 872/1000\n", + "26/26 [==============================] - 0s 368us/sample - loss: 5.8941e-05 - acc: 1.0000\n", + "Epoch 873/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 5.8804e-05 - acc: 1.0000\n", + "Epoch 874/1000\n", + "26/26 [==============================] - 0s 335us/sample - loss: 5.8675e-05 - acc: 1.0000\n", + "Epoch 875/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 5.8492e-05 - acc: 1.0000\n", + "Epoch 876/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 5.8363e-05 - acc: 1.0000\n", + "Epoch 877/1000\n", + "26/26 [==============================] - 0s 394us/sample - loss: 5.8235e-05 - acc: 1.0000\n", + "Epoch 878/1000\n", + "26/26 [==============================] - 0s 402us/sample - loss: 5.8111e-05 - acc: 1.0000\n", + "Epoch 879/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 5.7960e-05 - acc: 1.0000\n", + "Epoch 880/1000\n", + "26/26 [==============================] - 0s 405us/sample - loss: 5.7818e-05 - acc: 1.0000\n", + "Epoch 881/1000\n", + "26/26 [==============================] - 0s 462us/sample - loss: 5.7699e-05 - acc: 1.0000\n", + "Epoch 882/1000\n", + "26/26 [==============================] - 0s 459us/sample - loss: 5.7557e-05 - acc: 1.0000\n", + "Epoch 883/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 5.7419e-05 - acc: 1.0000\n", + "Epoch 884/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 5.7300e-05 - acc: 1.0000\n", + "Epoch 885/1000\n", + "26/26 [==============================] - 0s 333us/sample - loss: 5.7135e-05 - acc: 1.0000\n", + "Epoch 886/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 5.7002e-05 - acc: 1.0000\n", + "Epoch 887/1000\n", + "26/26 [==============================] - 0s 445us/sample - loss: 5.6869e-05 - acc: 1.0000\n", + "Epoch 888/1000\n", + "26/26 [==============================] - 0s 406us/sample - loss: 5.6713e-05 - acc: 1.0000\n", + "Epoch 889/1000\n", + "26/26 [==============================] - 0s 412us/sample - loss: 5.6557e-05 - acc: 1.0000\n", + "Epoch 890/1000\n", + "26/26 [==============================] - 0s 441us/sample - loss: 5.6429e-05 - acc: 1.0000\n", + "Epoch 891/1000\n", + "26/26 [==============================] - 0s 396us/sample - loss: 5.6259e-05 - acc: 1.0000\n", + "Epoch 892/1000\n", + "26/26 [==============================] - 0s 315us/sample - loss: 5.6131e-05 - acc: 1.0000\n", + "Epoch 893/1000\n", + "26/26 [==============================] - 0s 408us/sample - loss: 5.5993e-05 - acc: 1.0000\n", + "Epoch 894/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 5.5842e-05 - acc: 1.0000\n", + "Epoch 895/1000\n", + "26/26 [==============================] - 0s 324us/sample - loss: 5.5704e-05 - acc: 1.0000\n", + "Epoch 896/1000\n", + "26/26 [==============================] - 0s 377us/sample - loss: 5.5572e-05 - acc: 1.0000\n", + "Epoch 897/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 5.5434e-05 - acc: 1.0000\n", + "Epoch 898/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 5.5287e-05 - acc: 1.0000\n", + "Epoch 899/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 5.5150e-05 - acc: 1.0000\n", + "Epoch 900/1000\n", + "26/26 [==============================] - 0s 524us/sample - loss: 5.4998e-05 - acc: 1.0000\n", + "Epoch 901/1000\n", + "26/26 [==============================] - 0s 404us/sample - loss: 5.4847e-05 - acc: 1.0000\n", + "Epoch 902/1000\n", + "26/26 [==============================] - 0s 325us/sample - loss: 5.4705e-05 - acc: 1.0000\n", + "Epoch 903/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 5.4554e-05 - acc: 1.0000\n", + "Epoch 904/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 5.4430e-05 - acc: 1.0000\n", + "Epoch 905/1000\n", + "26/26 [==============================] - 0s 290us/sample - loss: 5.4274e-05 - acc: 1.0000\n", + "Epoch 906/1000\n", + "26/26 [==============================] - 0s 446us/sample - loss: 5.4146e-05 - acc: 1.0000\n", + "Epoch 907/1000\n", + "26/26 [==============================] - 0s 419us/sample - loss: 5.4013e-05 - acc: 1.0000\n", + "Epoch 908/1000\n", + "26/26 [==============================] - 0s 413us/sample - loss: 5.3903e-05 - acc: 1.0000\n", + "Epoch 909/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 5.3770e-05 - acc: 1.0000\n", + "Epoch 910/1000\n", + "26/26 [==============================] - 0s 607us/sample - loss: 5.3632e-05 - acc: 1.0000\n", + "Epoch 911/1000\n", + "26/26 [==============================] - 0s 527us/sample - loss: 5.3495e-05 - acc: 1.0000\n", + "Epoch 912/1000\n", + "26/26 [==============================] - 0s 527us/sample - loss: 5.3376e-05 - acc: 1.0000\n", + "Epoch 913/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 5.3261e-05 - acc: 1.0000\n", + "Epoch 914/1000\n", + "26/26 [==============================] - 0s 513us/sample - loss: 5.3128e-05 - acc: 1.0000\n", + "Epoch 915/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 5.2990e-05 - acc: 1.0000\n", + "Epoch 916/1000\n", + "26/26 [==============================] - 0s 662us/sample - loss: 5.2871e-05 - acc: 1.0000\n", + "Epoch 917/1000\n", + "26/26 [==============================] - 0s 432us/sample - loss: 5.2738e-05 - acc: 1.0000\n", + "Epoch 918/1000\n", + "26/26 [==============================] - 0s 403us/sample - loss: 5.2610e-05 - acc: 1.0000\n", + "Epoch 919/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 5.2472e-05 - acc: 1.0000\n", + "Epoch 920/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 5.2367e-05 - acc: 1.0000\n", + "Epoch 921/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 5.2225e-05 - acc: 1.0000\n", + "Epoch 922/1000\n", + "26/26 [==============================] - 0s 349us/sample - loss: 5.2096e-05 - acc: 1.0000\n", + "Epoch 923/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 5.1973e-05 - acc: 1.0000\n", + "Epoch 924/1000\n", + "26/26 [==============================] - 0s 478us/sample - loss: 5.1881e-05 - acc: 1.0000\n", + "Epoch 925/1000\n", + "26/26 [==============================] - 0s 494us/sample - loss: 5.1734e-05 - acc: 1.0000\n", + "Epoch 926/1000\n", + "26/26 [==============================] - 0s 442us/sample - loss: 5.1629e-05 - acc: 1.0000\n", + "Epoch 927/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 5.1491e-05 - acc: 1.0000\n", + "Epoch 928/1000\n", + "26/26 [==============================] - 0s 423us/sample - loss: 5.1363e-05 - acc: 1.0000\n", + "Epoch 929/1000\n", + "26/26 [==============================] - 0s 431us/sample - loss: 5.1221e-05 - acc: 1.0000\n", + "Epoch 930/1000\n", + "26/26 [==============================] - 0s 373us/sample - loss: 5.1111e-05 - acc: 1.0000\n", + "Epoch 931/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 5.0982e-05 - acc: 1.0000\n", + "Epoch 932/1000\n", + "26/26 [==============================] - 0s 327us/sample - loss: 5.0859e-05 - acc: 1.0000\n", + "Epoch 933/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 5.0721e-05 - acc: 1.0000\n", + "Epoch 934/1000\n", + "26/26 [==============================] - 0s 337us/sample - loss: 5.0588e-05 - acc: 1.0000\n", + "Epoch 935/1000\n", + "26/26 [==============================] - 0s 353us/sample - loss: 5.0446e-05 - acc: 1.0000\n", + "Epoch 936/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 5.0327e-05 - acc: 1.0000\n", + "Epoch 937/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 5.0180e-05 - acc: 1.0000\n", + "Epoch 938/1000\n", + "26/26 [==============================] - 0s 356us/sample - loss: 5.0079e-05 - acc: 1.0000\n", + "Epoch 939/1000\n", + "26/26 [==============================] - 0s 519us/sample - loss: 4.9928e-05 - acc: 1.0000\n", + "Epoch 940/1000\n", + "26/26 [==============================] - 0s 393us/sample - loss: 4.9823e-05 - acc: 1.0000\n", + "Epoch 941/1000\n", + "26/26 [==============================] - 0s 334us/sample - loss: 4.9694e-05 - acc: 1.0000\n", + "Epoch 942/1000\n", + "26/26 [==============================] - 0s 423us/sample - loss: 4.9570e-05 - acc: 1.0000\n", + "Epoch 943/1000\n", + "26/26 [==============================] - 0s 417us/sample - loss: 4.9433e-05 - acc: 1.0000\n", + "Epoch 944/1000\n", + "26/26 [==============================] - 0s 396us/sample - loss: 4.9323e-05 - acc: 1.0000\n", + "Epoch 945/1000\n", + "26/26 [==============================] - 0s 556us/sample - loss: 4.9231e-05 - acc: 1.0000\n", + "Epoch 946/1000\n", + "26/26 [==============================] - 0s 343us/sample - loss: 4.9117e-05 - acc: 1.0000\n", + "Epoch 947/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 4.9002e-05 - acc: 1.0000\n", + "Epoch 948/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 4.8874e-05 - acc: 1.0000\n", + "Epoch 949/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 4.8754e-05 - acc: 1.0000\n", + "Epoch 950/1000\n", + "26/26 [==============================] - 0s 429us/sample - loss: 4.8626e-05 - acc: 1.0000\n", + "Epoch 951/1000\n", + "26/26 [==============================] - 0s 402us/sample - loss: 4.8498e-05 - acc: 1.0000\n", + "Epoch 952/1000\n", + "26/26 [==============================] - 0s 405us/sample - loss: 4.8388e-05 - acc: 1.0000\n", + "Epoch 953/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 4.8268e-05 - acc: 1.0000\n", + "Epoch 954/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 4.8131e-05 - acc: 1.0000\n", + "Epoch 955/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 4.8035e-05 - acc: 1.0000\n", + "Epoch 956/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 4.7915e-05 - acc: 1.0000\n", + "Epoch 957/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 4.7810e-05 - acc: 1.0000\n", + "Epoch 958/1000\n", + "26/26 [==============================] - 0s 328us/sample - loss: 4.7714e-05 - acc: 1.0000\n", + "Epoch 959/1000\n", + "26/26 [==============================] - 0s 343us/sample - loss: 4.7599e-05 - acc: 1.0000\n", + "Epoch 960/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 4.7475e-05 - acc: 1.0000\n", + "Epoch 961/1000\n", + "26/26 [==============================] - 0s 439us/sample - loss: 4.7356e-05 - acc: 1.0000\n", + "Epoch 962/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 4.7260e-05 - acc: 1.0000\n", + "Epoch 963/1000\n", + "26/26 [==============================] - 0s 557us/sample - loss: 4.7159e-05 - acc: 1.0000\n", + "Epoch 964/1000\n", + "26/26 [==============================] - 0s 522us/sample - loss: 4.7017e-05 - acc: 1.0000\n", + "Epoch 965/1000\n", + "26/26 [==============================] - 0s 449us/sample - loss: 4.6898e-05 - acc: 1.0000\n", + "Epoch 966/1000\n", + "26/26 [==============================] - 0s 402us/sample - loss: 4.6792e-05 - acc: 1.0000\n", + "Epoch 967/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 4.6668e-05 - acc: 1.0000\n", + "Epoch 968/1000\n", + "26/26 [==============================] - 0s 389us/sample - loss: 4.6535e-05 - acc: 1.0000\n", + "Epoch 969/1000\n", + "26/26 [==============================] - 0s 431us/sample - loss: 4.6448e-05 - acc: 1.0000\n", + "Epoch 970/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 4.6343e-05 - acc: 1.0000\n", + "Epoch 971/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 4.6233e-05 - acc: 1.0000\n", + "Epoch 972/1000\n", + "26/26 [==============================] - 0s 368us/sample - loss: 4.6095e-05 - acc: 1.0000\n", + "Epoch 973/1000\n", + "26/26 [==============================] - 0s 332us/sample - loss: 4.5985e-05 - acc: 1.0000\n", + "Epoch 974/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 4.5866e-05 - acc: 1.0000\n", + "Epoch 975/1000\n", + "26/26 [==============================] - 0s 396us/sample - loss: 4.5761e-05 - acc: 1.0000\n", + "Epoch 976/1000\n", + "26/26 [==============================] - 0s 432us/sample - loss: 4.5664e-05 - acc: 1.0000\n", + "Epoch 977/1000\n", + "26/26 [==============================] - 0s 372us/sample - loss: 4.5554e-05 - acc: 1.0000\n", + "Epoch 978/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 4.5444e-05 - acc: 1.0000\n", + "Epoch 979/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 4.5316e-05 - acc: 1.0000\n", + "Epoch 980/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 4.5215e-05 - acc: 1.0000\n", + "Epoch 981/1000\n", + "26/26 [==============================] - 0s 383us/sample - loss: 4.5100e-05 - acc: 1.0000\n", + "Epoch 982/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 4.5004e-05 - acc: 1.0000\n", + "Epoch 983/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 4.4890e-05 - acc: 1.0000\n", + "Epoch 984/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 4.4761e-05 - acc: 1.0000\n", + "Epoch 985/1000\n", + "26/26 [==============================] - 0s 326us/sample - loss: 4.4679e-05 - acc: 1.0000\n", + "Epoch 986/1000\n", + "26/26 [==============================] - 0s 356us/sample - loss: 4.4546e-05 - acc: 1.0000\n", + "Epoch 987/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 4.4440e-05 - acc: 1.0000\n", + "Epoch 988/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 4.4335e-05 - acc: 1.0000\n", + "Epoch 989/1000\n", + "26/26 [==============================] - 0s 323us/sample - loss: 4.4229e-05 - acc: 1.0000\n", + "Epoch 990/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 4.4129e-05 - acc: 1.0000\n", + "Epoch 991/1000\n", + "26/26 [==============================] - 0s 353us/sample - loss: 4.4009e-05 - acc: 1.0000\n", + "Epoch 992/1000\n", + "26/26 [==============================] - 0s 394us/sample - loss: 4.3890e-05 - acc: 1.0000\n", + "Epoch 993/1000\n", + "26/26 [==============================] - 0s 399us/sample - loss: 4.3766e-05 - acc: 1.0000\n", + "Epoch 994/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 4.3661e-05 - acc: 1.0000\n", + "Epoch 995/1000\n", + "26/26 [==============================] - 0s 439us/sample - loss: 4.3565e-05 - acc: 1.0000\n", + "Epoch 996/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 4.3464e-05 - acc: 1.0000\n", + "Epoch 997/1000\n", + "26/26 [==============================] - 0s 436us/sample - loss: 4.3358e-05 - acc: 1.0000\n", + "Epoch 998/1000\n", + "26/26 [==============================] - 0s 508us/sample - loss: 4.3248e-05 - acc: 1.0000\n", + "Epoch 999/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 4.3157e-05 - acc: 1.0000\n", + "Epoch 1000/1000\n", + "26/26 [==============================] - 0s 360us/sample - loss: 4.3065e-05 - acc: 1.0000\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "def bag_of_words(s, words):\n", + " bag = [0 for _ in range(len(words))]\n", + "\n", + " s_words = nltk.word_tokenize(s)\n", + " s_words = [stemmer.stem(word.lower()) for word in s_words]\n", + "\n", + " for se in s_words:\n", + " for i, w in enumerate(words):\n", + " if w == se:\n", + " bag[i] = 1\n", + "\n", + " return numpy.array(bag)\n", + "\n", + "import tensorflow as tf\n", + "import numpy as np\n", + "import random\n", + "\n", + "\n", + "model = tf.keras.models.load_model('model.tflearn')\n", + "\n", + "def chat():\n", + " print(\"Start talking with the bot (type quit to stop)!\")\n", + " while True:\n", + " inp = input(\"You: \")\n", + " if inp.lower() == \"quit\":\n", + " break\n", + "\n", + " processed_input = bag_of_words(inp, words)\n", + " processed_input = np.array([processed_input])\n", + "\n", + "\n", + " results = model.predict(processed_input)\n", + " results_index = np.argmax(results)\n", + " tag = labels[results_index]\n", + "\n", + " for tg in data[\"intents\"]:\n", + " if tg['tag'] == tag:\n", + " responses = tg['responses']\n", + "\n", + " print(random.choice(responses))\n", + "\n", + "chat()\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "blSmwDWNAHRM", + "outputId": "a1ecc1d6-41bb-4541-aba6-ae31b2eb81a0" + }, + "execution_count": 35, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Start talking with the bot (type quit to stop)!\n", + "You: hi\n", + "Good to see you again!\n", + "You: hello\n", + "Hello!\n", + "You: what you doing\n", + "Hello!\n", + "You: quit\n" + ] + } + ] + } + ] +} \ No newline at end of file diff --git a/WEEK 2 YOLO_V4.ipynb b/WEEK 2 YOLO_V4.ipynb new file mode 100644 index 000000000..7d896b43e --- /dev/null +++ b/WEEK 2 YOLO_V4.ipynb @@ -0,0 +1,881 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "QLCpLo_belcv", + "outputId": "9c11c274-ad20-4883-b821-3cf6c1fba175" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mounted at /content/drive\n" + ] + } + ], + "source": [ + "from google.colab import drive\n", + "drive.mount('/content/drive')" + ] + }, + { + "cell_type": "code", + "source": [ + "import os" + ], + "metadata": { + "id": "vIcKIjDkepFY" + }, + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "path='/content/drive/My Drive/YOLO_V4'\n", + "os.chdir(path)" + ], + "metadata": { + "id": "Q-Y3T2GPfEYR" + }, + "execution_count": 3, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "!git clone https://github.com/AlexeyAB/darknet" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IWG1Bx5rfGws", + "outputId": "b73bd57a-47c2-4268-ca6b-833bdbaabf44" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Cloning into 'darknet'...\n", + "remote: Enumerating objects: 15530, done.\u001b[K\n", + "remote: Counting objects: 100% (16/16), done.\u001b[K\n", + "remote: Compressing objects: 100% (12/12), done.\u001b[K\n", + "remote: Total 15530 (delta 5), reused 13 (delta 4), pack-reused 15514\u001b[K\n", + "Receiving objects: 100% (15530/15530), 14.22 MiB | 5.78 MiB/s, done.\n", + "Resolving deltas: 100% (10417/10417), done.\n", + "Updating files: 100% (2058/2058), done.\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!/usr/local/cuda/bin/nvcc --version" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IZjyyzlSfJlx", + "outputId": "e1b193b2-8e99-4119-c6b0-15b25489d6af" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "nvcc: NVIDIA (R) Cuda compiler driver\n", + "Copyright (c) 2005-2022 NVIDIA Corporation\n", + "Built on Wed_Sep_21_10:33:58_PDT_2022\n", + "Cuda compilation tools, release 11.8, V11.8.89\n", + "Build cuda_11.8.r11.8/compiler.31833905_0\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "os.chdir('/content/drive/My Drive/YOLO_V4/darknet')\n", + "!make" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "4EqEEZN0fTgY", + "outputId": "c6b3c2aa-2e13-4c4d-9a0a-36882fa29dec" + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "mkdir -p ./obj/\n", + "mkdir -p backup\n", + "chmod +x *.sh\n", + "g++ -std=c++11 -std=c++11 -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/image_opencv.cpp -o obj/image_opencv.o\n", + "g++ -std=c++11 -std=c++11 -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/http_stream.cpp -o obj/http_stream.o\n", + "\u001b[01m\u001b[K./src/http_stream.cpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kbool JSON_sender::write(const char*)\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/http_stream.cpp:253:21:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kn\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 253 | int \u001b[01;35m\u001b[Kn\u001b[m\u001b[K = _write(client, outputbuf, outlen);\n", + " | \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/http_stream.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid set_track_id(detection*, int, float, float, float, int, int, int)\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/http_stream.cpp:867:27:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison of integer expressions of different signedness: ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’ and ‘\u001b[01m\u001b[Kstd::vector::size_type\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’} [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n", + " 867 | for (int i = 0; \u001b[01;35m\u001b[Ki < v.size()\u001b[m\u001b[K; ++i) {\n", + " | \u001b[01;35m\u001b[K~~^~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/http_stream.cpp:875:33:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison of integer expressions of different signedness: ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’ and ‘\u001b[01m\u001b[Kstd::vector::size_type\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’} [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n", + " 875 | for (int old_id = 0; \u001b[01;35m\u001b[Kold_id < old_dets.size()\u001b[m\u001b[K; ++old_id) {\n", + " | \u001b[01;35m\u001b[K~~~~~~~^~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/http_stream.cpp:894:31:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison of integer expressions of different signedness: ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’ and ‘\u001b[01m\u001b[Kstd::vector::size_type\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’} [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n", + " 894 | for (int index = 0; \u001b[01;35m\u001b[Kindex < new_dets_num*old_dets.size()\u001b[m\u001b[K; ++index) {\n", + " | \u001b[01;35m\u001b[K~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/http_stream.cpp:930:28:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison of integer expressions of different signedness: ‘\u001b[01m\u001b[Kstd::deque >::size_type\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’} and ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n", + " 930 | if (\u001b[01;35m\u001b[Kold_dets_dq.size() > deque_size\u001b[m\u001b[K) old_dets_dq.pop_front();\n", + " | \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~\u001b[m\u001b[K\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/gemm.c -o obj/gemm.o\n", + "\u001b[01m\u001b[K./src/gemm.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kconvolution_2d\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/gemm.c:2044:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kout_w\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 2044 | const int \u001b[01;35m\u001b[Kout_w\u001b[m\u001b[K = (w + 2 * pad - ksize) / stride + 1; // output_width=input_width for stride=1 and pad=1\n", + " | \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/gemm.c:2043:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kout_h\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 2043 | const int \u001b[01;35m\u001b[Kout_h\u001b[m\u001b[K = (h + 2 * pad - ksize) / stride + 1; // output_height=input_height for stride=1 and pad=1\n", + " | \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/utils.c -o obj/utils.o\n", + "\u001b[01m\u001b[K./src/utils.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kcustom_hash\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/utils.c:1082:12:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Ksuggest parentheses around assignment used as truth value [\u001b[01;35m\u001b[K-Wparentheses\u001b[m\u001b[K]\n", + " 1082 | while (\u001b[01;35m\u001b[Kc\u001b[m\u001b[K = *str++)\n", + " | \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n", + "In file included from \u001b[01m\u001b[K/usr/include/string.h:495\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[Kinclude/darknet.h:14\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[K./src/utils.h:3\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[K./src/utils.c:4\u001b[m\u001b[K:\n", + "In function ‘\u001b[01m\u001b[Kstrncpy\u001b[m\u001b[K’,\n", + " inlined from ‘\u001b[01m\u001b[Kcopy_string\u001b[m\u001b[K’ at \u001b[01m\u001b[K./src/utils.c:552:5\u001b[m\u001b[K:\n", + "\u001b[01m\u001b[K/usr/include/x86_64-linux-gnu/bits/string_fortified.h:106:10:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[K__builtin_strncpy\u001b[m\u001b[K’ specified bound depends on the length of the source argument [\u001b[01;35m\u001b[K-Wstringop-overflow=\u001b[m\u001b[K]\n", + " 106 | return \u001b[01;35m\u001b[K__builtin___strncpy_chk (__dest, __src, __len, __bos (__dest))\u001b[m\u001b[K;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/utils.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kcopy_string\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/utils.c:552:22:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Klength computed here\n", + " 552 | strncpy(copy, s, \u001b[01;36m\u001b[Kstrlen(s)\u001b[m\u001b[K+1);\n", + " | \u001b[01;36m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/dark_cuda.c -o obj/dark_cuda.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/convolutional_layer.c -o obj/convolutional_layer.o\n", + "\u001b[01m\u001b[K./src/convolutional_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kresize_convolutional_layer\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/convolutional_layer.c:898:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kold_h\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 898 | int \u001b[01;35m\u001b[Kold_h\u001b[m\u001b[K = l->h;\n", + " | \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/convolutional_layer.c:897:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kold_w\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 897 | int \u001b[01;35m\u001b[Kold_w\u001b[m\u001b[K = l->w;\n", + " | \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/convolutional_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kforward_convolutional_layer\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/convolutional_layer.c:1342:32:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kt_intput_size\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 1342 | size_t \u001b[01;35m\u001b[Kt_intput_size\u001b[m\u001b[K = binary_transpose_align_input(k, n, state.workspace, &l.t_bit_input, ldb_align, l.bit_align);\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/list.c -o obj/list.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/image.c -o obj/image.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/activations.c -o obj/activations.o\n", + "\u001b[01m\u001b[K./src/activations.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kactivate\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KRELU6\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n", + " 79 | \u001b[01;35m\u001b[Kswitch\u001b[m\u001b[K(a){\n", + " | \u001b[01;35m\u001b[K^~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KSWISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n", + "\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KMISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n", + "\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KHARD_MISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n", + "\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KNORM_CHAN\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n", + "\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KNORM_CHAN_SOFTMAX\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n", + "\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KNORM_CHAN_SOFTMAX_MAXVAL\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n", + "\u001b[01m\u001b[K./src/activations.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kgradient\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/activations.c:310:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KSWISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n", + " 310 | \u001b[01;35m\u001b[Kswitch\u001b[m\u001b[K(a){\n", + " | \u001b[01;35m\u001b[K^~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/activations.c:310:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KMISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n", + "\u001b[01m\u001b[K./src/activations.c:310:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KHARD_MISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/im2col.c -o obj/im2col.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/col2im.c -o obj/col2im.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/blas.c -o obj/blas.o\n", + "\u001b[01m\u001b[K./src/blas.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kbackward_shortcut_multilayer_cpu\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/blas.c:207:21:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kout_index\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 207 | int \u001b[01;35m\u001b[Kout_index\u001b[m\u001b[K = id;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/blas.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kfind_sim\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/blas.c:597:59:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Ksize_t\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’} [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n", + " 597 | printf(\" Error: find_sim(): sim isn't found: i = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, j = %d, z = %d \\n\", \u001b[32m\u001b[Ki\u001b[m\u001b[K, j, z);\n", + " | \u001b[01;35m\u001b[K~^\u001b[m\u001b[K \u001b[32m\u001b[K~\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[K|\u001b[m\u001b[K \u001b[32m\u001b[K|\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[Kint\u001b[m\u001b[K \u001b[32m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K\n", + " | \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/blas.c:597:67:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 3 has type ‘\u001b[01m\u001b[Ksize_t\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’} [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n", + " 597 | printf(\" Error: find_sim(): sim isn't found: i = %d, j = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, z = %d \\n\", i, \u001b[32m\u001b[Kj\u001b[m\u001b[K, z);\n", + " | \u001b[01;35m\u001b[K~^\u001b[m\u001b[K \u001b[32m\u001b[K~\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[K|\u001b[m\u001b[K \u001b[32m\u001b[K|\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[Kint\u001b[m\u001b[K \u001b[32m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K\n", + " | \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/blas.c:597:75:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 4 has type ‘\u001b[01m\u001b[Ksize_t\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’} [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n", + " 597 | printf(\" Error: find_sim(): sim isn't found: i = %d, j = %d, z = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K \\n\", i, j, \u001b[32m\u001b[Kz\u001b[m\u001b[K);\n", + " | \u001b[01;35m\u001b[K~^\u001b[m\u001b[K \u001b[32m\u001b[K~\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[K|\u001b[m\u001b[K \u001b[32m\u001b[K|\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[Kint\u001b[m\u001b[K \u001b[32m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K\n", + " | \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/blas.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kfind_P_constrastive\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/blas.c:611:68:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Ksize_t\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’} [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n", + " 611 | printf(\" Error: find_P_constrastive(): P isn't found: i = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, j = %d, z = %d \\n\", \u001b[32m\u001b[Ki\u001b[m\u001b[K, j, z);\n", + " | \u001b[01;35m\u001b[K~^\u001b[m\u001b[K \u001b[32m\u001b[K~\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[K|\u001b[m\u001b[K \u001b[32m\u001b[K|\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[Kint\u001b[m\u001b[K \u001b[32m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K\n", + " | \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/blas.c:611:76:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 3 has type ‘\u001b[01m\u001b[Ksize_t\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’} [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n", + " 611 | printf(\" Error: find_P_constrastive(): P isn't found: i = %d, j = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, z = %d \\n\", i, \u001b[32m\u001b[Kj\u001b[m\u001b[K, z);\n", + " | \u001b[01;35m\u001b[K~^\u001b[m\u001b[K \u001b[32m\u001b[K~\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[K|\u001b[m\u001b[K \u001b[32m\u001b[K|\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[Kint\u001b[m\u001b[K \u001b[32m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K\n", + " | \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/blas.c:611:84:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 4 has type ‘\u001b[01m\u001b[Ksize_t\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’} [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n", + " 611 | printf(\" Error: find_P_constrastive(): P isn't found: i = %d, j = %d, z = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K \\n\", i, j, \u001b[32m\u001b[Kz\u001b[m\u001b[K);\n", + " | \u001b[01;35m\u001b[K~^\u001b[m\u001b[K \u001b[32m\u001b[K~\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[K|\u001b[m\u001b[K \u001b[32m\u001b[K|\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[Kint\u001b[m\u001b[K \u001b[32m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K\n", + " | \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/blas.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KP_constrastive_f\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/blas.c:651:79:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 3 has type ‘\u001b[01m\u001b[Ksize_t\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’} [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n", + " 651 | fprintf(stderr, \" Error: in P_constrastive must be i != l, while i = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, l = %d \\n\", \u001b[32m\u001b[Ki\u001b[m\u001b[K, l);\n", + " | \u001b[01;35m\u001b[K~^\u001b[m\u001b[K \u001b[32m\u001b[K~\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[K|\u001b[m\u001b[K \u001b[32m\u001b[K|\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[Kint\u001b[m\u001b[K \u001b[32m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K\n", + " | \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/blas.c:651:87:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 4 has type ‘\u001b[01m\u001b[Ksize_t\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’} [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n", + " 651 | fprintf(stderr, \" Error: in P_constrastive must be i != l, while i = %d, l = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K \\n\", i, \u001b[32m\u001b[Kl\u001b[m\u001b[K);\n", + " | \u001b[01;35m\u001b[K~^\u001b[m\u001b[K \u001b[32m\u001b[K~\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[K|\u001b[m\u001b[K \u001b[32m\u001b[K|\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[Kint\u001b[m\u001b[K \u001b[32m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K\n", + " | \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/blas.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KP_constrastive\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/blas.c:785:79:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 3 has type ‘\u001b[01m\u001b[Ksize_t\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’} [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n", + " 785 | fprintf(stderr, \" Error: in P_constrastive must be i != l, while i = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, l = %d \\n\", \u001b[32m\u001b[Ki\u001b[m\u001b[K, l);\n", + " | \u001b[01;35m\u001b[K~^\u001b[m\u001b[K \u001b[32m\u001b[K~\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[K|\u001b[m\u001b[K \u001b[32m\u001b[K|\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[Kint\u001b[m\u001b[K \u001b[32m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K\n", + " | \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/blas.c:785:87:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 4 has type ‘\u001b[01m\u001b[Ksize_t\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’} [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n", + " 785 | fprintf(stderr, \" Error: in P_constrastive must be i != l, while i = %d, l = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K \\n\", i, \u001b[32m\u001b[Kl\u001b[m\u001b[K);\n", + " | \u001b[01;35m\u001b[K~^\u001b[m\u001b[K \u001b[32m\u001b[K~\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[K|\u001b[m\u001b[K \u001b[32m\u001b[K|\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[Kint\u001b[m\u001b[K \u001b[32m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K\n", + " | \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/crop_layer.c -o obj/crop_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/dropout_layer.c -o obj/dropout_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/maxpool_layer.c -o obj/maxpool_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/softmax_layer.c -o obj/softmax_layer.o\n", + "\u001b[01m\u001b[K./src/softmax_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kmake_contrastive_layer\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/softmax_layer.c:203:101:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 9 has type ‘\u001b[01m\u001b[Ksize_t\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Kconst long unsigned int\u001b[m\u001b[K’} [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n", + " 203 | fprintf(stderr, \"contrastive %4d x%4d x%4d x emb_size %4d x batch: %4d classes = %4d, step = \u001b[01;35m\u001b[K%4d\u001b[m\u001b[K \\n\", w, h, l.n, l.embedding_size, batch, l.classes, \u001b[32m\u001b[Kstep\u001b[m\u001b[K);\n", + " | \u001b[01;35m\u001b[K~~^\u001b[m\u001b[K \u001b[32m\u001b[K~~~~\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[K|\u001b[m\u001b[K \u001b[32m\u001b[K|\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[Kint\u001b[m\u001b[K \u001b[32m\u001b[Ksize_t {aka const long unsigned int}\u001b[m\u001b[K\n", + " | \u001b[32m\u001b[K%4ld\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/softmax_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kforward_contrastive_layer\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/softmax_layer.c:244:27:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kmax_truth\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n", + " 244 | float \u001b[01;35m\u001b[Kmax_truth\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/data.c -o obj/data.o\n", + "\u001b[01m\u001b[K./src/data.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kload_data_detection\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/data.c:1409:43:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kr_scale\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n", + " 1409 | float r1 = 0, r2 = 0, r3 = 0, r4 = 0, \u001b[01;35m\u001b[Kr_scale\u001b[m\u001b[K;\n", + " | \u001b[01;35m\u001b[K^~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/data.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kfill_truth_detection\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/data.c:440:33:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[K%s\u001b[m\u001b[K’ directive writing up to 4095 bytes into a region of size 251 [\u001b[01;35m\u001b[K-Wformat-overflow=\u001b[m\u001b[K]\n", + " 440 | sprintf(buff, \"echo \u001b[01;35m\u001b[K%s\u001b[m\u001b[K \\\"Wrong annotation: w = %f\\\" >> bad_label.list\", \u001b[32m\u001b[Klabelpath\u001b[m\u001b[K, w);\n", + " | \u001b[01;35m\u001b[K^~\u001b[m\u001b[K \u001b[32m\u001b[K~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/data.c:440:27:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kassuming directive output of 8 bytes\n", + " 440 | sprintf(buff, \u001b[01;36m\u001b[K\"echo %s \\\"Wrong annotation: w = %f\\\" >> bad_label.list\"\u001b[m\u001b[K, labelpath, w);\n", + " | \u001b[01;36m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "In file included from \u001b[01m\u001b[K/usr/include/stdio.h:867\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[Kinclude/darknet.h:13\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[K./src/data.h:5\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[K./src/data.c:1\u001b[m\u001b[K:\n", + "\u001b[01m\u001b[K/usr/include/x86_64-linux-gnu/bits/stdio2.h:36:10:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K‘\u001b[01m\u001b[K__builtin___sprintf_chk\u001b[m\u001b[K’ output between 52 and 4461 bytes into a destination of size 256\n", + " 36 | return \u001b[01;36m\u001b[K__builtin___sprintf_chk (__s, __USE_FORTIFY_LEVEL - 1,\u001b[m\u001b[K\n", + " | \u001b[01;36m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + " 37 | \u001b[01;36m\u001b[K __bos (__s), __fmt, __va_arg_pack ())\u001b[m\u001b[K;\n", + " | \u001b[01;36m\u001b[K~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/data.c:447:33:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[K%s\u001b[m\u001b[K’ directive writing up to 4095 bytes into a region of size 251 [\u001b[01;35m\u001b[K-Wformat-overflow=\u001b[m\u001b[K]\n", + " 447 | sprintf(buff, \"echo \u001b[01;35m\u001b[K%s\u001b[m\u001b[K \\\"Wrong annotation: h = %f\\\" >> bad_label.list\", \u001b[32m\u001b[Klabelpath\u001b[m\u001b[K, h);\n", + " | \u001b[01;35m\u001b[K^~\u001b[m\u001b[K \u001b[32m\u001b[K~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/data.c:447:27:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kassuming directive output of 8 bytes\n", + " 447 | sprintf(buff, \u001b[01;36m\u001b[K\"echo %s \\\"Wrong annotation: h = %f\\\" >> bad_label.list\"\u001b[m\u001b[K, labelpath, h);\n", + " | \u001b[01;36m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "In file included from \u001b[01m\u001b[K/usr/include/stdio.h:867\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[Kinclude/darknet.h:13\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[K./src/data.h:5\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[K./src/data.c:1\u001b[m\u001b[K:\n", + "\u001b[01m\u001b[K/usr/include/x86_64-linux-gnu/bits/stdio2.h:36:10:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K‘\u001b[01m\u001b[K__builtin___sprintf_chk\u001b[m\u001b[K’ output between 52 and 4461 bytes into a destination of size 256\n", + " 36 | return \u001b[01;36m\u001b[K__builtin___sprintf_chk (__s, __USE_FORTIFY_LEVEL - 1,\u001b[m\u001b[K\n", + " | \u001b[01;36m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + " 37 | \u001b[01;36m\u001b[K __bos (__s), __fmt, __va_arg_pack ())\u001b[m\u001b[K;\n", + " | \u001b[01;36m\u001b[K~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/data.c:432:33:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[K%s\u001b[m\u001b[K’ directive writing up to 4095 bytes into a region of size 251 [\u001b[01;35m\u001b[K-Wformat-overflow=\u001b[m\u001b[K]\n", + " 432 | sprintf(buff, \"echo \u001b[01;35m\u001b[K%s\u001b[m\u001b[K \\\"Wrong annotation: x = %f, y = %f\\\" >> bad_label.list\", \u001b[32m\u001b[Klabelpath\u001b[m\u001b[K, x, y);\n", + " | \u001b[01;35m\u001b[K^~\u001b[m\u001b[K \u001b[32m\u001b[K~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/data.c:432:27:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kassuming directive output of 8 bytes\n", + " 432 | sprintf(buff, \u001b[01;36m\u001b[K\"echo %s \\\"Wrong annotation: x = %f, y = %f\\\" >> bad_label.list\"\u001b[m\u001b[K, labelpath, x, y);\n", + " | \u001b[01;36m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/data.c:432:27:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kassuming directive output of 8 bytes\n", + "In file included from \u001b[01m\u001b[K/usr/include/stdio.h:867\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[Kinclude/darknet.h:13\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[K./src/data.h:5\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[K./src/data.c:1\u001b[m\u001b[K:\n", + "\u001b[01m\u001b[K/usr/include/x86_64-linux-gnu/bits/stdio2.h:36:10:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K‘\u001b[01m\u001b[K__builtin___sprintf_chk\u001b[m\u001b[K’ output between 61 and 4784 bytes into a destination of size 256\n", + " 36 | return \u001b[01;36m\u001b[K__builtin___sprintf_chk (__s, __USE_FORTIFY_LEVEL - 1,\u001b[m\u001b[K\n", + " | \u001b[01;36m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + " 37 | \u001b[01;36m\u001b[K __bos (__s), __fmt, __va_arg_pack ())\u001b[m\u001b[K;\n", + " | \u001b[01;36m\u001b[K~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/data.c:424:33:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[K%s\u001b[m\u001b[K’ directive writing up to 4095 bytes into a region of size 251 [\u001b[01;35m\u001b[K-Wformat-overflow=\u001b[m\u001b[K]\n", + " 424 | sprintf(buff, \"echo \u001b[01;35m\u001b[K%s\u001b[m\u001b[K \\\"Wrong annotation: x = 0 or y = 0\\\" >> bad_label.list\", \u001b[32m\u001b[Klabelpath\u001b[m\u001b[K);\n", + " | \u001b[01;35m\u001b[K^~\u001b[m\u001b[K \u001b[32m\u001b[K~~~~~~~~~\u001b[m\u001b[K\n", + "In file included from \u001b[01m\u001b[K/usr/include/stdio.h:867\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[Kinclude/darknet.h:13\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[K./src/data.h:5\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[K./src/data.c:1\u001b[m\u001b[K:\n", + "\u001b[01m\u001b[K/usr/include/x86_64-linux-gnu/bits/stdio2.h:36:10:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K‘\u001b[01m\u001b[K__builtin___sprintf_chk\u001b[m\u001b[K’ output between 59 and 4154 bytes into a destination of size 256\n", + " 36 | return \u001b[01;36m\u001b[K__builtin___sprintf_chk (__s, __USE_FORTIFY_LEVEL - 1,\u001b[m\u001b[K\n", + " | \u001b[01;36m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + " 37 | \u001b[01;36m\u001b[K __bos (__s), __fmt, __va_arg_pack ())\u001b[m\u001b[K;\n", + " | \u001b[01;36m\u001b[K~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/data.c:410:33:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[K%s\u001b[m\u001b[K’ directive writing up to 4095 bytes into a region of size 251 [\u001b[01;35m\u001b[K-Wformat-overflow=\u001b[m\u001b[K]\n", + " 410 | sprintf(buff, \"echo \u001b[01;35m\u001b[K%s\u001b[m\u001b[K \\\"Wrong annotation: class_id = %d. But class_id should be [from 0 to %d]\\\" >> bad_label.list\", \u001b[32m\u001b[Klabelpath\u001b[m\u001b[K, id, (classes-1));\n", + " | \u001b[01;35m\u001b[K^~\u001b[m\u001b[K \u001b[32m\u001b[K~~~~~~~~~\u001b[m\u001b[K\n", + "In file included from \u001b[01m\u001b[K/usr/include/stdio.h:867\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[Kinclude/darknet.h:13\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[K./src/data.h:5\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[K./src/data.c:1\u001b[m\u001b[K:\n", + "\u001b[01m\u001b[K/usr/include/x86_64-linux-gnu/bits/stdio2.h:36:10:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K‘\u001b[01m\u001b[K__builtin___sprintf_chk\u001b[m\u001b[K’ output between 95 and 4210 bytes into a destination of size 256\n", + " 36 | return \u001b[01;36m\u001b[K__builtin___sprintf_chk (__s, __USE_FORTIFY_LEVEL - 1,\u001b[m\u001b[K\n", + " | \u001b[01;36m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + " 37 | \u001b[01;36m\u001b[K __bos (__s), __fmt, __va_arg_pack ())\u001b[m\u001b[K;\n", + " | \u001b[01;36m\u001b[K~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/matrix.c -o obj/matrix.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/network.c -o obj/network.o\n", + "\u001b[01m\u001b[K./src/network.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Ktrain_network_waitkey\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/network.c:435:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kema_period\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 435 | int \u001b[01;35m\u001b[Kema_period\u001b[m\u001b[K = (net.max_batches - ema_start_point - 1000) * (1.0 - net.ema_alpha);\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~\u001b[m\u001b[K\n", + "At top level:\n", + "\u001b[01m\u001b[K./src/network.c:1269:14:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[Krelu\u001b[m\u001b[K’ defined but not used [\u001b[01;35m\u001b[K-Wunused-function\u001b[m\u001b[K]\n", + " 1269 | static float \u001b[01;35m\u001b[Krelu\u001b[m\u001b[K(float src) {\n", + " | \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/connected_layer.c -o obj/connected_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/cost_layer.c -o obj/cost_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/parser.c -o obj/parser.o\n", + "\u001b[01m\u001b[K./src/parser.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Ksave_implicit_weights\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/parser.c:1909:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ki\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 1909 | int \u001b[01;35m\u001b[Ki\u001b[m\u001b[K;\n", + " | \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/parser.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kget_classes_multipliers\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/parser.c:438:40:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kargument 1 range [18446744071562067968, 18446744073709551615] exceeds maximum object size 9223372036854775807 [\u001b[01;35m\u001b[K-Walloc-size-larger-than=\u001b[m\u001b[K]\n", + " 438 | classes_multipliers = (float *)\u001b[01;35m\u001b[Kcalloc(classes_counters, sizeof(float))\u001b[m\u001b[K;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "In file included from \u001b[01m\u001b[K./src/parser.c:3\u001b[m\u001b[K:\n", + "\u001b[01m\u001b[K/usr/include/stdlib.h:542:14:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kin a call to allocation function ‘\u001b[01m\u001b[Kcalloc\u001b[m\u001b[K’ declared here\n", + " 542 | extern void *\u001b[01;36m\u001b[Kcalloc\u001b[m\u001b[K (size_t __nmemb, size_t __size)\n", + " | \u001b[01;36m\u001b[K^~~~~~\u001b[m\u001b[K\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/option_list.c -o obj/option_list.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/darknet.c -o obj/darknet.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/detection_layer.c -o obj/detection_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/captcha.c -o obj/captcha.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/route_layer.c -o obj/route_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/writing.c -o obj/writing.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/box.c -o obj/box.o\n", + "\u001b[01m\u001b[K./src/box.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kbox_iou_kind\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/box.c:154:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KMSE\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n", + " 154 | \u001b[01;35m\u001b[Kswitch\u001b[m\u001b[K(iou_kind) {\n", + " | \u001b[01;35m\u001b[K^~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/box.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kdiounms_sort\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/box.c:898:27:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kbeta_prob\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 898 | float \u001b[01;35m\u001b[Kbeta_prob\u001b[m\u001b[K = pow(dets[j].prob[k], 2) / sum_prob;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/box.c:897:27:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kalpha_prob\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 897 | float \u001b[01;35m\u001b[Kalpha_prob\u001b[m\u001b[K = pow(dets[i].prob[k], 2) / sum_prob;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~\u001b[m\u001b[K\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/nightmare.c -o obj/nightmare.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/normalization_layer.c -o obj/normalization_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/avgpool_layer.c -o obj/avgpool_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/coco.c -o obj/coco.o\n", + "\u001b[01m\u001b[K./src/coco.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvalidate_coco_recall\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/coco.c:248:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kbase\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 248 | char *\u001b[01;35m\u001b[Kbase\u001b[m\u001b[K = \"results/comp4_det_test_\";\n", + " | \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/dice.c -o obj/dice.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/yolo.c -o obj/yolo.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/detector.c -o obj/detector.o\n", + "\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Ktrain_detector\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/detector.c:395:72:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Ksuggest parentheses around ‘\u001b[01m\u001b[K&&\u001b[m\u001b[K’ within ‘\u001b[01m\u001b[K||\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wparentheses\u001b[m\u001b[K]\n", + " 395 | \u001b[01;35m\u001b[K(iteration >= (iter_save + 1000) || iteration % 1000 == 0) && net.max_batches < 10000\u001b[m\u001b[K)\n", + " | \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/detector.c:328:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kdraw_precision\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n", + " 328 | int \u001b[01;35m\u001b[Kdraw_precision\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/detector.c:67:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kavg_contrastive_acc\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 67 | float \u001b[01;35m\u001b[Kavg_contrastive_acc\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Keliminate_bdd\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/detector.c:588:21:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kstatement with no effect [\u001b[01;35m\u001b[K-Wunused-value\u001b[m\u001b[K]\n", + " 588 | \u001b[01;35m\u001b[Kfor\u001b[m\u001b[K (k; buf[k + n] != '\\0'; k++)\n", + " | \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvalidate_detector\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/detector.c:709:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kmkd2\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 709 | int \u001b[01;35m\u001b[Kmkd2\u001b[m\u001b[K = make_directory(buff2, 0777);\n", + " | \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/detector.c:707:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kmkd\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 707 | int \u001b[01;35m\u001b[Kmkd\u001b[m\u001b[K = make_directory(buff, 0777);\n", + " | \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvalidate_detector_map\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/detector.c:1326:24:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kcur_prob\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n", + " 1326 | double \u001b[01;35m\u001b[Kcur_prob\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/detector.c:1347:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kclass_recall\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 1347 | float \u001b[01;35m\u001b[Kclass_recall\u001b[m\u001b[K = (float)tp_for_thresh_per_class[i] / ((float)tp_for_thresh_per_class[i] + (float)(truth_classes_count[i] - tp_for_thresh_per_class[i]));\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/detector.c:1346:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kclass_precision\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 1346 | float \u001b[01;35m\u001b[Kclass_precision\u001b[m\u001b[K = (float)tp_for_thresh_per_class[i] / ((float)tp_for_thresh_per_class[i] + (float)fp_for_thresh_per_class[i]);\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "At top level:\n", + "\u001b[01m\u001b[K./src/detector.c:461:12:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[Kget_coco_image_id\u001b[m\u001b[K’ defined but not used [\u001b[01;35m\u001b[K-Wunused-function\u001b[m\u001b[K]\n", + " 461 | static int \u001b[01;35m\u001b[Kget_coco_image_id\u001b[m\u001b[K(char *filename)\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/layer.c -o obj/layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/compare.c -o obj/compare.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/classifier.c -o obj/classifier.o\n", + "\u001b[01m\u001b[K./src/classifier.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Ktrain_classifier\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/classifier.c:190:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kdraw_precision\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n", + " 190 | int \u001b[01;35m\u001b[Kdraw_precision\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/classifier.c:146:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kcount\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 146 | int \u001b[01;35m\u001b[Kcount\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/classifier.c:35:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kavg_contrastive_acc\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 35 | float \u001b[01;35m\u001b[Kavg_contrastive_acc\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/classifier.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kpredict_classifier\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/classifier.c:855:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktime\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 855 | clock_t \u001b[01;35m\u001b[Ktime\u001b[m\u001b[K;\n", + " | \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/local_layer.c -o obj/local_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/swag.c -o obj/swag.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/shortcut_layer.c -o obj/shortcut_layer.o\n", + "\u001b[01m\u001b[K./src/shortcut_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kmake_shortcut_layer\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/shortcut_layer.c:55:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kscale\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 55 | float \u001b[01;35m\u001b[Kscale\u001b[m\u001b[K = sqrt(2. / l.nweights);\n", + " | \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/representation_layer.c -o obj/representation_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/activation_layer.c -o obj/activation_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/rnn_layer.c -o obj/rnn_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/gru_layer.c -o obj/gru_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/rnn.c -o obj/rnn.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/rnn_vid.c -o obj/rnn_vid.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/crnn_layer.c -o obj/crnn_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/demo.c -o obj/demo.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/tag.c -o obj/tag.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/cifar.c -o obj/cifar.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/go.c -o obj/go.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/batchnorm_layer.c -o obj/batchnorm_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/art.c -o obj/art.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/region_layer.c -o obj/region_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/reorg_layer.c -o obj/reorg_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/reorg_old_layer.c -o obj/reorg_old_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/super.c -o obj/super.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/voxel.c -o obj/voxel.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/tree.c -o obj/tree.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/yolo_layer.c -o obj/yolo_layer.o\n", + "\u001b[01m\u001b[K./src/yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kprocess_batch\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/yolo_layer.c:426:25:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kbest_match_t\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n", + " 426 | int \u001b[01;35m\u001b[Kbest_match_t\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kforward_yolo_layer\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/yolo_layer.c:707:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kavg_anyobj\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 707 | float \u001b[01;35m\u001b[Kavg_anyobj\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/yolo_layer.c:706:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kavg_obj\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 706 | float \u001b[01;35m\u001b[Kavg_obj\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/yolo_layer.c:705:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kavg_cat\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 705 | float \u001b[01;35m\u001b[Kavg_cat\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/yolo_layer.c:704:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Krecall75\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 704 | float \u001b[01;35m\u001b[Krecall75\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/yolo_layer.c:703:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Krecall\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 703 | float \u001b[01;35m\u001b[Krecall\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/yolo_layer.c:702:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktot_ciou_loss\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 702 | float \u001b[01;35m\u001b[Ktot_ciou_loss\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/yolo_layer.c:701:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktot_diou_loss\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 701 | float \u001b[01;35m\u001b[Ktot_diou_loss\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/yolo_layer.c:698:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktot_ciou\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 698 | float \u001b[01;35m\u001b[Ktot_ciou\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/yolo_layer.c:697:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktot_diou\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 697 | float \u001b[01;35m\u001b[Ktot_diou\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/yolo_layer.c:696:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktot_giou\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 696 | float \u001b[01;35m\u001b[Ktot_giou\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/gaussian_yolo_layer.c -o obj/gaussian_yolo_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/upsample_layer.c -o obj/upsample_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/lstm_layer.c -o obj/lstm_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/conv_lstm_layer.c -o obj/conv_lstm_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/scale_channels_layer.c -o obj/scale_channels_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/sam_layer.c -o obj/sam_layer.o\n", + "g++ -std=c++11 -std=c++11 -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast obj/image_opencv.o obj/http_stream.o obj/gemm.o obj/utils.o obj/dark_cuda.o obj/convolutional_layer.o obj/list.o obj/image.o obj/activations.o obj/im2col.o obj/col2im.o obj/blas.o obj/crop_layer.o obj/dropout_layer.o obj/maxpool_layer.o obj/softmax_layer.o obj/data.o obj/matrix.o obj/network.o obj/connected_layer.o obj/cost_layer.o obj/parser.o obj/option_list.o obj/darknet.o obj/detection_layer.o obj/captcha.o obj/route_layer.o obj/writing.o obj/box.o obj/nightmare.o obj/normalization_layer.o obj/avgpool_layer.o obj/coco.o obj/dice.o obj/yolo.o obj/detector.o obj/layer.o obj/compare.o obj/classifier.o obj/local_layer.o obj/swag.o obj/shortcut_layer.o obj/representation_layer.o obj/activation_layer.o obj/rnn_layer.o obj/gru_layer.o obj/rnn.o obj/rnn_vid.o obj/crnn_layer.o obj/demo.o obj/tag.o obj/cifar.o obj/go.o obj/batchnorm_layer.o obj/art.o obj/region_layer.o obj/reorg_layer.o obj/reorg_old_layer.o obj/super.o obj/voxel.o obj/tree.o obj/yolo_layer.o obj/gaussian_yolo_layer.o obj/upsample_layer.o obj/lstm_layer.o obj/conv_lstm_layer.o obj/scale_channels_layer.o obj/sam_layer.o -o darknet -lm -pthread\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!./darknet" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IM282uWWfW2n", + "outputId": "3123df19-cfb2-46f1-f709-e631518642e1" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "usage: ./darknet \n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!wget https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "kGMG34IBfqAO", + "outputId": "dbcfea6b-3e02-466a-d890-37a26a885651" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2023-07-13 09:05:10-- https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights\n", + "Resolving github.com (github.com)... 140.82.121.3\n", + "Connecting to github.com (github.com)|140.82.121.3|:443... connected.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: https://objects.githubusercontent.com/github-production-release-asset-2e65be/75388965/ba4b6380-889c-11ea-9751-f994f5961796?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20230713%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230713T090510Z&X-Amz-Expires=300&X-Amz-Signature=cea41ca1934f8fb97456ef5d2cc519bde7b60d1b7bec35fd51b2eb42dc06aca4&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=75388965&response-content-disposition=attachment%3B%20filename%3Dyolov4.weights&response-content-type=application%2Foctet-stream [following]\n", + "--2023-07-13 09:05:10-- https://objects.githubusercontent.com/github-production-release-asset-2e65be/75388965/ba4b6380-889c-11ea-9751-f994f5961796?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20230713%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230713T090510Z&X-Amz-Expires=300&X-Amz-Signature=cea41ca1934f8fb97456ef5d2cc519bde7b60d1b7bec35fd51b2eb42dc06aca4&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=75388965&response-content-disposition=attachment%3B%20filename%3Dyolov4.weights&response-content-type=application%2Foctet-stream\n", + "Resolving objects.githubusercontent.com (objects.githubusercontent.com)... 185.199.109.133, 185.199.111.133, 185.199.110.133, ...\n", + "Connecting to objects.githubusercontent.com (objects.githubusercontent.com)|185.199.109.133|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 257717640 (246M) [application/octet-stream]\n", + "Saving to: ‘yolov4.weights’\n", + "\n", + "yolov4.weights 100%[===================>] 245.78M 46.7MB/s in 5.3s \n", + "\n", + "2023-07-13 09:05:15 (46.7 MB/s) - ‘yolov4.weights’ saved [257717640/257717640]\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!./darknet detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights data/eagle.jpg" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "OKjVBw3hfwZ1", + "outputId": "0bdae556-9094-434c-e163-fd4308d17c0b" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " GPU isn't used \n", + " OpenCV isn't used - data augmentation will be slow \n", + "mini_batch = 1, batch = 8, time_steps = 1, train = 0 \n", + " layer filters size/strd(dil) input output\n", + " 0 conv 32 3 x 3/ 1 608 x 608 x 3 -> 608 x 608 x 32 0.639 BF\n", + " 1 conv 64 3 x 3/ 2 608 x 608 x 32 -> 304 x 304 x 64 3.407 BF\n", + " 2 conv 64 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 64 0.757 BF\n", + " 3 route 1 \t\t -> 304 x 304 x 64 \n", + " 4 conv 64 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 64 0.757 BF\n", + " 5 conv 32 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 32 0.379 BF\n", + " 6 conv 64 3 x 3/ 1 304 x 304 x 32 -> 304 x 304 x 64 3.407 BF\n", + " 7 Shortcut Layer: 4, wt = 0, wn = 0, outputs: 304 x 304 x 64 0.006 BF\n", + " 8 conv 64 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 64 0.757 BF\n", + " 9 route 8 2 \t -> 304 x 304 x 128 \n", + " 10 conv 64 1 x 1/ 1 304 x 304 x 128 -> 304 x 304 x 64 1.514 BF\n", + " 11 conv 128 3 x 3/ 2 304 x 304 x 64 -> 152 x 152 x 128 3.407 BF\n", + " 12 conv 64 1 x 1/ 1 152 x 152 x 128 -> 152 x 152 x 64 0.379 BF\n", + " 13 route 11 \t\t -> 152 x 152 x 128 \n", + " 14 conv 64 1 x 1/ 1 152 x 152 x 128 -> 152 x 152 x 64 0.379 BF\n", + " 15 conv 64 1 x 1/ 1 152 x 152 x 64 -> 152 x 152 x 64 0.189 BF\n", + " 16 conv 64 3 x 3/ 1 152 x 152 x 64 -> 152 x 152 x 64 1.703 BF\n", + " 17 Shortcut Layer: 14, wt = 0, wn = 0, outputs: 152 x 152 x 64 0.001 BF\n", + " 18 conv 64 1 x 1/ 1 152 x 152 x 64 -> 152 x 152 x 64 0.189 BF\n", + " 19 conv 64 3 x 3/ 1 152 x 152 x 64 -> 152 x 152 x 64 1.703 BF\n", + " 20 Shortcut Layer: 17, wt = 0, wn = 0, outputs: 152 x 152 x 64 0.001 BF\n", + " 21 conv 64 1 x 1/ 1 152 x 152 x 64 -> 152 x 152 x 64 0.189 BF\n", + " 22 route 21 12 \t -> 152 x 152 x 128 \n", + " 23 conv 128 1 x 1/ 1 152 x 152 x 128 -> 152 x 152 x 128 0.757 BF\n", + " 24 conv 256 3 x 3/ 2 152 x 152 x 128 -> 76 x 76 x 256 3.407 BF\n", + " 25 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF\n", + " 26 route 24 \t\t -> 76 x 76 x 256 \n", + " 27 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF\n", + " 28 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF\n", + " 29 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF\n", + " 30 Shortcut Layer: 27, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF\n", + " 31 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF\n", + " 32 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF\n", + " 33 Shortcut Layer: 30, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF\n", + " 34 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF\n", + " 35 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF\n", + " 36 Shortcut Layer: 33, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF\n", + " 37 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF\n", + " 38 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF\n", + " 39 Shortcut Layer: 36, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF\n", + " 40 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF\n", + " 41 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF\n", + " 42 Shortcut Layer: 39, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF\n", + " 43 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF\n", + " 44 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF\n", + " 45 Shortcut Layer: 42, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF\n", + " 46 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF\n", + " 47 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF\n", + " 48 Shortcut Layer: 45, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF\n", + " 49 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF\n", + " 50 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF\n", + " 51 Shortcut Layer: 48, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF\n", + " 52 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF\n", + " 53 route 52 25 \t -> 76 x 76 x 256 \n", + " 54 conv 256 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 256 0.757 BF\n", + " 55 conv 512 3 x 3/ 2 76 x 76 x 256 -> 38 x 38 x 512 3.407 BF\n", + " 56 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF\n", + " 57 route 55 \t\t -> 38 x 38 x 512 \n", + " 58 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF\n", + " 59 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF\n", + " 60 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF\n", + " 61 Shortcut Layer: 58, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF\n", + " 62 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF\n", + " 63 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF\n", + " 64 Shortcut Layer: 61, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF\n", + " 65 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF\n", + " 66 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF\n", + " 67 Shortcut Layer: 64, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF\n", + " 68 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF\n", + " 69 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF\n", + " 70 Shortcut Layer: 67, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF\n", + " 71 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF\n", + " 72 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF\n", + " 73 Shortcut Layer: 70, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF\n", + " 74 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF\n", + " 75 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF\n", + " 76 Shortcut Layer: 73, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF\n", + " 77 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF\n", + " 78 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF\n", + " 79 Shortcut Layer: 76, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF\n", + " 80 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF\n", + " 81 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF\n", + " 82 Shortcut Layer: 79, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF\n", + " 83 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF\n", + " 84 route 83 56 \t -> 38 x 38 x 512 \n", + " 85 conv 512 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 512 0.757 BF\n", + " 86 conv 1024 3 x 3/ 2 38 x 38 x 512 -> 19 x 19 x1024 3.407 BF\n", + " 87 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF\n", + " 88 route 86 \t\t -> 19 x 19 x1024 \n", + " 89 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF\n", + " 90 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF\n", + " 91 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF\n", + " 92 Shortcut Layer: 89, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF\n", + " 93 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF\n", + " 94 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF\n", + " 95 Shortcut Layer: 92, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF\n", + " 96 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF\n", + " 97 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF\n", + " 98 Shortcut Layer: 95, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF\n", + " 99 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF\n", + " 100 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF\n", + " 101 Shortcut Layer: 98, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF\n", + " 102 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF\n", + " 103 route 102 87 \t -> 19 x 19 x1024 \n", + " 104 conv 1024 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x1024 0.757 BF\n", + " 105 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF\n", + " 106 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF\n", + " 107 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF\n", + " 108 max 5x 5/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.005 BF\n", + " 109 route 107 \t\t -> 19 x 19 x 512 \n", + " 110 max 9x 9/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.015 BF\n", + " 111 route 107 \t\t -> 19 x 19 x 512 \n", + " 112 max 13x13/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.031 BF\n", + " 113 route 112 110 108 107 \t -> 19 x 19 x2048 \n", + " 114 conv 512 1 x 1/ 1 19 x 19 x2048 -> 19 x 19 x 512 0.757 BF\n", + " 115 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF\n", + " 116 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF\n", + " 117 conv 256 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 256 0.095 BF\n", + " 118 upsample 2x 19 x 19 x 256 -> 38 x 38 x 256\n", + " 119 route 85 \t\t -> 38 x 38 x 512 \n", + " 120 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF\n", + " 121 route 120 118 \t -> 38 x 38 x 512 \n", + " 122 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF\n", + " 123 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF\n", + " 124 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF\n", + " 125 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF\n", + " 126 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF\n", + " 127 conv 128 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 128 0.095 BF\n", + " 128 upsample 2x 38 x 38 x 128 -> 76 x 76 x 128\n", + " 129 route 54 \t\t -> 76 x 76 x 256 \n", + " 130 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF\n", + " 131 route 130 128 \t -> 76 x 76 x 256 \n", + " 132 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF\n", + " 133 conv 256 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BF\n", + " 134 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF\n", + " 135 conv 256 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BF\n", + " 136 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF\n", + " 137 conv 256 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BF\n", + " 138 conv 255 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 255 0.754 BF\n", + " 139 yolo\n", + "[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.20\n", + "nms_kind: greedynms (1), beta = 0.600000 \n", + " 140 route 136 \t\t -> 76 x 76 x 128 \n", + " 141 conv 256 3 x 3/ 2 76 x 76 x 128 -> 38 x 38 x 256 0.852 BF\n", + " 142 route 141 126 \t -> 38 x 38 x 512 \n", + " 143 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF\n", + " 144 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF\n", + " 145 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF\n", + " 146 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF\n", + " 147 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF\n", + " 148 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF\n", + " 149 conv 255 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 255 0.377 BF\n", + " 150 yolo\n", + "[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.10\n", + "nms_kind: greedynms (1), beta = 0.600000 \n", + " 151 route 147 \t\t -> 38 x 38 x 256 \n", + " 152 conv 512 3 x 3/ 2 38 x 38 x 256 -> 19 x 19 x 512 0.852 BF\n", + " 153 route 152 116 \t -> 19 x 19 x1024 \n", + " 154 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF\n", + " 155 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF\n", + " 156 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF\n", + " 157 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF\n", + " 158 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF\n", + " 159 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF\n", + " 160 conv 255 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 255 0.189 BF\n", + " 161 yolo\n", + "[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.05\n", + "nms_kind: greedynms (1), beta = 0.600000 \n", + "Total BFLOPS 128.459 \n", + "avg_outputs = 1068395 \n", + "Loading weights from yolov4.weights...\n", + " seen 64, trained: 32032 K-images (500 Kilo-batches_64) \n", + "Done! Loaded 162 layers from weights-file \n", + " Detection layer: 139 - type = 28 \n", + " Detection layer: 150 - type = 28 \n", + " Detection layer: 161 - type = 28 \n", + "data/eagle.jpg: Predicted in 26348.319000 milli-seconds.\n", + "bird: 97%\n", + "Not compiled with OpenCV, saving to predictions.png instead\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import cv2\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ], + "metadata": { + "id": "JUrmZwRAgCxz" + }, + "execution_count": 10, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "image=cv2.imread('predictions.jpg')\n", + "fig=plt.figure()\n", + "fig.set_size_inches(12,14)\n", + "plt.imshow(image)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 469 + }, + "id": "lt_Qlr6vgTJR", + "outputId": "70f66fdf-c2d5-4c24-c30e-a3ca7f5a1a8c" + }, + "execution_count": 11, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 11 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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+ }, + "metadata": {} + } + ] + } + ] +} \ No newline at end of file diff --git a/Week 3 Image Alignment.ipynb b/Week 3 Image Alignment.ipynb new file mode 100644 index 000000000..49e1dcc9a --- /dev/null +++ b/Week 3 Image Alignment.ipynb @@ -0,0 +1,148 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "8f72856b", + "metadata": {}, + "outputs": [], + "source": [ + "import cv2\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "e21dbb7d", + "metadata": {}, + "outputs": [], + "source": [ + "def align_image(image):\n", + " \n", + " gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n", + "\n", + " \n", + " sift = cv2.SIFT_create()\n", + "\n", + "\n", + " keypoints, descriptors = sift.detectAndCompute(gray, None)\n", + "\n", + "\n", + " matcher = cv2.BFMatcher()\n", + "\n", + "\n", + " matches = matcher.knnMatch(descriptors, descriptors, k=2)\n", + " \n", + " good_matches = []\n", + " for m, n in matches:\n", + " if m.distance < 0.75 * n.distance:\n", + " good_matches.append(m)\n", + "\n", + " src_points = np.float32([keypoints[m.queryIdx].pt for m in good_matches]).reshape(-1, 1, 2)\n", + " dst_points = np.float32([keypoints[m.trainIdx].pt for m in good_matches]).reshape(-1, 1, 2)\n", + "\n", + "\n", + " M, mask = cv2.findHomography(src_points, dst_points, cv2.RANSAC, 5.0)\n", + "\n", + "\n", + " aligned_image = cv2.warpPerspective(image, M, (image.shape[1], image.shape[0]))\n", + "\n", + " return aligned_image" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "5ad7015f", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "image = cv2.imread(\"C:\\\\Users\\\\91740\\\\OneDrive\\\\Desktop\\\\g.jpg\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2361d9d7", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "81e03c86", + "metadata": {}, + "outputs": [], + "source": [ + "clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))\n", + "enhanced_image = cv2.cvtColor(image1, cv2.COLOR_BGR2LAB)\n", + "enhanced_image[:, :, 0] = clahe.apply(enhanced_image1[:, :, 0])\n", + "enhanced_image = cv2.cvtColor(enhanced_image1, cv2.COLOR_LAB2BGR)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "b2a89ab1", + "metadata": {}, + "outputs": [], + "source": [ + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "d8c5e61e", + "metadata": {}, + "outputs": [], + "source": [ + "aligned_image = align_image(enhanced_image)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f2e30048", + "metadata": {}, + "outputs": [], + "source": [ + "cv2.imshow('Aligned Image', aligned_image)\n", + "cv2.waitKey(0)\n", + "cv2.destroyAllWindows()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f9fbe637", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + 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prices.merchantbrandcategoriesprice
0Bestbuy.comGrace DigitalElectronics,Home Audio & Theater,Home Audio,Al...92.99
1Bestbuy.comLenovoElectronics,Computers,Laptops,Laptops By Brand...229.99
2Bestbuy.comHouse of MarleyHeadphones,Consumer Electronics,Portable Audio...16.99
3Bestbuy.comSonyElectronics,Home Audio & Theater,Home Audio,Al...69.99
4bhphotovideo.comSonyDigital Cameras,Cameras & Photo,Used:Digital P...846.00
...............
5431Bestbuy.comAppleiPhones,All Cell Phones with Plans,iPhone SE,C...12.45
5432bhphotovideo.comZAGGComputers,Bags, Cases & Sleeves,Computer Acces...149.99
5433Bestbuy.com360flyCameras & Photo,360 Cameras,VR 360 Video,Camco...324.99
5434HotelectronicsAlpineAuto & Tires,Auto Electronics,Car Speakers and...61.55
5435Electronics Expo (Authorized Dealer)PioneerSpeaker Separates tdrbbzebscxdcufzwattw,Electr...249.99
\n", + "

5436 rows × 4 columns

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" + ], + "text/plain": [ + " 360fly AOC ASUS Acer Actiontec AfterShokz Aftershokz Aiwa \\\n", + "0 0 0 0 0 0 0 0 0 \n", + "1 0 0 0 0 0 0 0 0 \n", + "2 0 0 0 0 0 0 0 0 \n", + "3 0 0 0 0 0 0 0 0 \n", + "4 0 0 0 0 0 0 0 0 \n", + "... ... ... ... ... ... ... ... ... \n", + "5431 0 0 0 0 0 0 0 0 \n", + "5432 0 0 0 0 0 0 0 0 \n", + "5433 1 0 0 0 0 0 0 0 \n", + "5434 0 0 0 0 0 0 0 0 \n", + "5435 0 0 0 0 0 0 0 0 \n", + "\n", + " Alienware Alpine ... wirelessalliance wirelessmoo wwstereo \\\n", + "0 0 0 ... 0 0 0 \n", + "1 0 0 ... 0 0 0 \n", + "2 0 0 ... 0 0 0 \n", + "3 0 0 ... 0 0 0 \n", + "4 0 0 ... 0 0 0 \n", + "... ... ... ... ... ... ... \n", + "5431 0 0 ... 0 0 0 \n", + "5432 0 0 ... 0 0 0 \n", + "5433 0 0 ... 0 0 0 \n", + "5434 0 1 ... 0 0 0 \n", + "5435 0 0 ... 0 0 0 \n", + "\n", + " www-bestelectronicsoutlet-com www-sonicelectronix-com yogi-comp \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "2 0 0 0 \n", + "3 0 0 0 \n", + "4 0 0 0 \n", + "... ... ... ... \n", + "5431 0 0 0 \n", + "5432 0 0 0 \n", + "5433 0 0 0 \n", + "5434 0 0 0 \n", + "5435 0 0 0 \n", + "\n", + " your-best-store zal-digital zara4573 zoneusa \n", + "0 0 0 0 0 \n", + "1 0 0 0 0 \n", + "2 0 0 0 0 \n", + "3 0 0 0 0 \n", + "4 0 0 0 0 \n", + "... ... ... ... ... \n", + "5431 0 0 0 0 \n", + "5432 0 0 0 0 \n", + "5433 0 0 0 0 \n", + "5434 0 0 0 0 \n", + "5435 0 0 0 0 \n", + "\n", + "[5436 rows x 1632 columns]" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X=pd.concat([a,b,c],axis=\"columns\")\n", + "X\n" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "7a1cd811", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 92.99\n", + "1 229.99\n", + "2 16.99\n", + "3 69.99\n", + "4 846.00\n", + " ... \n", + "5431 12.45\n", + "5432 149.99\n", + "5433 324.99\n", + "5434 61.55\n", + "5435 249.99\n", + "Name: price, Length: 5436, dtype: float64" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Y=df.price\n", + "Y" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "9d0a0146", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn import linear_model\n", + "reg=linear_model.LinearRegression()" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "4ca31384", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
LinearRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "LinearRegression()" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "reg.fit(X,Y)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "7e3c1007", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9388549395038605" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "reg.score(X,Y)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "148f7c1c", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7676f489", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/author.json b/author.json index 3d4a531ce..562c2d2ca 100644 --- a/author.json +++ b/author.json @@ -2,3 +2,6 @@ "name": "", "entry_number": "" } +NAME=ANAY SINGH +ENTRY NUMBER= 2022ME12026 +me1222026@iitd.ac.in diff --git a/machine-learning/Chat_Bot Week4.ipynb b/machine-learning/Chat_Bot Week4.ipynb new file mode 100644 index 000000000..1573a27cc --- /dev/null +++ b/machine-learning/Chat_Bot Week4.ipynb @@ -0,0 +1,4365 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "P_QTQHHq4342" + }, + "outputs": [], + "source": [ + "import numpy\n", + "\n", + "import tensorflow\n", + "import random" + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "RFoJ82Lf5K4j" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import nltk\n", + "nltk.download('punkt')\n", + "from nltk.stem.lancaster import LancasterStemmer\n", + "stemmer = LancasterStemmer()" + ], + "metadata": { + "id": "FO6ClwGd46Ht", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "575b971d-f3f3-43c2-ecc5-d077eddde95d" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "[nltk_data] Downloading package punkt to /root/nltk_data...\n", + "[nltk_data] Unzipping tokenizers/punkt.zip.\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import json\n", + "\n", + "\n", + "json_file_path = \"/content/intents.json\"\n", + "\n", + "with open(json_file_path) as json_file:\n", + " data = json.load(json_file)\n" + ], + "metadata": { + "id": "Dp6FwiiO4-dq" + }, + "execution_count": 10, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "words = []\n", + "labels = []\n", + "docs_x = []\n", + "docs_y = []" + ], + "metadata": { + "id": "RIYdjslF5Qcx" + }, + "execution_count": 11, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "for intent in data['intents']:\n", + " for pattern in intent['patterns']:\n", + " wrds = nltk.word_tokenize(pattern)\n", + " words.extend(wrds)\n", + " docs_x.append(wrds)\n", + " docs_y.append(intent[\"tag\"])\n", + "\n", + " if intent['tag'] not in labels:\n", + " labels.append(intent['tag'])" + ], + "metadata": { + "id": "aetDZr5l7n9T" + }, + "execution_count": 12, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "words = [stemmer.stem(w.lower()) for w in words if w != \"?\"]\n", + "words = sorted(list(set(words)))\n", + "\n", + "labels = sorted(labels)" + ], + "metadata": { + "id": "nIXu0XZc8DfO" + }, + "execution_count": 13, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "training = []\n", + "output = []\n", + "\n", + "out_empty = [0 for _ in range(len(labels))]\n", + "\n", + "for x, doc in enumerate(docs_x):\n", + " bag = []\n", + "\n", + " wrds = [stemmer.stem(w.lower()) for w in doc]\n", + "\n", + " for w in words:\n", + " if w in wrds:\n", + " bag.append(1)\n", + " else:\n", + " bag.append(0)\n", + "\n", + " output_row = out_empty[:]\n", + " output_row[labels.index(docs_y[x])] = 1\n", + "\n", + " training.append(bag)\n", + " output.append(output_row)\n", + "\n", + "\n", + "training = numpy.array(training)\n", + "output = numpy.array(output)" + ], + "metadata": { + "id": "mCZmk41W8k-5" + }, + "execution_count": 14, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "!pip install tflearn" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "U7wwe4hZ8pJu", + "outputId": "1e1b9419-d937-4250-c654-cb8e83cf4dd0" + }, + "execution_count": 16, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting tflearn\n", + " Downloading tflearn-0.5.0.tar.gz (107 kB)\n", + "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/107.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m107.3/107.3 kB\u001b[0m \u001b[31m2.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from tflearn) (1.22.4)\n", + "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from tflearn) (1.16.0)\n", + "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from tflearn) (8.4.0)\n", + "Building wheels for collected packages: tflearn\n", + " Building wheel for tflearn (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for tflearn: filename=tflearn-0.5.0-py3-none-any.whl size=127283 sha256=a6f381c5e8399b87389ed4ee68a1595b8e5a28699b1ac506fe7833af19ae0709\n", + " Stored in directory: /root/.cache/pip/wheels/55/fb/7b/e06204a0ceefa45443930b9a250cb5ebe31def0e4e8245a465\n", + "Successfully built tflearn\n", + "Installing collected packages: tflearn\n", + "Successfully installed tflearn-0.5.0\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import tflearn" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IDIwBP4w9QzO", + "outputId": "23a5ccdf-2ec4-461e-fb52-889277544b6d" + }, + "execution_count": 17, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "WARNING:tensorflow:From /usr/local/lib/python3.10/dist-packages/tensorflow/python/compat/v2_compat.py:107: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "non-resource variables are not supported in the long term\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import tensorflow as tf\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense\n", + "\n", + "\n", + "model = Sequential([\n", + " Dense(8, input_shape=(len(training[0]),)),\n", + " Dense(8),\n", + " Dense(len(output[0]), activation=\"softmax\")\n", + "])\n", + "\n", + "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "model.fit(training, output, epochs=1000, batch_size=8)\n", + "\n", + "model.save('model.tflearn')\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "834kf2sG9oAs", + "outputId": "0587720f-cab1-42be-a961-5abc13494436" + }, + "execution_count": 25, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Train on 26 samples\n", + "Epoch 1/1000\n", + "26/26 [==============================] - 0s 9ms/sample - loss: 1.7451 - acc: 0.3077\n", + "Epoch 2/1000\n", + "26/26 [==============================] - 0s 547us/sample - loss: 1.7246 - acc: 0.3462\n", + "Epoch 3/1000\n", + "26/26 [==============================] - 0s 701us/sample - loss: 1.7090 - acc: 0.3462\n", + "Epoch 4/1000\n", + "26/26 [==============================] - 0s 455us/sample - loss: 1.6939 - acc: 0.3462\n", + "Epoch 5/1000\n", + "26/26 [==============================] - 0s 455us/sample - loss: 1.6792 - acc: 0.3462\n", + "Epoch 6/1000\n", + "26/26 [==============================] - 0s 431us/sample - loss: 1.6643 - acc: 0.3846\n", + "Epoch 7/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 1.6498 - acc: 0.3846\n", + "Epoch 8/1000\n", + "26/26 [==============================] - 0s 338us/sample - loss: 1.6356 - acc: 0.3846\n", + "Epoch 9/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 1.6215 - acc: 0.3846\n", + "Epoch 10/1000\n", + "26/26 [==============================] - 0s 413us/sample - loss: 1.6080 - acc: 0.4231\n", + "Epoch 11/1000\n", + "26/26 [==============================] - 0s 406us/sample - loss: 1.5936 - acc: 0.4231\n", + "Epoch 12/1000\n", + "26/26 [==============================] - 0s 536us/sample - loss: 1.5813 - acc: 0.4615\n", + "Epoch 13/1000\n", + "26/26 [==============================] - 0s 475us/sample - loss: 1.5654 - acc: 0.5000\n", + "Epoch 14/1000\n", + "26/26 [==============================] - 0s 521us/sample - loss: 1.5518 - acc: 0.5000\n", + "Epoch 15/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 1.5381 - acc: 0.5000\n", + "Epoch 16/1000\n", + "26/26 [==============================] - 0s 515us/sample - loss: 1.5239 - acc: 0.5000\n", + "Epoch 17/1000\n", + "26/26 [==============================] - 0s 395us/sample - loss: 1.5087 - acc: 0.5000\n", + "Epoch 18/1000\n", + "26/26 [==============================] - 0s 409us/sample - loss: 1.4943 - acc: 0.5000\n", + "Epoch 19/1000\n", + "26/26 [==============================] - 0s 453us/sample - loss: 1.4796 - acc: 0.5000\n", + "Epoch 20/1000\n", + "26/26 [==============================] - 0s 412us/sample - loss: 1.4645 - acc: 0.5769\n", + "Epoch 21/1000\n", + "26/26 [==============================] - 0s 416us/sample - loss: 1.4497 - acc: 0.5769\n", + "Epoch 22/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 1.4356 - acc: 0.5385\n", + "Epoch 23/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 1.4206 - acc: 0.5385\n", + "Epoch 24/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 1.4054 - acc: 0.5385\n", + "Epoch 25/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 1.3914 - acc: 0.5385\n", + "Epoch 26/1000\n", + "26/26 [==============================] - 0s 384us/sample - loss: 1.3770 - acc: 0.5385\n", + "Epoch 27/1000\n", + "26/26 [==============================] - 0s 417us/sample - loss: 1.3612 - acc: 0.5385\n", + "Epoch 28/1000\n", + "26/26 [==============================] - 0s 430us/sample - loss: 1.3468 - acc: 0.5385\n", + "Epoch 29/1000\n", + "26/26 [==============================] - 0s 399us/sample - loss: 1.3319 - acc: 0.5385\n", + "Epoch 30/1000\n", + "26/26 [==============================] - 0s 428us/sample - loss: 1.3163 - acc: 0.5385\n", + "Epoch 31/1000\n", + "26/26 [==============================] - 0s 402us/sample - loss: 1.3021 - acc: 0.5769\n", + "Epoch 32/1000\n", + "26/26 [==============================] - 0s 445us/sample - loss: 1.2863 - acc: 0.5769\n", + "Epoch 33/1000\n", + "26/26 [==============================] - 0s 360us/sample - loss: 1.2712 - acc: 0.5769\n", + "Epoch 34/1000\n", + "26/26 [==============================] - 0s 412us/sample - loss: 1.2558 - acc: 0.5769\n", + "Epoch 35/1000\n", + "26/26 [==============================] - 0s 410us/sample - loss: 1.2400 - acc: 0.5769\n", + "Epoch 36/1000\n", + "26/26 [==============================] - 0s 431us/sample - loss: 1.2245 - acc: 0.5769\n", + "Epoch 37/1000\n", + "26/26 [==============================] - 0s 429us/sample - loss: 1.2090 - acc: 0.5769\n", + "Epoch 38/1000\n", + "26/26 [==============================] - 0s 393us/sample - loss: 1.1923 - acc: 0.6154\n", + "Epoch 39/1000\n", + "26/26 [==============================] - 0s 514us/sample - loss: 1.1767 - acc: 0.6154\n", + "Epoch 40/1000\n", + "26/26 [==============================] - 0s 679us/sample - loss: 1.1609 - acc: 0.6538\n", + "Epoch 41/1000\n", + "26/26 [==============================] - 0s 505us/sample - loss: 1.1455 - acc: 0.6538\n", + "Epoch 42/1000\n", + "26/26 [==============================] - 0s 583us/sample - loss: 1.1291 - acc: 0.6538\n", + "Epoch 43/1000\n", + "26/26 [==============================] - 0s 428us/sample - loss: 1.1143 - acc: 0.6923\n", + "Epoch 44/1000\n", + "26/26 [==============================] - 0s 445us/sample - loss: 1.0985 - acc: 0.6923\n", + "Epoch 45/1000\n", + "26/26 [==============================] - 0s 398us/sample - loss: 1.0829 - acc: 0.6923\n", + "Epoch 46/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 1.0676 - acc: 0.6923\n", + "Epoch 47/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 1.0519 - acc: 0.6923\n", + "Epoch 48/1000\n", + "26/26 [==============================] - 0s 393us/sample - loss: 1.0366 - acc: 0.7308\n", + "Epoch 49/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 1.0210 - acc: 0.7308\n", + "Epoch 50/1000\n", + "26/26 [==============================] - 0s 425us/sample - loss: 1.0059 - acc: 0.7308\n", + "Epoch 51/1000\n", + "26/26 [==============================] - 0s 413us/sample - loss: 0.9903 - acc: 0.7308\n", + "Epoch 52/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 0.9756 - acc: 0.7308\n", + "Epoch 53/1000\n", + "26/26 [==============================] - 0s 554us/sample - loss: 0.9597 - acc: 0.7692\n", + "Epoch 54/1000\n", + "26/26 [==============================] - 0s 420us/sample - loss: 0.9455 - acc: 0.7692\n", + "Epoch 55/1000\n", + "26/26 [==============================] - 0s 413us/sample - loss: 0.9300 - acc: 0.7692\n", + "Epoch 56/1000\n", + "26/26 [==============================] - 0s 469us/sample - loss: 0.9167 - acc: 0.7692\n", + "Epoch 57/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 0.9017 - acc: 0.7692\n", + "Epoch 58/1000\n", + "26/26 [==============================] - 0s 378us/sample - loss: 0.8889 - acc: 0.7692\n", + "Epoch 59/1000\n", + "26/26 [==============================] - 0s 383us/sample - loss: 0.8739 - acc: 0.7692\n", + "Epoch 60/1000\n", + "26/26 [==============================] - 0s 375us/sample - loss: 0.8605 - acc: 0.7692\n", + "Epoch 61/1000\n", + "26/26 [==============================] - 0s 444us/sample - loss: 0.8466 - acc: 0.8077\n", + "Epoch 62/1000\n", + "26/26 [==============================] - 0s 449us/sample - loss: 0.8330 - acc: 0.8462\n", + "Epoch 63/1000\n", + "26/26 [==============================] - 0s 428us/sample - loss: 0.8188 - acc: 0.8462\n", + "Epoch 64/1000\n", + "26/26 [==============================] - 0s 417us/sample - loss: 0.8053 - acc: 0.8462\n", + "Epoch 65/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 0.7920 - acc: 0.8846\n", + "Epoch 66/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 0.7772 - acc: 0.8846\n", + "Epoch 67/1000\n", + "26/26 [==============================] - 0s 415us/sample - loss: 0.7643 - acc: 0.8846\n", + "Epoch 68/1000\n", + "26/26 [==============================] - 0s 393us/sample - loss: 0.7514 - acc: 0.8462\n", + "Epoch 69/1000\n", + "26/26 [==============================] - 0s 389us/sample - loss: 0.7376 - acc: 0.8846\n", + "Epoch 70/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 0.7254 - acc: 0.8846\n", + "Epoch 71/1000\n", + "26/26 [==============================] - 0s 415us/sample - loss: 0.7126 - acc: 0.8846\n", + "Epoch 72/1000\n", + "26/26 [==============================] - 0s 421us/sample - loss: 0.7013 - acc: 0.8846\n", + "Epoch 73/1000\n", + "26/26 [==============================] - 0s 554us/sample - loss: 0.6887 - acc: 0.8846\n", + "Epoch 74/1000\n", + "26/26 [==============================] - 0s 387us/sample - loss: 0.6770 - acc: 0.8846\n", + "Epoch 75/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 0.6653 - acc: 0.8846\n", + "Epoch 76/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 0.6537 - acc: 0.8846\n", + "Epoch 77/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 0.6424 - acc: 0.8846\n", + "Epoch 78/1000\n", + "26/26 [==============================] - 0s 370us/sample - loss: 0.6306 - acc: 0.8846\n", + "Epoch 79/1000\n", + "26/26 [==============================] - 0s 372us/sample - loss: 0.6197 - acc: 0.8846\n", + "Epoch 80/1000\n", + "26/26 [==============================] - 0s 416us/sample - loss: 0.6079 - acc: 0.8846\n", + "Epoch 81/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 0.5966 - acc: 0.8846\n", + "Epoch 82/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 0.5860 - acc: 0.8846\n", + "Epoch 83/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 0.5753 - acc: 0.8846\n", + "Epoch 84/1000\n", + "26/26 [==============================] - 0s 408us/sample - loss: 0.5640 - acc: 0.8846\n", + "Epoch 85/1000\n", + "26/26 [==============================] - 0s 408us/sample - loss: 0.5542 - acc: 0.8846\n", + "Epoch 86/1000\n", + "26/26 [==============================] - 0s 416us/sample - loss: 0.5434 - acc: 0.8846\n", + "Epoch 87/1000\n", + "26/26 [==============================] - 0s 464us/sample - loss: 0.5332 - acc: 0.9231\n", + "Epoch 88/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 0.5229 - acc: 0.9231\n", + "Epoch 89/1000\n", + "26/26 [==============================] - 0s 410us/sample - loss: 0.5125 - acc: 0.9231\n", + "Epoch 90/1000\n", + "26/26 [==============================] - 0s 455us/sample - loss: 0.5027 - acc: 0.9231\n", + "Epoch 91/1000\n", + "26/26 [==============================] - 0s 430us/sample - loss: 0.4932 - acc: 0.9231\n", + "Epoch 92/1000\n", + "26/26 [==============================] - 0s 476us/sample - loss: 0.4835 - acc: 0.9231\n", + "Epoch 93/1000\n", + "26/26 [==============================] - 0s 533us/sample - loss: 0.4742 - acc: 0.9231\n", + "Epoch 94/1000\n", + "26/26 [==============================] - 0s 601us/sample - loss: 0.4656 - acc: 0.9231\n", + "Epoch 95/1000\n", + "26/26 [==============================] - 0s 517us/sample - loss: 0.4566 - acc: 0.9231\n", + "Epoch 96/1000\n", + "26/26 [==============================] - 0s 468us/sample - loss: 0.4478 - acc: 0.9615\n", + "Epoch 97/1000\n", + "26/26 [==============================] - 0s 434us/sample - loss: 0.4399 - acc: 0.9615\n", + "Epoch 98/1000\n", + "26/26 [==============================] - 0s 423us/sample - loss: 0.4313 - acc: 0.9615\n", + "Epoch 99/1000\n", + "26/26 [==============================] - 0s 560us/sample - loss: 0.4220 - acc: 0.9615\n", + "Epoch 100/1000\n", + "26/26 [==============================] - 0s 445us/sample - loss: 0.4149 - acc: 0.9615\n", + "Epoch 101/1000\n", + "26/26 [==============================] - 0s 466us/sample - loss: 0.4072 - acc: 0.9615\n", + "Epoch 102/1000\n", + "26/26 [==============================] - 0s 579us/sample - loss: 0.3992 - acc: 0.9615\n", + "Epoch 103/1000\n", + "26/26 [==============================] - 0s 368us/sample - loss: 0.3911 - acc: 0.9615\n", + "Epoch 104/1000\n", + "26/26 [==============================] - 0s 375us/sample - loss: 0.3836 - acc: 1.0000\n", + "Epoch 105/1000\n", + "26/26 [==============================] - 0s 384us/sample - loss: 0.3763 - acc: 1.0000\n", + "Epoch 106/1000\n", + "26/26 [==============================] - 0s 409us/sample - loss: 0.3682 - acc: 1.0000\n", + "Epoch 107/1000\n", + "26/26 [==============================] - 0s 425us/sample - loss: 0.3611 - acc: 1.0000\n", + "Epoch 108/1000\n", + "26/26 [==============================] - 0s 423us/sample - loss: 0.3541 - acc: 1.0000\n", + "Epoch 109/1000\n", + "26/26 [==============================] - 0s 428us/sample - loss: 0.3475 - acc: 1.0000\n", + "Epoch 110/1000\n", + "26/26 [==============================] - 0s 450us/sample - loss: 0.3410 - acc: 1.0000\n", + "Epoch 111/1000\n", + "26/26 [==============================] - 0s 468us/sample - loss: 0.3349 - acc: 1.0000\n", + "Epoch 112/1000\n", + "26/26 [==============================] - 0s 438us/sample - loss: 0.3283 - acc: 1.0000\n", + "Epoch 113/1000\n", + "26/26 [==============================] - 0s 394us/sample - loss: 0.3219 - acc: 1.0000\n", + "Epoch 114/1000\n", + "26/26 [==============================] - 0s 425us/sample - loss: 0.3165 - acc: 1.0000\n", + "Epoch 115/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 0.3108 - acc: 1.0000\n", + "Epoch 116/1000\n", + "26/26 [==============================] - 0s 449us/sample - loss: 0.3047 - acc: 1.0000\n", + "Epoch 117/1000\n", + "26/26 [==============================] - 0s 509us/sample - loss: 0.2989 - acc: 1.0000\n", + "Epoch 118/1000\n", + "26/26 [==============================] - 0s 578us/sample - loss: 0.2931 - acc: 1.0000\n", + "Epoch 119/1000\n", + "26/26 [==============================] - 0s 563us/sample - loss: 0.2876 - acc: 1.0000\n", + "Epoch 120/1000\n", + "26/26 [==============================] - 0s 384us/sample - loss: 0.2825 - acc: 1.0000\n", + "Epoch 121/1000\n", + "26/26 [==============================] - 0s 408us/sample - loss: 0.2770 - acc: 1.0000\n", + "Epoch 122/1000\n", + "26/26 [==============================] - 0s 370us/sample - loss: 0.2715 - acc: 1.0000\n", + "Epoch 123/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 0.2664 - acc: 1.0000\n", + "Epoch 124/1000\n", + "26/26 [==============================] - 0s 398us/sample - loss: 0.2606 - acc: 1.0000\n", + "Epoch 125/1000\n", + "26/26 [==============================] - 0s 423us/sample - loss: 0.2553 - acc: 1.0000\n", + "Epoch 126/1000\n", + "26/26 [==============================] - 0s 427us/sample - loss: 0.2507 - acc: 1.0000\n", + "Epoch 127/1000\n", + "26/26 [==============================] - 0s 396us/sample - loss: 0.2459 - acc: 1.0000\n", + "Epoch 128/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 0.2411 - acc: 1.0000\n", + "Epoch 129/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 0.2368 - acc: 1.0000\n", + "Epoch 130/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 0.2325 - acc: 1.0000\n", + "Epoch 131/1000\n", + "26/26 [==============================] - 0s 368us/sample - loss: 0.2274 - acc: 1.0000\n", + "Epoch 132/1000\n", + "26/26 [==============================] - 0s 385us/sample - loss: 0.2232 - acc: 1.0000\n", + "Epoch 133/1000\n", + "26/26 [==============================] - 0s 395us/sample - loss: 0.2189 - acc: 1.0000\n", + "Epoch 134/1000\n", + "26/26 [==============================] - 0s 420us/sample - loss: 0.2147 - acc: 1.0000\n", + "Epoch 135/1000\n", + "26/26 [==============================] - 0s 378us/sample - loss: 0.2105 - acc: 1.0000\n", + "Epoch 136/1000\n", + "26/26 [==============================] - 0s 439us/sample - loss: 0.2063 - acc: 1.0000\n", + "Epoch 137/1000\n", + "26/26 [==============================] - 0s 442us/sample - loss: 0.2024 - acc: 1.0000\n", + "Epoch 138/1000\n", + "26/26 [==============================] - 0s 416us/sample - loss: 0.1987 - acc: 1.0000\n", + "Epoch 139/1000\n", + "26/26 [==============================] - 0s 611us/sample - loss: 0.1946 - acc: 1.0000\n", + "Epoch 140/1000\n", + "26/26 [==============================] - 0s 428us/sample - loss: 0.1906 - acc: 1.0000\n", + "Epoch 141/1000\n", + "26/26 [==============================] - 0s 401us/sample - loss: 0.1871 - acc: 1.0000\n", + "Epoch 142/1000\n", + "26/26 [==============================] - 0s 540us/sample - loss: 0.1836 - acc: 1.0000\n", + "Epoch 143/1000\n", + "26/26 [==============================] - 0s 450us/sample - loss: 0.1796 - acc: 1.0000\n", + "Epoch 144/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 0.1764 - acc: 1.0000\n", + "Epoch 145/1000\n", + "26/26 [==============================] - 0s 584us/sample - loss: 0.1726 - acc: 1.0000\n", + "Epoch 146/1000\n", + "26/26 [==============================] - 0s 411us/sample - loss: 0.1693 - acc: 1.0000\n", + "Epoch 147/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 0.1661 - acc: 1.0000\n", + "Epoch 148/1000\n", + "26/26 [==============================] - 0s 412us/sample - loss: 0.1629 - acc: 1.0000\n", + "Epoch 149/1000\n", + "26/26 [==============================] - 0s 391us/sample - loss: 0.1598 - acc: 1.0000\n", + "Epoch 150/1000\n", + "26/26 [==============================] - 0s 385us/sample - loss: 0.1565 - acc: 1.0000\n", + "Epoch 151/1000\n", + "26/26 [==============================] - 0s 395us/sample - loss: 0.1535 - acc: 1.0000\n", + "Epoch 152/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 0.1508 - acc: 1.0000\n", + "Epoch 153/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 0.1478 - acc: 1.0000\n", + "Epoch 154/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 0.1449 - acc: 1.0000\n", + "Epoch 155/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 0.1417 - acc: 1.0000\n", + "Epoch 156/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 0.1391 - acc: 1.0000\n", + "Epoch 157/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 0.1363 - acc: 1.0000\n", + "Epoch 158/1000\n", + "26/26 [==============================] - 0s 393us/sample - loss: 0.1339 - acc: 1.0000\n", + "Epoch 159/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 0.1314 - acc: 1.0000\n", + "Epoch 160/1000\n", + "26/26 [==============================] - 0s 551us/sample - loss: 0.1291 - acc: 1.0000\n", + "Epoch 161/1000\n", + "26/26 [==============================] - 0s 419us/sample - loss: 0.1268 - acc: 1.0000\n", + "Epoch 162/1000\n", + "26/26 [==============================] - 0s 376us/sample - loss: 0.1247 - acc: 1.0000\n", + "Epoch 163/1000\n", + "26/26 [==============================] - 0s 502us/sample - loss: 0.1226 - acc: 1.0000\n", + "Epoch 164/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 0.1205 - acc: 1.0000\n", + "Epoch 165/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 0.1184 - acc: 1.0000\n", + "Epoch 166/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 0.1164 - acc: 1.0000\n", + "Epoch 167/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 0.1144 - acc: 1.0000\n", + "Epoch 168/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 0.1126 - acc: 1.0000\n", + "Epoch 169/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 0.1105 - acc: 1.0000\n", + "Epoch 170/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 0.1087 - acc: 1.0000\n", + "Epoch 171/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 0.1070 - acc: 1.0000\n", + "Epoch 172/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 0.1052 - acc: 1.0000\n", + "Epoch 173/1000\n", + "26/26 [==============================] - 0s 375us/sample - loss: 0.1035 - acc: 1.0000\n", + "Epoch 174/1000\n", + "26/26 [==============================] - 0s 375us/sample - loss: 0.1019 - acc: 1.0000\n", + "Epoch 175/1000\n", + "26/26 [==============================] - 0s 454us/sample - loss: 0.0999 - acc: 1.0000\n", + "Epoch 176/1000\n", + "26/26 [==============================] - 0s 519us/sample - loss: 0.0983 - acc: 1.0000\n", + "Epoch 177/1000\n", + "26/26 [==============================] - 0s 498us/sample - loss: 0.0964 - acc: 1.0000\n", + "Epoch 178/1000\n", + "26/26 [==============================] - 0s 445us/sample - loss: 0.0948 - acc: 1.0000\n", + "Epoch 179/1000\n", + "26/26 [==============================] - 0s 428us/sample - loss: 0.0930 - acc: 1.0000\n", + "Epoch 180/1000\n", + "26/26 [==============================] - 0s 424us/sample - loss: 0.0915 - acc: 1.0000\n", + "Epoch 181/1000\n", + "26/26 [==============================] - 0s 415us/sample - loss: 0.0901 - acc: 1.0000\n", + "Epoch 182/1000\n", + "26/26 [==============================] - 0s 391us/sample - loss: 0.0883 - acc: 1.0000\n", + "Epoch 183/1000\n", + "26/26 [==============================] - 0s 401us/sample - loss: 0.0869 - acc: 1.0000\n", + "Epoch 184/1000\n", + "26/26 [==============================] - 0s 372us/sample - loss: 0.0855 - acc: 1.0000\n", + "Epoch 185/1000\n", + "26/26 [==============================] - 0s 387us/sample - loss: 0.0840 - acc: 1.0000\n", + "Epoch 186/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 0.0826 - acc: 1.0000\n", + "Epoch 187/1000\n", + "26/26 [==============================] - 0s 410us/sample - loss: 0.0814 - acc: 1.0000\n", + "Epoch 188/1000\n", + "26/26 [==============================] - 0s 593us/sample - loss: 0.0801 - acc: 1.0000\n", + "Epoch 189/1000\n", + "26/26 [==============================] - 0s 448us/sample - loss: 0.0789 - acc: 1.0000\n", + "Epoch 190/1000\n", + "26/26 [==============================] - 0s 431us/sample - loss: 0.0777 - acc: 1.0000\n", + "Epoch 191/1000\n", + "26/26 [==============================] - 0s 395us/sample - loss: 0.0764 - acc: 1.0000\n", + "Epoch 192/1000\n", + "26/26 [==============================] - 0s 395us/sample - loss: 0.0753 - acc: 1.0000\n", + "Epoch 193/1000\n", + "26/26 [==============================] - 0s 383us/sample - loss: 0.0739 - acc: 1.0000\n", + "Epoch 194/1000\n", + "26/26 [==============================] - 0s 402us/sample - loss: 0.0728 - acc: 1.0000\n", + "Epoch 195/1000\n", + "26/26 [==============================] - 0s 419us/sample - loss: 0.0717 - acc: 1.0000\n", + "Epoch 196/1000\n", + "26/26 [==============================] - 0s 375us/sample - loss: 0.0704 - acc: 1.0000\n", + "Epoch 197/1000\n", + "26/26 [==============================] - 0s 360us/sample - loss: 0.0694 - acc: 1.0000\n", + "Epoch 198/1000\n", + "26/26 [==============================] - 0s 360us/sample - loss: 0.0684 - acc: 1.0000\n", + "Epoch 199/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 0.0673 - acc: 1.0000\n", + "Epoch 200/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 0.0662 - acc: 1.0000\n", + "Epoch 201/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 0.0652 - acc: 1.0000\n", + "Epoch 202/1000\n", + "26/26 [==============================] - 0s 386us/sample - loss: 0.0641 - acc: 1.0000\n", + "Epoch 203/1000\n", + "26/26 [==============================] - 0s 449us/sample - loss: 0.0632 - acc: 1.0000\n", + "Epoch 204/1000\n", + "26/26 [==============================] - 0s 395us/sample - loss: 0.0623 - acc: 1.0000\n", + "Epoch 205/1000\n", + "26/26 [==============================] - 0s 386us/sample - loss: 0.0613 - acc: 1.0000\n", + "Epoch 206/1000\n", + "26/26 [==============================] - 0s 386us/sample - loss: 0.0604 - acc: 1.0000\n", + "Epoch 207/1000\n", + "26/26 [==============================] - 0s 442us/sample - loss: 0.0594 - acc: 1.0000\n", + "Epoch 208/1000\n", + "26/26 [==============================] - 0s 428us/sample - loss: 0.0586 - acc: 1.0000\n", + "Epoch 209/1000\n", + "26/26 [==============================] - 0s 378us/sample - loss: 0.0577 - acc: 1.0000\n", + "Epoch 210/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 0.0567 - acc: 1.0000\n", + "Epoch 211/1000\n", + "26/26 [==============================] - 0s 579us/sample - loss: 0.0559 - acc: 1.0000\n", + "Epoch 212/1000\n", + "26/26 [==============================] - 0s 490us/sample - loss: 0.0551 - acc: 1.0000\n", + "Epoch 213/1000\n", + "26/26 [==============================] - 0s 594us/sample - loss: 0.0544 - acc: 1.0000\n", + "Epoch 214/1000\n", + "26/26 [==============================] - 0s 432us/sample - loss: 0.0537 - acc: 1.0000\n", + "Epoch 215/1000\n", + "26/26 [==============================] - 0s 417us/sample - loss: 0.0529 - acc: 1.0000\n", + "Epoch 216/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 0.0522 - acc: 1.0000\n", + "Epoch 217/1000\n", + "26/26 [==============================] - 0s 450us/sample - loss: 0.0516 - acc: 1.0000\n", + "Epoch 218/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 0.0508 - acc: 1.0000\n", + "Epoch 219/1000\n", + "26/26 [==============================] - 0s 373us/sample - loss: 0.0500 - acc: 1.0000\n", + "Epoch 220/1000\n", + "26/26 [==============================] - 0s 572us/sample - loss: 0.0493 - acc: 1.0000\n", + "Epoch 221/1000\n", + "26/26 [==============================] - 0s 427us/sample - loss: 0.0486 - acc: 1.0000\n", + "Epoch 222/1000\n", + "26/26 [==============================] - 0s 372us/sample - loss: 0.0479 - acc: 1.0000\n", + "Epoch 223/1000\n", + "26/26 [==============================] - 0s 383us/sample - loss: 0.0473 - acc: 1.0000\n", + "Epoch 224/1000\n", + "26/26 [==============================] - 0s 626us/sample - loss: 0.0467 - acc: 1.0000\n", + "Epoch 225/1000\n", + "26/26 [==============================] - 0s 424us/sample - loss: 0.0460 - acc: 1.0000\n", + "Epoch 226/1000\n", + "26/26 [==============================] - 0s 331us/sample - loss: 0.0454 - acc: 1.0000\n", + "Epoch 227/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 0.0449 - acc: 1.0000\n", + "Epoch 228/1000\n", + "26/26 [==============================] - 0s 340us/sample - loss: 0.0443 - acc: 1.0000\n", + "Epoch 229/1000\n", + "26/26 [==============================] - 0s 387us/sample - loss: 0.0436 - acc: 1.0000\n", + "Epoch 230/1000\n", + "26/26 [==============================] - 0s 383us/sample - loss: 0.0430 - acc: 1.0000\n", + "Epoch 231/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 0.0424 - acc: 1.0000\n", + "Epoch 232/1000\n", + "26/26 [==============================] - 0s 403us/sample - loss: 0.0419 - acc: 1.0000\n", + "Epoch 233/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 0.0414 - acc: 1.0000\n", + "Epoch 234/1000\n", + "26/26 [==============================] - 0s 370us/sample - loss: 0.0408 - acc: 1.0000\n", + "Epoch 235/1000\n", + "26/26 [==============================] - 0s 368us/sample - loss: 0.0403 - acc: 1.0000\n", + "Epoch 236/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 0.0397 - acc: 1.0000\n", + "Epoch 237/1000\n", + "26/26 [==============================] - 0s 394us/sample - loss: 0.0392 - acc: 1.0000\n", + "Epoch 238/1000\n", + "26/26 [==============================] - 0s 353us/sample - loss: 0.0388 - acc: 1.0000\n", + "Epoch 239/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 0.0382 - acc: 1.0000\n", + "Epoch 240/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 0.0377 - acc: 1.0000\n", + "Epoch 241/1000\n", + "26/26 [==============================] - 0s 338us/sample - loss: 0.0373 - acc: 1.0000\n", + "Epoch 242/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 0.0368 - acc: 1.0000\n", + "Epoch 243/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 0.0364 - acc: 1.0000\n", + "Epoch 244/1000\n", + "26/26 [==============================] - 0s 353us/sample - loss: 0.0360 - acc: 1.0000\n", + "Epoch 245/1000\n", + "26/26 [==============================] - 0s 523us/sample - loss: 0.0355 - acc: 1.0000\n", + "Epoch 246/1000\n", + "26/26 [==============================] - 0s 424us/sample - loss: 0.0351 - acc: 1.0000\n", + "Epoch 247/1000\n", + "26/26 [==============================] - 0s 410us/sample - loss: 0.0347 - acc: 1.0000\n", + "Epoch 248/1000\n", + "26/26 [==============================] - 0s 472us/sample - loss: 0.0343 - acc: 1.0000\n", + "Epoch 249/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 0.0339 - acc: 1.0000\n", + "Epoch 250/1000\n", + "26/26 [==============================] - 0s 458us/sample - loss: 0.0334 - acc: 1.0000\n", + "Epoch 251/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 0.0331 - acc: 1.0000\n", + "Epoch 252/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 0.0327 - acc: 1.0000\n", + "Epoch 253/1000\n", + "26/26 [==============================] - 0s 375us/sample - loss: 0.0323 - acc: 1.0000\n", + "Epoch 254/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 0.0319 - acc: 1.0000\n", + "Epoch 255/1000\n", + "26/26 [==============================] - 0s 387us/sample - loss: 0.0315 - acc: 1.0000\n", + "Epoch 256/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 0.0311 - acc: 1.0000\n", + "Epoch 257/1000\n", + "26/26 [==============================] - 0s 375us/sample - loss: 0.0308 - acc: 1.0000\n", + "Epoch 258/1000\n", + "26/26 [==============================] - 0s 492us/sample - loss: 0.0304 - acc: 1.0000\n", + "Epoch 259/1000\n", + "26/26 [==============================] - 0s 459us/sample - loss: 0.0301 - acc: 1.0000\n", + "Epoch 260/1000\n", + "26/26 [==============================] - 0s 408us/sample - loss: 0.0297 - acc: 1.0000\n", + "Epoch 261/1000\n", + "26/26 [==============================] - 0s 383us/sample - loss: 0.0294 - acc: 1.0000\n", + "Epoch 262/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 0.0291 - acc: 1.0000\n", + "Epoch 263/1000\n", + "26/26 [==============================] - 0s 406us/sample - loss: 0.0287 - acc: 1.0000\n", + "Epoch 264/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 0.0284 - acc: 1.0000\n", + "Epoch 265/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 0.0281 - acc: 1.0000\n", + "Epoch 266/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 0.0278 - acc: 1.0000\n", + "Epoch 267/1000\n", + "26/26 [==============================] - 0s 404us/sample - loss: 0.0275 - acc: 1.0000\n", + "Epoch 268/1000\n", + "26/26 [==============================] - 0s 387us/sample - loss: 0.0272 - acc: 1.0000\n", + "Epoch 269/1000\n", + "26/26 [==============================] - 0s 385us/sample - loss: 0.0269 - acc: 1.0000\n", + "Epoch 270/1000\n", + "26/26 [==============================] - 0s 420us/sample - loss: 0.0266 - acc: 1.0000\n", + "Epoch 271/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 0.0263 - acc: 1.0000\n", + "Epoch 272/1000\n", + "26/26 [==============================] - 0s 419us/sample - loss: 0.0260 - acc: 1.0000\n", + "Epoch 273/1000\n", + "26/26 [==============================] - 0s 427us/sample - loss: 0.0257 - acc: 1.0000\n", + "Epoch 274/1000\n", + "26/26 [==============================] - 0s 385us/sample - loss: 0.0255 - acc: 1.0000\n", + "Epoch 275/1000\n", + "26/26 [==============================] - 0s 421us/sample - loss: 0.0252 - acc: 1.0000\n", + "Epoch 276/1000\n", + "26/26 [==============================] - 0s 568us/sample - loss: 0.0249 - acc: 1.0000\n", + "Epoch 277/1000\n", + "26/26 [==============================] - 0s 550us/sample - loss: 0.0246 - acc: 1.0000\n", + "Epoch 278/1000\n", + "26/26 [==============================] - 0s 503us/sample - loss: 0.0244 - acc: 1.0000\n", + "Epoch 279/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 0.0241 - acc: 1.0000\n", + "Epoch 280/1000\n", + "26/26 [==============================] - 0s 384us/sample - loss: 0.0239 - acc: 1.0000\n", + "Epoch 281/1000\n", + "26/26 [==============================] - 0s 370us/sample - loss: 0.0236 - acc: 1.0000\n", + "Epoch 282/1000\n", + "26/26 [==============================] - 0s 377us/sample - loss: 0.0234 - acc: 1.0000\n", + "Epoch 283/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 0.0231 - acc: 1.0000\n", + "Epoch 284/1000\n", + "26/26 [==============================] - 0s 412us/sample - loss: 0.0229 - acc: 1.0000\n", + "Epoch 285/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 0.0227 - acc: 1.0000\n", + "Epoch 286/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 0.0224 - acc: 1.0000\n", + "Epoch 287/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 0.0222 - acc: 1.0000\n", + "Epoch 288/1000\n", + "26/26 [==============================] - 0s 671us/sample - loss: 0.0220 - acc: 1.0000\n", + "Epoch 289/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 0.0217 - acc: 1.0000\n", + "Epoch 290/1000\n", + "26/26 [==============================] - 0s 389us/sample - loss: 0.0215 - acc: 1.0000\n", + "Epoch 291/1000\n", + "26/26 [==============================] - 0s 512us/sample - loss: 0.0213 - acc: 1.0000\n", + "Epoch 292/1000\n", + "26/26 [==============================] - 0s 490us/sample - loss: 0.0211 - acc: 1.0000\n", + "Epoch 293/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 0.0209 - acc: 1.0000\n", + "Epoch 294/1000\n", + "26/26 [==============================] - 0s 483us/sample - loss: 0.0206 - acc: 1.0000\n", + "Epoch 295/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 0.0204 - acc: 1.0000\n", + "Epoch 296/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 0.0202 - acc: 1.0000\n", + "Epoch 297/1000\n", + "26/26 [==============================] - 0s 421us/sample - loss: 0.0200 - acc: 1.0000\n", + "Epoch 298/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 0.0198 - acc: 1.0000\n", + "Epoch 299/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 0.0196 - acc: 1.0000\n", + "Epoch 300/1000\n", + "26/26 [==============================] - 0s 394us/sample - loss: 0.0194 - acc: 1.0000\n", + "Epoch 301/1000\n", + "26/26 [==============================] - 0s 350us/sample - loss: 0.0192 - acc: 1.0000\n", + "Epoch 302/1000\n", + "26/26 [==============================] - 0s 420us/sample - loss: 0.0190 - acc: 1.0000\n", + "Epoch 303/1000\n", + "26/26 [==============================] - 0s 418us/sample - loss: 0.0189 - acc: 1.0000\n", + "Epoch 304/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 0.0187 - acc: 1.0000\n", + "Epoch 305/1000\n", + "26/26 [==============================] - 0s 350us/sample - loss: 0.0185 - acc: 1.0000\n", + "Epoch 306/1000\n", + "26/26 [==============================] - 0s 349us/sample - loss: 0.0183 - acc: 1.0000\n", + "Epoch 307/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 0.0182 - acc: 1.0000\n", + "Epoch 308/1000\n", + "26/26 [==============================] - 0s 356us/sample - loss: 0.0180 - acc: 1.0000\n", + "Epoch 309/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 0.0178 - acc: 1.0000\n", + "Epoch 310/1000\n", + "26/26 [==============================] - 0s 326us/sample - loss: 0.0177 - acc: 1.0000\n", + "Epoch 311/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 0.0175 - acc: 1.0000\n", + "Epoch 312/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 0.0173 - acc: 1.0000\n", + "Epoch 313/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 0.0172 - acc: 1.0000\n", + "Epoch 314/1000\n", + "26/26 [==============================] - 0s 387us/sample - loss: 0.0170 - acc: 1.0000\n", + "Epoch 315/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 0.0169 - acc: 1.0000\n", + "Epoch 316/1000\n", + "26/26 [==============================] - 0s 378us/sample - loss: 0.0167 - acc: 1.0000\n", + "Epoch 317/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 0.0166 - acc: 1.0000\n", + "Epoch 318/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 0.0164 - acc: 1.0000\n", + "Epoch 319/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 0.0163 - acc: 1.0000\n", + "Epoch 320/1000\n", + "26/26 [==============================] - 0s 329us/sample - loss: 0.0161 - acc: 1.0000\n", + "Epoch 321/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 0.0160 - acc: 1.0000\n", + "Epoch 322/1000\n", + "26/26 [==============================] - 0s 348us/sample - loss: 0.0158 - acc: 1.0000\n", + "Epoch 323/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 0.0157 - acc: 1.0000\n", + "Epoch 324/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 0.0155 - acc: 1.0000\n", + "Epoch 325/1000\n", + "26/26 [==============================] - 0s 372us/sample - loss: 0.0154 - acc: 1.0000\n", + "Epoch 326/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 0.0152 - acc: 1.0000\n", + "Epoch 327/1000\n", + "26/26 [==============================] - 0s 421us/sample - loss: 0.0151 - acc: 1.0000\n", + "Epoch 328/1000\n", + "26/26 [==============================] - 0s 445us/sample - loss: 0.0150 - acc: 1.0000\n", + "Epoch 329/1000\n", + "26/26 [==============================] - 0s 425us/sample - loss: 0.0148 - acc: 1.0000\n", + "Epoch 330/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 0.0147 - acc: 1.0000\n", + "Epoch 331/1000\n", + "26/26 [==============================] - 0s 355us/sample - loss: 0.0146 - acc: 1.0000\n", + "Epoch 332/1000\n", + "26/26 [==============================] - 0s 447us/sample - loss: 0.0145 - acc: 1.0000\n", + "Epoch 333/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 0.0143 - acc: 1.0000\n", + "Epoch 334/1000\n", + "26/26 [==============================] - 0s 368us/sample - loss: 0.0142 - acc: 1.0000\n", + "Epoch 335/1000\n", + "26/26 [==============================] - 0s 466us/sample - loss: 0.0141 - acc: 1.0000\n", + "Epoch 336/1000\n", + "26/26 [==============================] - 0s 370us/sample - loss: 0.0140 - acc: 1.0000\n", + "Epoch 337/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 0.0138 - acc: 1.0000\n", + "Epoch 338/1000\n", + "26/26 [==============================] - 0s 384us/sample - loss: 0.0137 - acc: 1.0000\n", + "Epoch 339/1000\n", + "26/26 [==============================] - 0s 386us/sample - loss: 0.0136 - acc: 1.0000\n", + "Epoch 340/1000\n", + "26/26 [==============================] - 0s 393us/sample - loss: 0.0135 - acc: 1.0000\n", + "Epoch 341/1000\n", + "26/26 [==============================] - 0s 391us/sample - loss: 0.0134 - acc: 1.0000\n", + "Epoch 342/1000\n", + "26/26 [==============================] - 0s 401us/sample - loss: 0.0133 - acc: 1.0000\n", + "Epoch 343/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 0.0131 - acc: 1.0000\n", + "Epoch 344/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 0.0130 - acc: 1.0000\n", + "Epoch 345/1000\n", + "26/26 [==============================] - 0s 389us/sample - loss: 0.0129 - acc: 1.0000\n", + "Epoch 346/1000\n", + "26/26 [==============================] - 0s 370us/sample - loss: 0.0128 - acc: 1.0000\n", + "Epoch 347/1000\n", + "26/26 [==============================] - 0s 424us/sample - loss: 0.0127 - acc: 1.0000\n", + "Epoch 348/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 0.0126 - acc: 1.0000\n", + "Epoch 349/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 0.0125 - acc: 1.0000\n", + "Epoch 350/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 0.0124 - acc: 1.0000\n", + "Epoch 351/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 0.0123 - acc: 1.0000\n", + "Epoch 352/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 0.0123 - acc: 1.0000\n", + "Epoch 353/1000\n", + "26/26 [==============================] - 0s 360us/sample - loss: 0.0122 - acc: 1.0000\n", + "Epoch 354/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 0.0121 - acc: 1.0000\n", + "Epoch 355/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 0.0120 - acc: 1.0000\n", + "Epoch 356/1000\n", + "26/26 [==============================] - 0s 349us/sample - loss: 0.0118 - acc: 1.0000\n", + "Epoch 357/1000\n", + "26/26 [==============================] - 0s 393us/sample - loss: 0.0118 - acc: 1.0000\n", + "Epoch 358/1000\n", + "26/26 [==============================] - 0s 395us/sample - loss: 0.0117 - acc: 1.0000\n", + "Epoch 359/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 0.0116 - acc: 1.0000\n", + "Epoch 360/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 0.0115 - acc: 1.0000\n", + "Epoch 361/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 0.0114 - acc: 1.0000\n", + "Epoch 362/1000\n", + "26/26 [==============================] - 0s 398us/sample - loss: 0.0113 - acc: 1.0000\n", + "Epoch 363/1000\n", + "26/26 [==============================] - 0s 611us/sample - loss: 0.0112 - acc: 1.0000\n", + "Epoch 364/1000\n", + "26/26 [==============================] - 0s 522us/sample - loss: 0.0111 - acc: 1.0000\n", + "Epoch 365/1000\n", + "26/26 [==============================] - 0s 637us/sample - loss: 0.0111 - acc: 1.0000\n", + "Epoch 366/1000\n", + "26/26 [==============================] - 0s 716us/sample - loss: 0.0110 - acc: 1.0000\n", + "Epoch 367/1000\n", + "26/26 [==============================] - 0s 605us/sample - loss: 0.0109 - acc: 1.0000\n", + "Epoch 368/1000\n", + "26/26 [==============================] - 0s 495us/sample - loss: 0.0108 - acc: 1.0000\n", + "Epoch 369/1000\n", + "26/26 [==============================] - 0s 521us/sample - loss: 0.0107 - acc: 1.0000\n", + "Epoch 370/1000\n", + "26/26 [==============================] - 0s 620us/sample - loss: 0.0106 - acc: 1.0000\n", + "Epoch 371/1000\n", + "26/26 [==============================] - 0s 686us/sample - loss: 0.0105 - acc: 1.0000\n", + "Epoch 372/1000\n", + "26/26 [==============================] - 0s 676us/sample - loss: 0.0104 - acc: 1.0000\n", + "Epoch 373/1000\n", + "26/26 [==============================] - 0s 675us/sample - loss: 0.0104 - acc: 1.0000\n", + "Epoch 374/1000\n", + "26/26 [==============================] - 0s 453us/sample - loss: 0.0103 - acc: 1.0000\n", + "Epoch 375/1000\n", + "26/26 [==============================] - 0s 469us/sample - loss: 0.0102 - acc: 1.0000\n", + "Epoch 376/1000\n", + "26/26 [==============================] - 0s 456us/sample - loss: 0.0101 - acc: 1.0000\n", + "Epoch 377/1000\n", + "26/26 [==============================] - 0s 459us/sample - loss: 0.0101 - acc: 1.0000\n", + "Epoch 378/1000\n", + "26/26 [==============================] - 0s 395us/sample - loss: 0.0100 - acc: 1.0000\n", + "Epoch 379/1000\n", + "26/26 [==============================] - 0s 499us/sample - loss: 0.0099 - acc: 1.0000\n", + "Epoch 380/1000\n", + "26/26 [==============================] - 0s 446us/sample - loss: 0.0098 - acc: 1.0000\n", + "Epoch 381/1000\n", + "26/26 [==============================] - 0s 438us/sample - loss: 0.0098 - acc: 1.0000\n", + "Epoch 382/1000\n", + "26/26 [==============================] - 0s 554us/sample - loss: 0.0097 - acc: 1.0000\n", + "Epoch 383/1000\n", + "26/26 [==============================] - 0s 538us/sample - loss: 0.0096 - acc: 1.0000\n", + "Epoch 384/1000\n", + "26/26 [==============================] - 0s 517us/sample - loss: 0.0096 - acc: 1.0000\n", + "Epoch 385/1000\n", + "26/26 [==============================] - 0s 605us/sample - loss: 0.0095 - acc: 1.0000\n", + "Epoch 386/1000\n", + "26/26 [==============================] - 0s 394us/sample - loss: 0.0094 - acc: 1.0000\n", + "Epoch 387/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 0.0093 - acc: 1.0000\n", + "Epoch 388/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 0.0093 - acc: 1.0000\n", + "Epoch 389/1000\n", + "26/26 [==============================] - 0s 396us/sample - loss: 0.0092 - acc: 1.0000\n", + "Epoch 390/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 0.0091 - acc: 1.0000\n", + "Epoch 391/1000\n", + "26/26 [==============================] - 0s 396us/sample - loss: 0.0091 - acc: 1.0000\n", + "Epoch 392/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 0.0090 - acc: 1.0000\n", + "Epoch 393/1000\n", + "26/26 [==============================] - 0s 581us/sample - loss: 0.0090 - acc: 1.0000\n", + "Epoch 394/1000\n", + "26/26 [==============================] - 0s 433us/sample - loss: 0.0089 - acc: 1.0000\n", + "Epoch 395/1000\n", + "26/26 [==============================] - 0s 443us/sample - loss: 0.0088 - acc: 1.0000\n", + "Epoch 396/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 0.0088 - acc: 1.0000\n", + "Epoch 397/1000\n", + "26/26 [==============================] - 0s 423us/sample - loss: 0.0087 - acc: 1.0000\n", + "Epoch 398/1000\n", + "26/26 [==============================] - 0s 348us/sample - loss: 0.0087 - acc: 1.0000\n", + "Epoch 399/1000\n", + "26/26 [==============================] - 0s 322us/sample - loss: 0.0086 - acc: 1.0000\n", + "Epoch 400/1000\n", + "26/26 [==============================] - 0s 417us/sample - loss: 0.0085 - acc: 1.0000\n", + "Epoch 401/1000\n", + "26/26 [==============================] - 0s 403us/sample - loss: 0.0085 - acc: 1.0000\n", + "Epoch 402/1000\n", + "26/26 [==============================] - 0s 404us/sample - loss: 0.0084 - acc: 1.0000\n", + "Epoch 403/1000\n", + "26/26 [==============================] - 0s 394us/sample - loss: 0.0084 - acc: 1.0000\n", + "Epoch 404/1000\n", + "26/26 [==============================] - 0s 319us/sample - loss: 0.0083 - acc: 1.0000\n", + "Epoch 405/1000\n", + "26/26 [==============================] - 0s 489us/sample - loss: 0.0082 - acc: 1.0000\n", + "Epoch 406/1000\n", + "26/26 [==============================] - 0s 548us/sample - loss: 0.0082 - acc: 1.0000\n", + "Epoch 407/1000\n", + "26/26 [==============================] - 0s 535us/sample - loss: 0.0081 - acc: 1.0000\n", + "Epoch 408/1000\n", + "26/26 [==============================] - 0s 491us/sample - loss: 0.0081 - acc: 1.0000\n", + "Epoch 409/1000\n", + "26/26 [==============================] - 0s 425us/sample - loss: 0.0080 - acc: 1.0000\n", + "Epoch 410/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 0.0080 - acc: 1.0000\n", + "Epoch 411/1000\n", + "26/26 [==============================] - 0s 464us/sample - loss: 0.0079 - acc: 1.0000\n", + "Epoch 412/1000\n", + "26/26 [==============================] - 0s 557us/sample - loss: 0.0079 - acc: 1.0000\n", + "Epoch 413/1000\n", + "26/26 [==============================] - 0s 597us/sample - loss: 0.0078 - acc: 1.0000\n", + "Epoch 414/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 0.0078 - acc: 1.0000\n", + "Epoch 415/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 0.0077 - acc: 1.0000\n", + "Epoch 416/1000\n", + "26/26 [==============================] - 0s 350us/sample - loss: 0.0077 - acc: 1.0000\n", + "Epoch 417/1000\n", + "26/26 [==============================] - 0s 353us/sample - loss: 0.0076 - acc: 1.0000\n", + "Epoch 418/1000\n", + "26/26 [==============================] - 0s 340us/sample - loss: 0.0076 - acc: 1.0000\n", + "Epoch 419/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 0.0075 - acc: 1.0000\n", + "Epoch 420/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 0.0075 - acc: 1.0000\n", + "Epoch 421/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 0.0074 - acc: 1.0000\n", + "Epoch 422/1000\n", + "26/26 [==============================] - 0s 448us/sample - loss: 0.0074 - acc: 1.0000\n", + "Epoch 423/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 0.0073 - acc: 1.0000\n", + "Epoch 424/1000\n", + "26/26 [==============================] - 0s 450us/sample - loss: 0.0073 - acc: 1.0000\n", + "Epoch 425/1000\n", + "26/26 [==============================] - 0s 589us/sample - loss: 0.0073 - acc: 1.0000\n", + "Epoch 426/1000\n", + "26/26 [==============================] - 0s 702us/sample - loss: 0.0072 - acc: 1.0000\n", + "Epoch 427/1000\n", + "26/26 [==============================] - 0s 765us/sample - loss: 0.0072 - acc: 1.0000\n", + "Epoch 428/1000\n", + "26/26 [==============================] - 0s 722us/sample - loss: 0.0071 - acc: 1.0000\n", + "Epoch 429/1000\n", + "26/26 [==============================] - 0s 429us/sample - loss: 0.0071 - acc: 1.0000\n", + "Epoch 430/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 0.0070 - acc: 1.0000\n", + "Epoch 431/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 0.0070 - acc: 1.0000\n", + "Epoch 432/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 0.0070 - acc: 1.0000\n", + "Epoch 433/1000\n", + "26/26 [==============================] - 0s 499us/sample - loss: 0.0069 - acc: 1.0000\n", + "Epoch 434/1000\n", + "26/26 [==============================] - 0s 594us/sample - loss: 0.0069 - acc: 1.0000\n", + "Epoch 435/1000\n", + "26/26 [==============================] - 0s 470us/sample - loss: 0.0068 - acc: 1.0000\n", + "Epoch 436/1000\n", + "26/26 [==============================] - 0s 571us/sample - loss: 0.0068 - acc: 1.0000\n", + "Epoch 437/1000\n", + "26/26 [==============================] - 0s 491us/sample - loss: 0.0068 - acc: 1.0000\n", + "Epoch 438/1000\n", + "26/26 [==============================] - 0s 383us/sample - loss: 0.0067 - acc: 1.0000\n", + "Epoch 439/1000\n", + "26/26 [==============================] - 0s 550us/sample - loss: 0.0067 - acc: 1.0000\n", + "Epoch 440/1000\n", + "26/26 [==============================] - 0s 439us/sample - loss: 0.0066 - acc: 1.0000\n", + "Epoch 441/1000\n", + "26/26 [==============================] - 0s 356us/sample - loss: 0.0066 - acc: 1.0000\n", + "Epoch 442/1000\n", + "26/26 [==============================] - 0s 383us/sample - loss: 0.0066 - acc: 1.0000\n", + "Epoch 443/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 0.0065 - acc: 1.0000\n", + "Epoch 444/1000\n", + "26/26 [==============================] - 0s 429us/sample - loss: 0.0065 - acc: 1.0000\n", + "Epoch 445/1000\n", + "26/26 [==============================] - 0s 409us/sample - loss: 0.0064 - acc: 1.0000\n", + "Epoch 446/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 0.0064 - acc: 1.0000\n", + "Epoch 447/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 0.0064 - acc: 1.0000\n", + "Epoch 448/1000\n", + "26/26 [==============================] - 0s 334us/sample - loss: 0.0063 - acc: 1.0000\n", + "Epoch 449/1000\n", + "26/26 [==============================] - 0s 326us/sample - loss: 0.0063 - acc: 1.0000\n", + "Epoch 450/1000\n", + "26/26 [==============================] - 0s 328us/sample - loss: 0.0062 - acc: 1.0000\n", + "Epoch 451/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 0.0062 - acc: 1.0000\n", + "Epoch 452/1000\n", + "26/26 [==============================] - 0s 350us/sample - loss: 0.0062 - acc: 1.0000\n", + "Epoch 453/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 0.0061 - acc: 1.0000\n", + "Epoch 454/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 0.0061 - acc: 1.0000\n", + "Epoch 455/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 0.0061 - acc: 1.0000\n", + "Epoch 456/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 0.0060 - acc: 1.0000\n", + "Epoch 457/1000\n", + "26/26 [==============================] - 0s 434us/sample - loss: 0.0060 - acc: 1.0000\n", + "Epoch 458/1000\n", + "26/26 [==============================] - 0s 463us/sample - loss: 0.0060 - acc: 1.0000\n", + "Epoch 459/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 0.0059 - acc: 1.0000\n", + "Epoch 460/1000\n", + "26/26 [==============================] - 0s 340us/sample - loss: 0.0059 - acc: 1.0000\n", + "Epoch 461/1000\n", + "26/26 [==============================] - 0s 337us/sample - loss: 0.0058 - acc: 1.0000\n", + "Epoch 462/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 0.0058 - acc: 1.0000\n", + "Epoch 463/1000\n", + "26/26 [==============================] - 0s 360us/sample - loss: 0.0058 - acc: 1.0000\n", + "Epoch 464/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 0.0057 - acc: 1.0000\n", + "Epoch 465/1000\n", + "26/26 [==============================] - 0s 378us/sample - loss: 0.0057 - acc: 1.0000\n", + "Epoch 466/1000\n", + "26/26 [==============================] - 0s 350us/sample - loss: 0.0057 - acc: 1.0000\n", + "Epoch 467/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 0.0056 - acc: 1.0000\n", + "Epoch 468/1000\n", + "26/26 [==============================] - 0s 386us/sample - loss: 0.0056 - acc: 1.0000\n", + "Epoch 469/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 0.0056 - acc: 1.0000\n", + "Epoch 470/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 0.0055 - acc: 1.0000\n", + "Epoch 471/1000\n", + "26/26 [==============================] - 0s 402us/sample - loss: 0.0055 - acc: 1.0000\n", + "Epoch 472/1000\n", + "26/26 [==============================] - 0s 396us/sample - loss: 0.0055 - acc: 1.0000\n", + "Epoch 473/1000\n", + "26/26 [==============================] - 0s 438us/sample - loss: 0.0054 - acc: 1.0000\n", + "Epoch 474/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 0.0054 - acc: 1.0000\n", + "Epoch 475/1000\n", + "26/26 [==============================] - 0s 385us/sample - loss: 0.0054 - acc: 1.0000\n", + "Epoch 476/1000\n", + "26/26 [==============================] - 0s 563us/sample - loss: 0.0053 - acc: 1.0000\n", + "Epoch 477/1000\n", + "26/26 [==============================] - 0s 461us/sample - loss: 0.0053 - acc: 1.0000\n", + "Epoch 478/1000\n", + "26/26 [==============================] - 0s 476us/sample - loss: 0.0053 - acc: 1.0000\n", + "Epoch 479/1000\n", + "26/26 [==============================] - 0s 454us/sample - loss: 0.0052 - acc: 1.0000\n", + "Epoch 480/1000\n", + "26/26 [==============================] - 0s 372us/sample - loss: 0.0052 - acc: 1.0000\n", + "Epoch 481/1000\n", + "26/26 [==============================] - 0s 472us/sample - loss: 0.0052 - acc: 1.0000\n", + "Epoch 482/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 0.0052 - acc: 1.0000\n", + "Epoch 483/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 0.0051 - acc: 1.0000\n", + "Epoch 484/1000\n", + "26/26 [==============================] - 0s 395us/sample - loss: 0.0051 - acc: 1.0000\n", + "Epoch 485/1000\n", + "26/26 [==============================] - 0s 456us/sample - loss: 0.0051 - acc: 1.0000\n", + "Epoch 486/1000\n", + "26/26 [==============================] - 0s 476us/sample - loss: 0.0050 - acc: 1.0000\n", + "Epoch 487/1000\n", + "26/26 [==============================] - 0s 419us/sample - loss: 0.0050 - acc: 1.0000\n", + "Epoch 488/1000\n", + "26/26 [==============================] - 0s 420us/sample - loss: 0.0050 - acc: 1.0000\n", + "Epoch 489/1000\n", + "26/26 [==============================] - 0s 435us/sample - loss: 0.0049 - acc: 1.0000\n", + "Epoch 490/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 0.0049 - acc: 1.0000\n", + "Epoch 491/1000\n", + "26/26 [==============================] - 0s 394us/sample - loss: 0.0049 - acc: 1.0000\n", + "Epoch 492/1000\n", + "26/26 [==============================] - 0s 418us/sample - loss: 0.0049 - acc: 1.0000\n", + "Epoch 493/1000\n", + "26/26 [==============================] - 0s 418us/sample - loss: 0.0048 - acc: 1.0000\n", + "Epoch 494/1000\n", + "26/26 [==============================] - 0s 405us/sample - loss: 0.0048 - acc: 1.0000\n", + "Epoch 495/1000\n", + "26/26 [==============================] - 0s 348us/sample - loss: 0.0048 - acc: 1.0000\n", + "Epoch 496/1000\n", + "26/26 [==============================] - 0s 335us/sample - loss: 0.0048 - acc: 1.0000\n", + "Epoch 497/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 0.0047 - acc: 1.0000\n", + "Epoch 498/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 0.0047 - acc: 1.0000\n", + "Epoch 499/1000\n", + "26/26 [==============================] - 0s 350us/sample - loss: 0.0047 - acc: 1.0000\n", + "Epoch 500/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 0.0046 - acc: 1.0000\n", + "Epoch 501/1000\n", + "26/26 [==============================] - 0s 376us/sample - loss: 0.0046 - acc: 1.0000\n", + "Epoch 502/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 0.0046 - acc: 1.0000\n", + "Epoch 503/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 0.0046 - acc: 1.0000\n", + "Epoch 504/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 0.0045 - acc: 1.0000\n", + "Epoch 505/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 0.0045 - acc: 1.0000\n", + "Epoch 506/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 0.0045 - acc: 1.0000\n", + "Epoch 507/1000\n", + "26/26 [==============================] - 0s 326us/sample - loss: 0.0045 - acc: 1.0000\n", + "Epoch 508/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 0.0044 - acc: 1.0000\n", + "Epoch 509/1000\n", + "26/26 [==============================] - 0s 340us/sample - loss: 0.0044 - acc: 1.0000\n", + "Epoch 510/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 0.0044 - acc: 1.0000\n", + "Epoch 511/1000\n", + "26/26 [==============================] - 0s 421us/sample - loss: 0.0044 - acc: 1.0000\n", + "Epoch 512/1000\n", + "26/26 [==============================] - 0s 450us/sample - loss: 0.0043 - acc: 1.0000\n", + "Epoch 513/1000\n", + "26/26 [==============================] - 0s 487us/sample - loss: 0.0043 - acc: 1.0000\n", + "Epoch 514/1000\n", + "26/26 [==============================] - 0s 482us/sample - loss: 0.0043 - acc: 1.0000\n", + "Epoch 515/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 0.0043 - acc: 1.0000\n", + "Epoch 516/1000\n", + "26/26 [==============================] - 0s 604us/sample - loss: 0.0043 - acc: 1.0000\n", + "Epoch 517/1000\n", + "26/26 [==============================] - 0s 441us/sample - loss: 0.0042 - acc: 1.0000\n", + "Epoch 518/1000\n", + "26/26 [==============================] - 0s 573us/sample - loss: 0.0042 - acc: 1.0000\n", + "Epoch 519/1000\n", + "26/26 [==============================] - 0s 482us/sample - loss: 0.0042 - acc: 1.0000\n", + "Epoch 520/1000\n", + "26/26 [==============================] - 0s 376us/sample - loss: 0.0042 - acc: 1.0000\n", + "Epoch 521/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 0.0041 - acc: 1.0000\n", + "Epoch 522/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 0.0041 - acc: 1.0000\n", + "Epoch 523/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 0.0041 - acc: 1.0000\n", + "Epoch 524/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 0.0041 - acc: 1.0000\n", + "Epoch 525/1000\n", + "26/26 [==============================] - 0s 360us/sample - loss: 0.0041 - acc: 1.0000\n", + "Epoch 526/1000\n", + "26/26 [==============================] - 0s 343us/sample - loss: 0.0040 - acc: 1.0000\n", + "Epoch 527/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 0.0040 - acc: 1.0000\n", + "Epoch 528/1000\n", + "26/26 [==============================] - 0s 380us/sample - loss: 0.0040 - acc: 1.0000\n", + "Epoch 529/1000\n", + "26/26 [==============================] - 0s 690us/sample - loss: 0.0040 - acc: 1.0000\n", + "Epoch 530/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 0.0040 - acc: 1.0000\n", + "Epoch 531/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 0.0039 - acc: 1.0000\n", + "Epoch 532/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 0.0039 - acc: 1.0000\n", + "Epoch 533/1000\n", + "26/26 [==============================] - 0s 348us/sample - loss: 0.0039 - acc: 1.0000\n", + "Epoch 534/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 0.0039 - acc: 1.0000\n", + "Epoch 535/1000\n", + "26/26 [==============================] - 0s 482us/sample - loss: 0.0039 - acc: 1.0000\n", + "Epoch 536/1000\n", + "26/26 [==============================] - 0s 477us/sample - loss: 0.0038 - acc: 1.0000\n", + "Epoch 537/1000\n", + "26/26 [==============================] - 0s 519us/sample - loss: 0.0038 - acc: 1.0000\n", + "Epoch 538/1000\n", + "26/26 [==============================] - 0s 429us/sample - loss: 0.0038 - acc: 1.0000\n", + "Epoch 539/1000\n", + "26/26 [==============================] - 0s 377us/sample - loss: 0.0038 - acc: 1.0000\n", + "Epoch 540/1000\n", + "26/26 [==============================] - 0s 452us/sample - loss: 0.0038 - acc: 1.0000\n", + "Epoch 541/1000\n", + "26/26 [==============================] - 0s 391us/sample - loss: 0.0037 - acc: 1.0000\n", + "Epoch 542/1000\n", + "26/26 [==============================] - 0s 502us/sample - loss: 0.0037 - acc: 1.0000\n", + "Epoch 543/1000\n", + "26/26 [==============================] - 0s 508us/sample - loss: 0.0037 - acc: 1.0000\n", + "Epoch 544/1000\n", + "26/26 [==============================] - 0s 573us/sample - loss: 0.0037 - acc: 1.0000\n", + "Epoch 545/1000\n", + "26/26 [==============================] - 0s 477us/sample - loss: 0.0037 - acc: 1.0000\n", + "Epoch 546/1000\n", + "26/26 [==============================] - 0s 436us/sample - loss: 0.0036 - acc: 1.0000\n", + "Epoch 547/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 0.0036 - acc: 1.0000\n", + "Epoch 548/1000\n", + "26/26 [==============================] - 0s 396us/sample - loss: 0.0036 - acc: 1.0000\n", + "Epoch 549/1000\n", + "26/26 [==============================] - 0s 425us/sample - loss: 0.0036 - acc: 1.0000\n", + "Epoch 550/1000\n", + "26/26 [==============================] - 0s 457us/sample - loss: 0.0036 - acc: 1.0000\n", + "Epoch 551/1000\n", + "26/26 [==============================] - 0s 402us/sample - loss: 0.0036 - acc: 1.0000\n", + "Epoch 552/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 0.0035 - acc: 1.0000\n", + "Epoch 553/1000\n", + "26/26 [==============================] - 0s 410us/sample - loss: 0.0035 - acc: 1.0000\n", + "Epoch 554/1000\n", + "26/26 [==============================] - 0s 398us/sample - loss: 0.0035 - acc: 1.0000\n", + "Epoch 555/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 0.0035 - acc: 1.0000\n", + "Epoch 556/1000\n", + "26/26 [==============================] - 0s 348us/sample - loss: 0.0035 - acc: 1.0000\n", + "Epoch 557/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 0.0034 - acc: 1.0000\n", + "Epoch 558/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 0.0034 - acc: 1.0000\n", + "Epoch 559/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 0.0034 - acc: 1.0000\n", + "Epoch 560/1000\n", + "26/26 [==============================] - 0s 396us/sample - loss: 0.0034 - acc: 1.0000\n", + "Epoch 561/1000\n", + "26/26 [==============================] - 0s 436us/sample - loss: 0.0034 - acc: 1.0000\n", + "Epoch 562/1000\n", + "26/26 [==============================] - 0s 536us/sample - loss: 0.0034 - acc: 1.0000\n", + "Epoch 563/1000\n", + "26/26 [==============================] - 0s 442us/sample - loss: 0.0033 - acc: 1.0000\n", + "Epoch 564/1000\n", + "26/26 [==============================] - 0s 448us/sample - loss: 0.0033 - acc: 1.0000\n", + "Epoch 565/1000\n", + "26/26 [==============================] - 0s 428us/sample - loss: 0.0033 - acc: 1.0000\n", + "Epoch 566/1000\n", + "26/26 [==============================] - 0s 399us/sample - loss: 0.0033 - acc: 1.0000\n", + "Epoch 567/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 0.0033 - acc: 1.0000\n", + "Epoch 568/1000\n", + "26/26 [==============================] - 0s 389us/sample - loss: 0.0033 - acc: 1.0000\n", + "Epoch 569/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 0.0032 - acc: 1.0000\n", + "Epoch 570/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 0.0032 - acc: 1.0000\n", + "Epoch 571/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 0.0032 - acc: 1.0000\n", + "Epoch 572/1000\n", + "26/26 [==============================] - 0s 462us/sample - loss: 0.0032 - acc: 1.0000\n", + "Epoch 573/1000\n", + "26/26 [==============================] - 0s 506us/sample - loss: 0.0032 - acc: 1.0000\n", + "Epoch 574/1000\n", + "26/26 [==============================] - 0s 487us/sample - loss: 0.0032 - acc: 1.0000\n", + "Epoch 575/1000\n", + "26/26 [==============================] - 0s 444us/sample - loss: 0.0032 - acc: 1.0000\n", + "Epoch 576/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 0.0031 - acc: 1.0000\n", + "Epoch 577/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 0.0031 - acc: 1.0000\n", + "Epoch 578/1000\n", + "26/26 [==============================] - 0s 370us/sample - loss: 0.0031 - acc: 1.0000\n", + "Epoch 579/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 0.0031 - acc: 1.0000\n", + "Epoch 580/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 0.0031 - acc: 1.0000\n", + "Epoch 581/1000\n", + "26/26 [==============================] - 0s 384us/sample - loss: 0.0031 - acc: 1.0000\n", + "Epoch 582/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 0.0030 - acc: 1.0000\n", + "Epoch 583/1000\n", + "26/26 [==============================] - 0s 355us/sample - loss: 0.0030 - acc: 1.0000\n", + "Epoch 584/1000\n", + "26/26 [==============================] - 0s 424us/sample - loss: 0.0030 - acc: 1.0000\n", + "Epoch 585/1000\n", + "26/26 [==============================] - 0s 436us/sample - loss: 0.0030 - acc: 1.0000\n", + "Epoch 586/1000\n", + "26/26 [==============================] - 0s 660us/sample - loss: 0.0030 - acc: 1.0000\n", + "Epoch 587/1000\n", + "26/26 [==============================] - 0s 561us/sample - loss: 0.0030 - acc: 1.0000\n", + "Epoch 588/1000\n", + "26/26 [==============================] - 0s 600us/sample - loss: 0.0030 - acc: 1.0000\n", + "Epoch 589/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 0.0029 - acc: 1.0000\n", + "Epoch 590/1000\n", + "26/26 [==============================] - 0s 460us/sample - loss: 0.0029 - acc: 1.0000\n", + "Epoch 591/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 0.0029 - acc: 1.0000\n", + "Epoch 592/1000\n", + "26/26 [==============================] - 0s 474us/sample - loss: 0.0029 - acc: 1.0000\n", + "Epoch 593/1000\n", + "26/26 [==============================] - 0s 485us/sample - loss: 0.0029 - acc: 1.0000\n", + "Epoch 594/1000\n", + "26/26 [==============================] - 0s 594us/sample - loss: 0.0029 - acc: 1.0000\n", + "Epoch 595/1000\n", + "26/26 [==============================] - 0s 633us/sample - loss: 0.0029 - acc: 1.0000\n", + "Epoch 596/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 0.0029 - acc: 1.0000\n", + "Epoch 597/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 0.0028 - acc: 1.0000\n", + "Epoch 598/1000\n", + "26/26 [==============================] - 0s 372us/sample - loss: 0.0028 - acc: 1.0000\n", + "Epoch 599/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 0.0028 - acc: 1.0000\n", + "Epoch 600/1000\n", + "26/26 [==============================] - 0s 386us/sample - loss: 0.0028 - acc: 1.0000\n", + "Epoch 601/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 0.0028 - acc: 1.0000\n", + "Epoch 602/1000\n", + "26/26 [==============================] - 0s 391us/sample - loss: 0.0028 - acc: 1.0000\n", + "Epoch 603/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 0.0028 - acc: 1.0000\n", + "Epoch 604/1000\n", + "26/26 [==============================] - 0s 408us/sample - loss: 0.0028 - acc: 1.0000\n", + "Epoch 605/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 0.0027 - acc: 1.0000\n", + "Epoch 606/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 0.0027 - acc: 1.0000\n", + "Epoch 607/1000\n", + "26/26 [==============================] - 0s 447us/sample - loss: 0.0027 - acc: 1.0000\n", + "Epoch 608/1000\n", + "26/26 [==============================] - 0s 410us/sample - loss: 0.0027 - acc: 1.0000\n", + "Epoch 609/1000\n", + "26/26 [==============================] - 0s 398us/sample - loss: 0.0027 - acc: 1.0000\n", + "Epoch 610/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 0.0027 - acc: 1.0000\n", + "Epoch 611/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 0.0027 - acc: 1.0000\n", + "Epoch 612/1000\n", + "26/26 [==============================] - 0s 402us/sample - loss: 0.0027 - acc: 1.0000\n", + "Epoch 613/1000\n", + "26/26 [==============================] - 0s 349us/sample - loss: 0.0026 - acc: 1.0000\n", + "Epoch 614/1000\n", + "26/26 [==============================] - 0s 330us/sample - loss: 0.0026 - acc: 1.0000\n", + "Epoch 615/1000\n", + "26/26 [==============================] - 0s 340us/sample - loss: 0.0026 - acc: 1.0000\n", + "Epoch 616/1000\n", + "26/26 [==============================] - 0s 368us/sample - loss: 0.0026 - acc: 1.0000\n", + "Epoch 617/1000\n", + "26/26 [==============================] - 0s 404us/sample - loss: 0.0026 - acc: 1.0000\n", + "Epoch 618/1000\n", + "26/26 [==============================] - 0s 323us/sample - loss: 0.0026 - acc: 1.0000\n", + "Epoch 619/1000\n", + "26/26 [==============================] - 0s 396us/sample - loss: 0.0026 - acc: 1.0000\n", + "Epoch 620/1000\n", + "26/26 [==============================] - 0s 407us/sample - loss: 0.0026 - acc: 1.0000\n", + "Epoch 621/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 0.0026 - acc: 1.0000\n", + "Epoch 622/1000\n", + "26/26 [==============================] - 0s 420us/sample - loss: 0.0025 - acc: 1.0000\n", + "Epoch 623/1000\n", + "26/26 [==============================] - 0s 401us/sample - loss: 0.0025 - acc: 1.0000\n", + "Epoch 624/1000\n", + "26/26 [==============================] - 0s 471us/sample - loss: 0.0025 - acc: 1.0000\n", + "Epoch 625/1000\n", + "26/26 [==============================] - 0s 426us/sample - loss: 0.0025 - acc: 1.0000\n", + "Epoch 626/1000\n", + "26/26 [==============================] - 0s 498us/sample - loss: 0.0025 - acc: 1.0000\n", + "Epoch 627/1000\n", + "26/26 [==============================] - 0s 490us/sample - loss: 0.0025 - acc: 1.0000\n", + "Epoch 628/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 0.0025 - acc: 1.0000\n", + "Epoch 629/1000\n", + "26/26 [==============================] - 0s 540us/sample - loss: 0.0025 - acc: 1.0000\n", + "Epoch 630/1000\n", + "26/26 [==============================] - 0s 548us/sample - loss: 0.0025 - acc: 1.0000\n", + "Epoch 631/1000\n", + "26/26 [==============================] - 0s 444us/sample - loss: 0.0024 - acc: 1.0000\n", + "Epoch 632/1000\n", + "26/26 [==============================] - 0s 435us/sample - loss: 0.0024 - acc: 1.0000\n", + "Epoch 633/1000\n", + "26/26 [==============================] - 0s 410us/sample - loss: 0.0024 - acc: 1.0000\n", + "Epoch 634/1000\n", + "26/26 [==============================] - 0s 803us/sample - loss: 0.0024 - acc: 1.0000\n", + "Epoch 635/1000\n", + "26/26 [==============================] - 0s 785us/sample - loss: 0.0024 - acc: 1.0000\n", + "Epoch 636/1000\n", + "26/26 [==============================] - 0s 756us/sample - loss: 0.0024 - acc: 1.0000\n", + "Epoch 637/1000\n", + "26/26 [==============================] - 0s 421us/sample - loss: 0.0024 - acc: 1.0000\n", + "Epoch 638/1000\n", + "26/26 [==============================] - 0s 521us/sample - loss: 0.0024 - acc: 1.0000\n", + "Epoch 639/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 0.0023 - acc: 1.0000\n", + "Epoch 640/1000\n", + "26/26 [==============================] - 0s 505us/sample - loss: 0.0023 - acc: 1.0000\n", + "Epoch 641/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 0.0023 - acc: 1.0000\n", + "Epoch 642/1000\n", + "26/26 [==============================] - 0s 458us/sample - loss: 0.0023 - acc: 1.0000\n", + "Epoch 643/1000\n", + "26/26 [==============================] - 0s 515us/sample - loss: 0.0023 - acc: 1.0000\n", + "Epoch 644/1000\n", + "26/26 [==============================] - 0s 538us/sample - loss: 0.0023 - acc: 1.0000\n", + "Epoch 645/1000\n", + "26/26 [==============================] - 0s 416us/sample - loss: 0.0023 - acc: 1.0000\n", + "Epoch 646/1000\n", + "26/26 [==============================] - 0s 356us/sample - loss: 0.0023 - acc: 1.0000\n", + "Epoch 647/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 0.0023 - acc: 1.0000\n", + "Epoch 648/1000\n", + "26/26 [==============================] - 0s 387us/sample - loss: 0.0023 - acc: 1.0000\n", + "Epoch 649/1000\n", + "26/26 [==============================] - 0s 394us/sample - loss: 0.0022 - acc: 1.0000\n", + "Epoch 650/1000\n", + "26/26 [==============================] - 0s 433us/sample - loss: 0.0022 - acc: 1.0000\n", + "Epoch 651/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 0.0022 - acc: 1.0000\n", + "Epoch 652/1000\n", + "26/26 [==============================] - 0s 441us/sample - loss: 0.0022 - acc: 1.0000\n", + "Epoch 653/1000\n", + "26/26 [==============================] - 0s 455us/sample - loss: 0.0022 - acc: 1.0000\n", + "Epoch 654/1000\n", + "26/26 [==============================] - 0s 466us/sample - loss: 0.0022 - acc: 1.0000\n", + "Epoch 655/1000\n", + "26/26 [==============================] - 0s 440us/sample - loss: 0.0022 - acc: 1.0000\n", + "Epoch 656/1000\n", + "26/26 [==============================] - 0s 479us/sample - loss: 0.0022 - acc: 1.0000\n", + "Epoch 657/1000\n", + "26/26 [==============================] - 0s 604us/sample - loss: 0.0022 - acc: 1.0000\n", + "Epoch 658/1000\n", + "26/26 [==============================] - 0s 479us/sample - loss: 0.0022 - acc: 1.0000\n", + "Epoch 659/1000\n", + "26/26 [==============================] - 0s 531us/sample - loss: 0.0021 - acc: 1.0000\n", + "Epoch 660/1000\n", + "26/26 [==============================] - 0s 426us/sample - loss: 0.0021 - acc: 1.0000\n", + "Epoch 661/1000\n", + "26/26 [==============================] - 0s 457us/sample - loss: 0.0021 - acc: 1.0000\n", + "Epoch 662/1000\n", + "26/26 [==============================] - 0s 404us/sample - loss: 0.0021 - acc: 1.0000\n", + "Epoch 663/1000\n", + "26/26 [==============================] - 0s 449us/sample - loss: 0.0021 - acc: 1.0000\n", + "Epoch 664/1000\n", + "26/26 [==============================] - 0s 391us/sample - loss: 0.0021 - acc: 1.0000\n", + "Epoch 665/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 0.0021 - acc: 1.0000\n", + "Epoch 666/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 0.0021 - acc: 1.0000\n", + "Epoch 667/1000\n", + "26/26 [==============================] - 0s 391us/sample - loss: 0.0021 - acc: 1.0000\n", + "Epoch 668/1000\n", + "26/26 [==============================] - 0s 505us/sample - loss: 0.0021 - acc: 1.0000\n", + "Epoch 669/1000\n", + "26/26 [==============================] - 0s 376us/sample - loss: 0.0021 - acc: 1.0000\n", + "Epoch 670/1000\n", + "26/26 [==============================] - 0s 419us/sample - loss: 0.0020 - acc: 1.0000\n", + "Epoch 671/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 0.0020 - acc: 1.0000\n", + "Epoch 672/1000\n", + "26/26 [==============================] - 0s 420us/sample - loss: 0.0020 - acc: 1.0000\n", + "Epoch 673/1000\n", + "26/26 [==============================] - 0s 399us/sample - loss: 0.0020 - acc: 1.0000\n", + "Epoch 674/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 0.0020 - acc: 1.0000\n", + "Epoch 675/1000\n", + "26/26 [==============================] - 0s 349us/sample - loss: 0.0020 - acc: 1.0000\n", + "Epoch 676/1000\n", + "26/26 [==============================] - 0s 603us/sample - loss: 0.0020 - acc: 1.0000\n", + "Epoch 677/1000\n", + "26/26 [==============================] - 0s 509us/sample - loss: 0.0020 - acc: 1.0000\n", + "Epoch 678/1000\n", + "26/26 [==============================] - 0s 386us/sample - loss: 0.0020 - acc: 1.0000\n", + "Epoch 679/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 0.0020 - acc: 1.0000\n", + "Epoch 680/1000\n", + "26/26 [==============================] - 0s 427us/sample - loss: 0.0020 - acc: 1.0000\n", + "Epoch 681/1000\n", + "26/26 [==============================] - 0s 467us/sample - loss: 0.0020 - acc: 1.0000\n", + "Epoch 682/1000\n", + "26/26 [==============================] - 0s 412us/sample - loss: 0.0019 - acc: 1.0000\n", + "Epoch 683/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 0.0019 - acc: 1.0000\n", + "Epoch 684/1000\n", + "26/26 [==============================] - 0s 368us/sample - loss: 0.0019 - acc: 1.0000\n", + "Epoch 685/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 0.0019 - acc: 1.0000\n", + "Epoch 686/1000\n", + "26/26 [==============================] - 0s 483us/sample - loss: 0.0019 - acc: 1.0000\n", + "Epoch 687/1000\n", + "26/26 [==============================] - 0s 433us/sample - loss: 0.0019 - acc: 1.0000\n", + "Epoch 688/1000\n", + "26/26 [==============================] - 0s 438us/sample - loss: 0.0019 - acc: 1.0000\n", + "Epoch 689/1000\n", + "26/26 [==============================] - 0s 464us/sample - loss: 0.0019 - acc: 1.0000\n", + "Epoch 690/1000\n", + "26/26 [==============================] - 0s 630us/sample - loss: 0.0019 - acc: 1.0000\n", + "Epoch 691/1000\n", + "26/26 [==============================] - 0s 573us/sample - loss: 0.0019 - acc: 1.0000\n", + "Epoch 692/1000\n", + "26/26 [==============================] - 0s 546us/sample - loss: 0.0019 - acc: 1.0000\n", + "Epoch 693/1000\n", + "26/26 [==============================] - 0s 505us/sample - loss: 0.0019 - acc: 1.0000\n", + "Epoch 694/1000\n", + "26/26 [==============================] - 0s 378us/sample - loss: 0.0018 - acc: 1.0000\n", + "Epoch 695/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 0.0018 - acc: 1.0000\n", + "Epoch 696/1000\n", + "26/26 [==============================] - 0s 349us/sample - loss: 0.0018 - acc: 1.0000\n", + "Epoch 697/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 0.0018 - acc: 1.0000\n", + "Epoch 698/1000\n", + "26/26 [==============================] - 0s 349us/sample - loss: 0.0018 - acc: 1.0000\n", + "Epoch 699/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 0.0018 - acc: 1.0000\n", + "Epoch 700/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 0.0018 - acc: 1.0000\n", + "Epoch 701/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 0.0018 - acc: 1.0000\n", + "Epoch 702/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 0.0018 - acc: 1.0000\n", + "Epoch 703/1000\n", + "26/26 [==============================] - 0s 522us/sample - loss: 0.0018 - acc: 1.0000\n", + "Epoch 704/1000\n", + "26/26 [==============================] - 0s 576us/sample - loss: 0.0018 - acc: 1.0000\n", + "Epoch 705/1000\n", + "26/26 [==============================] - 0s 383us/sample - loss: 0.0018 - acc: 1.0000\n", + "Epoch 706/1000\n", + "26/26 [==============================] - 0s 355us/sample - loss: 0.0018 - acc: 1.0000\n", + "Epoch 707/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 708/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 709/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 710/1000\n", + "26/26 [==============================] - 0s 410us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 711/1000\n", + "26/26 [==============================] - 0s 583us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 712/1000\n", + "26/26 [==============================] - 0s 562us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 713/1000\n", + "26/26 [==============================] - 0s 586us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 714/1000\n", + "26/26 [==============================] - 0s 665us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 715/1000\n", + "26/26 [==============================] - 0s 827us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 716/1000\n", + "26/26 [==============================] - 0s 499us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 717/1000\n", + "26/26 [==============================] - 0s 478us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 718/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 719/1000\n", + "26/26 [==============================] - 0s 377us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 720/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 721/1000\n", + "26/26 [==============================] - 0s 281us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 722/1000\n", + "26/26 [==============================] - 0s 531us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 723/1000\n", + "26/26 [==============================] - 0s 461us/sample - loss: 0.0016 - acc: 1.0000\n", + "Epoch 724/1000\n", + "26/26 [==============================] - 0s 467us/sample - loss: 0.0016 - acc: 1.0000\n", + "Epoch 725/1000\n", + "26/26 [==============================] - 0s 481us/sample - loss: 0.0016 - acc: 1.0000\n", + "Epoch 726/1000\n", + "26/26 [==============================] - 0s 421us/sample - loss: 0.0016 - acc: 1.0000\n", + "Epoch 727/1000\n", + "26/26 [==============================] - 0s 480us/sample - loss: 0.0016 - acc: 1.0000\n", + "Epoch 728/1000\n", + "26/26 [==============================] - 0s 456us/sample - loss: 0.0016 - acc: 1.0000\n", + "Epoch 729/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 0.0016 - acc: 1.0000\n", + "Epoch 730/1000\n", + "26/26 [==============================] - 0s 478us/sample - loss: 0.0016 - acc: 1.0000\n", + "Epoch 731/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 0.0016 - acc: 1.0000\n", + "Epoch 732/1000\n", + "26/26 [==============================] - 0s 459us/sample - loss: 0.0016 - acc: 1.0000\n", + "Epoch 733/1000\n", + "26/26 [==============================] - 0s 550us/sample - loss: 0.0016 - acc: 1.0000\n", + "Epoch 734/1000\n", + "26/26 [==============================] - 0s 470us/sample - loss: 0.0016 - acc: 1.0000\n", + "Epoch 735/1000\n", + "26/26 [==============================] - 0s 376us/sample - loss: 0.0016 - acc: 1.0000\n", + "Epoch 736/1000\n", + "26/26 [==============================] - 0s 343us/sample - loss: 0.0016 - acc: 1.0000\n", + "Epoch 737/1000\n", + "26/26 [==============================] - 0s 398us/sample - loss: 0.0016 - acc: 1.0000\n", + "Epoch 738/1000\n", + "26/26 [==============================] - 0s 469us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 739/1000\n", + "26/26 [==============================] - 0s 582us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 740/1000\n", + "26/26 [==============================] - 0s 471us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 741/1000\n", + "26/26 [==============================] - 0s 461us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 742/1000\n", + "26/26 [==============================] - 0s 441us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 743/1000\n", + "26/26 [==============================] - 0s 425us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 744/1000\n", + "26/26 [==============================] - 0s 477us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 745/1000\n", + "26/26 [==============================] - 0s 435us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 746/1000\n", + "26/26 [==============================] - 0s 529us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 747/1000\n", + "26/26 [==============================] - 0s 553us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 748/1000\n", + "26/26 [==============================] - 0s 570us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 749/1000\n", + "26/26 [==============================] - 0s 308us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 750/1000\n", + "26/26 [==============================] - 0s 391us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 751/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 752/1000\n", + "26/26 [==============================] - 0s 368us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 753/1000\n", + "26/26 [==============================] - 0s 340us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 754/1000\n", + "26/26 [==============================] - 0s 372us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 755/1000\n", + "26/26 [==============================] - 0s 319us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 756/1000\n", + "26/26 [==============================] - 0s 322us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 757/1000\n", + "26/26 [==============================] - 0s 331us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 758/1000\n", + "26/26 [==============================] - 0s 335us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 759/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 760/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 761/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 762/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 763/1000\n", + "26/26 [==============================] - 0s 380us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 764/1000\n", + "26/26 [==============================] - 0s 401us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 765/1000\n", + "26/26 [==============================] - 0s 399us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 766/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 767/1000\n", + "26/26 [==============================] - 0s 451us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 768/1000\n", + "26/26 [==============================] - 0s 393us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 769/1000\n", + "26/26 [==============================] - 0s 404us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 770/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 771/1000\n", + "26/26 [==============================] - 0s 376us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 772/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 773/1000\n", + "26/26 [==============================] - 0s 370us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 774/1000\n", + "26/26 [==============================] - 0s 370us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 775/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 776/1000\n", + "26/26 [==============================] - 0s 343us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 777/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 778/1000\n", + "26/26 [==============================] - 0s 337us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 779/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 780/1000\n", + "26/26 [==============================] - 0s 373us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 781/1000\n", + "26/26 [==============================] - 0s 316us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 782/1000\n", + "26/26 [==============================] - 0s 329us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 783/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 784/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 785/1000\n", + "26/26 [==============================] - 0s 360us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 786/1000\n", + "26/26 [==============================] - 0s 389us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 787/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 788/1000\n", + "26/26 [==============================] - 0s 325us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 789/1000\n", + "26/26 [==============================] - 0s 334us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 790/1000\n", + "26/26 [==============================] - 0s 355us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 791/1000\n", + "26/26 [==============================] - 0s 505us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 792/1000\n", + "26/26 [==============================] - 0s 573us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 793/1000\n", + "26/26 [==============================] - 0s 511us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 794/1000\n", + "26/26 [==============================] - 0s 511us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 795/1000\n", + "26/26 [==============================] - 0s 506us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 796/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 797/1000\n", + "26/26 [==============================] - 0s 596us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 798/1000\n", + "26/26 [==============================] - 0s 646us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 799/1000\n", + "26/26 [==============================] - 0s 411us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 800/1000\n", + "26/26 [==============================] - 0s 375us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 801/1000\n", + "26/26 [==============================] - 0s 337us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 802/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 803/1000\n", + "26/26 [==============================] - 0s 407us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 804/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 805/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 806/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 807/1000\n", + "26/26 [==============================] - 0s 353us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 808/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 809/1000\n", + "26/26 [==============================] - 0s 355us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 810/1000\n", + "26/26 [==============================] - 0s 459us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 811/1000\n", + "26/26 [==============================] - 0s 577us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 812/1000\n", + "26/26 [==============================] - 0s 549us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 813/1000\n", + "26/26 [==============================] - 0s 391us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 814/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 815/1000\n", + "26/26 [==============================] - 0s 324us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 816/1000\n", + "26/26 [==============================] - 0s 321us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 817/1000\n", + "26/26 [==============================] - 0s 335us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 818/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 819/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 820/1000\n", + "26/26 [==============================] - 0s 348us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 821/1000\n", + "26/26 [==============================] - 0s 333us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 822/1000\n", + "26/26 [==============================] - 0s 335us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 823/1000\n", + "26/26 [==============================] - 0s 335us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 824/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 825/1000\n", + "26/26 [==============================] - 0s 353us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 826/1000\n", + "26/26 [==============================] - 0s 329us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 827/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 828/1000\n", + "26/26 [==============================] - 0s 320us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 829/1000\n", + "26/26 [==============================] - 0s 334us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 830/1000\n", + "26/26 [==============================] - 0s 334us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 831/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 832/1000\n", + "26/26 [==============================] - 0s 333us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 833/1000\n", + "26/26 [==============================] - 0s 425us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 834/1000\n", + "26/26 [==============================] - 0s 330us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 835/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 836/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 837/1000\n", + "26/26 [==============================] - 0s 356us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 838/1000\n", + "26/26 [==============================] - 0s 329us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 839/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 840/1000\n", + "26/26 [==============================] - 0s 315us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 841/1000\n", + "26/26 [==============================] - 0s 328us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 842/1000\n", + "26/26 [==============================] - 0s 313us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 843/1000\n", + "26/26 [==============================] - 0s 334us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 844/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 0.0010 - acc: 1.0000\n", + "Epoch 845/1000\n", + "26/26 [==============================] - 0s 308us/sample - loss: 0.0010 - acc: 1.0000\n", + "Epoch 846/1000\n", + "26/26 [==============================] - 0s 334us/sample - loss: 0.0010 - acc: 1.0000\n", + "Epoch 847/1000\n", + "26/26 [==============================] - 0s 313us/sample - loss: 0.0010 - acc: 1.0000\n", + "Epoch 848/1000\n", + "26/26 [==============================] - 0s 325us/sample - loss: 0.0010 - acc: 1.0000\n", + "Epoch 849/1000\n", + "26/26 [==============================] - 0s 302us/sample - loss: 0.0010 - acc: 1.0000\n", + "Epoch 850/1000\n", + "26/26 [==============================] - 0s 311us/sample - loss: 0.0010 - acc: 1.0000\n", + "Epoch 851/1000\n", + "26/26 [==============================] - 0s 322us/sample - loss: 0.0010 - acc: 1.0000\n", + "Epoch 852/1000\n", + "26/26 [==============================] - 0s 320us/sample - loss: 0.0010 - acc: 1.0000\n", + "Epoch 853/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 0.0010 - acc: 1.0000\n", + "Epoch 854/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 0.0010 - acc: 1.0000\n", + "Epoch 855/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 0.0010 - acc: 1.0000\n", + "Epoch 856/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 0.0010 - acc: 1.0000\n", + "Epoch 857/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 0.0010 - acc: 1.0000\n", + "Epoch 858/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 0.0010 - acc: 1.0000\n", + "Epoch 859/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 9.9701e-04 - acc: 1.0000\n", + "Epoch 860/1000\n", + "26/26 [==============================] - 0s 343us/sample - loss: 9.9342e-04 - acc: 1.0000\n", + "Epoch 861/1000\n", + "26/26 [==============================] - 0s 331us/sample - loss: 9.9023e-04 - acc: 1.0000\n", + "Epoch 862/1000\n", + "26/26 [==============================] - 0s 320us/sample - loss: 9.8631e-04 - acc: 1.0000\n", + "Epoch 863/1000\n", + "26/26 [==============================] - 0s 349us/sample - loss: 9.8267e-04 - acc: 1.0000\n", + "Epoch 864/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 9.7901e-04 - acc: 1.0000\n", + "Epoch 865/1000\n", + "26/26 [==============================] - 0s 328us/sample - loss: 9.7601e-04 - acc: 1.0000\n", + "Epoch 866/1000\n", + "26/26 [==============================] - 0s 396us/sample - loss: 9.7201e-04 - acc: 1.0000\n", + "Epoch 867/1000\n", + "26/26 [==============================] - 0s 409us/sample - loss: 9.6872e-04 - acc: 1.0000\n", + "Epoch 868/1000\n", + "26/26 [==============================] - 0s 348us/sample - loss: 9.6529e-04 - acc: 1.0000\n", + "Epoch 869/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 9.6207e-04 - acc: 1.0000\n", + "Epoch 870/1000\n", + "26/26 [==============================] - 0s 417us/sample - loss: 9.5898e-04 - acc: 1.0000\n", + "Epoch 871/1000\n", + "26/26 [==============================] - 0s 384us/sample - loss: 9.5517e-04 - acc: 1.0000\n", + "Epoch 872/1000\n", + "26/26 [==============================] - 0s 335us/sample - loss: 9.5133e-04 - acc: 1.0000\n", + "Epoch 873/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 9.4806e-04 - acc: 1.0000\n", + "Epoch 874/1000\n", + "26/26 [==============================] - 0s 348us/sample - loss: 9.4486e-04 - acc: 1.0000\n", + "Epoch 875/1000\n", + "26/26 [==============================] - 0s 310us/sample - loss: 9.4108e-04 - acc: 1.0000\n", + "Epoch 876/1000\n", + "26/26 [==============================] - 0s 327us/sample - loss: 9.3826e-04 - acc: 1.0000\n", + "Epoch 877/1000\n", + "26/26 [==============================] - 0s 375us/sample - loss: 9.3489e-04 - acc: 1.0000\n", + "Epoch 878/1000\n", + "26/26 [==============================] - 0s 531us/sample - loss: 9.3196e-04 - acc: 1.0000\n", + "Epoch 879/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 9.2895e-04 - acc: 1.0000\n", + "Epoch 880/1000\n", + "26/26 [==============================] - 0s 601us/sample - loss: 9.2584e-04 - acc: 1.0000\n", + "Epoch 881/1000\n", + "26/26 [==============================] - 0s 360us/sample - loss: 9.2291e-04 - acc: 1.0000\n", + "Epoch 882/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 9.2006e-04 - acc: 1.0000\n", + "Epoch 883/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 9.1699e-04 - acc: 1.0000\n", + "Epoch 884/1000\n", + "26/26 [==============================] - 0s 776us/sample - loss: 9.1408e-04 - acc: 1.0000\n", + "Epoch 885/1000\n", + "26/26 [==============================] - 0s 463us/sample - loss: 9.1137e-04 - acc: 1.0000\n", + "Epoch 886/1000\n", + "26/26 [==============================] - 0s 309us/sample - loss: 9.0805e-04 - acc: 1.0000\n", + "Epoch 887/1000\n", + "26/26 [==============================] - 0s 329us/sample - loss: 9.0477e-04 - acc: 1.0000\n", + "Epoch 888/1000\n", + "26/26 [==============================] - 0s 350us/sample - loss: 9.0106e-04 - acc: 1.0000\n", + "Epoch 889/1000\n", + "26/26 [==============================] - 0s 333us/sample - loss: 8.9827e-04 - acc: 1.0000\n", + "Epoch 890/1000\n", + "26/26 [==============================] - 0s 395us/sample - loss: 8.9475e-04 - acc: 1.0000\n", + "Epoch 891/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 8.9123e-04 - acc: 1.0000\n", + "Epoch 892/1000\n", + "26/26 [==============================] - 0s 355us/sample - loss: 8.8832e-04 - acc: 1.0000\n", + "Epoch 893/1000\n", + "26/26 [==============================] - 0s 310us/sample - loss: 8.8509e-04 - acc: 1.0000\n", + "Epoch 894/1000\n", + "26/26 [==============================] - 0s 312us/sample - loss: 8.8189e-04 - acc: 1.0000\n", + "Epoch 895/1000\n", + "26/26 [==============================] - 0s 309us/sample - loss: 8.7851e-04 - acc: 1.0000\n", + "Epoch 896/1000\n", + "26/26 [==============================] - 0s 329us/sample - loss: 8.7573e-04 - acc: 1.0000\n", + "Epoch 897/1000\n", + "26/26 [==============================] - 0s 572us/sample - loss: 8.7279e-04 - acc: 1.0000\n", + "Epoch 898/1000\n", + "26/26 [==============================] - 0s 416us/sample - loss: 8.7003e-04 - acc: 1.0000\n", + "Epoch 899/1000\n", + "26/26 [==============================] - 0s 389us/sample - loss: 8.6728e-04 - acc: 1.0000\n", + "Epoch 900/1000\n", + "26/26 [==============================] - 0s 510us/sample - loss: 8.6435e-04 - acc: 1.0000\n", + "Epoch 901/1000\n", + "26/26 [==============================] - 0s 378us/sample - loss: 8.6132e-04 - acc: 1.0000\n", + "Epoch 902/1000\n", + "26/26 [==============================] - 0s 375us/sample - loss: 8.5855e-04 - acc: 1.0000\n", + "Epoch 903/1000\n", + "26/26 [==============================] - 0s 551us/sample - loss: 8.5554e-04 - acc: 1.0000\n", + "Epoch 904/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 8.5273e-04 - acc: 1.0000\n", + "Epoch 905/1000\n", + "26/26 [==============================] - 0s 321us/sample - loss: 8.4990e-04 - acc: 1.0000\n", + "Epoch 906/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 8.4710e-04 - acc: 1.0000\n", + "Epoch 907/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 8.4430e-04 - acc: 1.0000\n", + "Epoch 908/1000\n", + "26/26 [==============================] - 0s 404us/sample - loss: 8.4181e-04 - acc: 1.0000\n", + "Epoch 909/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 8.3837e-04 - acc: 1.0000\n", + "Epoch 910/1000\n", + "26/26 [==============================] - 0s 340us/sample - loss: 8.3549e-04 - acc: 1.0000\n", + "Epoch 911/1000\n", + "26/26 [==============================] - 0s 297us/sample - loss: 8.3257e-04 - acc: 1.0000\n", + "Epoch 912/1000\n", + "26/26 [==============================] - 0s 491us/sample - loss: 8.2977e-04 - acc: 1.0000\n", + "Epoch 913/1000\n", + "26/26 [==============================] - 0s 326us/sample - loss: 8.2689e-04 - acc: 1.0000\n", + "Epoch 914/1000\n", + "26/26 [==============================] - 0s 376us/sample - loss: 8.2446e-04 - acc: 1.0000\n", + "Epoch 915/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 8.2171e-04 - acc: 1.0000\n", + "Epoch 916/1000\n", + "26/26 [==============================] - 0s 373us/sample - loss: 8.1915e-04 - acc: 1.0000\n", + "Epoch 917/1000\n", + "26/26 [==============================] - 0s 304us/sample - loss: 8.1657e-04 - acc: 1.0000\n", + "Epoch 918/1000\n", + "26/26 [==============================] - 0s 340us/sample - loss: 8.1368e-04 - acc: 1.0000\n", + "Epoch 919/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 8.1075e-04 - acc: 1.0000\n", + "Epoch 920/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 8.0821e-04 - acc: 1.0000\n", + "Epoch 921/1000\n", + "26/26 [==============================] - 0s 330us/sample - loss: 8.0514e-04 - acc: 1.0000\n", + "Epoch 922/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 8.0250e-04 - acc: 1.0000\n", + "Epoch 923/1000\n", + "26/26 [==============================] - 0s 496us/sample - loss: 7.9946e-04 - acc: 1.0000\n", + "Epoch 924/1000\n", + "26/26 [==============================] - 0s 429us/sample - loss: 7.9721e-04 - acc: 1.0000\n", + "Epoch 925/1000\n", + "26/26 [==============================] - 0s 349us/sample - loss: 7.9391e-04 - acc: 1.0000\n", + "Epoch 926/1000\n", + "26/26 [==============================] - 0s 560us/sample - loss: 7.9110e-04 - acc: 1.0000\n", + "Epoch 927/1000\n", + "26/26 [==============================] - 0s 336us/sample - loss: 7.8843e-04 - acc: 1.0000\n", + "Epoch 928/1000\n", + "26/26 [==============================] - 0s 465us/sample - loss: 7.8576e-04 - acc: 1.0000\n", + "Epoch 929/1000\n", + "26/26 [==============================] - 0s 427us/sample - loss: 7.8293e-04 - acc: 1.0000\n", + "Epoch 930/1000\n", + "26/26 [==============================] - 0s 337us/sample - loss: 7.8071e-04 - acc: 1.0000\n", + "Epoch 931/1000\n", + "26/26 [==============================] - 0s 444us/sample - loss: 7.7809e-04 - acc: 1.0000\n", + "Epoch 932/1000\n", + "26/26 [==============================] - 0s 299us/sample - loss: 7.7553e-04 - acc: 1.0000\n", + "Epoch 933/1000\n", + "26/26 [==============================] - 0s 325us/sample - loss: 7.7312e-04 - acc: 1.0000\n", + "Epoch 934/1000\n", + "26/26 [==============================] - 0s 318us/sample - loss: 7.7052e-04 - acc: 1.0000\n", + "Epoch 935/1000\n", + "26/26 [==============================] - 0s 442us/sample - loss: 7.6810e-04 - acc: 1.0000\n", + "Epoch 936/1000\n", + "26/26 [==============================] - 0s 463us/sample - loss: 7.6561e-04 - acc: 1.0000\n", + "Epoch 937/1000\n", + "26/26 [==============================] - 0s 350us/sample - loss: 7.6323e-04 - acc: 1.0000\n", + "Epoch 938/1000\n", + "26/26 [==============================] - 0s 430us/sample - loss: 7.6079e-04 - acc: 1.0000\n", + "Epoch 939/1000\n", + "26/26 [==============================] - 0s 515us/sample - loss: 7.5830e-04 - acc: 1.0000\n", + "Epoch 940/1000\n", + "26/26 [==============================] - 0s 440us/sample - loss: 7.5604e-04 - acc: 1.0000\n", + "Epoch 941/1000\n", + "26/26 [==============================] - 0s 318us/sample - loss: 7.5326e-04 - acc: 1.0000\n", + "Epoch 942/1000\n", + "26/26 [==============================] - 0s 491us/sample - loss: 7.5096e-04 - acc: 1.0000\n", + "Epoch 943/1000\n", + "26/26 [==============================] - 0s 440us/sample - loss: 7.4852e-04 - acc: 1.0000\n", + "Epoch 944/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 7.4616e-04 - acc: 1.0000\n", + "Epoch 945/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 7.4382e-04 - acc: 1.0000\n", + "Epoch 946/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 7.4155e-04 - acc: 1.0000\n", + "Epoch 947/1000\n", + "26/26 [==============================] - 0s 448us/sample - loss: 7.3885e-04 - acc: 1.0000\n", + "Epoch 948/1000\n", + "26/26 [==============================] - 0s 441us/sample - loss: 7.3667e-04 - acc: 1.0000\n", + "Epoch 949/1000\n", + "26/26 [==============================] - 0s 525us/sample - loss: 7.3447e-04 - acc: 1.0000\n", + "Epoch 950/1000\n", + "26/26 [==============================] - 0s 405us/sample - loss: 7.3181e-04 - acc: 1.0000\n", + "Epoch 951/1000\n", + "26/26 [==============================] - 0s 421us/sample - loss: 7.2932e-04 - acc: 1.0000\n", + "Epoch 952/1000\n", + "26/26 [==============================] - 0s 368us/sample - loss: 7.2682e-04 - acc: 1.0000\n", + "Epoch 953/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 7.2428e-04 - acc: 1.0000\n", + "Epoch 954/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 7.2192e-04 - acc: 1.0000\n", + "Epoch 955/1000\n", + "26/26 [==============================] - 0s 332us/sample - loss: 7.1955e-04 - acc: 1.0000\n", + "Epoch 956/1000\n", + "26/26 [==============================] - 0s 303us/sample - loss: 7.1718e-04 - acc: 1.0000\n", + "Epoch 957/1000\n", + "26/26 [==============================] - 0s 325us/sample - loss: 7.1498e-04 - acc: 1.0000\n", + "Epoch 958/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 7.1282e-04 - acc: 1.0000\n", + "Epoch 959/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 7.1044e-04 - acc: 1.0000\n", + "Epoch 960/1000\n", + "26/26 [==============================] - 0s 332us/sample - loss: 7.0836e-04 - acc: 1.0000\n", + "Epoch 961/1000\n", + "26/26 [==============================] - 0s 300us/sample - loss: 7.0611e-04 - acc: 1.0000\n", + "Epoch 962/1000\n", + "26/26 [==============================] - 0s 385us/sample - loss: 7.0399e-04 - acc: 1.0000\n", + "Epoch 963/1000\n", + "26/26 [==============================] - 0s 294us/sample - loss: 7.0178e-04 - acc: 1.0000\n", + "Epoch 964/1000\n", + "26/26 [==============================] - 0s 281us/sample - loss: 6.9945e-04 - acc: 1.0000\n", + "Epoch 965/1000\n", + "26/26 [==============================] - 0s 311us/sample - loss: 6.9712e-04 - acc: 1.0000\n", + "Epoch 966/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 6.9452e-04 - acc: 1.0000\n", + "Epoch 967/1000\n", + "26/26 [==============================] - 0s 306us/sample - loss: 6.9264e-04 - acc: 1.0000\n", + "Epoch 968/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 6.9005e-04 - acc: 1.0000\n", + "Epoch 969/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 6.8808e-04 - acc: 1.0000\n", + "Epoch 970/1000\n", + "26/26 [==============================] - 0s 326us/sample - loss: 6.8596e-04 - acc: 1.0000\n", + "Epoch 971/1000\n", + "26/26 [==============================] - 0s 462us/sample - loss: 6.8387e-04 - acc: 1.0000\n", + "Epoch 972/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 6.8177e-04 - acc: 1.0000\n", + "Epoch 973/1000\n", + "26/26 [==============================] - 0s 327us/sample - loss: 6.7971e-04 - acc: 1.0000\n", + "Epoch 974/1000\n", + "26/26 [==============================] - 0s 405us/sample - loss: 6.7773e-04 - acc: 1.0000\n", + "Epoch 975/1000\n", + "26/26 [==============================] - 0s 426us/sample - loss: 6.7538e-04 - acc: 1.0000\n", + "Epoch 976/1000\n", + "26/26 [==============================] - 0s 333us/sample - loss: 6.7323e-04 - acc: 1.0000\n", + "Epoch 977/1000\n", + "26/26 [==============================] - 0s 315us/sample - loss: 6.7110e-04 - acc: 1.0000\n", + "Epoch 978/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 6.6902e-04 - acc: 1.0000\n", + "Epoch 979/1000\n", + "26/26 [==============================] - 0s 420us/sample - loss: 6.6703e-04 - acc: 1.0000\n", + "Epoch 980/1000\n", + "26/26 [==============================] - 0s 419us/sample - loss: 6.6497e-04 - acc: 1.0000\n", + "Epoch 981/1000\n", + "26/26 [==============================] - 0s 452us/sample - loss: 6.6300e-04 - acc: 1.0000\n", + "Epoch 982/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 6.6105e-04 - acc: 1.0000\n", + "Epoch 983/1000\n", + "26/26 [==============================] - 0s 507us/sample - loss: 6.5898e-04 - acc: 1.0000\n", + "Epoch 984/1000\n", + "26/26 [==============================] - 0s 373us/sample - loss: 6.5700e-04 - acc: 1.0000\n", + "Epoch 985/1000\n", + "26/26 [==============================] - 0s 393us/sample - loss: 6.5506e-04 - acc: 1.0000\n", + "Epoch 986/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 6.5288e-04 - acc: 1.0000\n", + "Epoch 987/1000\n", + "26/26 [==============================] - 0s 435us/sample - loss: 6.5099e-04 - acc: 1.0000\n", + "Epoch 988/1000\n", + "26/26 [==============================] - 0s 423us/sample - loss: 6.4897e-04 - acc: 1.0000\n", + "Epoch 989/1000\n", + "26/26 [==============================] - 0s 314us/sample - loss: 6.4694e-04 - acc: 1.0000\n", + "Epoch 990/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 6.4497e-04 - acc: 1.0000\n", + "Epoch 991/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 6.4279e-04 - acc: 1.0000\n", + "Epoch 992/1000\n", + "26/26 [==============================] - 0s 443us/sample - loss: 6.4098e-04 - acc: 1.0000\n", + "Epoch 993/1000\n", + "26/26 [==============================] - 0s 423us/sample - loss: 6.3900e-04 - acc: 1.0000\n", + "Epoch 994/1000\n", + "26/26 [==============================] - 0s 479us/sample - loss: 6.3675e-04 - acc: 1.0000\n", + "Epoch 995/1000\n", + "26/26 [==============================] - 0s 428us/sample - loss: 6.3473e-04 - acc: 1.0000\n", + "Epoch 996/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 6.3240e-04 - acc: 1.0000\n", + "Epoch 997/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 6.3059e-04 - acc: 1.0000\n", + "Epoch 998/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 6.2870e-04 - acc: 1.0000\n", + "Epoch 999/1000\n", + "26/26 [==============================] - 0s 297us/sample - loss: 6.2625e-04 - acc: 1.0000\n", + "Epoch 1000/1000\n", + "26/26 [==============================] - 0s 344us/sample - loss: 6.2455e-04 - acc: 1.0000\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "\n", + "try:\n", + " model.load(\"model.tflearn\")\n", + "except:\n", + " model.fit(training, output, epochs=1000, batch_size=8)\n", + " model.save(\"model.tflearn\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "yMA7TxSt8tuR", + "outputId": "4fe84cc9-a2b1-4a00-a6db-9d577a663bc9" + }, + "execution_count": 28, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Train on 26 samples\n", + "Epoch 1/1000\n", + "26/26 [==============================] - 0s 489us/sample - loss: 6.2248e-04 - acc: 1.0000\n", + "Epoch 2/1000\n", + "26/26 [==============================] - 0s 399us/sample - loss: 6.2066e-04 - acc: 1.0000\n", + "Epoch 3/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 6.1863e-04 - acc: 1.0000\n", + "Epoch 4/1000\n", + "26/26 [==============================] - 0s 493us/sample - loss: 6.1703e-04 - acc: 1.0000\n", + "Epoch 5/1000\n", + "26/26 [==============================] - 0s 429us/sample - loss: 6.1497e-04 - acc: 1.0000\n", + "Epoch 6/1000\n", + "26/26 [==============================] - 0s 447us/sample - loss: 6.1282e-04 - acc: 1.0000\n", + "Epoch 7/1000\n", + "26/26 [==============================] - 0s 419us/sample - loss: 6.1096e-04 - acc: 1.0000\n", + "Epoch 8/1000\n", + "26/26 [==============================] - 0s 419us/sample - loss: 6.0934e-04 - acc: 1.0000\n", + "Epoch 9/1000\n", + "26/26 [==============================] - 0s 412us/sample - loss: 6.0735e-04 - acc: 1.0000\n", + "Epoch 10/1000\n", + "26/26 [==============================] - 0s 435us/sample - loss: 6.0566e-04 - acc: 1.0000\n", + "Epoch 11/1000\n", + "26/26 [==============================] - 0s 423us/sample - loss: 6.0332e-04 - acc: 1.0000\n", + "Epoch 12/1000\n", + "26/26 [==============================] - 0s 438us/sample - loss: 6.0162e-04 - acc: 1.0000\n", + "Epoch 13/1000\n", + "26/26 [==============================] - 0s 421us/sample - loss: 5.9995e-04 - acc: 1.0000\n", + "Epoch 14/1000\n", + "26/26 [==============================] - 0s 418us/sample - loss: 5.9787e-04 - acc: 1.0000\n", + "Epoch 15/1000\n", + "26/26 [==============================] - 0s 524us/sample - loss: 5.9599e-04 - acc: 1.0000\n", + "Epoch 16/1000\n", + "26/26 [==============================] - 0s 412us/sample - loss: 5.9400e-04 - acc: 1.0000\n", + "Epoch 17/1000\n", + "26/26 [==============================] - 0s 403us/sample - loss: 5.9228e-04 - acc: 1.0000\n", + "Epoch 18/1000\n", + "26/26 [==============================] - 0s 432us/sample - loss: 5.9041e-04 - acc: 1.0000\n", + "Epoch 19/1000\n", + "26/26 [==============================] - 0s 427us/sample - loss: 5.8872e-04 - acc: 1.0000\n", + "Epoch 20/1000\n", + "26/26 [==============================] - 0s 537us/sample - loss: 5.8697e-04 - acc: 1.0000\n", + "Epoch 21/1000\n", + "26/26 [==============================] - 0s 609us/sample - loss: 5.8517e-04 - acc: 1.0000\n", + "Epoch 22/1000\n", + "26/26 [==============================] - 0s 427us/sample - loss: 5.8335e-04 - acc: 1.0000\n", + "Epoch 23/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 5.8174e-04 - acc: 1.0000\n", + "Epoch 24/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 5.7976e-04 - acc: 1.0000\n", + "Epoch 25/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 5.7799e-04 - acc: 1.0000\n", + "Epoch 26/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 5.7619e-04 - acc: 1.0000\n", + "Epoch 27/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 5.7449e-04 - acc: 1.0000\n", + "Epoch 28/1000\n", + "26/26 [==============================] - 0s 383us/sample - loss: 5.7280e-04 - acc: 1.0000\n", + "Epoch 29/1000\n", + "26/26 [==============================] - 0s 416us/sample - loss: 5.7085e-04 - acc: 1.0000\n", + "Epoch 30/1000\n", + "26/26 [==============================] - 0s 384us/sample - loss: 5.6909e-04 - acc: 1.0000\n", + "Epoch 31/1000\n", + "26/26 [==============================] - 0s 411us/sample - loss: 5.6732e-04 - acc: 1.0000\n", + "Epoch 32/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 5.6559e-04 - acc: 1.0000\n", + "Epoch 33/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 5.6381e-04 - acc: 1.0000\n", + "Epoch 34/1000\n", + "26/26 [==============================] - 0s 426us/sample - loss: 5.6217e-04 - acc: 1.0000\n", + "Epoch 35/1000\n", + "26/26 [==============================] - 0s 391us/sample - loss: 5.6038e-04 - acc: 1.0000\n", + "Epoch 36/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 5.5837e-04 - acc: 1.0000\n", + "Epoch 37/1000\n", + "26/26 [==============================] - 0s 380us/sample - loss: 5.5687e-04 - acc: 1.0000\n", + "Epoch 38/1000\n", + "26/26 [==============================] - 0s 378us/sample - loss: 5.5503e-04 - acc: 1.0000\n", + "Epoch 39/1000\n", + "26/26 [==============================] - 0s 427us/sample - loss: 5.5335e-04 - acc: 1.0000\n", + "Epoch 40/1000\n", + "26/26 [==============================] - 0s 380us/sample - loss: 5.5153e-04 - acc: 1.0000\n", + "Epoch 41/1000\n", + "26/26 [==============================] - 0s 389us/sample - loss: 5.4990e-04 - acc: 1.0000\n", + "Epoch 42/1000\n", + "26/26 [==============================] - 0s 380us/sample - loss: 5.4792e-04 - acc: 1.0000\n", + "Epoch 43/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 5.4625e-04 - acc: 1.0000\n", + "Epoch 44/1000\n", + "26/26 [==============================] - 0s 401us/sample - loss: 5.4460e-04 - acc: 1.0000\n", + "Epoch 45/1000\n", + "26/26 [==============================] - 0s 398us/sample - loss: 5.4289e-04 - acc: 1.0000\n", + "Epoch 46/1000\n", + "26/26 [==============================] - 0s 401us/sample - loss: 5.4130e-04 - acc: 1.0000\n", + "Epoch 47/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 5.3942e-04 - acc: 1.0000\n", + "Epoch 48/1000\n", + "26/26 [==============================] - 0s 407us/sample - loss: 5.3757e-04 - acc: 1.0000\n", + "Epoch 49/1000\n", + "26/26 [==============================] - 0s 326us/sample - loss: 5.3597e-04 - acc: 1.0000\n", + "Epoch 50/1000\n", + "26/26 [==============================] - 0s 360us/sample - loss: 5.3446e-04 - acc: 1.0000\n", + "Epoch 51/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 5.3264e-04 - acc: 1.0000\n", + "Epoch 52/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 5.3109e-04 - acc: 1.0000\n", + "Epoch 53/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 5.2951e-04 - acc: 1.0000\n", + "Epoch 54/1000\n", + "26/26 [==============================] - 0s 458us/sample - loss: 5.2789e-04 - acc: 1.0000\n", + "Epoch 55/1000\n", + "26/26 [==============================] - 0s 407us/sample - loss: 5.2624e-04 - acc: 1.0000\n", + "Epoch 56/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 5.2468e-04 - acc: 1.0000\n", + "Epoch 57/1000\n", + "26/26 [==============================] - 0s 454us/sample - loss: 5.2304e-04 - acc: 1.0000\n", + "Epoch 58/1000\n", + "26/26 [==============================] - 0s 425us/sample - loss: 5.2157e-04 - acc: 1.0000\n", + "Epoch 59/1000\n", + "26/26 [==============================] - 0s 376us/sample - loss: 5.1991e-04 - acc: 1.0000\n", + "Epoch 60/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 5.1817e-04 - acc: 1.0000\n", + "Epoch 61/1000\n", + "26/26 [==============================] - 0s 416us/sample - loss: 5.1680e-04 - acc: 1.0000\n", + "Epoch 62/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 5.1523e-04 - acc: 1.0000\n", + "Epoch 63/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 5.1350e-04 - acc: 1.0000\n", + "Epoch 64/1000\n", + "26/26 [==============================] - 0s 372us/sample - loss: 5.1207e-04 - acc: 1.0000\n", + "Epoch 65/1000\n", + "26/26 [==============================] - 0s 343us/sample - loss: 5.1046e-04 - acc: 1.0000\n", + "Epoch 66/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 5.0885e-04 - acc: 1.0000\n", + "Epoch 67/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 5.0737e-04 - acc: 1.0000\n", + "Epoch 68/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 5.0597e-04 - acc: 1.0000\n", + "Epoch 69/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 5.0452e-04 - acc: 1.0000\n", + "Epoch 70/1000\n", + "26/26 [==============================] - 0s 344us/sample - loss: 5.0289e-04 - acc: 1.0000\n", + "Epoch 71/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 5.0147e-04 - acc: 1.0000\n", + "Epoch 72/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 5.0005e-04 - acc: 1.0000\n", + "Epoch 73/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 4.9858e-04 - acc: 1.0000\n", + "Epoch 74/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 4.9726e-04 - acc: 1.0000\n", + "Epoch 75/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 4.9565e-04 - acc: 1.0000\n", + "Epoch 76/1000\n", + "26/26 [==============================] - 0s 336us/sample - loss: 4.9422e-04 - acc: 1.0000\n", + "Epoch 77/1000\n", + "26/26 [==============================] - 0s 322us/sample - loss: 4.9277e-04 - acc: 1.0000\n", + "Epoch 78/1000\n", + "26/26 [==============================] - 0s 326us/sample - loss: 4.9121e-04 - acc: 1.0000\n", + "Epoch 79/1000\n", + "26/26 [==============================] - 0s 494us/sample - loss: 4.8976e-04 - acc: 1.0000\n", + "Epoch 80/1000\n", + "26/26 [==============================] - 0s 520us/sample - loss: 4.8833e-04 - acc: 1.0000\n", + "Epoch 81/1000\n", + "26/26 [==============================] - 0s 334us/sample - loss: 4.8673e-04 - acc: 1.0000\n", + "Epoch 82/1000\n", + "26/26 [==============================] - 0s 328us/sample - loss: 4.8530e-04 - acc: 1.0000\n", + "Epoch 83/1000\n", + "26/26 [==============================] - 0s 330us/sample - loss: 4.8383e-04 - acc: 1.0000\n", + "Epoch 84/1000\n", + "26/26 [==============================] - 0s 496us/sample - loss: 4.8240e-04 - acc: 1.0000\n", + "Epoch 85/1000\n", + "26/26 [==============================] - 0s 370us/sample - loss: 4.8099e-04 - acc: 1.0000\n", + "Epoch 86/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 4.7955e-04 - acc: 1.0000\n", + "Epoch 87/1000\n", + "26/26 [==============================] - 0s 411us/sample - loss: 4.7813e-04 - acc: 1.0000\n", + "Epoch 88/1000\n", + "26/26 [==============================] - 0s 405us/sample - loss: 4.7681e-04 - acc: 1.0000\n", + "Epoch 89/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 4.7548e-04 - acc: 1.0000\n", + "Epoch 90/1000\n", + "26/26 [==============================] - 0s 391us/sample - loss: 4.7394e-04 - acc: 1.0000\n", + "Epoch 91/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 4.7261e-04 - acc: 1.0000\n", + "Epoch 92/1000\n", + "26/26 [==============================] - 0s 474us/sample - loss: 4.7114e-04 - acc: 1.0000\n", + "Epoch 93/1000\n", + "26/26 [==============================] - 0s 402us/sample - loss: 4.6956e-04 - acc: 1.0000\n", + "Epoch 94/1000\n", + "26/26 [==============================] - 0s 393us/sample - loss: 4.6812e-04 - acc: 1.0000\n", + "Epoch 95/1000\n", + "26/26 [==============================] - 0s 387us/sample - loss: 4.6673e-04 - acc: 1.0000\n", + "Epoch 96/1000\n", + "26/26 [==============================] - 0s 327us/sample - loss: 4.6538e-04 - acc: 1.0000\n", + "Epoch 97/1000\n", + "26/26 [==============================] - 0s 333us/sample - loss: 4.6409e-04 - acc: 1.0000\n", + "Epoch 98/1000\n", + "26/26 [==============================] - 0s 434us/sample - loss: 4.6280e-04 - acc: 1.0000\n", + "Epoch 99/1000\n", + "26/26 [==============================] - 0s 350us/sample - loss: 4.6143e-04 - acc: 1.0000\n", + "Epoch 100/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 4.6023e-04 - acc: 1.0000\n", + "Epoch 101/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 4.5888e-04 - acc: 1.0000\n", + "Epoch 102/1000\n", + "26/26 [==============================] - 0s 393us/sample - loss: 4.5768e-04 - acc: 1.0000\n", + "Epoch 103/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 4.5639e-04 - acc: 1.0000\n", + "Epoch 104/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 4.5495e-04 - acc: 1.0000\n", + "Epoch 105/1000\n", + "26/26 [==============================] - 0s 380us/sample - loss: 4.5361e-04 - acc: 1.0000\n", + "Epoch 106/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 4.5236e-04 - acc: 1.0000\n", + "Epoch 107/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 4.5087e-04 - acc: 1.0000\n", + "Epoch 108/1000\n", + "26/26 [==============================] - 0s 387us/sample - loss: 4.4956e-04 - acc: 1.0000\n", + "Epoch 109/1000\n", + "26/26 [==============================] - 0s 423us/sample - loss: 4.4813e-04 - acc: 1.0000\n", + "Epoch 110/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 4.4693e-04 - acc: 1.0000\n", + "Epoch 111/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 4.4548e-04 - acc: 1.0000\n", + "Epoch 112/1000\n", + "26/26 [==============================] - 0s 425us/sample - loss: 4.4421e-04 - acc: 1.0000\n", + "Epoch 113/1000\n", + "26/26 [==============================] - 0s 325us/sample - loss: 4.4296e-04 - acc: 1.0000\n", + "Epoch 114/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 4.4168e-04 - acc: 1.0000\n", + "Epoch 115/1000\n", + "26/26 [==============================] - 0s 384us/sample - loss: 4.4049e-04 - acc: 1.0000\n", + "Epoch 116/1000\n", + "26/26 [==============================] - 0s 385us/sample - loss: 4.3917e-04 - acc: 1.0000\n", + "Epoch 117/1000\n", + "26/26 [==============================] - 0s 394us/sample - loss: 4.3786e-04 - acc: 1.0000\n", + "Epoch 118/1000\n", + "26/26 [==============================] - 0s 406us/sample - loss: 4.3642e-04 - acc: 1.0000\n", + "Epoch 119/1000\n", + "26/26 [==============================] - 0s 437us/sample - loss: 4.3526e-04 - acc: 1.0000\n", + "Epoch 120/1000\n", + "26/26 [==============================] - 0s 409us/sample - loss: 4.3361e-04 - acc: 1.0000\n", + "Epoch 121/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 4.3237e-04 - acc: 1.0000\n", + "Epoch 122/1000\n", + "26/26 [==============================] - 0s 389us/sample - loss: 4.3097e-04 - acc: 1.0000\n", + "Epoch 123/1000\n", + "26/26 [==============================] - 0s 375us/sample - loss: 4.2969e-04 - acc: 1.0000\n", + "Epoch 124/1000\n", + "26/26 [==============================] - 0s 373us/sample - loss: 4.2855e-04 - acc: 1.0000\n", + "Epoch 125/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 4.2705e-04 - acc: 1.0000\n", + "Epoch 126/1000\n", + "26/26 [==============================] - 0s 386us/sample - loss: 4.2577e-04 - acc: 1.0000\n", + "Epoch 127/1000\n", + "26/26 [==============================] - 0s 387us/sample - loss: 4.2442e-04 - acc: 1.0000\n", + "Epoch 128/1000\n", + "26/26 [==============================] - 0s 391us/sample - loss: 4.2311e-04 - acc: 1.0000\n", + "Epoch 129/1000\n", + "26/26 [==============================] - 0s 404us/sample - loss: 4.2183e-04 - acc: 1.0000\n", + "Epoch 130/1000\n", + "26/26 [==============================] - 0s 428us/sample - loss: 4.2044e-04 - acc: 1.0000\n", + "Epoch 131/1000\n", + "26/26 [==============================] - 0s 404us/sample - loss: 4.1890e-04 - acc: 1.0000\n", + "Epoch 132/1000\n", + "26/26 [==============================] - 0s 454us/sample - loss: 4.1774e-04 - acc: 1.0000\n", + "Epoch 133/1000\n", + "26/26 [==============================] - 0s 393us/sample - loss: 4.1641e-04 - acc: 1.0000\n", + "Epoch 134/1000\n", + "26/26 [==============================] - 0s 580us/sample - loss: 4.1510e-04 - acc: 1.0000\n", + "Epoch 135/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 4.1385e-04 - acc: 1.0000\n", + "Epoch 136/1000\n", + "26/26 [==============================] - 0s 368us/sample - loss: 4.1266e-04 - acc: 1.0000\n", + "Epoch 137/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 4.1138e-04 - acc: 1.0000\n", + "Epoch 138/1000\n", + "26/26 [==============================] - 0s 386us/sample - loss: 4.1019e-04 - acc: 1.0000\n", + "Epoch 139/1000\n", + "26/26 [==============================] - 0s 375us/sample - loss: 4.0880e-04 - acc: 1.0000\n", + "Epoch 140/1000\n", + "26/26 [==============================] - 0s 372us/sample - loss: 4.0776e-04 - acc: 1.0000\n", + "Epoch 141/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 4.0666e-04 - acc: 1.0000\n", + "Epoch 142/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 4.0545e-04 - acc: 1.0000\n", + "Epoch 143/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 4.0426e-04 - acc: 1.0000\n", + "Epoch 144/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 4.0318e-04 - acc: 1.0000\n", + "Epoch 145/1000\n", + "26/26 [==============================] - 0s 387us/sample - loss: 4.0199e-04 - acc: 1.0000\n", + "Epoch 146/1000\n", + "26/26 [==============================] - 0s 547us/sample - loss: 4.0076e-04 - acc: 1.0000\n", + "Epoch 147/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 3.9951e-04 - acc: 1.0000\n", + "Epoch 148/1000\n", + "26/26 [==============================] - 0s 405us/sample - loss: 3.9826e-04 - acc: 1.0000\n", + "Epoch 149/1000\n", + "26/26 [==============================] - 0s 597us/sample - loss: 3.9715e-04 - acc: 1.0000\n", + "Epoch 150/1000\n", + "26/26 [==============================] - 0s 534us/sample - loss: 3.9590e-04 - acc: 1.0000\n", + "Epoch 151/1000\n", + "26/26 [==============================] - 0s 645us/sample - loss: 3.9481e-04 - acc: 1.0000\n", + "Epoch 152/1000\n", + "26/26 [==============================] - 0s 496us/sample - loss: 3.9368e-04 - acc: 1.0000\n", + "Epoch 153/1000\n", + "26/26 [==============================] - 0s 398us/sample - loss: 3.9251e-04 - acc: 1.0000\n", + "Epoch 154/1000\n", + "26/26 [==============================] - 0s 441us/sample - loss: 3.9124e-04 - acc: 1.0000\n", + "Epoch 155/1000\n", + "26/26 [==============================] - 0s 408us/sample - loss: 3.9008e-04 - acc: 1.0000\n", + "Epoch 156/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 3.8893e-04 - acc: 1.0000\n", + "Epoch 157/1000\n", + "26/26 [==============================] - 0s 387us/sample - loss: 3.8780e-04 - acc: 1.0000\n", + "Epoch 158/1000\n", + "26/26 [==============================] - 0s 355us/sample - loss: 3.8666e-04 - acc: 1.0000\n", + "Epoch 159/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 3.8555e-04 - acc: 1.0000\n", + "Epoch 160/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 3.8424e-04 - acc: 1.0000\n", + "Epoch 161/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 3.8309e-04 - acc: 1.0000\n", + "Epoch 162/1000\n", + "26/26 [==============================] - 0s 404us/sample - loss: 3.8202e-04 - acc: 1.0000\n", + "Epoch 163/1000\n", + "26/26 [==============================] - 0s 356us/sample - loss: 3.8078e-04 - acc: 1.0000\n", + "Epoch 164/1000\n", + "26/26 [==============================] - 0s 376us/sample - loss: 3.7967e-04 - acc: 1.0000\n", + "Epoch 165/1000\n", + "26/26 [==============================] - 0s 464us/sample - loss: 3.7846e-04 - acc: 1.0000\n", + "Epoch 166/1000\n", + "26/26 [==============================] - 0s 387us/sample - loss: 3.7740e-04 - acc: 1.0000\n", + "Epoch 167/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 3.7616e-04 - acc: 1.0000\n", + "Epoch 168/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 3.7519e-04 - acc: 1.0000\n", + "Epoch 169/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 3.7416e-04 - acc: 1.0000\n", + "Epoch 170/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 3.7314e-04 - acc: 1.0000\n", + "Epoch 171/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 3.7219e-04 - acc: 1.0000\n", + "Epoch 172/1000\n", + "26/26 [==============================] - 0s 377us/sample - loss: 3.7108e-04 - acc: 1.0000\n", + "Epoch 173/1000\n", + "26/26 [==============================] - 0s 421us/sample - loss: 3.7013e-04 - acc: 1.0000\n", + "Epoch 174/1000\n", + "26/26 [==============================] - 0s 408us/sample - loss: 3.6919e-04 - acc: 1.0000\n", + "Epoch 175/1000\n", + "26/26 [==============================] - 0s 353us/sample - loss: 3.6812e-04 - acc: 1.0000\n", + "Epoch 176/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 3.6692e-04 - acc: 1.0000\n", + "Epoch 177/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 3.6580e-04 - acc: 1.0000\n", + "Epoch 178/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 3.6477e-04 - acc: 1.0000\n", + "Epoch 179/1000\n", + "26/26 [==============================] - 0s 356us/sample - loss: 3.6375e-04 - acc: 1.0000\n", + "Epoch 180/1000\n", + "26/26 [==============================] - 0s 370us/sample - loss: 3.6264e-04 - acc: 1.0000\n", + "Epoch 181/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 3.6147e-04 - acc: 1.0000\n", + "Epoch 182/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 3.6052e-04 - acc: 1.0000\n", + "Epoch 183/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 3.5941e-04 - acc: 1.0000\n", + "Epoch 184/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 3.5835e-04 - acc: 1.0000\n", + "Epoch 185/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 3.5740e-04 - acc: 1.0000\n", + "Epoch 186/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 3.5625e-04 - acc: 1.0000\n", + "Epoch 187/1000\n", + "26/26 [==============================] - 0s 406us/sample - loss: 3.5537e-04 - acc: 1.0000\n", + "Epoch 188/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 3.5412e-04 - acc: 1.0000\n", + "Epoch 189/1000\n", + "26/26 [==============================] - 0s 383us/sample - loss: 3.5301e-04 - acc: 1.0000\n", + "Epoch 190/1000\n", + "26/26 [==============================] - 0s 353us/sample - loss: 3.5195e-04 - acc: 1.0000\n", + "Epoch 191/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 3.5090e-04 - acc: 1.0000\n", + "Epoch 192/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 3.4978e-04 - acc: 1.0000\n", + "Epoch 193/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 3.4898e-04 - acc: 1.0000\n", + "Epoch 194/1000\n", + "26/26 [==============================] - 0s 331us/sample - loss: 3.4778e-04 - acc: 1.0000\n", + "Epoch 195/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 3.4682e-04 - acc: 1.0000\n", + "Epoch 196/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 3.4591e-04 - acc: 1.0000\n", + "Epoch 197/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 3.4486e-04 - acc: 1.0000\n", + "Epoch 198/1000\n", + "26/26 [==============================] - 0s 435us/sample - loss: 3.4399e-04 - acc: 1.0000\n", + "Epoch 199/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 3.4315e-04 - acc: 1.0000\n", + "Epoch 200/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 3.4212e-04 - acc: 1.0000\n", + "Epoch 201/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 3.4122e-04 - acc: 1.0000\n", + "Epoch 202/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 3.4026e-04 - acc: 1.0000\n", + "Epoch 203/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 3.3921e-04 - acc: 1.0000\n", + "Epoch 204/1000\n", + "26/26 [==============================] - 0s 368us/sample - loss: 3.3838e-04 - acc: 1.0000\n", + "Epoch 205/1000\n", + "26/26 [==============================] - 0s 370us/sample - loss: 3.3737e-04 - acc: 1.0000\n", + "Epoch 206/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 3.3644e-04 - acc: 1.0000\n", + "Epoch 207/1000\n", + "26/26 [==============================] - 0s 356us/sample - loss: 3.3533e-04 - acc: 1.0000\n", + "Epoch 208/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 3.3433e-04 - acc: 1.0000\n", + "Epoch 209/1000\n", + "26/26 [==============================] - 0s 398us/sample - loss: 3.3337e-04 - acc: 1.0000\n", + "Epoch 210/1000\n", + "26/26 [==============================] - 0s 445us/sample - loss: 3.3237e-04 - acc: 1.0000\n", + "Epoch 211/1000\n", + "26/26 [==============================] - 0s 598us/sample - loss: 3.3145e-04 - acc: 1.0000\n", + "Epoch 212/1000\n", + "26/26 [==============================] - 0s 427us/sample - loss: 3.3054e-04 - acc: 1.0000\n", + "Epoch 213/1000\n", + "26/26 [==============================] - 0s 427us/sample - loss: 3.2956e-04 - acc: 1.0000\n", + "Epoch 214/1000\n", + "26/26 [==============================] - 0s 454us/sample - loss: 3.2869e-04 - acc: 1.0000\n", + "Epoch 215/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 3.2783e-04 - acc: 1.0000\n", + "Epoch 216/1000\n", + "26/26 [==============================] - 0s 380us/sample - loss: 3.2686e-04 - acc: 1.0000\n", + "Epoch 217/1000\n", + "26/26 [==============================] - 0s 399us/sample - loss: 3.2591e-04 - acc: 1.0000\n", + "Epoch 218/1000\n", + "26/26 [==============================] - 0s 376us/sample - loss: 3.2486e-04 - acc: 1.0000\n", + "Epoch 219/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 3.2388e-04 - acc: 1.0000\n", + "Epoch 220/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 3.2296e-04 - acc: 1.0000\n", + "Epoch 221/1000\n", + "26/26 [==============================] - 0s 333us/sample - loss: 3.2212e-04 - acc: 1.0000\n", + "Epoch 222/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 3.2120e-04 - acc: 1.0000\n", + "Epoch 223/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 3.2033e-04 - acc: 1.0000\n", + "Epoch 224/1000\n", + "26/26 [==============================] - 0s 353us/sample - loss: 3.1947e-04 - acc: 1.0000\n", + "Epoch 225/1000\n", + "26/26 [==============================] - 0s 356us/sample - loss: 3.1854e-04 - acc: 1.0000\n", + "Epoch 226/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 3.1776e-04 - acc: 1.0000\n", + "Epoch 227/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 3.1695e-04 - acc: 1.0000\n", + "Epoch 228/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 3.1607e-04 - acc: 1.0000\n", + "Epoch 229/1000\n", + "26/26 [==============================] - 0s 385us/sample - loss: 3.1524e-04 - acc: 1.0000\n", + "Epoch 230/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 3.1406e-04 - acc: 1.0000\n", + "Epoch 231/1000\n", + "26/26 [==============================] - 0s 408us/sample - loss: 3.1306e-04 - acc: 1.0000\n", + "Epoch 232/1000\n", + "26/26 [==============================] - 0s 373us/sample - loss: 3.1218e-04 - acc: 1.0000\n", + "Epoch 233/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 3.1125e-04 - acc: 1.0000\n", + "Epoch 234/1000\n", + "26/26 [==============================] - 0s 376us/sample - loss: 3.1037e-04 - acc: 1.0000\n", + "Epoch 235/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 3.0947e-04 - acc: 1.0000\n", + "Epoch 236/1000\n", + "26/26 [==============================] - 0s 375us/sample - loss: 3.0859e-04 - acc: 1.0000\n", + "Epoch 237/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 3.0773e-04 - acc: 1.0000\n", + "Epoch 238/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 3.0669e-04 - acc: 1.0000\n", + "Epoch 239/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 3.0594e-04 - acc: 1.0000\n", + "Epoch 240/1000\n", + "26/26 [==============================] - 0s 330us/sample - loss: 3.0485e-04 - acc: 1.0000\n", + "Epoch 241/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 3.0411e-04 - acc: 1.0000\n", + "Epoch 242/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 3.0312e-04 - acc: 1.0000\n", + "Epoch 243/1000\n", + "26/26 [==============================] - 0s 329us/sample - loss: 3.0236e-04 - acc: 1.0000\n", + "Epoch 244/1000\n", + "26/26 [==============================] - 0s 326us/sample - loss: 3.0151e-04 - acc: 1.0000\n", + "Epoch 245/1000\n", + "26/26 [==============================] - 0s 348us/sample - loss: 3.0056e-04 - acc: 1.0000\n", + "Epoch 246/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 2.9972e-04 - acc: 1.0000\n", + "Epoch 247/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 2.9881e-04 - acc: 1.0000\n", + "Epoch 248/1000\n", + "26/26 [==============================] - 0s 356us/sample - loss: 2.9802e-04 - acc: 1.0000\n", + "Epoch 249/1000\n", + "26/26 [==============================] - 0s 337us/sample - loss: 2.9726e-04 - acc: 1.0000\n", + "Epoch 250/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 2.9638e-04 - acc: 1.0000\n", + "Epoch 251/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 2.9573e-04 - acc: 1.0000\n", + "Epoch 252/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 2.9484e-04 - acc: 1.0000\n", + "Epoch 253/1000\n", + "26/26 [==============================] - 0s 380us/sample - loss: 2.9405e-04 - acc: 1.0000\n", + "Epoch 254/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 2.9316e-04 - acc: 1.0000\n", + "Epoch 255/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 2.9238e-04 - acc: 1.0000\n", + "Epoch 256/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 2.9157e-04 - acc: 1.0000\n", + "Epoch 257/1000\n", + "26/26 [==============================] - 0s 589us/sample - loss: 2.9080e-04 - acc: 1.0000\n", + "Epoch 258/1000\n", + "26/26 [==============================] - 0s 455us/sample - loss: 2.9004e-04 - acc: 1.0000\n", + "Epoch 259/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 2.8929e-04 - acc: 1.0000\n", + "Epoch 260/1000\n", + "26/26 [==============================] - 0s 452us/sample - loss: 2.8852e-04 - acc: 1.0000\n", + "Epoch 261/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 2.8779e-04 - acc: 1.0000\n", + "Epoch 262/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 2.8697e-04 - acc: 1.0000\n", + "Epoch 263/1000\n", + "26/26 [==============================] - 0s 449us/sample - loss: 2.8615e-04 - acc: 1.0000\n", + "Epoch 264/1000\n", + "26/26 [==============================] - 0s 372us/sample - loss: 2.8541e-04 - acc: 1.0000\n", + "Epoch 265/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 2.8456e-04 - acc: 1.0000\n", + "Epoch 266/1000\n", + "26/26 [==============================] - 0s 419us/sample - loss: 2.8375e-04 - acc: 1.0000\n", + "Epoch 267/1000\n", + "26/26 [==============================] - 0s 368us/sample - loss: 2.8294e-04 - acc: 1.0000\n", + "Epoch 268/1000\n", + "26/26 [==============================] - 0s 380us/sample - loss: 2.8213e-04 - acc: 1.0000\n", + "Epoch 269/1000\n", + "26/26 [==============================] - 0s 405us/sample - loss: 2.8135e-04 - acc: 1.0000\n", + "Epoch 270/1000\n", + "26/26 [==============================] - 0s 417us/sample - loss: 2.8050e-04 - acc: 1.0000\n", + "Epoch 271/1000\n", + "26/26 [==============================] - 0s 370us/sample - loss: 2.7965e-04 - acc: 1.0000\n", + "Epoch 272/1000\n", + "26/26 [==============================] - 0s 370us/sample - loss: 2.7887e-04 - acc: 1.0000\n", + "Epoch 273/1000\n", + "26/26 [==============================] - 0s 331us/sample - loss: 2.7803e-04 - acc: 1.0000\n", + "Epoch 274/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 2.7721e-04 - acc: 1.0000\n", + "Epoch 275/1000\n", + "26/26 [==============================] - 0s 404us/sample - loss: 2.7646e-04 - acc: 1.0000\n", + "Epoch 276/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 2.7566e-04 - acc: 1.0000\n", + "Epoch 277/1000\n", + "26/26 [==============================] - 0s 442us/sample - loss: 2.7489e-04 - acc: 1.0000\n", + "Epoch 278/1000\n", + "26/26 [==============================] - 0s 494us/sample - loss: 2.7412e-04 - acc: 1.0000\n", + "Epoch 279/1000\n", + "26/26 [==============================] - 0s 494us/sample - loss: 2.7339e-04 - acc: 1.0000\n", + "Epoch 280/1000\n", + "26/26 [==============================] - 0s 542us/sample - loss: 2.7262e-04 - acc: 1.0000\n", + "Epoch 281/1000\n", + "26/26 [==============================] - 0s 413us/sample - loss: 2.7190e-04 - acc: 1.0000\n", + "Epoch 282/1000\n", + "26/26 [==============================] - 0s 409us/sample - loss: 2.7113e-04 - acc: 1.0000\n", + "Epoch 283/1000\n", + "26/26 [==============================] - 0s 636us/sample - loss: 2.7037e-04 - acc: 1.0000\n", + "Epoch 284/1000\n", + "26/26 [==============================] - 0s 504us/sample - loss: 2.6961e-04 - acc: 1.0000\n", + "Epoch 285/1000\n", + "26/26 [==============================] - 0s 594us/sample - loss: 2.6897e-04 - acc: 1.0000\n", + "Epoch 286/1000\n", + "26/26 [==============================] - 0s 410us/sample - loss: 2.6817e-04 - acc: 1.0000\n", + "Epoch 287/1000\n", + "26/26 [==============================] - 0s 470us/sample - loss: 2.6757e-04 - acc: 1.0000\n", + "Epoch 288/1000\n", + "26/26 [==============================] - 0s 378us/sample - loss: 2.6677e-04 - acc: 1.0000\n", + "Epoch 289/1000\n", + "26/26 [==============================] - 0s 344us/sample - loss: 2.6610e-04 - acc: 1.0000\n", + "Epoch 290/1000\n", + "26/26 [==============================] - 0s 638us/sample - loss: 2.6534e-04 - acc: 1.0000\n", + "Epoch 291/1000\n", + "26/26 [==============================] - 0s 378us/sample - loss: 2.6457e-04 - acc: 1.0000\n", + "Epoch 292/1000\n", + "26/26 [==============================] - 0s 332us/sample - loss: 2.6381e-04 - acc: 1.0000\n", + "Epoch 293/1000\n", + "26/26 [==============================] - 0s 329us/sample - loss: 2.6301e-04 - acc: 1.0000\n", + "Epoch 294/1000\n", + "26/26 [==============================] - 0s 430us/sample - loss: 2.6217e-04 - acc: 1.0000\n", + "Epoch 295/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 2.6147e-04 - acc: 1.0000\n", + "Epoch 296/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 2.6074e-04 - acc: 1.0000\n", + "Epoch 297/1000\n", + "26/26 [==============================] - 0s 543us/sample - loss: 2.6007e-04 - acc: 1.0000\n", + "Epoch 298/1000\n", + "26/26 [==============================] - 0s 324us/sample - loss: 2.5931e-04 - acc: 1.0000\n", + "Epoch 299/1000\n", + "26/26 [==============================] - 0s 323us/sample - loss: 2.5868e-04 - acc: 1.0000\n", + "Epoch 300/1000\n", + "26/26 [==============================] - 0s 303us/sample - loss: 2.5799e-04 - acc: 1.0000\n", + "Epoch 301/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 2.5726e-04 - acc: 1.0000\n", + "Epoch 302/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 2.5665e-04 - acc: 1.0000\n", + "Epoch 303/1000\n", + "26/26 [==============================] - 0s 322us/sample - loss: 2.5590e-04 - acc: 1.0000\n", + "Epoch 304/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 2.5522e-04 - acc: 1.0000\n", + "Epoch 305/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 2.5457e-04 - acc: 1.0000\n", + "Epoch 306/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 2.5383e-04 - acc: 1.0000\n", + "Epoch 307/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 2.5306e-04 - acc: 1.0000\n", + "Epoch 308/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 2.5241e-04 - acc: 1.0000\n", + "Epoch 309/1000\n", + "26/26 [==============================] - 0s 407us/sample - loss: 2.5162e-04 - acc: 1.0000\n", + "Epoch 310/1000\n", + "26/26 [==============================] - 0s 432us/sample - loss: 2.5088e-04 - acc: 1.0000\n", + "Epoch 311/1000\n", + "26/26 [==============================] - 0s 368us/sample - loss: 2.5025e-04 - acc: 1.0000\n", + "Epoch 312/1000\n", + "26/26 [==============================] - 0s 331us/sample - loss: 2.4951e-04 - acc: 1.0000\n", + "Epoch 313/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 2.4893e-04 - acc: 1.0000\n", + "Epoch 314/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 2.4823e-04 - acc: 1.0000\n", + "Epoch 315/1000\n", + "26/26 [==============================] - 0s 325us/sample - loss: 2.4755e-04 - acc: 1.0000\n", + "Epoch 316/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 2.4690e-04 - acc: 1.0000\n", + "Epoch 317/1000\n", + "26/26 [==============================] - 0s 336us/sample - loss: 2.4608e-04 - acc: 1.0000\n", + "Epoch 318/1000\n", + "26/26 [==============================] - 0s 511us/sample - loss: 2.4566e-04 - acc: 1.0000\n", + "Epoch 319/1000\n", + "26/26 [==============================] - 0s 391us/sample - loss: 2.4475e-04 - acc: 1.0000\n", + "Epoch 320/1000\n", + "26/26 [==============================] - 0s 350us/sample - loss: 2.4399e-04 - acc: 1.0000\n", + "Epoch 321/1000\n", + "26/26 [==============================] - 0s 325us/sample - loss: 2.4329e-04 - acc: 1.0000\n", + "Epoch 322/1000\n", + "26/26 [==============================] - 0s 373us/sample - loss: 2.4260e-04 - acc: 1.0000\n", + "Epoch 323/1000\n", + "26/26 [==============================] - 0s 328us/sample - loss: 2.4190e-04 - acc: 1.0000\n", + "Epoch 324/1000\n", + "26/26 [==============================] - 0s 340us/sample - loss: 2.4122e-04 - acc: 1.0000\n", + "Epoch 325/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 2.4058e-04 - acc: 1.0000\n", + "Epoch 326/1000\n", + "26/26 [==============================] - 0s 356us/sample - loss: 2.3995e-04 - acc: 1.0000\n", + "Epoch 327/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 2.3926e-04 - acc: 1.0000\n", + "Epoch 328/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 2.3859e-04 - acc: 1.0000\n", + "Epoch 329/1000\n", + "26/26 [==============================] - 0s 323us/sample - loss: 2.3790e-04 - acc: 1.0000\n", + "Epoch 330/1000\n", + "26/26 [==============================] - 0s 309us/sample - loss: 2.3726e-04 - acc: 1.0000\n", + "Epoch 331/1000\n", + "26/26 [==============================] - 0s 315us/sample - loss: 2.3659e-04 - acc: 1.0000\n", + "Epoch 332/1000\n", + "26/26 [==============================] - 0s 298us/sample - loss: 2.3590e-04 - acc: 1.0000\n", + "Epoch 333/1000\n", + "26/26 [==============================] - 0s 310us/sample - loss: 2.3523e-04 - acc: 1.0000\n", + "Epoch 334/1000\n", + "26/26 [==============================] - 0s 293us/sample - loss: 2.3464e-04 - acc: 1.0000\n", + "Epoch 335/1000\n", + "26/26 [==============================] - 0s 286us/sample - loss: 2.3400e-04 - acc: 1.0000\n", + "Epoch 336/1000\n", + "26/26 [==============================] - 0s 344us/sample - loss: 2.3331e-04 - acc: 1.0000\n", + "Epoch 337/1000\n", + "26/26 [==============================] - 0s 317us/sample - loss: 2.3267e-04 - acc: 1.0000\n", + "Epoch 338/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 2.3206e-04 - acc: 1.0000\n", + "Epoch 339/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 2.3138e-04 - acc: 1.0000\n", + "Epoch 340/1000\n", + "26/26 [==============================] - 0s 325us/sample - loss: 2.3071e-04 - acc: 1.0000\n", + "Epoch 341/1000\n", + "26/26 [==============================] - 0s 320us/sample - loss: 2.3019e-04 - acc: 1.0000\n", + "Epoch 342/1000\n", + "26/26 [==============================] - 0s 520us/sample - loss: 2.2946e-04 - acc: 1.0000\n", + "Epoch 343/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 2.2875e-04 - acc: 1.0000\n", + "Epoch 344/1000\n", + "26/26 [==============================] - 0s 344us/sample - loss: 2.2818e-04 - acc: 1.0000\n", + "Epoch 345/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 2.2755e-04 - acc: 1.0000\n", + "Epoch 346/1000\n", + "26/26 [==============================] - 0s 461us/sample - loss: 2.2696e-04 - acc: 1.0000\n", + "Epoch 347/1000\n", + "26/26 [==============================] - 0s 368us/sample - loss: 2.2631e-04 - acc: 1.0000\n", + "Epoch 348/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 2.2572e-04 - acc: 1.0000\n", + "Epoch 349/1000\n", + "26/26 [==============================] - 0s 433us/sample - loss: 2.2508e-04 - acc: 1.0000\n", + "Epoch 350/1000\n", + "26/26 [==============================] - 0s 431us/sample - loss: 2.2452e-04 - acc: 1.0000\n", + "Epoch 351/1000\n", + "26/26 [==============================] - 0s 396us/sample - loss: 2.2401e-04 - acc: 1.0000\n", + "Epoch 352/1000\n", + "26/26 [==============================] - 0s 385us/sample - loss: 2.2339e-04 - acc: 1.0000\n", + "Epoch 353/1000\n", + "26/26 [==============================] - 0s 429us/sample - loss: 2.2286e-04 - acc: 1.0000\n", + "Epoch 354/1000\n", + "26/26 [==============================] - 0s 333us/sample - loss: 2.2235e-04 - acc: 1.0000\n", + "Epoch 355/1000\n", + "26/26 [==============================] - 0s 373us/sample - loss: 2.2176e-04 - acc: 1.0000\n", + "Epoch 356/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 2.2122e-04 - acc: 1.0000\n", + "Epoch 357/1000\n", + "26/26 [==============================] - 0s 410us/sample - loss: 2.2063e-04 - acc: 1.0000\n", + "Epoch 358/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 2.1996e-04 - acc: 1.0000\n", + "Epoch 359/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 2.1935e-04 - acc: 1.0000\n", + "Epoch 360/1000\n", + "26/26 [==============================] - 0s 406us/sample - loss: 2.1873e-04 - acc: 1.0000\n", + "Epoch 361/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 2.1814e-04 - acc: 1.0000\n", + "Epoch 362/1000\n", + "26/26 [==============================] - 0s 470us/sample - loss: 2.1755e-04 - acc: 1.0000\n", + "Epoch 363/1000\n", + "26/26 [==============================] - 0s 432us/sample - loss: 2.1690e-04 - acc: 1.0000\n", + "Epoch 364/1000\n", + "26/26 [==============================] - 0s 494us/sample - loss: 2.1640e-04 - acc: 1.0000\n", + "Epoch 365/1000\n", + "26/26 [==============================] - 0s 463us/sample - loss: 2.1583e-04 - acc: 1.0000\n", + "Epoch 366/1000\n", + "26/26 [==============================] - 0s 417us/sample - loss: 2.1532e-04 - acc: 1.0000\n", + "Epoch 367/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 2.1482e-04 - acc: 1.0000\n", + "Epoch 368/1000\n", + "26/26 [==============================] - 0s 386us/sample - loss: 2.1423e-04 - acc: 1.0000\n", + "Epoch 369/1000\n", + "26/26 [==============================] - 0s 407us/sample - loss: 2.1367e-04 - acc: 1.0000\n", + "Epoch 370/1000\n", + "26/26 [==============================] - 0s 469us/sample - loss: 2.1316e-04 - acc: 1.0000\n", + "Epoch 371/1000\n", + "26/26 [==============================] - 0s 545us/sample - loss: 2.1259e-04 - acc: 1.0000\n", + "Epoch 372/1000\n", + "26/26 [==============================] - 0s 411us/sample - loss: 2.1207e-04 - acc: 1.0000\n", + "Epoch 373/1000\n", + "26/26 [==============================] - 0s 380us/sample - loss: 2.1153e-04 - acc: 1.0000\n", + "Epoch 374/1000\n", + "26/26 [==============================] - 0s 355us/sample - loss: 2.1096e-04 - acc: 1.0000\n", + "Epoch 375/1000\n", + "26/26 [==============================] - 0s 327us/sample - loss: 2.1042e-04 - acc: 1.0000\n", + "Epoch 376/1000\n", + "26/26 [==============================] - 0s 375us/sample - loss: 2.0986e-04 - acc: 1.0000\n", + "Epoch 377/1000\n", + "26/26 [==============================] - 0s 372us/sample - loss: 2.0933e-04 - acc: 1.0000\n", + "Epoch 378/1000\n", + "26/26 [==============================] - 0s 336us/sample - loss: 2.0871e-04 - acc: 1.0000\n", + "Epoch 379/1000\n", + "26/26 [==============================] - 0s 353us/sample - loss: 2.0823e-04 - acc: 1.0000\n", + "Epoch 380/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 2.0757e-04 - acc: 1.0000\n", + "Epoch 381/1000\n", + "26/26 [==============================] - 0s 320us/sample - loss: 2.0711e-04 - acc: 1.0000\n", + "Epoch 382/1000\n", + "26/26 [==============================] - 0s 334us/sample - loss: 2.0659e-04 - acc: 1.0000\n", + "Epoch 383/1000\n", + "26/26 [==============================] - 0s 360us/sample - loss: 2.0599e-04 - acc: 1.0000\n", + "Epoch 384/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 2.0552e-04 - acc: 1.0000\n", + "Epoch 385/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 2.0501e-04 - acc: 1.0000\n", + "Epoch 386/1000\n", + "26/26 [==============================] - 0s 333us/sample - loss: 2.0444e-04 - acc: 1.0000\n", + "Epoch 387/1000\n", + "26/26 [==============================] - 0s 315us/sample - loss: 2.0394e-04 - acc: 1.0000\n", + "Epoch 388/1000\n", + "26/26 [==============================] - 0s 340us/sample - loss: 2.0331e-04 - acc: 1.0000\n", + "Epoch 389/1000\n", + "26/26 [==============================] - 0s 356us/sample - loss: 2.0276e-04 - acc: 1.0000\n", + "Epoch 390/1000\n", + "26/26 [==============================] - 0s 334us/sample - loss: 2.0224e-04 - acc: 1.0000\n", + "Epoch 391/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 2.0169e-04 - acc: 1.0000\n", + "Epoch 392/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 2.0109e-04 - acc: 1.0000\n", + "Epoch 393/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 2.0056e-04 - acc: 1.0000\n", + "Epoch 394/1000\n", + "26/26 [==============================] - 0s 389us/sample - loss: 1.9998e-04 - acc: 1.0000\n", + "Epoch 395/1000\n", + "26/26 [==============================] - 0s 353us/sample - loss: 1.9946e-04 - acc: 1.0000\n", + "Epoch 396/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 1.9895e-04 - acc: 1.0000\n", + "Epoch 397/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 1.9837e-04 - acc: 1.0000\n", + "Epoch 398/1000\n", + "26/26 [==============================] - 0s 335us/sample - loss: 1.9782e-04 - acc: 1.0000\n", + "Epoch 399/1000\n", + "26/26 [==============================] - 0s 337us/sample - loss: 1.9733e-04 - acc: 1.0000\n", + "Epoch 400/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 1.9670e-04 - acc: 1.0000\n", + "Epoch 401/1000\n", + "26/26 [==============================] - 0s 353us/sample - loss: 1.9611e-04 - acc: 1.0000\n", + "Epoch 402/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 1.9560e-04 - acc: 1.0000\n", + "Epoch 403/1000\n", + "26/26 [==============================] - 0s 376us/sample - loss: 1.9508e-04 - acc: 1.0000\n", + "Epoch 404/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 1.9454e-04 - acc: 1.0000\n", + "Epoch 405/1000\n", + "26/26 [==============================] - 0s 333us/sample - loss: 1.9402e-04 - acc: 1.0000\n", + "Epoch 406/1000\n", + "26/26 [==============================] - 0s 323us/sample - loss: 1.9357e-04 - acc: 1.0000\n", + "Epoch 407/1000\n", + "26/26 [==============================] - 0s 329us/sample - loss: 1.9299e-04 - acc: 1.0000\n", + "Epoch 408/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 1.9245e-04 - acc: 1.0000\n", + "Epoch 409/1000\n", + "26/26 [==============================] - 0s 386us/sample - loss: 1.9186e-04 - acc: 1.0000\n", + "Epoch 410/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 1.9137e-04 - acc: 1.0000\n", + "Epoch 411/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 1.9081e-04 - acc: 1.0000\n", + "Epoch 412/1000\n", + "26/26 [==============================] - 0s 445us/sample - loss: 1.9030e-04 - acc: 1.0000\n", + "Epoch 413/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 1.8973e-04 - acc: 1.0000\n", + "Epoch 414/1000\n", + "26/26 [==============================] - 0s 378us/sample - loss: 1.8921e-04 - acc: 1.0000\n", + "Epoch 415/1000\n", + "26/26 [==============================] - 0s 335us/sample - loss: 1.8865e-04 - acc: 1.0000\n", + "Epoch 416/1000\n", + "26/26 [==============================] - 0s 336us/sample - loss: 1.8814e-04 - acc: 1.0000\n", + "Epoch 417/1000\n", + "26/26 [==============================] - 0s 348us/sample - loss: 1.8760e-04 - acc: 1.0000\n", + "Epoch 418/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 1.8708e-04 - acc: 1.0000\n", + "Epoch 419/1000\n", + "26/26 [==============================] - 0s 340us/sample - loss: 1.8656e-04 - acc: 1.0000\n", + "Epoch 420/1000\n", + "26/26 [==============================] - 0s 349us/sample - loss: 1.8609e-04 - acc: 1.0000\n", + "Epoch 421/1000\n", + "26/26 [==============================] - 0s 325us/sample - loss: 1.8557e-04 - acc: 1.0000\n", + "Epoch 422/1000\n", + "26/26 [==============================] - 0s 349us/sample - loss: 1.8506e-04 - acc: 1.0000\n", + "Epoch 423/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 1.8460e-04 - acc: 1.0000\n", + "Epoch 424/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 1.8412e-04 - acc: 1.0000\n", + "Epoch 425/1000\n", + "26/26 [==============================] - 0s 337us/sample - loss: 1.8361e-04 - acc: 1.0000\n", + "Epoch 426/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 1.8308e-04 - acc: 1.0000\n", + "Epoch 427/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 1.8252e-04 - acc: 1.0000\n", + "Epoch 428/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 1.8204e-04 - acc: 1.0000\n", + "Epoch 429/1000\n", + "26/26 [==============================] - 0s 360us/sample - loss: 1.8153e-04 - acc: 1.0000\n", + "Epoch 430/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 1.8104e-04 - acc: 1.0000\n", + "Epoch 431/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 1.8052e-04 - acc: 1.0000\n", + "Epoch 432/1000\n", + "26/26 [==============================] - 0s 306us/sample - loss: 1.8003e-04 - acc: 1.0000\n", + "Epoch 433/1000\n", + "26/26 [==============================] - 0s 330us/sample - loss: 1.7958e-04 - acc: 1.0000\n", + "Epoch 434/1000\n", + "26/26 [==============================] - 0s 325us/sample - loss: 1.7909e-04 - acc: 1.0000\n", + "Epoch 435/1000\n", + "26/26 [==============================] - 0s 329us/sample - loss: 1.7865e-04 - acc: 1.0000\n", + "Epoch 436/1000\n", + "26/26 [==============================] - 0s 430us/sample - loss: 1.7820e-04 - acc: 1.0000\n", + "Epoch 437/1000\n", + "26/26 [==============================] - 0s 494us/sample - loss: 1.7773e-04 - acc: 1.0000\n", + "Epoch 438/1000\n", + "26/26 [==============================] - 0s 355us/sample - loss: 1.7730e-04 - acc: 1.0000\n", + "Epoch 439/1000\n", + "26/26 [==============================] - 0s 498us/sample - loss: 1.7684e-04 - acc: 1.0000\n", + "Epoch 440/1000\n", + "26/26 [==============================] - 0s 472us/sample - loss: 1.7636e-04 - acc: 1.0000\n", + "Epoch 441/1000\n", + "26/26 [==============================] - 0s 566us/sample - loss: 1.7584e-04 - acc: 1.0000\n", + "Epoch 442/1000\n", + "26/26 [==============================] - 0s 498us/sample - loss: 1.7542e-04 - acc: 1.0000\n", + "Epoch 443/1000\n", + "26/26 [==============================] - 0s 608us/sample - loss: 1.7497e-04 - acc: 1.0000\n", + "Epoch 444/1000\n", + "26/26 [==============================] - 0s 445us/sample - loss: 1.7453e-04 - acc: 1.0000\n", + "Epoch 445/1000\n", + "26/26 [==============================] - 0s 348us/sample - loss: 1.7408e-04 - acc: 1.0000\n", + "Epoch 446/1000\n", + "26/26 [==============================] - 0s 507us/sample - loss: 1.7364e-04 - acc: 1.0000\n", + "Epoch 447/1000\n", + "26/26 [==============================] - 0s 407us/sample - loss: 1.7321e-04 - acc: 1.0000\n", + "Epoch 448/1000\n", + "26/26 [==============================] - 0s 542us/sample - loss: 1.7278e-04 - acc: 1.0000\n", + "Epoch 449/1000\n", + "26/26 [==============================] - 0s 468us/sample - loss: 1.7233e-04 - acc: 1.0000\n", + "Epoch 450/1000\n", + "26/26 [==============================] - 0s 380us/sample - loss: 1.7177e-04 - acc: 1.0000\n", + "Epoch 451/1000\n", + "26/26 [==============================] - 0s 545us/sample - loss: 1.7130e-04 - acc: 1.0000\n", + "Epoch 452/1000\n", + "26/26 [==============================] - 0s 430us/sample - loss: 1.7095e-04 - acc: 1.0000\n", + "Epoch 453/1000\n", + "26/26 [==============================] - 0s 555us/sample - loss: 1.7051e-04 - acc: 1.0000\n", + "Epoch 454/1000\n", + "26/26 [==============================] - 0s 610us/sample - loss: 1.7007e-04 - acc: 1.0000\n", + "Epoch 455/1000\n", + "26/26 [==============================] - 0s 449us/sample - loss: 1.6958e-04 - acc: 1.0000\n", + "Epoch 456/1000\n", + "26/26 [==============================] - 0s 431us/sample - loss: 1.6918e-04 - acc: 1.0000\n", + "Epoch 457/1000\n", + "26/26 [==============================] - 0s 513us/sample - loss: 1.6872e-04 - acc: 1.0000\n", + "Epoch 458/1000\n", + "26/26 [==============================] - 0s 437us/sample - loss: 1.6825e-04 - acc: 1.0000\n", + "Epoch 459/1000\n", + "26/26 [==============================] - 0s 446us/sample - loss: 1.6780e-04 - acc: 1.0000\n", + "Epoch 460/1000\n", + "26/26 [==============================] - 0s 372us/sample - loss: 1.6739e-04 - acc: 1.0000\n", + "Epoch 461/1000\n", + "26/26 [==============================] - 0s 384us/sample - loss: 1.6693e-04 - acc: 1.0000\n", + "Epoch 462/1000\n", + "26/26 [==============================] - 0s 621us/sample - loss: 1.6645e-04 - acc: 1.0000\n", + "Epoch 463/1000\n", + "26/26 [==============================] - 0s 484us/sample - loss: 1.6606e-04 - acc: 1.0000\n", + "Epoch 464/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 1.6560e-04 - acc: 1.0000\n", + "Epoch 465/1000\n", + "26/26 [==============================] - 0s 525us/sample - loss: 1.6518e-04 - acc: 1.0000\n", + "Epoch 466/1000\n", + "26/26 [==============================] - 0s 504us/sample - loss: 1.6475e-04 - acc: 1.0000\n", + "Epoch 467/1000\n", + "26/26 [==============================] - 0s 398us/sample - loss: 1.6428e-04 - acc: 1.0000\n", + "Epoch 468/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 1.6377e-04 - acc: 1.0000\n", + "Epoch 469/1000\n", + "26/26 [==============================] - 0s 424us/sample - loss: 1.6341e-04 - acc: 1.0000\n", + "Epoch 470/1000\n", + "26/26 [==============================] - 0s 618us/sample - loss: 1.6292e-04 - acc: 1.0000\n", + "Epoch 471/1000\n", + "26/26 [==============================] - 0s 482us/sample - loss: 1.6249e-04 - acc: 1.0000\n", + "Epoch 472/1000\n", + "26/26 [==============================] - 0s 444us/sample - loss: 1.6202e-04 - acc: 1.0000\n", + "Epoch 473/1000\n", + "26/26 [==============================] - 0s 408us/sample - loss: 1.6154e-04 - acc: 1.0000\n", + "Epoch 474/1000\n", + "26/26 [==============================] - 0s 376us/sample - loss: 1.6110e-04 - acc: 1.0000\n", + "Epoch 475/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 1.6077e-04 - acc: 1.0000\n", + "Epoch 476/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 1.6025e-04 - acc: 1.0000\n", + "Epoch 477/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 1.5984e-04 - acc: 1.0000\n", + "Epoch 478/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 1.5945e-04 - acc: 1.0000\n", + "Epoch 479/1000\n", + "26/26 [==============================] - 0s 429us/sample - loss: 1.5904e-04 - acc: 1.0000\n", + "Epoch 480/1000\n", + "26/26 [==============================] - 0s 476us/sample - loss: 1.5862e-04 - acc: 1.0000\n", + "Epoch 481/1000\n", + "26/26 [==============================] - 0s 385us/sample - loss: 1.5816e-04 - acc: 1.0000\n", + "Epoch 482/1000\n", + "26/26 [==============================] - 0s 344us/sample - loss: 1.5771e-04 - acc: 1.0000\n", + "Epoch 483/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 1.5731e-04 - acc: 1.0000\n", + "Epoch 484/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 1.5691e-04 - acc: 1.0000\n", + "Epoch 485/1000\n", + "26/26 [==============================] - 0s 335us/sample - loss: 1.5646e-04 - acc: 1.0000\n", + "Epoch 486/1000\n", + "26/26 [==============================] - 0s 336us/sample - loss: 1.5604e-04 - acc: 1.0000\n", + "Epoch 487/1000\n", + "26/26 [==============================] - 0s 323us/sample - loss: 1.5558e-04 - acc: 1.0000\n", + "Epoch 488/1000\n", + "26/26 [==============================] - 0s 343us/sample - loss: 1.5520e-04 - acc: 1.0000\n", + "Epoch 489/1000\n", + "26/26 [==============================] - 0s 398us/sample - loss: 1.5482e-04 - acc: 1.0000\n", + "Epoch 490/1000\n", + "26/26 [==============================] - 0s 437us/sample - loss: 1.5439e-04 - acc: 1.0000\n", + "Epoch 491/1000\n", + "26/26 [==============================] - 0s 408us/sample - loss: 1.5402e-04 - acc: 1.0000\n", + "Epoch 492/1000\n", + "26/26 [==============================] - 0s 609us/sample - loss: 1.5363e-04 - acc: 1.0000\n", + "Epoch 493/1000\n", + "26/26 [==============================] - 0s 498us/sample - loss: 1.5326e-04 - acc: 1.0000\n", + "Epoch 494/1000\n", + "26/26 [==============================] - 0s 542us/sample - loss: 1.5288e-04 - acc: 1.0000\n", + "Epoch 495/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 1.5252e-04 - acc: 1.0000\n", + "Epoch 496/1000\n", + "26/26 [==============================] - 0s 373us/sample - loss: 1.5210e-04 - acc: 1.0000\n", + "Epoch 497/1000\n", + "26/26 [==============================] - 0s 403us/sample - loss: 1.5170e-04 - acc: 1.0000\n", + "Epoch 498/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 1.5132e-04 - acc: 1.0000\n", + "Epoch 499/1000\n", + "26/26 [==============================] - 0s 577us/sample - loss: 1.5093e-04 - acc: 1.0000\n", + "Epoch 500/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 1.5052e-04 - acc: 1.0000\n", + "Epoch 501/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 1.5010e-04 - acc: 1.0000\n", + "Epoch 502/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 1.4964e-04 - acc: 1.0000\n", + "Epoch 503/1000\n", + "26/26 [==============================] - 0s 418us/sample - loss: 1.4927e-04 - acc: 1.0000\n", + "Epoch 504/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 1.4883e-04 - acc: 1.0000\n", + "Epoch 505/1000\n", + "26/26 [==============================] - 0s 470us/sample - loss: 1.4845e-04 - acc: 1.0000\n", + "Epoch 506/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 1.4800e-04 - acc: 1.0000\n", + "Epoch 507/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 1.4762e-04 - acc: 1.0000\n", + "Epoch 508/1000\n", + "26/26 [==============================] - 0s 447us/sample - loss: 1.4727e-04 - acc: 1.0000\n", + "Epoch 509/1000\n", + "26/26 [==============================] - 0s 446us/sample - loss: 1.4687e-04 - acc: 1.0000\n", + "Epoch 510/1000\n", + "26/26 [==============================] - 0s 453us/sample - loss: 1.4644e-04 - acc: 1.0000\n", + "Epoch 511/1000\n", + "26/26 [==============================] - 0s 577us/sample - loss: 1.4611e-04 - acc: 1.0000\n", + "Epoch 512/1000\n", + "26/26 [==============================] - 0s 527us/sample - loss: 1.4567e-04 - acc: 1.0000\n", + "Epoch 513/1000\n", + "26/26 [==============================] - 0s 493us/sample - loss: 1.4531e-04 - acc: 1.0000\n", + "Epoch 514/1000\n", + "26/26 [==============================] - 0s 484us/sample - loss: 1.4491e-04 - acc: 1.0000\n", + "Epoch 515/1000\n", + "26/26 [==============================] - 0s 404us/sample - loss: 1.4457e-04 - acc: 1.0000\n", + "Epoch 516/1000\n", + "26/26 [==============================] - 0s 391us/sample - loss: 1.4421e-04 - acc: 1.0000\n", + "Epoch 517/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 1.4387e-04 - acc: 1.0000\n", + "Epoch 518/1000\n", + "26/26 [==============================] - 0s 474us/sample - loss: 1.4346e-04 - acc: 1.0000\n", + "Epoch 519/1000\n", + "26/26 [==============================] - 0s 485us/sample - loss: 1.4308e-04 - acc: 1.0000\n", + "Epoch 520/1000\n", + "26/26 [==============================] - 0s 433us/sample - loss: 1.4269e-04 - acc: 1.0000\n", + "Epoch 521/1000\n", + "26/26 [==============================] - 0s 409us/sample - loss: 1.4230e-04 - acc: 1.0000\n", + "Epoch 522/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 1.4188e-04 - acc: 1.0000\n", + "Epoch 523/1000\n", + "26/26 [==============================] - 0s 416us/sample - loss: 1.4151e-04 - acc: 1.0000\n", + "Epoch 524/1000\n", + "26/26 [==============================] - 0s 408us/sample - loss: 1.4117e-04 - acc: 1.0000\n", + "Epoch 525/1000\n", + "26/26 [==============================] - 0s 439us/sample - loss: 1.4076e-04 - acc: 1.0000\n", + "Epoch 526/1000\n", + "26/26 [==============================] - 0s 496us/sample - loss: 1.4040e-04 - acc: 1.0000\n", + "Epoch 527/1000\n", + "26/26 [==============================] - 0s 583us/sample - loss: 1.4004e-04 - acc: 1.0000\n", + "Epoch 528/1000\n", + "26/26 [==============================] - 0s 403us/sample - loss: 1.3971e-04 - acc: 1.0000\n", + "Epoch 529/1000\n", + "26/26 [==============================] - 0s 539us/sample - loss: 1.3935e-04 - acc: 1.0000\n", + "Epoch 530/1000\n", + "26/26 [==============================] - 0s 766us/sample - loss: 1.3896e-04 - acc: 1.0000\n", + "Epoch 531/1000\n", + "26/26 [==============================] - 0s 596us/sample - loss: 1.3864e-04 - acc: 1.0000\n", + "Epoch 532/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 1.3830e-04 - acc: 1.0000\n", + "Epoch 533/1000\n", + "26/26 [==============================] - 0s 377us/sample - loss: 1.3795e-04 - acc: 1.0000\n", + "Epoch 534/1000\n", + "26/26 [==============================] - 0s 355us/sample - loss: 1.3762e-04 - acc: 1.0000\n", + "Epoch 535/1000\n", + "26/26 [==============================] - 0s 344us/sample - loss: 1.3729e-04 - acc: 1.0000\n", + "Epoch 536/1000\n", + "26/26 [==============================] - 0s 335us/sample - loss: 1.3692e-04 - acc: 1.0000\n", + "Epoch 537/1000\n", + "26/26 [==============================] - 0s 325us/sample - loss: 1.3657e-04 - acc: 1.0000\n", + "Epoch 538/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 1.3626e-04 - acc: 1.0000\n", + "Epoch 539/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 1.3589e-04 - acc: 1.0000\n", + "Epoch 540/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 1.3551e-04 - acc: 1.0000\n", + "Epoch 541/1000\n", + "26/26 [==============================] - 0s 349us/sample - loss: 1.3522e-04 - acc: 1.0000\n", + "Epoch 542/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 1.3488e-04 - acc: 1.0000\n", + "Epoch 543/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 1.3453e-04 - acc: 1.0000\n", + "Epoch 544/1000\n", + "26/26 [==============================] - 0s 372us/sample - loss: 1.3423e-04 - acc: 1.0000\n", + "Epoch 545/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 1.3395e-04 - acc: 1.0000\n", + "Epoch 546/1000\n", + "26/26 [==============================] - 0s 316us/sample - loss: 1.3360e-04 - acc: 1.0000\n", + "Epoch 547/1000\n", + "26/26 [==============================] - 0s 332us/sample - loss: 1.3329e-04 - acc: 1.0000\n", + "Epoch 548/1000\n", + "26/26 [==============================] - 0s 334us/sample - loss: 1.3296e-04 - acc: 1.0000\n", + "Epoch 549/1000\n", + "26/26 [==============================] - 0s 343us/sample - loss: 1.3260e-04 - acc: 1.0000\n", + "Epoch 550/1000\n", + "26/26 [==============================] - 0s 389us/sample - loss: 1.3228e-04 - acc: 1.0000\n", + "Epoch 551/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 1.3184e-04 - acc: 1.0000\n", + "Epoch 552/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 1.3157e-04 - acc: 1.0000\n", + "Epoch 553/1000\n", + "26/26 [==============================] - 0s 437us/sample - loss: 1.3115e-04 - acc: 1.0000\n", + "Epoch 554/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 1.3081e-04 - acc: 1.0000\n", + "Epoch 555/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 1.3044e-04 - acc: 1.0000\n", + "Epoch 556/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 1.3007e-04 - acc: 1.0000\n", + "Epoch 557/1000\n", + "26/26 [==============================] - 0s 411us/sample - loss: 1.2974e-04 - acc: 1.0000\n", + "Epoch 558/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 1.2944e-04 - acc: 1.0000\n", + "Epoch 559/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 1.2912e-04 - acc: 1.0000\n", + "Epoch 560/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 1.2880e-04 - acc: 1.0000\n", + "Epoch 561/1000\n", + "26/26 [==============================] - 0s 373us/sample - loss: 1.2849e-04 - acc: 1.0000\n", + "Epoch 562/1000\n", + "26/26 [==============================] - 0s 343us/sample - loss: 1.2816e-04 - acc: 1.0000\n", + "Epoch 563/1000\n", + "26/26 [==============================] - 0s 404us/sample - loss: 1.2785e-04 - acc: 1.0000\n", + "Epoch 564/1000\n", + "26/26 [==============================] - 0s 403us/sample - loss: 1.2754e-04 - acc: 1.0000\n", + "Epoch 565/1000\n", + "26/26 [==============================] - 0s 410us/sample - loss: 1.2720e-04 - acc: 1.0000\n", + "Epoch 566/1000\n", + "26/26 [==============================] - 0s 404us/sample - loss: 1.2689e-04 - acc: 1.0000\n", + "Epoch 567/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 1.2657e-04 - acc: 1.0000\n", + "Epoch 568/1000\n", + "26/26 [==============================] - 0s 355us/sample - loss: 1.2629e-04 - acc: 1.0000\n", + "Epoch 569/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 1.2596e-04 - acc: 1.0000\n", + "Epoch 570/1000\n", + "26/26 [==============================] - 0s 377us/sample - loss: 1.2563e-04 - acc: 1.0000\n", + "Epoch 571/1000\n", + "26/26 [==============================] - 0s 332us/sample - loss: 1.2531e-04 - acc: 1.0000\n", + "Epoch 572/1000\n", + "26/26 [==============================] - 0s 472us/sample - loss: 1.2496e-04 - acc: 1.0000\n", + "Epoch 573/1000\n", + "26/26 [==============================] - 0s 384us/sample - loss: 1.2461e-04 - acc: 1.0000\n", + "Epoch 574/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 1.2428e-04 - acc: 1.0000\n", + "Epoch 575/1000\n", + "26/26 [==============================] - 0s 343us/sample - loss: 1.2394e-04 - acc: 1.0000\n", + "Epoch 576/1000\n", + "26/26 [==============================] - 0s 395us/sample - loss: 1.2363e-04 - acc: 1.0000\n", + "Epoch 577/1000\n", + "26/26 [==============================] - 0s 370us/sample - loss: 1.2325e-04 - acc: 1.0000\n", + "Epoch 578/1000\n", + "26/26 [==============================] - 0s 403us/sample - loss: 1.2295e-04 - acc: 1.0000\n", + "Epoch 579/1000\n", + "26/26 [==============================] - 0s 592us/sample - loss: 1.2262e-04 - acc: 1.0000\n", + "Epoch 580/1000\n", + "26/26 [==============================] - 0s 653us/sample - loss: 1.2233e-04 - acc: 1.0000\n", + "Epoch 581/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 1.2201e-04 - acc: 1.0000\n", + "Epoch 582/1000\n", + "26/26 [==============================] - 0s 557us/sample - loss: 1.2173e-04 - acc: 1.0000\n", + "Epoch 583/1000\n", + "26/26 [==============================] - 0s 492us/sample - loss: 1.2142e-04 - acc: 1.0000\n", + "Epoch 584/1000\n", + "26/26 [==============================] - 0s 469us/sample - loss: 1.2113e-04 - acc: 1.0000\n", + "Epoch 585/1000\n", + "26/26 [==============================] - 0s 477us/sample - loss: 1.2083e-04 - acc: 1.0000\n", + "Epoch 586/1000\n", + "26/26 [==============================] - 0s 626us/sample - loss: 1.2053e-04 - acc: 1.0000\n", + "Epoch 587/1000\n", + "26/26 [==============================] - 0s 502us/sample - loss: 1.2026e-04 - acc: 1.0000\n", + "Epoch 588/1000\n", + "26/26 [==============================] - 0s 550us/sample - loss: 1.1996e-04 - acc: 1.0000\n", + "Epoch 589/1000\n", + "26/26 [==============================] - 0s 419us/sample - loss: 1.1971e-04 - acc: 1.0000\n", + "Epoch 590/1000\n", + "26/26 [==============================] - 0s 401us/sample - loss: 1.1942e-04 - acc: 1.0000\n", + "Epoch 591/1000\n", + "26/26 [==============================] - 0s 428us/sample - loss: 1.1911e-04 - acc: 1.0000\n", + "Epoch 592/1000\n", + "26/26 [==============================] - 0s 597us/sample - loss: 1.1884e-04 - acc: 1.0000\n", + "Epoch 593/1000\n", + "26/26 [==============================] - 0s 491us/sample - loss: 1.1854e-04 - acc: 1.0000\n", + "Epoch 594/1000\n", + "26/26 [==============================] - 0s 551us/sample - loss: 1.1822e-04 - acc: 1.0000\n", + "Epoch 595/1000\n", + "26/26 [==============================] - 0s 513us/sample - loss: 1.1798e-04 - acc: 1.0000\n", + "Epoch 596/1000\n", + "26/26 [==============================] - 0s 455us/sample - loss: 1.1766e-04 - acc: 1.0000\n", + "Epoch 597/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 1.1740e-04 - acc: 1.0000\n", + "Epoch 598/1000\n", + "26/26 [==============================] - 0s 465us/sample - loss: 1.1711e-04 - acc: 1.0000\n", + "Epoch 599/1000\n", + "26/26 [==============================] - 0s 452us/sample - loss: 1.1685e-04 - acc: 1.0000\n", + "Epoch 600/1000\n", + "26/26 [==============================] - 0s 492us/sample - loss: 1.1655e-04 - acc: 1.0000\n", + "Epoch 601/1000\n", + "26/26 [==============================] - 0s 480us/sample - loss: 1.1628e-04 - acc: 1.0000\n", + "Epoch 602/1000\n", + "26/26 [==============================] - 0s 420us/sample - loss: 1.1595e-04 - acc: 1.0000\n", + "Epoch 603/1000\n", + "26/26 [==============================] - 0s 385us/sample - loss: 1.1568e-04 - acc: 1.0000\n", + "Epoch 604/1000\n", + "26/26 [==============================] - 0s 343us/sample - loss: 1.1539e-04 - acc: 1.0000\n", + "Epoch 605/1000\n", + "26/26 [==============================] - 0s 321us/sample - loss: 1.1510e-04 - acc: 1.0000\n", + "Epoch 606/1000\n", + "26/26 [==============================] - 0s 314us/sample - loss: 1.1475e-04 - acc: 1.0000\n", + "Epoch 607/1000\n", + "26/26 [==============================] - 0s 333us/sample - loss: 1.1447e-04 - acc: 1.0000\n", + "Epoch 608/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 1.1417e-04 - acc: 1.0000\n", + "Epoch 609/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 1.1387e-04 - acc: 1.0000\n", + "Epoch 610/1000\n", + "26/26 [==============================] - 0s 337us/sample - loss: 1.1356e-04 - acc: 1.0000\n", + "Epoch 611/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 1.1324e-04 - acc: 1.0000\n", + "Epoch 612/1000\n", + "26/26 [==============================] - 0s 377us/sample - loss: 1.1295e-04 - acc: 1.0000\n", + "Epoch 613/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 1.1267e-04 - acc: 1.0000\n", + "Epoch 614/1000\n", + "26/26 [==============================] - 0s 334us/sample - loss: 1.1238e-04 - acc: 1.0000\n", + "Epoch 615/1000\n", + "26/26 [==============================] - 0s 326us/sample - loss: 1.1208e-04 - acc: 1.0000\n", + "Epoch 616/1000\n", + "26/26 [==============================] - 0s 325us/sample - loss: 1.1180e-04 - acc: 1.0000\n", + "Epoch 617/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 1.1152e-04 - acc: 1.0000\n", + "Epoch 618/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 1.1125e-04 - acc: 1.0000\n", + "Epoch 619/1000\n", + "26/26 [==============================] - 0s 515us/sample - loss: 1.1094e-04 - acc: 1.0000\n", + "Epoch 620/1000\n", + "26/26 [==============================] - 0s 389us/sample - loss: 1.1066e-04 - acc: 1.0000\n", + "Epoch 621/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 1.1039e-04 - acc: 1.0000\n", + "Epoch 622/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 1.1013e-04 - acc: 1.0000\n", + "Epoch 623/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 1.0985e-04 - acc: 1.0000\n", + "Epoch 624/1000\n", + "26/26 [==============================] - 0s 384us/sample - loss: 1.0956e-04 - acc: 1.0000\n", + "Epoch 625/1000\n", + "26/26 [==============================] - 0s 423us/sample - loss: 1.0925e-04 - acc: 1.0000\n", + "Epoch 626/1000\n", + "26/26 [==============================] - 0s 720us/sample - loss: 1.0895e-04 - acc: 1.0000\n", + "Epoch 627/1000\n", + "26/26 [==============================] - 0s 488us/sample - loss: 1.0867e-04 - acc: 1.0000\n", + "Epoch 628/1000\n", + "26/26 [==============================] - 0s 489us/sample - loss: 1.0834e-04 - acc: 1.0000\n", + "Epoch 629/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 1.0807e-04 - acc: 1.0000\n", + "Epoch 630/1000\n", + "26/26 [==============================] - 0s 451us/sample - loss: 1.0780e-04 - acc: 1.0000\n", + "Epoch 631/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 1.0754e-04 - acc: 1.0000\n", + "Epoch 632/1000\n", + "26/26 [==============================] - 0s 405us/sample - loss: 1.0725e-04 - acc: 1.0000\n", + "Epoch 633/1000\n", + "26/26 [==============================] - 0s 389us/sample - loss: 1.0699e-04 - acc: 1.0000\n", + "Epoch 634/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 1.0674e-04 - acc: 1.0000\n", + "Epoch 635/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 1.0650e-04 - acc: 1.0000\n", + "Epoch 636/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 1.0620e-04 - acc: 1.0000\n", + "Epoch 637/1000\n", + "26/26 [==============================] - 0s 599us/sample - loss: 1.0597e-04 - acc: 1.0000\n", + "Epoch 638/1000\n", + "26/26 [==============================] - 0s 447us/sample - loss: 1.0574e-04 - acc: 1.0000\n", + "Epoch 639/1000\n", + "26/26 [==============================] - 0s 413us/sample - loss: 1.0541e-04 - acc: 1.0000\n", + "Epoch 640/1000\n", + "26/26 [==============================] - 0s 409us/sample - loss: 1.0511e-04 - acc: 1.0000\n", + "Epoch 641/1000\n", + "26/26 [==============================] - 0s 467us/sample - loss: 1.0485e-04 - acc: 1.0000\n", + "Epoch 642/1000\n", + "26/26 [==============================] - 0s 447us/sample - loss: 1.0459e-04 - acc: 1.0000\n", + "Epoch 643/1000\n", + "26/26 [==============================] - 0s 486us/sample - loss: 1.0434e-04 - acc: 1.0000\n", + "Epoch 644/1000\n", + "26/26 [==============================] - 0s 453us/sample - loss: 1.0408e-04 - acc: 1.0000\n", + "Epoch 645/1000\n", + "26/26 [==============================] - 0s 487us/sample - loss: 1.0381e-04 - acc: 1.0000\n", + "Epoch 646/1000\n", + "26/26 [==============================] - 0s 410us/sample - loss: 1.0355e-04 - acc: 1.0000\n", + "Epoch 647/1000\n", + "26/26 [==============================] - 0s 438us/sample - loss: 1.0326e-04 - acc: 1.0000\n", + "Epoch 648/1000\n", + "26/26 [==============================] - 0s 477us/sample - loss: 1.0304e-04 - acc: 1.0000\n", + "Epoch 649/1000\n", + "26/26 [==============================] - 0s 398us/sample - loss: 1.0280e-04 - acc: 1.0000\n", + "Epoch 650/1000\n", + "26/26 [==============================] - 0s 540us/sample - loss: 1.0254e-04 - acc: 1.0000\n", + "Epoch 651/1000\n", + "26/26 [==============================] - 0s 409us/sample - loss: 1.0230e-04 - acc: 1.0000\n", + "Epoch 652/1000\n", + "26/26 [==============================] - 0s 494us/sample - loss: 1.0203e-04 - acc: 1.0000\n", + "Epoch 653/1000\n", + "26/26 [==============================] - 0s 469us/sample - loss: 1.0174e-04 - acc: 1.0000\n", + "Epoch 654/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 1.0150e-04 - acc: 1.0000\n", + "Epoch 655/1000\n", + "26/26 [==============================] - 0s 499us/sample - loss: 1.0121e-04 - acc: 1.0000\n", + "Epoch 656/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 1.0091e-04 - acc: 1.0000\n", + "Epoch 657/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 1.0067e-04 - acc: 1.0000\n", + "Epoch 658/1000\n", + "26/26 [==============================] - 0s 478us/sample - loss: 1.0036e-04 - acc: 1.0000\n", + "Epoch 659/1000\n", + "26/26 [==============================] - 0s 401us/sample - loss: 1.0011e-04 - acc: 1.0000\n", + "Epoch 660/1000\n", + "26/26 [==============================] - 0s 416us/sample - loss: 9.9874e-05 - acc: 1.0000\n", + "Epoch 661/1000\n", + "26/26 [==============================] - 0s 444us/sample - loss: 9.9617e-05 - acc: 1.0000\n", + "Epoch 662/1000\n", + "26/26 [==============================] - 0s 463us/sample - loss: 9.9379e-05 - acc: 1.0000\n", + "Epoch 663/1000\n", + "26/26 [==============================] - 0s 399us/sample - loss: 9.9113e-05 - acc: 1.0000\n", + "Epoch 664/1000\n", + "26/26 [==============================] - 0s 393us/sample - loss: 9.8879e-05 - acc: 1.0000\n", + "Epoch 665/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 9.8627e-05 - acc: 1.0000\n", + "Epoch 666/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 9.8348e-05 - acc: 1.0000\n", + "Epoch 667/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 9.8082e-05 - acc: 1.0000\n", + "Epoch 668/1000\n", + "26/26 [==============================] - 0s 350us/sample - loss: 9.7843e-05 - acc: 1.0000\n", + "Epoch 669/1000\n", + "26/26 [==============================] - 0s 332us/sample - loss: 9.7550e-05 - acc: 1.0000\n", + "Epoch 670/1000\n", + "26/26 [==============================] - 0s 360us/sample - loss: 9.7321e-05 - acc: 1.0000\n", + "Epoch 671/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 9.7087e-05 - acc: 1.0000\n", + "Epoch 672/1000\n", + "26/26 [==============================] - 0s 377us/sample - loss: 9.6844e-05 - acc: 1.0000\n", + "Epoch 673/1000\n", + "26/26 [==============================] - 0s 350us/sample - loss: 9.6619e-05 - acc: 1.0000\n", + "Epoch 674/1000\n", + "26/26 [==============================] - 0s 360us/sample - loss: 9.6395e-05 - acc: 1.0000\n", + "Epoch 675/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 9.6138e-05 - acc: 1.0000\n", + "Epoch 676/1000\n", + "26/26 [==============================] - 0s 348us/sample - loss: 9.5927e-05 - acc: 1.0000\n", + "Epoch 677/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 9.5652e-05 - acc: 1.0000\n", + "Epoch 678/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 9.5405e-05 - acc: 1.0000\n", + "Epoch 679/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 9.5153e-05 - acc: 1.0000\n", + "Epoch 680/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 9.4914e-05 - acc: 1.0000\n", + "Epoch 681/1000\n", + "26/26 [==============================] - 0s 335us/sample - loss: 9.4667e-05 - acc: 1.0000\n", + "Epoch 682/1000\n", + "26/26 [==============================] - 0s 373us/sample - loss: 9.4447e-05 - acc: 1.0000\n", + "Epoch 683/1000\n", + "26/26 [==============================] - 0s 328us/sample - loss: 9.4217e-05 - acc: 1.0000\n", + "Epoch 684/1000\n", + "26/26 [==============================] - 0s 328us/sample - loss: 9.3984e-05 - acc: 1.0000\n", + "Epoch 685/1000\n", + "26/26 [==============================] - 0s 329us/sample - loss: 9.3759e-05 - acc: 1.0000\n", + "Epoch 686/1000\n", + "26/26 [==============================] - 0s 321us/sample - loss: 9.3488e-05 - acc: 1.0000\n", + "Epoch 687/1000\n", + "26/26 [==============================] - 0s 317us/sample - loss: 9.3287e-05 - acc: 1.0000\n", + "Epoch 688/1000\n", + "26/26 [==============================] - 0s 312us/sample - loss: 9.3016e-05 - acc: 1.0000\n", + "Epoch 689/1000\n", + "26/26 [==============================] - 0s 325us/sample - loss: 9.2783e-05 - acc: 1.0000\n", + "Epoch 690/1000\n", + "26/26 [==============================] - 0s 319us/sample - loss: 9.2549e-05 - acc: 1.0000\n", + "Epoch 691/1000\n", + "26/26 [==============================] - 0s 328us/sample - loss: 9.2315e-05 - acc: 1.0000\n", + "Epoch 692/1000\n", + "26/26 [==============================] - 0s 325us/sample - loss: 9.2081e-05 - acc: 1.0000\n", + "Epoch 693/1000\n", + "26/26 [==============================] - 0s 389us/sample - loss: 9.1866e-05 - acc: 1.0000\n", + "Epoch 694/1000\n", + "26/26 [==============================] - 0s 355us/sample - loss: 9.1636e-05 - acc: 1.0000\n", + "Epoch 695/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 9.1416e-05 - acc: 1.0000\n", + "Epoch 696/1000\n", + "26/26 [==============================] - 0s 337us/sample - loss: 9.1187e-05 - acc: 1.0000\n", + "Epoch 697/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 9.0976e-05 - acc: 1.0000\n", + "Epoch 698/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 9.0752e-05 - acc: 1.0000\n", + "Epoch 699/1000\n", + "26/26 [==============================] - 0s 335us/sample - loss: 9.0518e-05 - acc: 1.0000\n", + "Epoch 700/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 9.0280e-05 - acc: 1.0000\n", + "Epoch 701/1000\n", + "26/26 [==============================] - 0s 428us/sample - loss: 9.0096e-05 - acc: 1.0000\n", + "Epoch 702/1000\n", + "26/26 [==============================] - 0s 386us/sample - loss: 8.9817e-05 - acc: 1.0000\n", + "Epoch 703/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 8.9592e-05 - acc: 1.0000\n", + "Epoch 704/1000\n", + "26/26 [==============================] - 0s 329us/sample - loss: 8.9372e-05 - acc: 1.0000\n", + "Epoch 705/1000\n", + "26/26 [==============================] - 0s 1ms/sample - loss: 8.9111e-05 - acc: 1.0000\n", + "Epoch 706/1000\n", + "26/26 [==============================] - 0s 766us/sample - loss: 8.8840e-05 - acc: 1.0000\n", + "Epoch 707/1000\n", + "26/26 [==============================] - 0s 564us/sample - loss: 8.8643e-05 - acc: 1.0000\n", + "Epoch 708/1000\n", + "26/26 [==============================] - 0s 475us/sample - loss: 8.8428e-05 - acc: 1.0000\n", + "Epoch 709/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 8.8189e-05 - acc: 1.0000\n", + "Epoch 710/1000\n", + "26/26 [==============================] - 0s 530us/sample - loss: 8.7983e-05 - acc: 1.0000\n", + "Epoch 711/1000\n", + "26/26 [==============================] - 0s 449us/sample - loss: 8.7754e-05 - acc: 1.0000\n", + "Epoch 712/1000\n", + "26/26 [==============================] - 0s 449us/sample - loss: 8.7529e-05 - acc: 1.0000\n", + "Epoch 713/1000\n", + "26/26 [==============================] - 0s 754us/sample - loss: 8.7300e-05 - acc: 1.0000\n", + "Epoch 714/1000\n", + "26/26 [==============================] - 0s 425us/sample - loss: 8.7098e-05 - acc: 1.0000\n", + "Epoch 715/1000\n", + "26/26 [==============================] - 0s 396us/sample - loss: 8.6873e-05 - acc: 1.0000\n", + "Epoch 716/1000\n", + "26/26 [==============================] - 0s 403us/sample - loss: 8.6658e-05 - acc: 1.0000\n", + "Epoch 717/1000\n", + "26/26 [==============================] - 0s 401us/sample - loss: 8.6461e-05 - acc: 1.0000\n", + "Epoch 718/1000\n", + "26/26 [==============================] - 0s 336us/sample - loss: 8.6250e-05 - acc: 1.0000\n", + "Epoch 719/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 8.6012e-05 - acc: 1.0000\n", + "Epoch 720/1000\n", + "26/26 [==============================] - 0s 383us/sample - loss: 8.5778e-05 - acc: 1.0000\n", + "Epoch 721/1000\n", + "26/26 [==============================] - 0s 329us/sample - loss: 8.5562e-05 - acc: 1.0000\n", + "Epoch 722/1000\n", + "26/26 [==============================] - 0s 320us/sample - loss: 8.5319e-05 - acc: 1.0000\n", + "Epoch 723/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 8.5063e-05 - acc: 1.0000\n", + "Epoch 724/1000\n", + "26/26 [==============================] - 0s 355us/sample - loss: 8.4861e-05 - acc: 1.0000\n", + "Epoch 725/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 8.4632e-05 - acc: 1.0000\n", + "Epoch 726/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 8.4398e-05 - acc: 1.0000\n", + "Epoch 727/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 8.4183e-05 - acc: 1.0000\n", + "Epoch 728/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 8.3995e-05 - acc: 1.0000\n", + "Epoch 729/1000\n", + "26/26 [==============================] - 0s 372us/sample - loss: 8.3752e-05 - acc: 1.0000\n", + "Epoch 730/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 8.3573e-05 - acc: 1.0000\n", + "Epoch 731/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 8.3348e-05 - acc: 1.0000\n", + "Epoch 732/1000\n", + "26/26 [==============================] - 0s 423us/sample - loss: 8.3119e-05 - acc: 1.0000\n", + "Epoch 733/1000\n", + "26/26 [==============================] - 0s 469us/sample - loss: 8.2913e-05 - acc: 1.0000\n", + "Epoch 734/1000\n", + "26/26 [==============================] - 0s 451us/sample - loss: 8.2693e-05 - acc: 1.0000\n", + "Epoch 735/1000\n", + "26/26 [==============================] - 0s 438us/sample - loss: 8.2473e-05 - acc: 1.0000\n", + "Epoch 736/1000\n", + "26/26 [==============================] - 0s 502us/sample - loss: 8.2262e-05 - acc: 1.0000\n", + "Epoch 737/1000\n", + "26/26 [==============================] - 0s 446us/sample - loss: 8.2065e-05 - acc: 1.0000\n", + "Epoch 738/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 8.1886e-05 - acc: 1.0000\n", + "Epoch 739/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 8.1675e-05 - acc: 1.0000\n", + "Epoch 740/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 8.1492e-05 - acc: 1.0000\n", + "Epoch 741/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 8.1290e-05 - acc: 1.0000\n", + "Epoch 742/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 8.1107e-05 - acc: 1.0000\n", + "Epoch 743/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 8.0900e-05 - acc: 1.0000\n", + "Epoch 744/1000\n", + "26/26 [==============================] - 0s 468us/sample - loss: 8.0671e-05 - acc: 1.0000\n", + "Epoch 745/1000\n", + "26/26 [==============================] - 0s 420us/sample - loss: 8.0465e-05 - acc: 1.0000\n", + "Epoch 746/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 8.0281e-05 - acc: 1.0000\n", + "Epoch 747/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 8.0103e-05 - acc: 1.0000\n", + "Epoch 748/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 7.9896e-05 - acc: 1.0000\n", + "Epoch 749/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 7.9704e-05 - acc: 1.0000\n", + "Epoch 750/1000\n", + "26/26 [==============================] - 0s 335us/sample - loss: 7.9497e-05 - acc: 1.0000\n", + "Epoch 751/1000\n", + "26/26 [==============================] - 0s 321us/sample - loss: 7.9273e-05 - acc: 1.0000\n", + "Epoch 752/1000\n", + "26/26 [==============================] - 0s 296us/sample - loss: 7.9089e-05 - acc: 1.0000\n", + "Epoch 753/1000\n", + "26/26 [==============================] - 0s 287us/sample - loss: 7.8874e-05 - acc: 1.0000\n", + "Epoch 754/1000\n", + "26/26 [==============================] - 0s 280us/sample - loss: 7.8681e-05 - acc: 1.0000\n", + "Epoch 755/1000\n", + "26/26 [==============================] - 0s 277us/sample - loss: 7.8493e-05 - acc: 1.0000\n", + "Epoch 756/1000\n", + "26/26 [==============================] - 0s 296us/sample - loss: 7.8301e-05 - acc: 1.0000\n", + "Epoch 757/1000\n", + "26/26 [==============================] - 0s 304us/sample - loss: 7.8145e-05 - acc: 1.0000\n", + "Epoch 758/1000\n", + "26/26 [==============================] - 0s 310us/sample - loss: 7.7971e-05 - acc: 1.0000\n", + "Epoch 759/1000\n", + "26/26 [==============================] - 0s 327us/sample - loss: 7.7742e-05 - acc: 1.0000\n", + "Epoch 760/1000\n", + "26/26 [==============================] - 0s 243us/sample - loss: 7.7586e-05 - acc: 1.0000\n", + "Epoch 761/1000\n", + "26/26 [==============================] - 0s 336us/sample - loss: 7.7384e-05 - acc: 1.0000\n", + "Epoch 762/1000\n", + "26/26 [==============================] - 0s 287us/sample - loss: 7.7210e-05 - acc: 1.0000\n", + "Epoch 763/1000\n", + "26/26 [==============================] - 0s 288us/sample - loss: 7.7017e-05 - acc: 1.0000\n", + "Epoch 764/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 7.6843e-05 - acc: 1.0000\n", + "Epoch 765/1000\n", + "26/26 [==============================] - 0s 329us/sample - loss: 7.6600e-05 - acc: 1.0000\n", + "Epoch 766/1000\n", + "26/26 [==============================] - 0s 326us/sample - loss: 7.6412e-05 - acc: 1.0000\n", + "Epoch 767/1000\n", + "26/26 [==============================] - 0s 296us/sample - loss: 7.6210e-05 - acc: 1.0000\n", + "Epoch 768/1000\n", + "26/26 [==============================] - 0s 335us/sample - loss: 7.6036e-05 - acc: 1.0000\n", + "Epoch 769/1000\n", + "26/26 [==============================] - 0s 292us/sample - loss: 7.5853e-05 - acc: 1.0000\n", + "Epoch 770/1000\n", + "26/26 [==============================] - 0s 300us/sample - loss: 7.5670e-05 - acc: 1.0000\n", + "Epoch 771/1000\n", + "26/26 [==============================] - 0s 291us/sample - loss: 7.5472e-05 - acc: 1.0000\n", + "Epoch 772/1000\n", + "26/26 [==============================] - 0s 283us/sample - loss: 7.5307e-05 - acc: 1.0000\n", + "Epoch 773/1000\n", + "26/26 [==============================] - 0s 286us/sample - loss: 7.5087e-05 - acc: 1.0000\n", + "Epoch 774/1000\n", + "26/26 [==============================] - 0s 287us/sample - loss: 7.4890e-05 - acc: 1.0000\n", + "Epoch 775/1000\n", + "26/26 [==============================] - 0s 327us/sample - loss: 7.4743e-05 - acc: 1.0000\n", + "Epoch 776/1000\n", + "26/26 [==============================] - 0s 432us/sample - loss: 7.4533e-05 - acc: 1.0000\n", + "Epoch 777/1000\n", + "26/26 [==============================] - 0s 373us/sample - loss: 7.4354e-05 - acc: 1.0000\n", + "Epoch 778/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 7.4166e-05 - acc: 1.0000\n", + "Epoch 779/1000\n", + "26/26 [==============================] - 0s 317us/sample - loss: 7.3992e-05 - acc: 1.0000\n", + "Epoch 780/1000\n", + "26/26 [==============================] - 0s 430us/sample - loss: 7.3822e-05 - acc: 1.0000\n", + "Epoch 781/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 7.3625e-05 - acc: 1.0000\n", + "Epoch 782/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 7.3446e-05 - acc: 1.0000\n", + "Epoch 783/1000\n", + "26/26 [==============================] - 0s 384us/sample - loss: 7.3272e-05 - acc: 1.0000\n", + "Epoch 784/1000\n", + "26/26 [==============================] - 0s 324us/sample - loss: 7.3066e-05 - acc: 1.0000\n", + "Epoch 785/1000\n", + "26/26 [==============================] - 0s 340us/sample - loss: 7.2882e-05 - acc: 1.0000\n", + "Epoch 786/1000\n", + "26/26 [==============================] - 0s 404us/sample - loss: 7.2722e-05 - acc: 1.0000\n", + "Epoch 787/1000\n", + "26/26 [==============================] - 0s 407us/sample - loss: 7.2529e-05 - acc: 1.0000\n", + "Epoch 788/1000\n", + "26/26 [==============================] - 0s 489us/sample - loss: 7.2346e-05 - acc: 1.0000\n", + "Epoch 789/1000\n", + "26/26 [==============================] - 0s 456us/sample - loss: 7.2153e-05 - acc: 1.0000\n", + "Epoch 790/1000\n", + "26/26 [==============================] - 0s 458us/sample - loss: 7.2002e-05 - acc: 1.0000\n", + "Epoch 791/1000\n", + "26/26 [==============================] - 0s 493us/sample - loss: 7.1814e-05 - acc: 1.0000\n", + "Epoch 792/1000\n", + "26/26 [==============================] - 0s 520us/sample - loss: 7.1649e-05 - acc: 1.0000\n", + "Epoch 793/1000\n", + "26/26 [==============================] - 0s 471us/sample - loss: 7.1429e-05 - acc: 1.0000\n", + "Epoch 794/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 7.1246e-05 - acc: 1.0000\n", + "Epoch 795/1000\n", + "26/26 [==============================] - 0s 424us/sample - loss: 7.1094e-05 - acc: 1.0000\n", + "Epoch 796/1000\n", + "26/26 [==============================] - 0s 443us/sample - loss: 7.0920e-05 - acc: 1.0000\n", + "Epoch 797/1000\n", + "26/26 [==============================] - 0s 432us/sample - loss: 7.0741e-05 - acc: 1.0000\n", + "Epoch 798/1000\n", + "26/26 [==============================] - 0s 443us/sample - loss: 7.0576e-05 - acc: 1.0000\n", + "Epoch 799/1000\n", + "26/26 [==============================] - 0s 417us/sample - loss: 7.0430e-05 - acc: 1.0000\n", + "Epoch 800/1000\n", + "26/26 [==============================] - 0s 310us/sample - loss: 7.0210e-05 - acc: 1.0000\n", + "Epoch 801/1000\n", + "26/26 [==============================] - 0s 444us/sample - loss: 7.0045e-05 - acc: 1.0000\n", + "Epoch 802/1000\n", + "26/26 [==============================] - 0s 385us/sample - loss: 6.9884e-05 - acc: 1.0000\n", + "Epoch 803/1000\n", + "26/26 [==============================] - 0s 467us/sample - loss: 6.9728e-05 - acc: 1.0000\n", + "Epoch 804/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 6.9527e-05 - acc: 1.0000\n", + "Epoch 805/1000\n", + "26/26 [==============================] - 0s 452us/sample - loss: 6.9357e-05 - acc: 1.0000\n", + "Epoch 806/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 6.9206e-05 - acc: 1.0000\n", + "Epoch 807/1000\n", + "26/26 [==============================] - 0s 442us/sample - loss: 6.9018e-05 - acc: 1.0000\n", + "Epoch 808/1000\n", + "26/26 [==============================] - 0s 424us/sample - loss: 6.8848e-05 - acc: 1.0000\n", + "Epoch 809/1000\n", + "26/26 [==============================] - 0s 435us/sample - loss: 6.8692e-05 - acc: 1.0000\n", + "Epoch 810/1000\n", + "26/26 [==============================] - 0s 348us/sample - loss: 6.8513e-05 - acc: 1.0000\n", + "Epoch 811/1000\n", + "26/26 [==============================] - 0s 343us/sample - loss: 6.8344e-05 - acc: 1.0000\n", + "Epoch 812/1000\n", + "26/26 [==============================] - 0s 353us/sample - loss: 6.8192e-05 - acc: 1.0000\n", + "Epoch 813/1000\n", + "26/26 [==============================] - 0s 457us/sample - loss: 6.8000e-05 - acc: 1.0000\n", + "Epoch 814/1000\n", + "26/26 [==============================] - 0s 417us/sample - loss: 6.7817e-05 - acc: 1.0000\n", + "Epoch 815/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 6.7670e-05 - acc: 1.0000\n", + "Epoch 816/1000\n", + "26/26 [==============================] - 0s 492us/sample - loss: 6.7519e-05 - acc: 1.0000\n", + "Epoch 817/1000\n", + "26/26 [==============================] - 0s 429us/sample - loss: 6.7344e-05 - acc: 1.0000\n", + "Epoch 818/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 6.7161e-05 - acc: 1.0000\n", + "Epoch 819/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 6.6991e-05 - acc: 1.0000\n", + "Epoch 820/1000\n", + "26/26 [==============================] - 0s 340us/sample - loss: 6.6817e-05 - acc: 1.0000\n", + "Epoch 821/1000\n", + "26/26 [==============================] - 0s 329us/sample - loss: 6.6657e-05 - acc: 1.0000\n", + "Epoch 822/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 6.6473e-05 - acc: 1.0000\n", + "Epoch 823/1000\n", + "26/26 [==============================] - 0s 396us/sample - loss: 6.6322e-05 - acc: 1.0000\n", + "Epoch 824/1000\n", + "26/26 [==============================] - 0s 429us/sample - loss: 6.6175e-05 - acc: 1.0000\n", + "Epoch 825/1000\n", + "26/26 [==============================] - 0s 396us/sample - loss: 6.6024e-05 - acc: 1.0000\n", + "Epoch 826/1000\n", + "26/26 [==============================] - 0s 434us/sample - loss: 6.5873e-05 - acc: 1.0000\n", + "Epoch 827/1000\n", + "26/26 [==============================] - 0s 432us/sample - loss: 6.5703e-05 - acc: 1.0000\n", + "Epoch 828/1000\n", + "26/26 [==============================] - 0s 421us/sample - loss: 6.5529e-05 - acc: 1.0000\n", + "Epoch 829/1000\n", + "26/26 [==============================] - 0s 440us/sample - loss: 6.5378e-05 - acc: 1.0000\n", + "Epoch 830/1000\n", + "26/26 [==============================] - 0s 405us/sample - loss: 6.5190e-05 - acc: 1.0000\n", + "Epoch 831/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 6.5034e-05 - acc: 1.0000\n", + "Epoch 832/1000\n", + "26/26 [==============================] - 0s 474us/sample - loss: 6.4869e-05 - acc: 1.0000\n", + "Epoch 833/1000\n", + "26/26 [==============================] - 0s 415us/sample - loss: 6.4722e-05 - acc: 1.0000\n", + "Epoch 834/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 6.4575e-05 - acc: 1.0000\n", + "Epoch 835/1000\n", + "26/26 [==============================] - 0s 433us/sample - loss: 6.4429e-05 - acc: 1.0000\n", + "Epoch 836/1000\n", + "26/26 [==============================] - 0s 332us/sample - loss: 6.4241e-05 - acc: 1.0000\n", + "Epoch 837/1000\n", + "26/26 [==============================] - 0s 323us/sample - loss: 6.4103e-05 - acc: 1.0000\n", + "Epoch 838/1000\n", + "26/26 [==============================] - 0s 377us/sample - loss: 6.3961e-05 - acc: 1.0000\n", + "Epoch 839/1000\n", + "26/26 [==============================] - 0s 373us/sample - loss: 6.3814e-05 - acc: 1.0000\n", + "Epoch 840/1000\n", + "26/26 [==============================] - 0s 445us/sample - loss: 6.3672e-05 - acc: 1.0000\n", + "Epoch 841/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 6.3507e-05 - acc: 1.0000\n", + "Epoch 842/1000\n", + "26/26 [==============================] - 0s 328us/sample - loss: 6.3361e-05 - acc: 1.0000\n", + "Epoch 843/1000\n", + "26/26 [==============================] - 0s 431us/sample - loss: 6.3205e-05 - acc: 1.0000\n", + "Epoch 844/1000\n", + "26/26 [==============================] - 0s 478us/sample - loss: 6.3035e-05 - acc: 1.0000\n", + "Epoch 845/1000\n", + "26/26 [==============================] - 0s 356us/sample - loss: 6.2911e-05 - acc: 1.0000\n", + "Epoch 846/1000\n", + "26/26 [==============================] - 0s 395us/sample - loss: 6.2751e-05 - acc: 1.0000\n", + "Epoch 847/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 6.2595e-05 - acc: 1.0000\n", + "Epoch 848/1000\n", + "26/26 [==============================] - 0s 510us/sample - loss: 6.2448e-05 - acc: 1.0000\n", + "Epoch 849/1000\n", + "26/26 [==============================] - 0s 432us/sample - loss: 6.2302e-05 - acc: 1.0000\n", + "Epoch 850/1000\n", + "26/26 [==============================] - 0s 376us/sample - loss: 6.2159e-05 - acc: 1.0000\n", + "Epoch 851/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 6.1985e-05 - acc: 1.0000\n", + "Epoch 852/1000\n", + "26/26 [==============================] - 0s 439us/sample - loss: 6.1861e-05 - acc: 1.0000\n", + "Epoch 853/1000\n", + "26/26 [==============================] - 0s 496us/sample - loss: 6.1715e-05 - acc: 1.0000\n", + "Epoch 854/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 6.1568e-05 - acc: 1.0000\n", + "Epoch 855/1000\n", + "26/26 [==============================] - 0s 464us/sample - loss: 6.1403e-05 - acc: 1.0000\n", + "Epoch 856/1000\n", + "26/26 [==============================] - 0s 398us/sample - loss: 6.1252e-05 - acc: 1.0000\n", + "Epoch 857/1000\n", + "26/26 [==============================] - 0s 446us/sample - loss: 6.1110e-05 - acc: 1.0000\n", + "Epoch 858/1000\n", + "26/26 [==============================] - 0s 454us/sample - loss: 6.0972e-05 - acc: 1.0000\n", + "Epoch 859/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 6.0825e-05 - acc: 1.0000\n", + "Epoch 860/1000\n", + "26/26 [==============================] - 0s 415us/sample - loss: 6.0669e-05 - acc: 1.0000\n", + "Epoch 861/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 6.0532e-05 - acc: 1.0000\n", + "Epoch 862/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 6.0404e-05 - acc: 1.0000\n", + "Epoch 863/1000\n", + "26/26 [==============================] - 0s 394us/sample - loss: 6.0261e-05 - acc: 1.0000\n", + "Epoch 864/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 6.0106e-05 - acc: 1.0000\n", + "Epoch 865/1000\n", + "26/26 [==============================] - 0s 355us/sample - loss: 5.9977e-05 - acc: 1.0000\n", + "Epoch 866/1000\n", + "26/26 [==============================] - 0s 321us/sample - loss: 5.9817e-05 - acc: 1.0000\n", + "Epoch 867/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 5.9670e-05 - acc: 1.0000\n", + "Epoch 868/1000\n", + "26/26 [==============================] - 0s 376us/sample - loss: 5.9537e-05 - acc: 1.0000\n", + "Epoch 869/1000\n", + "26/26 [==============================] - 0s 410us/sample - loss: 5.9418e-05 - acc: 1.0000\n", + "Epoch 870/1000\n", + "26/26 [==============================] - 0s 528us/sample - loss: 5.9262e-05 - acc: 1.0000\n", + "Epoch 871/1000\n", + "26/26 [==============================] - 0s 588us/sample - loss: 5.9102e-05 - acc: 1.0000\n", + "Epoch 872/1000\n", + "26/26 [==============================] - 0s 368us/sample - loss: 5.8941e-05 - acc: 1.0000\n", + "Epoch 873/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 5.8804e-05 - acc: 1.0000\n", + "Epoch 874/1000\n", + "26/26 [==============================] - 0s 335us/sample - loss: 5.8675e-05 - acc: 1.0000\n", + "Epoch 875/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 5.8492e-05 - acc: 1.0000\n", + "Epoch 876/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 5.8363e-05 - acc: 1.0000\n", + "Epoch 877/1000\n", + "26/26 [==============================] - 0s 394us/sample - loss: 5.8235e-05 - acc: 1.0000\n", + "Epoch 878/1000\n", + "26/26 [==============================] - 0s 402us/sample - loss: 5.8111e-05 - acc: 1.0000\n", + "Epoch 879/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 5.7960e-05 - acc: 1.0000\n", + "Epoch 880/1000\n", + "26/26 [==============================] - 0s 405us/sample - loss: 5.7818e-05 - acc: 1.0000\n", + "Epoch 881/1000\n", + "26/26 [==============================] - 0s 462us/sample - loss: 5.7699e-05 - acc: 1.0000\n", + "Epoch 882/1000\n", + "26/26 [==============================] - 0s 459us/sample - loss: 5.7557e-05 - acc: 1.0000\n", + "Epoch 883/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 5.7419e-05 - acc: 1.0000\n", + "Epoch 884/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 5.7300e-05 - acc: 1.0000\n", + "Epoch 885/1000\n", + "26/26 [==============================] - 0s 333us/sample - loss: 5.7135e-05 - acc: 1.0000\n", + "Epoch 886/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 5.7002e-05 - acc: 1.0000\n", + "Epoch 887/1000\n", + "26/26 [==============================] - 0s 445us/sample - loss: 5.6869e-05 - acc: 1.0000\n", + "Epoch 888/1000\n", + "26/26 [==============================] - 0s 406us/sample - loss: 5.6713e-05 - acc: 1.0000\n", + "Epoch 889/1000\n", + "26/26 [==============================] - 0s 412us/sample - loss: 5.6557e-05 - acc: 1.0000\n", + "Epoch 890/1000\n", + "26/26 [==============================] - 0s 441us/sample - loss: 5.6429e-05 - acc: 1.0000\n", + "Epoch 891/1000\n", + "26/26 [==============================] - 0s 396us/sample - loss: 5.6259e-05 - acc: 1.0000\n", + "Epoch 892/1000\n", + "26/26 [==============================] - 0s 315us/sample - loss: 5.6131e-05 - acc: 1.0000\n", + "Epoch 893/1000\n", + "26/26 [==============================] - 0s 408us/sample - loss: 5.5993e-05 - acc: 1.0000\n", + "Epoch 894/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 5.5842e-05 - acc: 1.0000\n", + "Epoch 895/1000\n", + "26/26 [==============================] - 0s 324us/sample - loss: 5.5704e-05 - acc: 1.0000\n", + "Epoch 896/1000\n", + "26/26 [==============================] - 0s 377us/sample - loss: 5.5572e-05 - acc: 1.0000\n", + "Epoch 897/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 5.5434e-05 - acc: 1.0000\n", + "Epoch 898/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 5.5287e-05 - acc: 1.0000\n", + "Epoch 899/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 5.5150e-05 - acc: 1.0000\n", + "Epoch 900/1000\n", + "26/26 [==============================] - 0s 524us/sample - loss: 5.4998e-05 - acc: 1.0000\n", + "Epoch 901/1000\n", + "26/26 [==============================] - 0s 404us/sample - loss: 5.4847e-05 - acc: 1.0000\n", + "Epoch 902/1000\n", + "26/26 [==============================] - 0s 325us/sample - loss: 5.4705e-05 - acc: 1.0000\n", + "Epoch 903/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 5.4554e-05 - acc: 1.0000\n", + "Epoch 904/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 5.4430e-05 - acc: 1.0000\n", + "Epoch 905/1000\n", + "26/26 [==============================] - 0s 290us/sample - loss: 5.4274e-05 - acc: 1.0000\n", + "Epoch 906/1000\n", + "26/26 [==============================] - 0s 446us/sample - loss: 5.4146e-05 - acc: 1.0000\n", + "Epoch 907/1000\n", + "26/26 [==============================] - 0s 419us/sample - loss: 5.4013e-05 - acc: 1.0000\n", + "Epoch 908/1000\n", + "26/26 [==============================] - 0s 413us/sample - loss: 5.3903e-05 - acc: 1.0000\n", + "Epoch 909/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 5.3770e-05 - acc: 1.0000\n", + "Epoch 910/1000\n", + "26/26 [==============================] - 0s 607us/sample - loss: 5.3632e-05 - acc: 1.0000\n", + "Epoch 911/1000\n", + "26/26 [==============================] - 0s 527us/sample - loss: 5.3495e-05 - acc: 1.0000\n", + "Epoch 912/1000\n", + "26/26 [==============================] - 0s 527us/sample - loss: 5.3376e-05 - acc: 1.0000\n", + "Epoch 913/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 5.3261e-05 - acc: 1.0000\n", + "Epoch 914/1000\n", + "26/26 [==============================] - 0s 513us/sample - loss: 5.3128e-05 - acc: 1.0000\n", + "Epoch 915/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 5.2990e-05 - acc: 1.0000\n", + "Epoch 916/1000\n", + "26/26 [==============================] - 0s 662us/sample - loss: 5.2871e-05 - acc: 1.0000\n", + "Epoch 917/1000\n", + "26/26 [==============================] - 0s 432us/sample - loss: 5.2738e-05 - acc: 1.0000\n", + "Epoch 918/1000\n", + "26/26 [==============================] - 0s 403us/sample - loss: 5.2610e-05 - acc: 1.0000\n", + "Epoch 919/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 5.2472e-05 - acc: 1.0000\n", + "Epoch 920/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 5.2367e-05 - acc: 1.0000\n", + "Epoch 921/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 5.2225e-05 - acc: 1.0000\n", + "Epoch 922/1000\n", + "26/26 [==============================] - 0s 349us/sample - loss: 5.2096e-05 - acc: 1.0000\n", + "Epoch 923/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 5.1973e-05 - acc: 1.0000\n", + "Epoch 924/1000\n", + "26/26 [==============================] - 0s 478us/sample - loss: 5.1881e-05 - acc: 1.0000\n", + "Epoch 925/1000\n", + "26/26 [==============================] - 0s 494us/sample - loss: 5.1734e-05 - acc: 1.0000\n", + "Epoch 926/1000\n", + "26/26 [==============================] - 0s 442us/sample - loss: 5.1629e-05 - acc: 1.0000\n", + "Epoch 927/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 5.1491e-05 - acc: 1.0000\n", + "Epoch 928/1000\n", + "26/26 [==============================] - 0s 423us/sample - loss: 5.1363e-05 - acc: 1.0000\n", + "Epoch 929/1000\n", + "26/26 [==============================] - 0s 431us/sample - loss: 5.1221e-05 - acc: 1.0000\n", + "Epoch 930/1000\n", + "26/26 [==============================] - 0s 373us/sample - loss: 5.1111e-05 - acc: 1.0000\n", + "Epoch 931/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 5.0982e-05 - acc: 1.0000\n", + "Epoch 932/1000\n", + "26/26 [==============================] - 0s 327us/sample - loss: 5.0859e-05 - acc: 1.0000\n", + "Epoch 933/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 5.0721e-05 - acc: 1.0000\n", + "Epoch 934/1000\n", + "26/26 [==============================] - 0s 337us/sample - loss: 5.0588e-05 - acc: 1.0000\n", + "Epoch 935/1000\n", + "26/26 [==============================] - 0s 353us/sample - loss: 5.0446e-05 - acc: 1.0000\n", + "Epoch 936/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 5.0327e-05 - acc: 1.0000\n", + "Epoch 937/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 5.0180e-05 - acc: 1.0000\n", + "Epoch 938/1000\n", + "26/26 [==============================] - 0s 356us/sample - loss: 5.0079e-05 - acc: 1.0000\n", + "Epoch 939/1000\n", + "26/26 [==============================] - 0s 519us/sample - loss: 4.9928e-05 - acc: 1.0000\n", + "Epoch 940/1000\n", + "26/26 [==============================] - 0s 393us/sample - loss: 4.9823e-05 - acc: 1.0000\n", + "Epoch 941/1000\n", + "26/26 [==============================] - 0s 334us/sample - loss: 4.9694e-05 - acc: 1.0000\n", + "Epoch 942/1000\n", + "26/26 [==============================] - 0s 423us/sample - loss: 4.9570e-05 - acc: 1.0000\n", + "Epoch 943/1000\n", + "26/26 [==============================] - 0s 417us/sample - loss: 4.9433e-05 - acc: 1.0000\n", + "Epoch 944/1000\n", + "26/26 [==============================] - 0s 396us/sample - loss: 4.9323e-05 - acc: 1.0000\n", + "Epoch 945/1000\n", + "26/26 [==============================] - 0s 556us/sample - loss: 4.9231e-05 - acc: 1.0000\n", + "Epoch 946/1000\n", + "26/26 [==============================] - 0s 343us/sample - loss: 4.9117e-05 - acc: 1.0000\n", + "Epoch 947/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 4.9002e-05 - acc: 1.0000\n", + "Epoch 948/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 4.8874e-05 - acc: 1.0000\n", + "Epoch 949/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 4.8754e-05 - acc: 1.0000\n", + "Epoch 950/1000\n", + "26/26 [==============================] - 0s 429us/sample - loss: 4.8626e-05 - acc: 1.0000\n", + "Epoch 951/1000\n", + "26/26 [==============================] - 0s 402us/sample - loss: 4.8498e-05 - acc: 1.0000\n", + "Epoch 952/1000\n", + "26/26 [==============================] - 0s 405us/sample - loss: 4.8388e-05 - acc: 1.0000\n", + "Epoch 953/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 4.8268e-05 - acc: 1.0000\n", + "Epoch 954/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 4.8131e-05 - acc: 1.0000\n", + "Epoch 955/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 4.8035e-05 - acc: 1.0000\n", + "Epoch 956/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 4.7915e-05 - acc: 1.0000\n", + "Epoch 957/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 4.7810e-05 - acc: 1.0000\n", + "Epoch 958/1000\n", + "26/26 [==============================] - 0s 328us/sample - loss: 4.7714e-05 - acc: 1.0000\n", + "Epoch 959/1000\n", + "26/26 [==============================] - 0s 343us/sample - loss: 4.7599e-05 - acc: 1.0000\n", + "Epoch 960/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 4.7475e-05 - acc: 1.0000\n", + "Epoch 961/1000\n", + "26/26 [==============================] - 0s 439us/sample - loss: 4.7356e-05 - acc: 1.0000\n", + "Epoch 962/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 4.7260e-05 - acc: 1.0000\n", + "Epoch 963/1000\n", + "26/26 [==============================] - 0s 557us/sample - loss: 4.7159e-05 - acc: 1.0000\n", + "Epoch 964/1000\n", + "26/26 [==============================] - 0s 522us/sample - loss: 4.7017e-05 - acc: 1.0000\n", + "Epoch 965/1000\n", + "26/26 [==============================] - 0s 449us/sample - loss: 4.6898e-05 - acc: 1.0000\n", + "Epoch 966/1000\n", + "26/26 [==============================] - 0s 402us/sample - loss: 4.6792e-05 - acc: 1.0000\n", + "Epoch 967/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 4.6668e-05 - acc: 1.0000\n", + "Epoch 968/1000\n", + "26/26 [==============================] - 0s 389us/sample - loss: 4.6535e-05 - acc: 1.0000\n", + "Epoch 969/1000\n", + "26/26 [==============================] - 0s 431us/sample - loss: 4.6448e-05 - acc: 1.0000\n", + "Epoch 970/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 4.6343e-05 - acc: 1.0000\n", + "Epoch 971/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 4.6233e-05 - acc: 1.0000\n", + "Epoch 972/1000\n", + "26/26 [==============================] - 0s 368us/sample - loss: 4.6095e-05 - acc: 1.0000\n", + "Epoch 973/1000\n", + "26/26 [==============================] - 0s 332us/sample - loss: 4.5985e-05 - acc: 1.0000\n", + "Epoch 974/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 4.5866e-05 - acc: 1.0000\n", + "Epoch 975/1000\n", + "26/26 [==============================] - 0s 396us/sample - loss: 4.5761e-05 - acc: 1.0000\n", + "Epoch 976/1000\n", + "26/26 [==============================] - 0s 432us/sample - loss: 4.5664e-05 - acc: 1.0000\n", + "Epoch 977/1000\n", + "26/26 [==============================] - 0s 372us/sample - loss: 4.5554e-05 - acc: 1.0000\n", + "Epoch 978/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 4.5444e-05 - acc: 1.0000\n", + "Epoch 979/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 4.5316e-05 - acc: 1.0000\n", + "Epoch 980/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 4.5215e-05 - acc: 1.0000\n", + "Epoch 981/1000\n", + "26/26 [==============================] - 0s 383us/sample - loss: 4.5100e-05 - acc: 1.0000\n", + "Epoch 982/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 4.5004e-05 - acc: 1.0000\n", + "Epoch 983/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 4.4890e-05 - acc: 1.0000\n", + "Epoch 984/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 4.4761e-05 - acc: 1.0000\n", + "Epoch 985/1000\n", + "26/26 [==============================] - 0s 326us/sample - loss: 4.4679e-05 - acc: 1.0000\n", + "Epoch 986/1000\n", + "26/26 [==============================] - 0s 356us/sample - loss: 4.4546e-05 - acc: 1.0000\n", + "Epoch 987/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 4.4440e-05 - acc: 1.0000\n", + "Epoch 988/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 4.4335e-05 - acc: 1.0000\n", + "Epoch 989/1000\n", + "26/26 [==============================] - 0s 323us/sample - loss: 4.4229e-05 - acc: 1.0000\n", + "Epoch 990/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 4.4129e-05 - acc: 1.0000\n", + "Epoch 991/1000\n", + "26/26 [==============================] - 0s 353us/sample - loss: 4.4009e-05 - acc: 1.0000\n", + "Epoch 992/1000\n", + "26/26 [==============================] - 0s 394us/sample - loss: 4.3890e-05 - acc: 1.0000\n", + "Epoch 993/1000\n", + "26/26 [==============================] - 0s 399us/sample - loss: 4.3766e-05 - acc: 1.0000\n", + "Epoch 994/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 4.3661e-05 - acc: 1.0000\n", + "Epoch 995/1000\n", + "26/26 [==============================] - 0s 439us/sample - loss: 4.3565e-05 - acc: 1.0000\n", + "Epoch 996/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 4.3464e-05 - acc: 1.0000\n", + "Epoch 997/1000\n", + "26/26 [==============================] - 0s 436us/sample - loss: 4.3358e-05 - acc: 1.0000\n", + "Epoch 998/1000\n", + "26/26 [==============================] - 0s 508us/sample - loss: 4.3248e-05 - acc: 1.0000\n", + "Epoch 999/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 4.3157e-05 - acc: 1.0000\n", + "Epoch 1000/1000\n", + "26/26 [==============================] - 0s 360us/sample - loss: 4.3065e-05 - acc: 1.0000\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "def bag_of_words(s, words):\n", + " bag = [0 for _ in range(len(words))]\n", + "\n", + " s_words = nltk.word_tokenize(s)\n", + " s_words = [stemmer.stem(word.lower()) for word in s_words]\n", + "\n", + " for se in s_words:\n", + " for i, w in enumerate(words):\n", + " if w == se:\n", + " bag[i] = 1\n", + "\n", + " return numpy.array(bag)\n", + "\n", + "import tensorflow as tf\n", + "import numpy as np\n", + "import random\n", + "\n", + "\n", + "model = tf.keras.models.load_model('model.tflearn')\n", + "\n", + "def chat():\n", + " print(\"Start talking with the bot (type quit to stop)!\")\n", + " while True:\n", + " inp = input(\"You: \")\n", + " if inp.lower() == \"quit\":\n", + " break\n", + "\n", + " processed_input = bag_of_words(inp, words)\n", + " processed_input = np.array([processed_input])\n", + "\n", + "\n", + " results = model.predict(processed_input)\n", + " results_index = np.argmax(results)\n", + " tag = labels[results_index]\n", + "\n", + " for tg in data[\"intents\"]:\n", + " if tg['tag'] == tag:\n", + " responses = tg['responses']\n", + "\n", + " print(random.choice(responses))\n", + "\n", + "chat()\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "blSmwDWNAHRM", + "outputId": "a1ecc1d6-41bb-4541-aba6-ae31b2eb81a0" + }, + "execution_count": 35, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Start talking with the bot (type quit to stop)!\n", + "You: hi\n", + "Good to see you again!\n", + "You: hello\n", + "Hello!\n", + "You: what you doing\n", + "Hello!\n", + "You: quit\n" + ] + } + ] + } + ] +} \ No newline at end of file diff --git a/machine-learning/WEEK 2 YOLO_V4.ipynb b/machine-learning/WEEK 2 YOLO_V4.ipynb new file mode 100644 index 000000000..7d896b43e --- /dev/null +++ b/machine-learning/WEEK 2 YOLO_V4.ipynb @@ -0,0 +1,881 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "QLCpLo_belcv", + "outputId": "9c11c274-ad20-4883-b821-3cf6c1fba175" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mounted at /content/drive\n" + ] + } + ], + "source": [ + "from google.colab import drive\n", + "drive.mount('/content/drive')" + ] + }, + { + "cell_type": "code", + "source": [ + "import os" + ], + "metadata": { + "id": "vIcKIjDkepFY" + }, + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "path='/content/drive/My Drive/YOLO_V4'\n", + "os.chdir(path)" + ], + "metadata": { + "id": "Q-Y3T2GPfEYR" + }, + "execution_count": 3, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "!git clone https://github.com/AlexeyAB/darknet" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IWG1Bx5rfGws", + "outputId": "b73bd57a-47c2-4268-ca6b-833bdbaabf44" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Cloning into 'darknet'...\n", + "remote: Enumerating objects: 15530, done.\u001b[K\n", + "remote: Counting objects: 100% (16/16), done.\u001b[K\n", + "remote: Compressing objects: 100% (12/12), done.\u001b[K\n", + "remote: Total 15530 (delta 5), reused 13 (delta 4), pack-reused 15514\u001b[K\n", + "Receiving objects: 100% (15530/15530), 14.22 MiB | 5.78 MiB/s, done.\n", + "Resolving deltas: 100% (10417/10417), done.\n", + "Updating files: 100% (2058/2058), done.\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!/usr/local/cuda/bin/nvcc --version" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IZjyyzlSfJlx", + "outputId": "e1b193b2-8e99-4119-c6b0-15b25489d6af" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "nvcc: NVIDIA (R) Cuda compiler driver\n", + "Copyright (c) 2005-2022 NVIDIA Corporation\n", + "Built on Wed_Sep_21_10:33:58_PDT_2022\n", + "Cuda compilation tools, release 11.8, V11.8.89\n", + "Build cuda_11.8.r11.8/compiler.31833905_0\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "os.chdir('/content/drive/My Drive/YOLO_V4/darknet')\n", + "!make" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "4EqEEZN0fTgY", + "outputId": "c6b3c2aa-2e13-4c4d-9a0a-36882fa29dec" + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "mkdir -p ./obj/\n", + "mkdir -p backup\n", + "chmod +x *.sh\n", + "g++ -std=c++11 -std=c++11 -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/image_opencv.cpp -o obj/image_opencv.o\n", + "g++ -std=c++11 -std=c++11 -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/http_stream.cpp -o obj/http_stream.o\n", + "\u001b[01m\u001b[K./src/http_stream.cpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kbool JSON_sender::write(const char*)\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/http_stream.cpp:253:21:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kn\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 253 | int \u001b[01;35m\u001b[Kn\u001b[m\u001b[K = _write(client, outputbuf, outlen);\n", + " | \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/http_stream.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid set_track_id(detection*, int, float, float, float, int, int, int)\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/http_stream.cpp:867:27:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison of integer expressions of different signedness: ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’ and ‘\u001b[01m\u001b[Kstd::vector::size_type\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’} [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n", + " 867 | for (int i = 0; \u001b[01;35m\u001b[Ki < v.size()\u001b[m\u001b[K; ++i) {\n", + " | \u001b[01;35m\u001b[K~~^~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/http_stream.cpp:875:33:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison of integer expressions of different signedness: ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’ and ‘\u001b[01m\u001b[Kstd::vector::size_type\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’} [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n", + " 875 | for (int old_id = 0; \u001b[01;35m\u001b[Kold_id < old_dets.size()\u001b[m\u001b[K; ++old_id) {\n", + " | \u001b[01;35m\u001b[K~~~~~~~^~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/http_stream.cpp:894:31:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison of integer expressions of different signedness: ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’ and ‘\u001b[01m\u001b[Kstd::vector::size_type\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’} [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n", + " 894 | for (int index = 0; \u001b[01;35m\u001b[Kindex < new_dets_num*old_dets.size()\u001b[m\u001b[K; ++index) {\n", + " | \u001b[01;35m\u001b[K~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/http_stream.cpp:930:28:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison of integer expressions of different signedness: ‘\u001b[01m\u001b[Kstd::deque >::size_type\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’} and ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n", + " 930 | if (\u001b[01;35m\u001b[Kold_dets_dq.size() > deque_size\u001b[m\u001b[K) old_dets_dq.pop_front();\n", + " | \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~\u001b[m\u001b[K\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/gemm.c -o obj/gemm.o\n", + "\u001b[01m\u001b[K./src/gemm.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kconvolution_2d\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/gemm.c:2044:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kout_w\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 2044 | const int \u001b[01;35m\u001b[Kout_w\u001b[m\u001b[K = (w + 2 * pad - ksize) / stride + 1; // output_width=input_width for stride=1 and pad=1\n", + " | \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/gemm.c:2043:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kout_h\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 2043 | const int \u001b[01;35m\u001b[Kout_h\u001b[m\u001b[K = (h + 2 * pad - ksize) / stride + 1; // output_height=input_height for stride=1 and pad=1\n", + " | \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/utils.c -o obj/utils.o\n", + "\u001b[01m\u001b[K./src/utils.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kcustom_hash\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/utils.c:1082:12:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Ksuggest parentheses around assignment used as truth value [\u001b[01;35m\u001b[K-Wparentheses\u001b[m\u001b[K]\n", + " 1082 | while (\u001b[01;35m\u001b[Kc\u001b[m\u001b[K = *str++)\n", + " | \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n", + "In file included from \u001b[01m\u001b[K/usr/include/string.h:495\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[Kinclude/darknet.h:14\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[K./src/utils.h:3\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[K./src/utils.c:4\u001b[m\u001b[K:\n", + "In function ‘\u001b[01m\u001b[Kstrncpy\u001b[m\u001b[K’,\n", + " inlined from ‘\u001b[01m\u001b[Kcopy_string\u001b[m\u001b[K’ at \u001b[01m\u001b[K./src/utils.c:552:5\u001b[m\u001b[K:\n", + "\u001b[01m\u001b[K/usr/include/x86_64-linux-gnu/bits/string_fortified.h:106:10:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[K__builtin_strncpy\u001b[m\u001b[K’ specified bound depends on the length of the source argument [\u001b[01;35m\u001b[K-Wstringop-overflow=\u001b[m\u001b[K]\n", + " 106 | return \u001b[01;35m\u001b[K__builtin___strncpy_chk (__dest, __src, __len, __bos (__dest))\u001b[m\u001b[K;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/utils.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kcopy_string\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/utils.c:552:22:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Klength computed here\n", + " 552 | strncpy(copy, s, \u001b[01;36m\u001b[Kstrlen(s)\u001b[m\u001b[K+1);\n", + " | \u001b[01;36m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/dark_cuda.c -o obj/dark_cuda.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/convolutional_layer.c -o obj/convolutional_layer.o\n", + "\u001b[01m\u001b[K./src/convolutional_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kresize_convolutional_layer\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/convolutional_layer.c:898:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kold_h\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 898 | int \u001b[01;35m\u001b[Kold_h\u001b[m\u001b[K = l->h;\n", + " | \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/convolutional_layer.c:897:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kold_w\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 897 | int \u001b[01;35m\u001b[Kold_w\u001b[m\u001b[K = l->w;\n", + " | \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/convolutional_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kforward_convolutional_layer\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/convolutional_layer.c:1342:32:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kt_intput_size\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 1342 | size_t \u001b[01;35m\u001b[Kt_intput_size\u001b[m\u001b[K = binary_transpose_align_input(k, n, state.workspace, &l.t_bit_input, ldb_align, l.bit_align);\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/list.c -o obj/list.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/image.c -o obj/image.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/activations.c -o obj/activations.o\n", + "\u001b[01m\u001b[K./src/activations.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kactivate\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KRELU6\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n", + " 79 | \u001b[01;35m\u001b[Kswitch\u001b[m\u001b[K(a){\n", + " | \u001b[01;35m\u001b[K^~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KSWISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n", + "\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KMISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n", + "\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KHARD_MISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n", + "\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KNORM_CHAN\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n", + "\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KNORM_CHAN_SOFTMAX\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n", + "\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KNORM_CHAN_SOFTMAX_MAXVAL\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n", + "\u001b[01m\u001b[K./src/activations.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kgradient\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/activations.c:310:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KSWISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n", + " 310 | \u001b[01;35m\u001b[Kswitch\u001b[m\u001b[K(a){\n", + " | \u001b[01;35m\u001b[K^~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/activations.c:310:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KMISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n", + "\u001b[01m\u001b[K./src/activations.c:310:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KHARD_MISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/im2col.c -o obj/im2col.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/col2im.c -o obj/col2im.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/blas.c -o obj/blas.o\n", + "\u001b[01m\u001b[K./src/blas.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kbackward_shortcut_multilayer_cpu\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/blas.c:207:21:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kout_index\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 207 | int \u001b[01;35m\u001b[Kout_index\u001b[m\u001b[K = id;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/blas.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kfind_sim\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/blas.c:597:59:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Ksize_t\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’} [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n", + " 597 | printf(\" Error: find_sim(): sim isn't found: i = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, j = %d, z = %d \\n\", \u001b[32m\u001b[Ki\u001b[m\u001b[K, j, z);\n", + " | \u001b[01;35m\u001b[K~^\u001b[m\u001b[K \u001b[32m\u001b[K~\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[K|\u001b[m\u001b[K \u001b[32m\u001b[K|\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[Kint\u001b[m\u001b[K \u001b[32m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K\n", + " | \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/blas.c:597:67:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 3 has type ‘\u001b[01m\u001b[Ksize_t\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’} [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n", + " 597 | printf(\" Error: find_sim(): sim isn't found: i = %d, j = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, z = %d \\n\", i, \u001b[32m\u001b[Kj\u001b[m\u001b[K, z);\n", + " | \u001b[01;35m\u001b[K~^\u001b[m\u001b[K \u001b[32m\u001b[K~\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[K|\u001b[m\u001b[K \u001b[32m\u001b[K|\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[Kint\u001b[m\u001b[K \u001b[32m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K\n", + " | \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/blas.c:597:75:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 4 has type ‘\u001b[01m\u001b[Ksize_t\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’} [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n", + " 597 | printf(\" Error: find_sim(): sim isn't found: i = %d, j = %d, z = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K \\n\", i, j, \u001b[32m\u001b[Kz\u001b[m\u001b[K);\n", + " | \u001b[01;35m\u001b[K~^\u001b[m\u001b[K \u001b[32m\u001b[K~\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[K|\u001b[m\u001b[K \u001b[32m\u001b[K|\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[Kint\u001b[m\u001b[K \u001b[32m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K\n", + " | \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/blas.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kfind_P_constrastive\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/blas.c:611:68:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Ksize_t\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’} [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n", + " 611 | printf(\" Error: find_P_constrastive(): P isn't found: i = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, j = %d, z = %d \\n\", \u001b[32m\u001b[Ki\u001b[m\u001b[K, j, z);\n", + " | \u001b[01;35m\u001b[K~^\u001b[m\u001b[K \u001b[32m\u001b[K~\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[K|\u001b[m\u001b[K \u001b[32m\u001b[K|\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[Kint\u001b[m\u001b[K \u001b[32m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K\n", + " | \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/blas.c:611:76:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 3 has type ‘\u001b[01m\u001b[Ksize_t\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’} [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n", + " 611 | printf(\" Error: find_P_constrastive(): P isn't found: i = %d, j = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, z = %d \\n\", i, \u001b[32m\u001b[Kj\u001b[m\u001b[K, z);\n", + " | \u001b[01;35m\u001b[K~^\u001b[m\u001b[K \u001b[32m\u001b[K~\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[K|\u001b[m\u001b[K \u001b[32m\u001b[K|\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[Kint\u001b[m\u001b[K \u001b[32m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K\n", + " | \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/blas.c:611:84:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 4 has type ‘\u001b[01m\u001b[Ksize_t\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’} [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n", + " 611 | printf(\" Error: find_P_constrastive(): P isn't found: i = %d, j = %d, z = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K \\n\", i, j, \u001b[32m\u001b[Kz\u001b[m\u001b[K);\n", + " | \u001b[01;35m\u001b[K~^\u001b[m\u001b[K \u001b[32m\u001b[K~\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[K|\u001b[m\u001b[K \u001b[32m\u001b[K|\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[Kint\u001b[m\u001b[K \u001b[32m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K\n", + " | \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/blas.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KP_constrastive_f\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/blas.c:651:79:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 3 has type ‘\u001b[01m\u001b[Ksize_t\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’} [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n", + " 651 | fprintf(stderr, \" Error: in P_constrastive must be i != l, while i = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, l = %d \\n\", \u001b[32m\u001b[Ki\u001b[m\u001b[K, l);\n", + " | \u001b[01;35m\u001b[K~^\u001b[m\u001b[K \u001b[32m\u001b[K~\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[K|\u001b[m\u001b[K \u001b[32m\u001b[K|\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[Kint\u001b[m\u001b[K \u001b[32m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K\n", + " | \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/blas.c:651:87:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 4 has type ‘\u001b[01m\u001b[Ksize_t\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’} [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n", + " 651 | fprintf(stderr, \" Error: in P_constrastive must be i != l, while i = %d, l = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K \\n\", i, \u001b[32m\u001b[Kl\u001b[m\u001b[K);\n", + " | \u001b[01;35m\u001b[K~^\u001b[m\u001b[K \u001b[32m\u001b[K~\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[K|\u001b[m\u001b[K \u001b[32m\u001b[K|\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[Kint\u001b[m\u001b[K \u001b[32m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K\n", + " | \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/blas.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KP_constrastive\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/blas.c:785:79:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 3 has type ‘\u001b[01m\u001b[Ksize_t\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’} [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n", + " 785 | fprintf(stderr, \" Error: in P_constrastive must be i != l, while i = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, l = %d \\n\", \u001b[32m\u001b[Ki\u001b[m\u001b[K, l);\n", + " | \u001b[01;35m\u001b[K~^\u001b[m\u001b[K \u001b[32m\u001b[K~\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[K|\u001b[m\u001b[K \u001b[32m\u001b[K|\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[Kint\u001b[m\u001b[K \u001b[32m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K\n", + " | \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/blas.c:785:87:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 4 has type ‘\u001b[01m\u001b[Ksize_t\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’} [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n", + " 785 | fprintf(stderr, \" Error: in P_constrastive must be i != l, while i = %d, l = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K \\n\", i, \u001b[32m\u001b[Kl\u001b[m\u001b[K);\n", + " | \u001b[01;35m\u001b[K~^\u001b[m\u001b[K \u001b[32m\u001b[K~\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[K|\u001b[m\u001b[K \u001b[32m\u001b[K|\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[Kint\u001b[m\u001b[K \u001b[32m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K\n", + " | \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/crop_layer.c -o obj/crop_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/dropout_layer.c -o obj/dropout_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/maxpool_layer.c -o obj/maxpool_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/softmax_layer.c -o obj/softmax_layer.o\n", + "\u001b[01m\u001b[K./src/softmax_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kmake_contrastive_layer\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/softmax_layer.c:203:101:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 9 has type ‘\u001b[01m\u001b[Ksize_t\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Kconst long unsigned int\u001b[m\u001b[K’} [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n", + " 203 | fprintf(stderr, \"contrastive %4d x%4d x%4d x emb_size %4d x batch: %4d classes = %4d, step = \u001b[01;35m\u001b[K%4d\u001b[m\u001b[K \\n\", w, h, l.n, l.embedding_size, batch, l.classes, \u001b[32m\u001b[Kstep\u001b[m\u001b[K);\n", + " | \u001b[01;35m\u001b[K~~^\u001b[m\u001b[K \u001b[32m\u001b[K~~~~\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[K|\u001b[m\u001b[K \u001b[32m\u001b[K|\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[Kint\u001b[m\u001b[K \u001b[32m\u001b[Ksize_t {aka const long unsigned int}\u001b[m\u001b[K\n", + " | \u001b[32m\u001b[K%4ld\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/softmax_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kforward_contrastive_layer\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/softmax_layer.c:244:27:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kmax_truth\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n", + " 244 | float \u001b[01;35m\u001b[Kmax_truth\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/data.c -o obj/data.o\n", + "\u001b[01m\u001b[K./src/data.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kload_data_detection\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/data.c:1409:43:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kr_scale\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n", + " 1409 | float r1 = 0, r2 = 0, r3 = 0, r4 = 0, \u001b[01;35m\u001b[Kr_scale\u001b[m\u001b[K;\n", + " | \u001b[01;35m\u001b[K^~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/data.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kfill_truth_detection\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/data.c:440:33:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[K%s\u001b[m\u001b[K’ directive writing up to 4095 bytes into a region of size 251 [\u001b[01;35m\u001b[K-Wformat-overflow=\u001b[m\u001b[K]\n", + " 440 | sprintf(buff, \"echo \u001b[01;35m\u001b[K%s\u001b[m\u001b[K \\\"Wrong annotation: w = %f\\\" >> bad_label.list\", \u001b[32m\u001b[Klabelpath\u001b[m\u001b[K, w);\n", + " | \u001b[01;35m\u001b[K^~\u001b[m\u001b[K \u001b[32m\u001b[K~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/data.c:440:27:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kassuming directive output of 8 bytes\n", + " 440 | sprintf(buff, \u001b[01;36m\u001b[K\"echo %s \\\"Wrong annotation: w = %f\\\" >> bad_label.list\"\u001b[m\u001b[K, labelpath, w);\n", + " | \u001b[01;36m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "In file included from \u001b[01m\u001b[K/usr/include/stdio.h:867\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[Kinclude/darknet.h:13\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[K./src/data.h:5\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[K./src/data.c:1\u001b[m\u001b[K:\n", + "\u001b[01m\u001b[K/usr/include/x86_64-linux-gnu/bits/stdio2.h:36:10:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K‘\u001b[01m\u001b[K__builtin___sprintf_chk\u001b[m\u001b[K’ output between 52 and 4461 bytes into a destination of size 256\n", + " 36 | return \u001b[01;36m\u001b[K__builtin___sprintf_chk (__s, __USE_FORTIFY_LEVEL - 1,\u001b[m\u001b[K\n", + " | \u001b[01;36m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + " 37 | \u001b[01;36m\u001b[K __bos (__s), __fmt, __va_arg_pack ())\u001b[m\u001b[K;\n", + " | \u001b[01;36m\u001b[K~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/data.c:447:33:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[K%s\u001b[m\u001b[K’ directive writing up to 4095 bytes into a region of size 251 [\u001b[01;35m\u001b[K-Wformat-overflow=\u001b[m\u001b[K]\n", + " 447 | sprintf(buff, \"echo \u001b[01;35m\u001b[K%s\u001b[m\u001b[K \\\"Wrong annotation: h = %f\\\" >> bad_label.list\", \u001b[32m\u001b[Klabelpath\u001b[m\u001b[K, h);\n", + " | \u001b[01;35m\u001b[K^~\u001b[m\u001b[K \u001b[32m\u001b[K~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/data.c:447:27:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kassuming directive output of 8 bytes\n", + " 447 | sprintf(buff, \u001b[01;36m\u001b[K\"echo %s \\\"Wrong annotation: h = %f\\\" >> bad_label.list\"\u001b[m\u001b[K, labelpath, h);\n", + " | \u001b[01;36m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "In file included from \u001b[01m\u001b[K/usr/include/stdio.h:867\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[Kinclude/darknet.h:13\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[K./src/data.h:5\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[K./src/data.c:1\u001b[m\u001b[K:\n", + "\u001b[01m\u001b[K/usr/include/x86_64-linux-gnu/bits/stdio2.h:36:10:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K‘\u001b[01m\u001b[K__builtin___sprintf_chk\u001b[m\u001b[K’ output between 52 and 4461 bytes into a destination of size 256\n", + " 36 | return \u001b[01;36m\u001b[K__builtin___sprintf_chk (__s, __USE_FORTIFY_LEVEL - 1,\u001b[m\u001b[K\n", + " | \u001b[01;36m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + " 37 | \u001b[01;36m\u001b[K __bos (__s), __fmt, __va_arg_pack ())\u001b[m\u001b[K;\n", + " | \u001b[01;36m\u001b[K~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/data.c:432:33:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[K%s\u001b[m\u001b[K’ directive writing up to 4095 bytes into a region of size 251 [\u001b[01;35m\u001b[K-Wformat-overflow=\u001b[m\u001b[K]\n", + " 432 | sprintf(buff, \"echo \u001b[01;35m\u001b[K%s\u001b[m\u001b[K \\\"Wrong annotation: x = %f, y = %f\\\" >> bad_label.list\", \u001b[32m\u001b[Klabelpath\u001b[m\u001b[K, x, y);\n", + " | \u001b[01;35m\u001b[K^~\u001b[m\u001b[K \u001b[32m\u001b[K~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/data.c:432:27:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kassuming directive output of 8 bytes\n", + " 432 | sprintf(buff, \u001b[01;36m\u001b[K\"echo %s \\\"Wrong annotation: x = %f, y = %f\\\" >> bad_label.list\"\u001b[m\u001b[K, labelpath, x, y);\n", + " | \u001b[01;36m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/data.c:432:27:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kassuming directive output of 8 bytes\n", + "In file included from \u001b[01m\u001b[K/usr/include/stdio.h:867\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[Kinclude/darknet.h:13\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[K./src/data.h:5\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[K./src/data.c:1\u001b[m\u001b[K:\n", + "\u001b[01m\u001b[K/usr/include/x86_64-linux-gnu/bits/stdio2.h:36:10:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K‘\u001b[01m\u001b[K__builtin___sprintf_chk\u001b[m\u001b[K’ output between 61 and 4784 bytes into a destination of size 256\n", + " 36 | return \u001b[01;36m\u001b[K__builtin___sprintf_chk (__s, __USE_FORTIFY_LEVEL - 1,\u001b[m\u001b[K\n", + " | \u001b[01;36m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + " 37 | \u001b[01;36m\u001b[K __bos (__s), __fmt, __va_arg_pack ())\u001b[m\u001b[K;\n", + " | \u001b[01;36m\u001b[K~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/data.c:424:33:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[K%s\u001b[m\u001b[K’ directive writing up to 4095 bytes into a region of size 251 [\u001b[01;35m\u001b[K-Wformat-overflow=\u001b[m\u001b[K]\n", + " 424 | sprintf(buff, \"echo \u001b[01;35m\u001b[K%s\u001b[m\u001b[K \\\"Wrong annotation: x = 0 or y = 0\\\" >> bad_label.list\", \u001b[32m\u001b[Klabelpath\u001b[m\u001b[K);\n", + " | \u001b[01;35m\u001b[K^~\u001b[m\u001b[K \u001b[32m\u001b[K~~~~~~~~~\u001b[m\u001b[K\n", + "In file included from \u001b[01m\u001b[K/usr/include/stdio.h:867\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[Kinclude/darknet.h:13\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[K./src/data.h:5\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[K./src/data.c:1\u001b[m\u001b[K:\n", + "\u001b[01m\u001b[K/usr/include/x86_64-linux-gnu/bits/stdio2.h:36:10:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K‘\u001b[01m\u001b[K__builtin___sprintf_chk\u001b[m\u001b[K’ output between 59 and 4154 bytes into a destination of size 256\n", + " 36 | return \u001b[01;36m\u001b[K__builtin___sprintf_chk (__s, __USE_FORTIFY_LEVEL - 1,\u001b[m\u001b[K\n", + " | \u001b[01;36m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + " 37 | \u001b[01;36m\u001b[K __bos (__s), __fmt, __va_arg_pack ())\u001b[m\u001b[K;\n", + " | \u001b[01;36m\u001b[K~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/data.c:410:33:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[K%s\u001b[m\u001b[K’ directive writing up to 4095 bytes into a region of size 251 [\u001b[01;35m\u001b[K-Wformat-overflow=\u001b[m\u001b[K]\n", + " 410 | sprintf(buff, \"echo \u001b[01;35m\u001b[K%s\u001b[m\u001b[K \\\"Wrong annotation: class_id = %d. But class_id should be [from 0 to %d]\\\" >> bad_label.list\", \u001b[32m\u001b[Klabelpath\u001b[m\u001b[K, id, (classes-1));\n", + " | \u001b[01;35m\u001b[K^~\u001b[m\u001b[K \u001b[32m\u001b[K~~~~~~~~~\u001b[m\u001b[K\n", + "In file included from \u001b[01m\u001b[K/usr/include/stdio.h:867\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[Kinclude/darknet.h:13\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[K./src/data.h:5\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[K./src/data.c:1\u001b[m\u001b[K:\n", + "\u001b[01m\u001b[K/usr/include/x86_64-linux-gnu/bits/stdio2.h:36:10:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K‘\u001b[01m\u001b[K__builtin___sprintf_chk\u001b[m\u001b[K’ output between 95 and 4210 bytes into a destination of size 256\n", + " 36 | return \u001b[01;36m\u001b[K__builtin___sprintf_chk (__s, __USE_FORTIFY_LEVEL - 1,\u001b[m\u001b[K\n", + " | \u001b[01;36m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + " 37 | \u001b[01;36m\u001b[K __bos (__s), __fmt, __va_arg_pack ())\u001b[m\u001b[K;\n", + " | \u001b[01;36m\u001b[K~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/matrix.c -o obj/matrix.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/network.c -o obj/network.o\n", + "\u001b[01m\u001b[K./src/network.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Ktrain_network_waitkey\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/network.c:435:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kema_period\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 435 | int \u001b[01;35m\u001b[Kema_period\u001b[m\u001b[K = (net.max_batches - ema_start_point - 1000) * (1.0 - net.ema_alpha);\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~\u001b[m\u001b[K\n", + "At top level:\n", + "\u001b[01m\u001b[K./src/network.c:1269:14:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[Krelu\u001b[m\u001b[K’ defined but not used [\u001b[01;35m\u001b[K-Wunused-function\u001b[m\u001b[K]\n", + " 1269 | static float \u001b[01;35m\u001b[Krelu\u001b[m\u001b[K(float src) {\n", + " | \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/connected_layer.c -o obj/connected_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/cost_layer.c -o obj/cost_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/parser.c -o obj/parser.o\n", + "\u001b[01m\u001b[K./src/parser.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Ksave_implicit_weights\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/parser.c:1909:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ki\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 1909 | int \u001b[01;35m\u001b[Ki\u001b[m\u001b[K;\n", + " | \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/parser.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kget_classes_multipliers\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/parser.c:438:40:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kargument 1 range [18446744071562067968, 18446744073709551615] exceeds maximum object size 9223372036854775807 [\u001b[01;35m\u001b[K-Walloc-size-larger-than=\u001b[m\u001b[K]\n", + " 438 | classes_multipliers = (float *)\u001b[01;35m\u001b[Kcalloc(classes_counters, sizeof(float))\u001b[m\u001b[K;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "In file included from \u001b[01m\u001b[K./src/parser.c:3\u001b[m\u001b[K:\n", + "\u001b[01m\u001b[K/usr/include/stdlib.h:542:14:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kin a call to allocation function ‘\u001b[01m\u001b[Kcalloc\u001b[m\u001b[K’ declared here\n", + " 542 | extern void *\u001b[01;36m\u001b[Kcalloc\u001b[m\u001b[K (size_t __nmemb, size_t __size)\n", + " | \u001b[01;36m\u001b[K^~~~~~\u001b[m\u001b[K\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/option_list.c -o obj/option_list.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/darknet.c -o obj/darknet.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/detection_layer.c -o obj/detection_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/captcha.c -o obj/captcha.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/route_layer.c -o obj/route_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/writing.c -o obj/writing.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/box.c -o obj/box.o\n", + "\u001b[01m\u001b[K./src/box.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kbox_iou_kind\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/box.c:154:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KMSE\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n", + " 154 | \u001b[01;35m\u001b[Kswitch\u001b[m\u001b[K(iou_kind) {\n", + " | \u001b[01;35m\u001b[K^~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/box.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kdiounms_sort\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/box.c:898:27:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kbeta_prob\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 898 | float \u001b[01;35m\u001b[Kbeta_prob\u001b[m\u001b[K = pow(dets[j].prob[k], 2) / sum_prob;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/box.c:897:27:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kalpha_prob\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 897 | float \u001b[01;35m\u001b[Kalpha_prob\u001b[m\u001b[K = pow(dets[i].prob[k], 2) / sum_prob;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~\u001b[m\u001b[K\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/nightmare.c -o obj/nightmare.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/normalization_layer.c -o obj/normalization_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/avgpool_layer.c -o obj/avgpool_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/coco.c -o obj/coco.o\n", + "\u001b[01m\u001b[K./src/coco.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvalidate_coco_recall\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/coco.c:248:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kbase\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 248 | char *\u001b[01;35m\u001b[Kbase\u001b[m\u001b[K = \"results/comp4_det_test_\";\n", + " | \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/dice.c -o obj/dice.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/yolo.c -o obj/yolo.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/detector.c -o obj/detector.o\n", + "\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Ktrain_detector\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/detector.c:395:72:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Ksuggest parentheses around ‘\u001b[01m\u001b[K&&\u001b[m\u001b[K’ within ‘\u001b[01m\u001b[K||\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wparentheses\u001b[m\u001b[K]\n", + " 395 | \u001b[01;35m\u001b[K(iteration >= (iter_save + 1000) || iteration % 1000 == 0) && net.max_batches < 10000\u001b[m\u001b[K)\n", + " | \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/detector.c:328:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kdraw_precision\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n", + " 328 | int \u001b[01;35m\u001b[Kdraw_precision\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/detector.c:67:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kavg_contrastive_acc\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 67 | float \u001b[01;35m\u001b[Kavg_contrastive_acc\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Keliminate_bdd\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/detector.c:588:21:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kstatement with no effect [\u001b[01;35m\u001b[K-Wunused-value\u001b[m\u001b[K]\n", + " 588 | \u001b[01;35m\u001b[Kfor\u001b[m\u001b[K (k; buf[k + n] != '\\0'; k++)\n", + " | \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvalidate_detector\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/detector.c:709:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kmkd2\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 709 | int \u001b[01;35m\u001b[Kmkd2\u001b[m\u001b[K = make_directory(buff2, 0777);\n", + " | \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/detector.c:707:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kmkd\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 707 | int \u001b[01;35m\u001b[Kmkd\u001b[m\u001b[K = make_directory(buff, 0777);\n", + " | \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvalidate_detector_map\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/detector.c:1326:24:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kcur_prob\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n", + " 1326 | double \u001b[01;35m\u001b[Kcur_prob\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/detector.c:1347:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kclass_recall\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 1347 | float \u001b[01;35m\u001b[Kclass_recall\u001b[m\u001b[K = (float)tp_for_thresh_per_class[i] / ((float)tp_for_thresh_per_class[i] + (float)(truth_classes_count[i] - tp_for_thresh_per_class[i]));\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/detector.c:1346:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kclass_precision\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 1346 | float \u001b[01;35m\u001b[Kclass_precision\u001b[m\u001b[K = (float)tp_for_thresh_per_class[i] / ((float)tp_for_thresh_per_class[i] + (float)fp_for_thresh_per_class[i]);\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "At top level:\n", + "\u001b[01m\u001b[K./src/detector.c:461:12:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[Kget_coco_image_id\u001b[m\u001b[K’ defined but not used [\u001b[01;35m\u001b[K-Wunused-function\u001b[m\u001b[K]\n", + " 461 | static int \u001b[01;35m\u001b[Kget_coco_image_id\u001b[m\u001b[K(char *filename)\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/layer.c -o obj/layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/compare.c -o obj/compare.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/classifier.c -o obj/classifier.o\n", + "\u001b[01m\u001b[K./src/classifier.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Ktrain_classifier\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/classifier.c:190:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kdraw_precision\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n", + " 190 | int \u001b[01;35m\u001b[Kdraw_precision\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/classifier.c:146:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kcount\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 146 | int \u001b[01;35m\u001b[Kcount\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/classifier.c:35:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kavg_contrastive_acc\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 35 | float \u001b[01;35m\u001b[Kavg_contrastive_acc\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/classifier.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kpredict_classifier\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/classifier.c:855:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktime\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 855 | clock_t \u001b[01;35m\u001b[Ktime\u001b[m\u001b[K;\n", + " | \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/local_layer.c -o obj/local_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/swag.c -o obj/swag.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/shortcut_layer.c -o obj/shortcut_layer.o\n", + "\u001b[01m\u001b[K./src/shortcut_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kmake_shortcut_layer\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/shortcut_layer.c:55:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kscale\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 55 | float \u001b[01;35m\u001b[Kscale\u001b[m\u001b[K = sqrt(2. / l.nweights);\n", + " | \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/representation_layer.c -o obj/representation_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/activation_layer.c -o obj/activation_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/rnn_layer.c -o obj/rnn_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/gru_layer.c -o obj/gru_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/rnn.c -o obj/rnn.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/rnn_vid.c -o obj/rnn_vid.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/crnn_layer.c -o obj/crnn_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/demo.c -o obj/demo.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/tag.c -o obj/tag.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/cifar.c -o obj/cifar.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/go.c -o obj/go.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/batchnorm_layer.c -o obj/batchnorm_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/art.c -o obj/art.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/region_layer.c -o obj/region_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/reorg_layer.c -o obj/reorg_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/reorg_old_layer.c -o obj/reorg_old_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/super.c -o obj/super.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/voxel.c -o obj/voxel.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/tree.c -o obj/tree.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/yolo_layer.c -o obj/yolo_layer.o\n", + "\u001b[01m\u001b[K./src/yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kprocess_batch\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/yolo_layer.c:426:25:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kbest_match_t\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n", + " 426 | int \u001b[01;35m\u001b[Kbest_match_t\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kforward_yolo_layer\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/yolo_layer.c:707:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kavg_anyobj\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 707 | float \u001b[01;35m\u001b[Kavg_anyobj\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/yolo_layer.c:706:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kavg_obj\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 706 | float \u001b[01;35m\u001b[Kavg_obj\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/yolo_layer.c:705:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kavg_cat\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 705 | float \u001b[01;35m\u001b[Kavg_cat\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/yolo_layer.c:704:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Krecall75\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 704 | float \u001b[01;35m\u001b[Krecall75\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/yolo_layer.c:703:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Krecall\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 703 | float \u001b[01;35m\u001b[Krecall\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/yolo_layer.c:702:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktot_ciou_loss\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 702 | float \u001b[01;35m\u001b[Ktot_ciou_loss\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/yolo_layer.c:701:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktot_diou_loss\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 701 | float \u001b[01;35m\u001b[Ktot_diou_loss\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/yolo_layer.c:698:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktot_ciou\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 698 | float \u001b[01;35m\u001b[Ktot_ciou\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/yolo_layer.c:697:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktot_diou\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 697 | float \u001b[01;35m\u001b[Ktot_diou\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/yolo_layer.c:696:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktot_giou\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 696 | float \u001b[01;35m\u001b[Ktot_giou\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/gaussian_yolo_layer.c -o obj/gaussian_yolo_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/upsample_layer.c -o obj/upsample_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/lstm_layer.c -o obj/lstm_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/conv_lstm_layer.c -o obj/conv_lstm_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/scale_channels_layer.c -o obj/scale_channels_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/sam_layer.c -o obj/sam_layer.o\n", + "g++ -std=c++11 -std=c++11 -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast obj/image_opencv.o obj/http_stream.o obj/gemm.o obj/utils.o obj/dark_cuda.o obj/convolutional_layer.o obj/list.o obj/image.o obj/activations.o obj/im2col.o obj/col2im.o obj/blas.o obj/crop_layer.o obj/dropout_layer.o obj/maxpool_layer.o obj/softmax_layer.o obj/data.o obj/matrix.o obj/network.o obj/connected_layer.o obj/cost_layer.o obj/parser.o obj/option_list.o obj/darknet.o obj/detection_layer.o obj/captcha.o obj/route_layer.o obj/writing.o obj/box.o obj/nightmare.o obj/normalization_layer.o obj/avgpool_layer.o obj/coco.o obj/dice.o obj/yolo.o obj/detector.o obj/layer.o obj/compare.o obj/classifier.o obj/local_layer.o obj/swag.o obj/shortcut_layer.o obj/representation_layer.o obj/activation_layer.o obj/rnn_layer.o obj/gru_layer.o obj/rnn.o obj/rnn_vid.o obj/crnn_layer.o obj/demo.o obj/tag.o obj/cifar.o obj/go.o obj/batchnorm_layer.o obj/art.o obj/region_layer.o obj/reorg_layer.o obj/reorg_old_layer.o obj/super.o obj/voxel.o obj/tree.o obj/yolo_layer.o obj/gaussian_yolo_layer.o obj/upsample_layer.o obj/lstm_layer.o obj/conv_lstm_layer.o obj/scale_channels_layer.o obj/sam_layer.o -o darknet -lm -pthread\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!./darknet" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IM282uWWfW2n", + "outputId": "3123df19-cfb2-46f1-f709-e631518642e1" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "usage: ./darknet \n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!wget https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "kGMG34IBfqAO", + "outputId": "dbcfea6b-3e02-466a-d890-37a26a885651" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2023-07-13 09:05:10-- https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights\n", + "Resolving github.com (github.com)... 140.82.121.3\n", + "Connecting to github.com (github.com)|140.82.121.3|:443... connected.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: https://objects.githubusercontent.com/github-production-release-asset-2e65be/75388965/ba4b6380-889c-11ea-9751-f994f5961796?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20230713%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230713T090510Z&X-Amz-Expires=300&X-Amz-Signature=cea41ca1934f8fb97456ef5d2cc519bde7b60d1b7bec35fd51b2eb42dc06aca4&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=75388965&response-content-disposition=attachment%3B%20filename%3Dyolov4.weights&response-content-type=application%2Foctet-stream [following]\n", + "--2023-07-13 09:05:10-- https://objects.githubusercontent.com/github-production-release-asset-2e65be/75388965/ba4b6380-889c-11ea-9751-f994f5961796?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20230713%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230713T090510Z&X-Amz-Expires=300&X-Amz-Signature=cea41ca1934f8fb97456ef5d2cc519bde7b60d1b7bec35fd51b2eb42dc06aca4&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=75388965&response-content-disposition=attachment%3B%20filename%3Dyolov4.weights&response-content-type=application%2Foctet-stream\n", + "Resolving objects.githubusercontent.com (objects.githubusercontent.com)... 185.199.109.133, 185.199.111.133, 185.199.110.133, ...\n", + "Connecting to objects.githubusercontent.com (objects.githubusercontent.com)|185.199.109.133|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 257717640 (246M) [application/octet-stream]\n", + "Saving to: ‘yolov4.weights’\n", + "\n", + "yolov4.weights 100%[===================>] 245.78M 46.7MB/s in 5.3s \n", + "\n", + "2023-07-13 09:05:15 (46.7 MB/s) - ‘yolov4.weights’ saved [257717640/257717640]\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!./darknet detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights data/eagle.jpg" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "OKjVBw3hfwZ1", + "outputId": "0bdae556-9094-434c-e163-fd4308d17c0b" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " GPU isn't used \n", + " OpenCV isn't used - data augmentation will be slow \n", + "mini_batch = 1, batch = 8, time_steps = 1, train = 0 \n", + " layer filters size/strd(dil) input output\n", + " 0 conv 32 3 x 3/ 1 608 x 608 x 3 -> 608 x 608 x 32 0.639 BF\n", + " 1 conv 64 3 x 3/ 2 608 x 608 x 32 -> 304 x 304 x 64 3.407 BF\n", + " 2 conv 64 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 64 0.757 BF\n", + " 3 route 1 \t\t -> 304 x 304 x 64 \n", + " 4 conv 64 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 64 0.757 BF\n", + " 5 conv 32 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 32 0.379 BF\n", + " 6 conv 64 3 x 3/ 1 304 x 304 x 32 -> 304 x 304 x 64 3.407 BF\n", + " 7 Shortcut Layer: 4, wt = 0, wn = 0, outputs: 304 x 304 x 64 0.006 BF\n", + " 8 conv 64 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 64 0.757 BF\n", + " 9 route 8 2 \t -> 304 x 304 x 128 \n", + " 10 conv 64 1 x 1/ 1 304 x 304 x 128 -> 304 x 304 x 64 1.514 BF\n", + " 11 conv 128 3 x 3/ 2 304 x 304 x 64 -> 152 x 152 x 128 3.407 BF\n", + " 12 conv 64 1 x 1/ 1 152 x 152 x 128 -> 152 x 152 x 64 0.379 BF\n", + " 13 route 11 \t\t -> 152 x 152 x 128 \n", + " 14 conv 64 1 x 1/ 1 152 x 152 x 128 -> 152 x 152 x 64 0.379 BF\n", + " 15 conv 64 1 x 1/ 1 152 x 152 x 64 -> 152 x 152 x 64 0.189 BF\n", + " 16 conv 64 3 x 3/ 1 152 x 152 x 64 -> 152 x 152 x 64 1.703 BF\n", + " 17 Shortcut Layer: 14, wt = 0, wn = 0, outputs: 152 x 152 x 64 0.001 BF\n", + " 18 conv 64 1 x 1/ 1 152 x 152 x 64 -> 152 x 152 x 64 0.189 BF\n", + " 19 conv 64 3 x 3/ 1 152 x 152 x 64 -> 152 x 152 x 64 1.703 BF\n", + " 20 Shortcut Layer: 17, wt = 0, wn = 0, outputs: 152 x 152 x 64 0.001 BF\n", + " 21 conv 64 1 x 1/ 1 152 x 152 x 64 -> 152 x 152 x 64 0.189 BF\n", + " 22 route 21 12 \t -> 152 x 152 x 128 \n", + " 23 conv 128 1 x 1/ 1 152 x 152 x 128 -> 152 x 152 x 128 0.757 BF\n", + " 24 conv 256 3 x 3/ 2 152 x 152 x 128 -> 76 x 76 x 256 3.407 BF\n", + " 25 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF\n", + " 26 route 24 \t\t -> 76 x 76 x 256 \n", + " 27 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF\n", + " 28 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF\n", + " 29 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF\n", + " 30 Shortcut Layer: 27, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF\n", + " 31 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF\n", + " 32 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF\n", + " 33 Shortcut Layer: 30, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF\n", + " 34 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF\n", + " 35 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF\n", + " 36 Shortcut Layer: 33, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF\n", + " 37 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF\n", + " 38 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF\n", + " 39 Shortcut Layer: 36, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF\n", + " 40 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF\n", + " 41 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF\n", + " 42 Shortcut Layer: 39, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF\n", + " 43 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF\n", + " 44 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF\n", + " 45 Shortcut Layer: 42, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF\n", + " 46 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF\n", + " 47 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF\n", + " 48 Shortcut Layer: 45, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF\n", + " 49 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF\n", + " 50 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF\n", + " 51 Shortcut Layer: 48, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF\n", + " 52 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF\n", + " 53 route 52 25 \t -> 76 x 76 x 256 \n", + " 54 conv 256 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 256 0.757 BF\n", + " 55 conv 512 3 x 3/ 2 76 x 76 x 256 -> 38 x 38 x 512 3.407 BF\n", + " 56 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF\n", + " 57 route 55 \t\t -> 38 x 38 x 512 \n", + " 58 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF\n", + " 59 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF\n", + " 60 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF\n", + " 61 Shortcut Layer: 58, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF\n", + " 62 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF\n", + " 63 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF\n", + " 64 Shortcut Layer: 61, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF\n", + " 65 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF\n", + " 66 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF\n", + " 67 Shortcut Layer: 64, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF\n", + " 68 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF\n", + " 69 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF\n", + " 70 Shortcut Layer: 67, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF\n", + " 71 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF\n", + " 72 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF\n", + " 73 Shortcut Layer: 70, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF\n", + " 74 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF\n", + " 75 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF\n", + " 76 Shortcut Layer: 73, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF\n", + " 77 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF\n", + " 78 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF\n", + " 79 Shortcut Layer: 76, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF\n", + " 80 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF\n", + " 81 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF\n", + " 82 Shortcut Layer: 79, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF\n", + " 83 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF\n", + " 84 route 83 56 \t -> 38 x 38 x 512 \n", + " 85 conv 512 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 512 0.757 BF\n", + " 86 conv 1024 3 x 3/ 2 38 x 38 x 512 -> 19 x 19 x1024 3.407 BF\n", + " 87 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF\n", + " 88 route 86 \t\t -> 19 x 19 x1024 \n", + " 89 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF\n", + " 90 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF\n", + " 91 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF\n", + " 92 Shortcut Layer: 89, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF\n", + " 93 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF\n", + " 94 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF\n", + " 95 Shortcut Layer: 92, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF\n", + " 96 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF\n", + " 97 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF\n", + " 98 Shortcut Layer: 95, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF\n", + " 99 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF\n", + " 100 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF\n", + " 101 Shortcut Layer: 98, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF\n", + " 102 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF\n", + " 103 route 102 87 \t -> 19 x 19 x1024 \n", + " 104 conv 1024 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x1024 0.757 BF\n", + " 105 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF\n", + " 106 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF\n", + " 107 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF\n", + " 108 max 5x 5/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.005 BF\n", + " 109 route 107 \t\t -> 19 x 19 x 512 \n", + " 110 max 9x 9/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.015 BF\n", + " 111 route 107 \t\t -> 19 x 19 x 512 \n", + " 112 max 13x13/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.031 BF\n", + " 113 route 112 110 108 107 \t -> 19 x 19 x2048 \n", + " 114 conv 512 1 x 1/ 1 19 x 19 x2048 -> 19 x 19 x 512 0.757 BF\n", + " 115 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF\n", + " 116 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF\n", + " 117 conv 256 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 256 0.095 BF\n", + " 118 upsample 2x 19 x 19 x 256 -> 38 x 38 x 256\n", + " 119 route 85 \t\t -> 38 x 38 x 512 \n", + " 120 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF\n", + " 121 route 120 118 \t -> 38 x 38 x 512 \n", + " 122 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF\n", + " 123 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF\n", + " 124 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF\n", + " 125 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF\n", + " 126 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF\n", + " 127 conv 128 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 128 0.095 BF\n", + " 128 upsample 2x 38 x 38 x 128 -> 76 x 76 x 128\n", + " 129 route 54 \t\t -> 76 x 76 x 256 \n", + " 130 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF\n", + " 131 route 130 128 \t -> 76 x 76 x 256 \n", + " 132 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF\n", + " 133 conv 256 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BF\n", + " 134 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF\n", + " 135 conv 256 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BF\n", + " 136 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF\n", + " 137 conv 256 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BF\n", + " 138 conv 255 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 255 0.754 BF\n", + " 139 yolo\n", + "[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.20\n", + "nms_kind: greedynms (1), beta = 0.600000 \n", + " 140 route 136 \t\t -> 76 x 76 x 128 \n", + " 141 conv 256 3 x 3/ 2 76 x 76 x 128 -> 38 x 38 x 256 0.852 BF\n", + " 142 route 141 126 \t -> 38 x 38 x 512 \n", + " 143 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF\n", + " 144 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF\n", + " 145 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF\n", + " 146 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF\n", + " 147 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF\n", + " 148 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF\n", + " 149 conv 255 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 255 0.377 BF\n", + " 150 yolo\n", + "[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.10\n", + "nms_kind: greedynms (1), beta = 0.600000 \n", + " 151 route 147 \t\t -> 38 x 38 x 256 \n", + " 152 conv 512 3 x 3/ 2 38 x 38 x 256 -> 19 x 19 x 512 0.852 BF\n", + " 153 route 152 116 \t -> 19 x 19 x1024 \n", + " 154 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF\n", + " 155 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF\n", + " 156 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF\n", + " 157 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF\n", + " 158 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF\n", + " 159 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF\n", + " 160 conv 255 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 255 0.189 BF\n", + " 161 yolo\n", + "[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.05\n", + "nms_kind: greedynms (1), beta = 0.600000 \n", + "Total BFLOPS 128.459 \n", + "avg_outputs = 1068395 \n", + "Loading weights from yolov4.weights...\n", + " seen 64, trained: 32032 K-images (500 Kilo-batches_64) \n", + "Done! Loaded 162 layers from weights-file \n", + " Detection layer: 139 - type = 28 \n", + " Detection layer: 150 - type = 28 \n", + " Detection layer: 161 - type = 28 \n", + "data/eagle.jpg: Predicted in 26348.319000 milli-seconds.\n", + "bird: 97%\n", + "Not compiled with OpenCV, saving to predictions.png instead\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import cv2\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ], + "metadata": { + "id": "JUrmZwRAgCxz" + }, + "execution_count": 10, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "image=cv2.imread('predictions.jpg')\n", + "fig=plt.figure()\n", + "fig.set_size_inches(12,14)\n", + "plt.imshow(image)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 469 + }, + "id": "lt_Qlr6vgTJR", + "outputId": "70f66fdf-c2d5-4c24-c30e-a3ca7f5a1a8c" + }, + "execution_count": 11, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 11 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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+ }, + "metadata": {} + } + ] + } + ] +} \ No newline at end of file diff --git a/machine-learning/Week 3 Image Alignment.ipynb b/machine-learning/Week 3 Image Alignment.ipynb new file mode 100644 index 000000000..49e1dcc9a --- /dev/null +++ b/machine-learning/Week 3 Image Alignment.ipynb @@ -0,0 +1,148 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "8f72856b", + "metadata": {}, + "outputs": [], + "source": [ + "import cv2\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "e21dbb7d", + "metadata": {}, + "outputs": [], + "source": [ + "def align_image(image):\n", + " \n", + " gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n", + "\n", + " \n", + " sift = cv2.SIFT_create()\n", + "\n", + "\n", + " keypoints, descriptors = sift.detectAndCompute(gray, None)\n", + "\n", + "\n", + " matcher = cv2.BFMatcher()\n", + "\n", + "\n", + " matches = matcher.knnMatch(descriptors, descriptors, k=2)\n", + " \n", + " good_matches = []\n", + " for m, n in matches:\n", + " if m.distance < 0.75 * n.distance:\n", + " good_matches.append(m)\n", + "\n", + " src_points = np.float32([keypoints[m.queryIdx].pt for m in good_matches]).reshape(-1, 1, 2)\n", + " dst_points = np.float32([keypoints[m.trainIdx].pt for m in good_matches]).reshape(-1, 1, 2)\n", + "\n", + "\n", + " M, mask = cv2.findHomography(src_points, dst_points, cv2.RANSAC, 5.0)\n", + "\n", + "\n", + " aligned_image = cv2.warpPerspective(image, M, (image.shape[1], image.shape[0]))\n", + "\n", + " return aligned_image" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "5ad7015f", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "image = cv2.imread(\"C:\\\\Users\\\\91740\\\\OneDrive\\\\Desktop\\\\g.jpg\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2361d9d7", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "81e03c86", + "metadata": {}, + "outputs": [], + "source": [ + "clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))\n", + "enhanced_image = cv2.cvtColor(image1, cv2.COLOR_BGR2LAB)\n", + "enhanced_image[:, :, 0] = clahe.apply(enhanced_image1[:, :, 0])\n", + "enhanced_image = cv2.cvtColor(enhanced_image1, cv2.COLOR_LAB2BGR)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "b2a89ab1", + "metadata": {}, + "outputs": [], + "source": [ + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "d8c5e61e", + "metadata": {}, + "outputs": [], + "source": [ + "aligned_image = align_image(enhanced_image)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f2e30048", + "metadata": {}, + "outputs": [], + "source": [ + "cv2.imshow('Aligned Image', aligned_image)\n", + "cv2.waitKey(0)\n", + "cv2.destroyAllWindows()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f9fbe637", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + 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prices.merchantbrandcategoriesprice
0Bestbuy.comGrace DigitalElectronics,Home Audio & Theater,Home Audio,Al...92.99
1Bestbuy.comLenovoElectronics,Computers,Laptops,Laptops By Brand...229.99
2Bestbuy.comHouse of MarleyHeadphones,Consumer Electronics,Portable Audio...16.99
3Bestbuy.comSonyElectronics,Home Audio & Theater,Home Audio,Al...69.99
4bhphotovideo.comSonyDigital Cameras,Cameras & Photo,Used:Digital P...846.00
...............
5431Bestbuy.comAppleiPhones,All Cell Phones with Plans,iPhone SE,C...12.45
5432bhphotovideo.comZAGGComputers,Bags, Cases & Sleeves,Computer Acces...149.99
5433Bestbuy.com360flyCameras & Photo,360 Cameras,VR 360 Video,Camco...324.99
5434HotelectronicsAlpineAuto & Tires,Auto Electronics,Car Speakers and...61.55
5435Electronics Expo (Authorized Dealer)PioneerSpeaker Separates tdrbbzebscxdcufzwattw,Electr...249.99
\n", + "

5436 rows × 4 columns

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LinearRegression()
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1Bestbuy.comLenovoElectronics,Computers,Laptops,Laptops By Brand...229.99
2Bestbuy.comHouse of MarleyHeadphones,Consumer Electronics,Portable Audio...16.99
3Bestbuy.comSonyElectronics,Home Audio & Theater,Home Audio,Al...69.99
4bhphotovideo.comSonyDigital Cameras,Cameras & Photo,Used:Digital P...846.00
...............
5431Bestbuy.comAppleiPhones,All Cell Phones with Plans,iPhone SE,C...12.45
5432bhphotovideo.comZAGGComputers,Bags, Cases & Sleeves,Computer Acces...149.99
5433Bestbuy.com360flyCameras & Photo,360 Cameras,VR 360 Video,Camco...324.99
5434HotelectronicsAlpineAuto & Tires,Auto Electronics,Car Speakers and...61.55
5435Electronics Expo (Authorized Dealer)PioneerSpeaker Separates tdrbbzebscxdcufzwattw,Electr...249.99
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0Bestbuy.comGrace DigitalElectronics,Home Audio & Theater,Home Audio,Al...92.99
1Bestbuy.comLenovoElectronics,Computers,Laptops,Laptops By Brand...229.99
2Bestbuy.comHouse of MarleyHeadphones,Consumer Electronics,Portable Audio...16.99
3Bestbuy.comSonyElectronics,Home Audio & Theater,Home Audio,Al...69.99
4bhphotovideo.comSonyDigital Cameras,Cameras & Photo,Used:Digital P...846.00
...............
5431Bestbuy.comAppleiPhones,All Cell Phones with Plans,iPhone SE,C...12.45
5432bhphotovideo.comZAGGComputers,Bags, Cases & Sleeves,Computer Acces...149.99
5433Bestbuy.com360flyCameras & Photo,360 Cameras,VR 360 Video,Camco...324.99
5434HotelectronicsAlpineAuto & Tires,Auto Electronics,Car Speakers and...61.55
5435Electronics Expo (Authorized Dealer)PioneerSpeaker Separates tdrbbzebscxdcufzwattw,Electr...249.99
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LinearRegression()
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"text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/machine-learning/week2/WEEK 2 YOLO_V4.ipynb b/machine-learning/week2/WEEK 2 YOLO_V4.ipynb new file mode 100644 index 000000000..7d896b43e --- /dev/null +++ b/machine-learning/week2/WEEK 2 YOLO_V4.ipynb @@ -0,0 +1,881 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "QLCpLo_belcv", + "outputId": "9c11c274-ad20-4883-b821-3cf6c1fba175" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mounted at /content/drive\n" + ] + } + ], + "source": [ + "from google.colab import drive\n", + "drive.mount('/content/drive')" + ] + }, + { + "cell_type": "code", + "source": [ + "import os" + ], + "metadata": { + "id": "vIcKIjDkepFY" + }, + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "path='/content/drive/My Drive/YOLO_V4'\n", + "os.chdir(path)" + ], + "metadata": { + "id": "Q-Y3T2GPfEYR" + }, + "execution_count": 3, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "!git clone https://github.com/AlexeyAB/darknet" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IWG1Bx5rfGws", + "outputId": "b73bd57a-47c2-4268-ca6b-833bdbaabf44" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Cloning into 'darknet'...\n", + "remote: Enumerating objects: 15530, done.\u001b[K\n", + "remote: Counting objects: 100% (16/16), done.\u001b[K\n", + "remote: Compressing objects: 100% (12/12), done.\u001b[K\n", + "remote: Total 15530 (delta 5), reused 13 (delta 4), pack-reused 15514\u001b[K\n", + "Receiving objects: 100% (15530/15530), 14.22 MiB | 5.78 MiB/s, done.\n", + "Resolving deltas: 100% (10417/10417), done.\n", + "Updating files: 100% (2058/2058), done.\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!/usr/local/cuda/bin/nvcc --version" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IZjyyzlSfJlx", + "outputId": "e1b193b2-8e99-4119-c6b0-15b25489d6af" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "nvcc: NVIDIA (R) Cuda compiler driver\n", + "Copyright (c) 2005-2022 NVIDIA Corporation\n", + "Built on Wed_Sep_21_10:33:58_PDT_2022\n", + "Cuda compilation tools, release 11.8, V11.8.89\n", + "Build cuda_11.8.r11.8/compiler.31833905_0\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "os.chdir('/content/drive/My Drive/YOLO_V4/darknet')\n", + "!make" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "4EqEEZN0fTgY", + "outputId": "c6b3c2aa-2e13-4c4d-9a0a-36882fa29dec" + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "mkdir -p ./obj/\n", + "mkdir -p backup\n", + "chmod +x *.sh\n", + "g++ -std=c++11 -std=c++11 -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/image_opencv.cpp -o obj/image_opencv.o\n", + "g++ -std=c++11 -std=c++11 -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/http_stream.cpp -o obj/http_stream.o\n", + "\u001b[01m\u001b[K./src/http_stream.cpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kbool JSON_sender::write(const char*)\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/http_stream.cpp:253:21:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kn\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 253 | int \u001b[01;35m\u001b[Kn\u001b[m\u001b[K = _write(client, outputbuf, outlen);\n", + " | \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/http_stream.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid set_track_id(detection*, int, float, float, float, int, int, int)\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/http_stream.cpp:867:27:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison of integer expressions of different signedness: ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’ and ‘\u001b[01m\u001b[Kstd::vector::size_type\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’} [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n", + " 867 | for (int i = 0; \u001b[01;35m\u001b[Ki < v.size()\u001b[m\u001b[K; ++i) {\n", + " | \u001b[01;35m\u001b[K~~^~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/http_stream.cpp:875:33:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison of integer expressions of different signedness: ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’ and ‘\u001b[01m\u001b[Kstd::vector::size_type\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’} [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n", + " 875 | for (int old_id = 0; \u001b[01;35m\u001b[Kold_id < old_dets.size()\u001b[m\u001b[K; ++old_id) {\n", + " | \u001b[01;35m\u001b[K~~~~~~~^~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/http_stream.cpp:894:31:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison of integer expressions of different signedness: ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’ and ‘\u001b[01m\u001b[Kstd::vector::size_type\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’} [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n", + " 894 | for (int index = 0; \u001b[01;35m\u001b[Kindex < new_dets_num*old_dets.size()\u001b[m\u001b[K; ++index) {\n", + " | \u001b[01;35m\u001b[K~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/http_stream.cpp:930:28:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison of integer expressions of different signedness: ‘\u001b[01m\u001b[Kstd::deque >::size_type\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’} and ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n", + " 930 | if (\u001b[01;35m\u001b[Kold_dets_dq.size() > deque_size\u001b[m\u001b[K) old_dets_dq.pop_front();\n", + " | \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~\u001b[m\u001b[K\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/gemm.c -o obj/gemm.o\n", + "\u001b[01m\u001b[K./src/gemm.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kconvolution_2d\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/gemm.c:2044:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kout_w\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 2044 | const int \u001b[01;35m\u001b[Kout_w\u001b[m\u001b[K = (w + 2 * pad - ksize) / stride + 1; // output_width=input_width for stride=1 and pad=1\n", + " | \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/gemm.c:2043:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kout_h\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 2043 | const int \u001b[01;35m\u001b[Kout_h\u001b[m\u001b[K = (h + 2 * pad - ksize) / stride + 1; // output_height=input_height for stride=1 and pad=1\n", + " | \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/utils.c -o obj/utils.o\n", + "\u001b[01m\u001b[K./src/utils.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kcustom_hash\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/utils.c:1082:12:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Ksuggest parentheses around assignment used as truth value [\u001b[01;35m\u001b[K-Wparentheses\u001b[m\u001b[K]\n", + " 1082 | while (\u001b[01;35m\u001b[Kc\u001b[m\u001b[K = *str++)\n", + " | \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n", + "In file included from \u001b[01m\u001b[K/usr/include/string.h:495\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[Kinclude/darknet.h:14\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[K./src/utils.h:3\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[K./src/utils.c:4\u001b[m\u001b[K:\n", + "In function ‘\u001b[01m\u001b[Kstrncpy\u001b[m\u001b[K’,\n", + " inlined from ‘\u001b[01m\u001b[Kcopy_string\u001b[m\u001b[K’ at \u001b[01m\u001b[K./src/utils.c:552:5\u001b[m\u001b[K:\n", + "\u001b[01m\u001b[K/usr/include/x86_64-linux-gnu/bits/string_fortified.h:106:10:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[K__builtin_strncpy\u001b[m\u001b[K’ specified bound depends on the length of the source argument [\u001b[01;35m\u001b[K-Wstringop-overflow=\u001b[m\u001b[K]\n", + " 106 | return \u001b[01;35m\u001b[K__builtin___strncpy_chk (__dest, __src, __len, __bos (__dest))\u001b[m\u001b[K;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/utils.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kcopy_string\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/utils.c:552:22:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Klength computed here\n", + " 552 | strncpy(copy, s, \u001b[01;36m\u001b[Kstrlen(s)\u001b[m\u001b[K+1);\n", + " | \u001b[01;36m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/dark_cuda.c -o obj/dark_cuda.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/convolutional_layer.c -o obj/convolutional_layer.o\n", + "\u001b[01m\u001b[K./src/convolutional_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kresize_convolutional_layer\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/convolutional_layer.c:898:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kold_h\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 898 | int \u001b[01;35m\u001b[Kold_h\u001b[m\u001b[K = l->h;\n", + " | \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/convolutional_layer.c:897:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kold_w\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 897 | int \u001b[01;35m\u001b[Kold_w\u001b[m\u001b[K = l->w;\n", + " | \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/convolutional_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kforward_convolutional_layer\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/convolutional_layer.c:1342:32:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kt_intput_size\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 1342 | size_t \u001b[01;35m\u001b[Kt_intput_size\u001b[m\u001b[K = binary_transpose_align_input(k, n, state.workspace, &l.t_bit_input, ldb_align, l.bit_align);\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/list.c -o obj/list.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/image.c -o obj/image.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/activations.c -o obj/activations.o\n", + "\u001b[01m\u001b[K./src/activations.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kactivate\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KRELU6\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n", + " 79 | \u001b[01;35m\u001b[Kswitch\u001b[m\u001b[K(a){\n", + " | \u001b[01;35m\u001b[K^~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KSWISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n", + "\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KMISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n", + "\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KHARD_MISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n", + "\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KNORM_CHAN\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n", + "\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KNORM_CHAN_SOFTMAX\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n", + "\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KNORM_CHAN_SOFTMAX_MAXVAL\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n", + "\u001b[01m\u001b[K./src/activations.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kgradient\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/activations.c:310:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KSWISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n", + " 310 | \u001b[01;35m\u001b[Kswitch\u001b[m\u001b[K(a){\n", + " | \u001b[01;35m\u001b[K^~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/activations.c:310:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KMISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n", + "\u001b[01m\u001b[K./src/activations.c:310:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KHARD_MISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/im2col.c -o obj/im2col.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/col2im.c -o obj/col2im.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/blas.c -o obj/blas.o\n", + "\u001b[01m\u001b[K./src/blas.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kbackward_shortcut_multilayer_cpu\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/blas.c:207:21:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kout_index\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 207 | int \u001b[01;35m\u001b[Kout_index\u001b[m\u001b[K = id;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/blas.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kfind_sim\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/blas.c:597:59:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Ksize_t\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’} [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n", + " 597 | printf(\" Error: find_sim(): sim isn't found: i = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, j = %d, z = %d \\n\", \u001b[32m\u001b[Ki\u001b[m\u001b[K, j, z);\n", + " | \u001b[01;35m\u001b[K~^\u001b[m\u001b[K \u001b[32m\u001b[K~\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[K|\u001b[m\u001b[K \u001b[32m\u001b[K|\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[Kint\u001b[m\u001b[K \u001b[32m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K\n", + " | \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/blas.c:597:67:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 3 has type ‘\u001b[01m\u001b[Ksize_t\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’} [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n", + " 597 | printf(\" Error: find_sim(): sim isn't found: i = %d, j = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, z = %d \\n\", i, \u001b[32m\u001b[Kj\u001b[m\u001b[K, z);\n", + " | \u001b[01;35m\u001b[K~^\u001b[m\u001b[K \u001b[32m\u001b[K~\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[K|\u001b[m\u001b[K \u001b[32m\u001b[K|\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[Kint\u001b[m\u001b[K \u001b[32m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K\n", + " | \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/blas.c:597:75:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 4 has type ‘\u001b[01m\u001b[Ksize_t\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’} [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n", + " 597 | printf(\" Error: find_sim(): sim isn't found: i = %d, j = %d, z = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K \\n\", i, j, \u001b[32m\u001b[Kz\u001b[m\u001b[K);\n", + " | \u001b[01;35m\u001b[K~^\u001b[m\u001b[K \u001b[32m\u001b[K~\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[K|\u001b[m\u001b[K \u001b[32m\u001b[K|\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[Kint\u001b[m\u001b[K \u001b[32m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K\n", + " | \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/blas.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kfind_P_constrastive\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/blas.c:611:68:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Ksize_t\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’} [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n", + " 611 | printf(\" Error: find_P_constrastive(): P isn't found: i = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, j = %d, z = %d \\n\", \u001b[32m\u001b[Ki\u001b[m\u001b[K, j, z);\n", + " | \u001b[01;35m\u001b[K~^\u001b[m\u001b[K \u001b[32m\u001b[K~\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[K|\u001b[m\u001b[K \u001b[32m\u001b[K|\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[Kint\u001b[m\u001b[K \u001b[32m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K\n", + " | \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/blas.c:611:76:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 3 has type ‘\u001b[01m\u001b[Ksize_t\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’} [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n", + " 611 | printf(\" Error: find_P_constrastive(): P isn't found: i = %d, j = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, z = %d \\n\", i, \u001b[32m\u001b[Kj\u001b[m\u001b[K, z);\n", + " | \u001b[01;35m\u001b[K~^\u001b[m\u001b[K \u001b[32m\u001b[K~\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[K|\u001b[m\u001b[K \u001b[32m\u001b[K|\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[Kint\u001b[m\u001b[K \u001b[32m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K\n", + " | \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/blas.c:611:84:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 4 has type ‘\u001b[01m\u001b[Ksize_t\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’} [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n", + " 611 | printf(\" Error: find_P_constrastive(): P isn't found: i = %d, j = %d, z = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K \\n\", i, j, \u001b[32m\u001b[Kz\u001b[m\u001b[K);\n", + " | \u001b[01;35m\u001b[K~^\u001b[m\u001b[K \u001b[32m\u001b[K~\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[K|\u001b[m\u001b[K \u001b[32m\u001b[K|\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[Kint\u001b[m\u001b[K \u001b[32m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K\n", + " | \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/blas.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KP_constrastive_f\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/blas.c:651:79:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 3 has type ‘\u001b[01m\u001b[Ksize_t\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’} [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n", + " 651 | fprintf(stderr, \" Error: in P_constrastive must be i != l, while i = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, l = %d \\n\", \u001b[32m\u001b[Ki\u001b[m\u001b[K, l);\n", + " | \u001b[01;35m\u001b[K~^\u001b[m\u001b[K \u001b[32m\u001b[K~\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[K|\u001b[m\u001b[K \u001b[32m\u001b[K|\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[Kint\u001b[m\u001b[K \u001b[32m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K\n", + " | \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/blas.c:651:87:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 4 has type ‘\u001b[01m\u001b[Ksize_t\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’} [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n", + " 651 | fprintf(stderr, \" Error: in P_constrastive must be i != l, while i = %d, l = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K \\n\", i, \u001b[32m\u001b[Kl\u001b[m\u001b[K);\n", + " | \u001b[01;35m\u001b[K~^\u001b[m\u001b[K \u001b[32m\u001b[K~\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[K|\u001b[m\u001b[K \u001b[32m\u001b[K|\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[Kint\u001b[m\u001b[K \u001b[32m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K\n", + " | \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/blas.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KP_constrastive\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/blas.c:785:79:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 3 has type ‘\u001b[01m\u001b[Ksize_t\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’} [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n", + " 785 | fprintf(stderr, \" Error: in P_constrastive must be i != l, while i = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, l = %d \\n\", \u001b[32m\u001b[Ki\u001b[m\u001b[K, l);\n", + " | \u001b[01;35m\u001b[K~^\u001b[m\u001b[K \u001b[32m\u001b[K~\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[K|\u001b[m\u001b[K \u001b[32m\u001b[K|\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[Kint\u001b[m\u001b[K \u001b[32m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K\n", + " | \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/blas.c:785:87:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 4 has type ‘\u001b[01m\u001b[Ksize_t\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’} [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n", + " 785 | fprintf(stderr, \" Error: in P_constrastive must be i != l, while i = %d, l = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K \\n\", i, \u001b[32m\u001b[Kl\u001b[m\u001b[K);\n", + " | \u001b[01;35m\u001b[K~^\u001b[m\u001b[K \u001b[32m\u001b[K~\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[K|\u001b[m\u001b[K \u001b[32m\u001b[K|\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[Kint\u001b[m\u001b[K \u001b[32m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K\n", + " | \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/crop_layer.c -o obj/crop_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/dropout_layer.c -o obj/dropout_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/maxpool_layer.c -o obj/maxpool_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/softmax_layer.c -o obj/softmax_layer.o\n", + "\u001b[01m\u001b[K./src/softmax_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kmake_contrastive_layer\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/softmax_layer.c:203:101:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 9 has type ‘\u001b[01m\u001b[Ksize_t\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Kconst long unsigned int\u001b[m\u001b[K’} [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n", + " 203 | fprintf(stderr, \"contrastive %4d x%4d x%4d x emb_size %4d x batch: %4d classes = %4d, step = \u001b[01;35m\u001b[K%4d\u001b[m\u001b[K \\n\", w, h, l.n, l.embedding_size, batch, l.classes, \u001b[32m\u001b[Kstep\u001b[m\u001b[K);\n", + " | \u001b[01;35m\u001b[K~~^\u001b[m\u001b[K \u001b[32m\u001b[K~~~~\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[K|\u001b[m\u001b[K \u001b[32m\u001b[K|\u001b[m\u001b[K\n", + " | \u001b[01;35m\u001b[Kint\u001b[m\u001b[K \u001b[32m\u001b[Ksize_t {aka const long unsigned int}\u001b[m\u001b[K\n", + " | \u001b[32m\u001b[K%4ld\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/softmax_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kforward_contrastive_layer\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/softmax_layer.c:244:27:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kmax_truth\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n", + " 244 | float \u001b[01;35m\u001b[Kmax_truth\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/data.c -o obj/data.o\n", + "\u001b[01m\u001b[K./src/data.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kload_data_detection\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/data.c:1409:43:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kr_scale\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n", + " 1409 | float r1 = 0, r2 = 0, r3 = 0, r4 = 0, \u001b[01;35m\u001b[Kr_scale\u001b[m\u001b[K;\n", + " | \u001b[01;35m\u001b[K^~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/data.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kfill_truth_detection\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/data.c:440:33:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[K%s\u001b[m\u001b[K’ directive writing up to 4095 bytes into a region of size 251 [\u001b[01;35m\u001b[K-Wformat-overflow=\u001b[m\u001b[K]\n", + " 440 | sprintf(buff, \"echo \u001b[01;35m\u001b[K%s\u001b[m\u001b[K \\\"Wrong annotation: w = %f\\\" >> bad_label.list\", \u001b[32m\u001b[Klabelpath\u001b[m\u001b[K, w);\n", + " | \u001b[01;35m\u001b[K^~\u001b[m\u001b[K \u001b[32m\u001b[K~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/data.c:440:27:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kassuming directive output of 8 bytes\n", + " 440 | sprintf(buff, \u001b[01;36m\u001b[K\"echo %s \\\"Wrong annotation: w = %f\\\" >> bad_label.list\"\u001b[m\u001b[K, labelpath, w);\n", + " | \u001b[01;36m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "In file included from \u001b[01m\u001b[K/usr/include/stdio.h:867\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[Kinclude/darknet.h:13\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[K./src/data.h:5\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[K./src/data.c:1\u001b[m\u001b[K:\n", + "\u001b[01m\u001b[K/usr/include/x86_64-linux-gnu/bits/stdio2.h:36:10:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K‘\u001b[01m\u001b[K__builtin___sprintf_chk\u001b[m\u001b[K’ output between 52 and 4461 bytes into a destination of size 256\n", + " 36 | return \u001b[01;36m\u001b[K__builtin___sprintf_chk (__s, __USE_FORTIFY_LEVEL - 1,\u001b[m\u001b[K\n", + " | \u001b[01;36m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + " 37 | \u001b[01;36m\u001b[K __bos (__s), __fmt, __va_arg_pack ())\u001b[m\u001b[K;\n", + " | \u001b[01;36m\u001b[K~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/data.c:447:33:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[K%s\u001b[m\u001b[K’ directive writing up to 4095 bytes into a region of size 251 [\u001b[01;35m\u001b[K-Wformat-overflow=\u001b[m\u001b[K]\n", + " 447 | sprintf(buff, \"echo \u001b[01;35m\u001b[K%s\u001b[m\u001b[K \\\"Wrong annotation: h = %f\\\" >> bad_label.list\", \u001b[32m\u001b[Klabelpath\u001b[m\u001b[K, h);\n", + " | \u001b[01;35m\u001b[K^~\u001b[m\u001b[K \u001b[32m\u001b[K~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/data.c:447:27:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kassuming directive output of 8 bytes\n", + " 447 | sprintf(buff, \u001b[01;36m\u001b[K\"echo %s \\\"Wrong annotation: h = %f\\\" >> bad_label.list\"\u001b[m\u001b[K, labelpath, h);\n", + " | \u001b[01;36m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "In file included from \u001b[01m\u001b[K/usr/include/stdio.h:867\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[Kinclude/darknet.h:13\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[K./src/data.h:5\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[K./src/data.c:1\u001b[m\u001b[K:\n", + "\u001b[01m\u001b[K/usr/include/x86_64-linux-gnu/bits/stdio2.h:36:10:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K‘\u001b[01m\u001b[K__builtin___sprintf_chk\u001b[m\u001b[K’ output between 52 and 4461 bytes into a destination of size 256\n", + " 36 | return \u001b[01;36m\u001b[K__builtin___sprintf_chk (__s, __USE_FORTIFY_LEVEL - 1,\u001b[m\u001b[K\n", + " | \u001b[01;36m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + " 37 | \u001b[01;36m\u001b[K __bos (__s), __fmt, __va_arg_pack ())\u001b[m\u001b[K;\n", + " | \u001b[01;36m\u001b[K~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/data.c:432:33:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[K%s\u001b[m\u001b[K’ directive writing up to 4095 bytes into a region of size 251 [\u001b[01;35m\u001b[K-Wformat-overflow=\u001b[m\u001b[K]\n", + " 432 | sprintf(buff, \"echo \u001b[01;35m\u001b[K%s\u001b[m\u001b[K \\\"Wrong annotation: x = %f, y = %f\\\" >> bad_label.list\", \u001b[32m\u001b[Klabelpath\u001b[m\u001b[K, x, y);\n", + " | \u001b[01;35m\u001b[K^~\u001b[m\u001b[K \u001b[32m\u001b[K~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/data.c:432:27:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kassuming directive output of 8 bytes\n", + " 432 | sprintf(buff, \u001b[01;36m\u001b[K\"echo %s \\\"Wrong annotation: x = %f, y = %f\\\" >> bad_label.list\"\u001b[m\u001b[K, labelpath, x, y);\n", + " | \u001b[01;36m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/data.c:432:27:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kassuming directive output of 8 bytes\n", + "In file included from \u001b[01m\u001b[K/usr/include/stdio.h:867\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[Kinclude/darknet.h:13\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[K./src/data.h:5\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[K./src/data.c:1\u001b[m\u001b[K:\n", + "\u001b[01m\u001b[K/usr/include/x86_64-linux-gnu/bits/stdio2.h:36:10:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K‘\u001b[01m\u001b[K__builtin___sprintf_chk\u001b[m\u001b[K’ output between 61 and 4784 bytes into a destination of size 256\n", + " 36 | return \u001b[01;36m\u001b[K__builtin___sprintf_chk (__s, __USE_FORTIFY_LEVEL - 1,\u001b[m\u001b[K\n", + " | \u001b[01;36m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + " 37 | \u001b[01;36m\u001b[K __bos (__s), __fmt, __va_arg_pack ())\u001b[m\u001b[K;\n", + " | \u001b[01;36m\u001b[K~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/data.c:424:33:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[K%s\u001b[m\u001b[K’ directive writing up to 4095 bytes into a region of size 251 [\u001b[01;35m\u001b[K-Wformat-overflow=\u001b[m\u001b[K]\n", + " 424 | sprintf(buff, \"echo \u001b[01;35m\u001b[K%s\u001b[m\u001b[K \\\"Wrong annotation: x = 0 or y = 0\\\" >> bad_label.list\", \u001b[32m\u001b[Klabelpath\u001b[m\u001b[K);\n", + " | \u001b[01;35m\u001b[K^~\u001b[m\u001b[K \u001b[32m\u001b[K~~~~~~~~~\u001b[m\u001b[K\n", + "In file included from \u001b[01m\u001b[K/usr/include/stdio.h:867\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[Kinclude/darknet.h:13\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[K./src/data.h:5\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[K./src/data.c:1\u001b[m\u001b[K:\n", + "\u001b[01m\u001b[K/usr/include/x86_64-linux-gnu/bits/stdio2.h:36:10:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K‘\u001b[01m\u001b[K__builtin___sprintf_chk\u001b[m\u001b[K’ output between 59 and 4154 bytes into a destination of size 256\n", + " 36 | return \u001b[01;36m\u001b[K__builtin___sprintf_chk (__s, __USE_FORTIFY_LEVEL - 1,\u001b[m\u001b[K\n", + " | \u001b[01;36m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + " 37 | \u001b[01;36m\u001b[K __bos (__s), __fmt, __va_arg_pack ())\u001b[m\u001b[K;\n", + " | \u001b[01;36m\u001b[K~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/data.c:410:33:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[K%s\u001b[m\u001b[K’ directive writing up to 4095 bytes into a region of size 251 [\u001b[01;35m\u001b[K-Wformat-overflow=\u001b[m\u001b[K]\n", + " 410 | sprintf(buff, \"echo \u001b[01;35m\u001b[K%s\u001b[m\u001b[K \\\"Wrong annotation: class_id = %d. But class_id should be [from 0 to %d]\\\" >> bad_label.list\", \u001b[32m\u001b[Klabelpath\u001b[m\u001b[K, id, (classes-1));\n", + " | \u001b[01;35m\u001b[K^~\u001b[m\u001b[K \u001b[32m\u001b[K~~~~~~~~~\u001b[m\u001b[K\n", + "In file included from \u001b[01m\u001b[K/usr/include/stdio.h:867\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[Kinclude/darknet.h:13\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[K./src/data.h:5\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[K./src/data.c:1\u001b[m\u001b[K:\n", + "\u001b[01m\u001b[K/usr/include/x86_64-linux-gnu/bits/stdio2.h:36:10:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K‘\u001b[01m\u001b[K__builtin___sprintf_chk\u001b[m\u001b[K’ output between 95 and 4210 bytes into a destination of size 256\n", + " 36 | return \u001b[01;36m\u001b[K__builtin___sprintf_chk (__s, __USE_FORTIFY_LEVEL - 1,\u001b[m\u001b[K\n", + " | \u001b[01;36m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + " 37 | \u001b[01;36m\u001b[K __bos (__s), __fmt, __va_arg_pack ())\u001b[m\u001b[K;\n", + " | \u001b[01;36m\u001b[K~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/matrix.c -o obj/matrix.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/network.c -o obj/network.o\n", + "\u001b[01m\u001b[K./src/network.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Ktrain_network_waitkey\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/network.c:435:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kema_period\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 435 | int \u001b[01;35m\u001b[Kema_period\u001b[m\u001b[K = (net.max_batches - ema_start_point - 1000) * (1.0 - net.ema_alpha);\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~\u001b[m\u001b[K\n", + "At top level:\n", + "\u001b[01m\u001b[K./src/network.c:1269:14:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[Krelu\u001b[m\u001b[K’ defined but not used [\u001b[01;35m\u001b[K-Wunused-function\u001b[m\u001b[K]\n", + " 1269 | static float \u001b[01;35m\u001b[Krelu\u001b[m\u001b[K(float src) {\n", + " | \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/connected_layer.c -o obj/connected_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/cost_layer.c -o obj/cost_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/parser.c -o obj/parser.o\n", + "\u001b[01m\u001b[K./src/parser.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Ksave_implicit_weights\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/parser.c:1909:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ki\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 1909 | int \u001b[01;35m\u001b[Ki\u001b[m\u001b[K;\n", + " | \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/parser.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kget_classes_multipliers\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/parser.c:438:40:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kargument 1 range [18446744071562067968, 18446744073709551615] exceeds maximum object size 9223372036854775807 [\u001b[01;35m\u001b[K-Walloc-size-larger-than=\u001b[m\u001b[K]\n", + " 438 | classes_multipliers = (float *)\u001b[01;35m\u001b[Kcalloc(classes_counters, sizeof(float))\u001b[m\u001b[K;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "In file included from \u001b[01m\u001b[K./src/parser.c:3\u001b[m\u001b[K:\n", + "\u001b[01m\u001b[K/usr/include/stdlib.h:542:14:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kin a call to allocation function ‘\u001b[01m\u001b[Kcalloc\u001b[m\u001b[K’ declared here\n", + " 542 | extern void *\u001b[01;36m\u001b[Kcalloc\u001b[m\u001b[K (size_t __nmemb, size_t __size)\n", + " | \u001b[01;36m\u001b[K^~~~~~\u001b[m\u001b[K\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/option_list.c -o obj/option_list.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/darknet.c -o obj/darknet.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/detection_layer.c -o obj/detection_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/captcha.c -o obj/captcha.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/route_layer.c -o obj/route_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/writing.c -o obj/writing.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/box.c -o obj/box.o\n", + "\u001b[01m\u001b[K./src/box.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kbox_iou_kind\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/box.c:154:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KMSE\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n", + " 154 | \u001b[01;35m\u001b[Kswitch\u001b[m\u001b[K(iou_kind) {\n", + " | \u001b[01;35m\u001b[K^~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/box.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kdiounms_sort\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/box.c:898:27:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kbeta_prob\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 898 | float \u001b[01;35m\u001b[Kbeta_prob\u001b[m\u001b[K = pow(dets[j].prob[k], 2) / sum_prob;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/box.c:897:27:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kalpha_prob\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 897 | float \u001b[01;35m\u001b[Kalpha_prob\u001b[m\u001b[K = pow(dets[i].prob[k], 2) / sum_prob;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~\u001b[m\u001b[K\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/nightmare.c -o obj/nightmare.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/normalization_layer.c -o obj/normalization_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/avgpool_layer.c -o obj/avgpool_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/coco.c -o obj/coco.o\n", + "\u001b[01m\u001b[K./src/coco.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvalidate_coco_recall\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/coco.c:248:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kbase\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 248 | char *\u001b[01;35m\u001b[Kbase\u001b[m\u001b[K = \"results/comp4_det_test_\";\n", + " | \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/dice.c -o obj/dice.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/yolo.c -o obj/yolo.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/detector.c -o obj/detector.o\n", + "\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Ktrain_detector\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/detector.c:395:72:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Ksuggest parentheses around ‘\u001b[01m\u001b[K&&\u001b[m\u001b[K’ within ‘\u001b[01m\u001b[K||\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wparentheses\u001b[m\u001b[K]\n", + " 395 | \u001b[01;35m\u001b[K(iteration >= (iter_save + 1000) || iteration % 1000 == 0) && net.max_batches < 10000\u001b[m\u001b[K)\n", + " | \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/detector.c:328:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kdraw_precision\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n", + " 328 | int \u001b[01;35m\u001b[Kdraw_precision\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/detector.c:67:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kavg_contrastive_acc\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 67 | float \u001b[01;35m\u001b[Kavg_contrastive_acc\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Keliminate_bdd\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/detector.c:588:21:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kstatement with no effect [\u001b[01;35m\u001b[K-Wunused-value\u001b[m\u001b[K]\n", + " 588 | \u001b[01;35m\u001b[Kfor\u001b[m\u001b[K (k; buf[k + n] != '\\0'; k++)\n", + " | \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvalidate_detector\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/detector.c:709:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kmkd2\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 709 | int \u001b[01;35m\u001b[Kmkd2\u001b[m\u001b[K = make_directory(buff2, 0777);\n", + " | \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/detector.c:707:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kmkd\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 707 | int \u001b[01;35m\u001b[Kmkd\u001b[m\u001b[K = make_directory(buff, 0777);\n", + " | \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvalidate_detector_map\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/detector.c:1326:24:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kcur_prob\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n", + " 1326 | double \u001b[01;35m\u001b[Kcur_prob\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/detector.c:1347:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kclass_recall\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 1347 | float \u001b[01;35m\u001b[Kclass_recall\u001b[m\u001b[K = (float)tp_for_thresh_per_class[i] / ((float)tp_for_thresh_per_class[i] + (float)(truth_classes_count[i] - tp_for_thresh_per_class[i]));\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/detector.c:1346:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kclass_precision\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 1346 | float \u001b[01;35m\u001b[Kclass_precision\u001b[m\u001b[K = (float)tp_for_thresh_per_class[i] / ((float)tp_for_thresh_per_class[i] + (float)fp_for_thresh_per_class[i]);\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "At top level:\n", + "\u001b[01m\u001b[K./src/detector.c:461:12:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[Kget_coco_image_id\u001b[m\u001b[K’ defined but not used [\u001b[01;35m\u001b[K-Wunused-function\u001b[m\u001b[K]\n", + " 461 | static int \u001b[01;35m\u001b[Kget_coco_image_id\u001b[m\u001b[K(char *filename)\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/layer.c -o obj/layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/compare.c -o obj/compare.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/classifier.c -o obj/classifier.o\n", + "\u001b[01m\u001b[K./src/classifier.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Ktrain_classifier\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/classifier.c:190:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kdraw_precision\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n", + " 190 | int \u001b[01;35m\u001b[Kdraw_precision\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/classifier.c:146:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kcount\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 146 | int \u001b[01;35m\u001b[Kcount\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/classifier.c:35:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kavg_contrastive_acc\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 35 | float \u001b[01;35m\u001b[Kavg_contrastive_acc\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/classifier.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kpredict_classifier\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/classifier.c:855:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktime\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 855 | clock_t \u001b[01;35m\u001b[Ktime\u001b[m\u001b[K;\n", + " | \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/local_layer.c -o obj/local_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/swag.c -o obj/swag.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/shortcut_layer.c -o obj/shortcut_layer.o\n", + "\u001b[01m\u001b[K./src/shortcut_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kmake_shortcut_layer\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/shortcut_layer.c:55:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kscale\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 55 | float \u001b[01;35m\u001b[Kscale\u001b[m\u001b[K = sqrt(2. / l.nweights);\n", + " | \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/representation_layer.c -o obj/representation_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/activation_layer.c -o obj/activation_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/rnn_layer.c -o obj/rnn_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/gru_layer.c -o obj/gru_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/rnn.c -o obj/rnn.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/rnn_vid.c -o obj/rnn_vid.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/crnn_layer.c -o obj/crnn_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/demo.c -o obj/demo.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/tag.c -o obj/tag.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/cifar.c -o obj/cifar.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/go.c -o obj/go.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/batchnorm_layer.c -o obj/batchnorm_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/art.c -o obj/art.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/region_layer.c -o obj/region_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/reorg_layer.c -o obj/reorg_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/reorg_old_layer.c -o obj/reorg_old_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/super.c -o obj/super.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/voxel.c -o obj/voxel.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/tree.c -o obj/tree.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/yolo_layer.c -o obj/yolo_layer.o\n", + "\u001b[01m\u001b[K./src/yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kprocess_batch\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/yolo_layer.c:426:25:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kbest_match_t\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n", + " 426 | int \u001b[01;35m\u001b[Kbest_match_t\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kforward_yolo_layer\u001b[m\u001b[K’:\n", + "\u001b[01m\u001b[K./src/yolo_layer.c:707:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kavg_anyobj\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 707 | float \u001b[01;35m\u001b[Kavg_anyobj\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/yolo_layer.c:706:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kavg_obj\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 706 | float \u001b[01;35m\u001b[Kavg_obj\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/yolo_layer.c:705:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kavg_cat\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 705 | float \u001b[01;35m\u001b[Kavg_cat\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/yolo_layer.c:704:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Krecall75\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 704 | float \u001b[01;35m\u001b[Krecall75\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/yolo_layer.c:703:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Krecall\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 703 | float \u001b[01;35m\u001b[Krecall\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/yolo_layer.c:702:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktot_ciou_loss\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 702 | float \u001b[01;35m\u001b[Ktot_ciou_loss\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/yolo_layer.c:701:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktot_diou_loss\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 701 | float \u001b[01;35m\u001b[Ktot_diou_loss\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/yolo_layer.c:698:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktot_ciou\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 698 | float \u001b[01;35m\u001b[Ktot_ciou\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/yolo_layer.c:697:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktot_diou\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 697 | float \u001b[01;35m\u001b[Ktot_diou\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K./src/yolo_layer.c:696:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktot_giou\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n", + " 696 | float \u001b[01;35m\u001b[Ktot_giou\u001b[m\u001b[K = 0;\n", + " | \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/gaussian_yolo_layer.c -o obj/gaussian_yolo_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/upsample_layer.c -o obj/upsample_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/lstm_layer.c -o obj/lstm_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/conv_lstm_layer.c -o obj/conv_lstm_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/scale_channels_layer.c -o obj/scale_channels_layer.o\n", + "gcc -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast -c ./src/sam_layer.c -o obj/sam_layer.o\n", + "g++ -std=c++11 -std=c++11 -Iinclude/ -I3rdparty/stb/include -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic -Ofast obj/image_opencv.o obj/http_stream.o obj/gemm.o obj/utils.o obj/dark_cuda.o obj/convolutional_layer.o obj/list.o obj/image.o obj/activations.o obj/im2col.o obj/col2im.o obj/blas.o obj/crop_layer.o obj/dropout_layer.o obj/maxpool_layer.o obj/softmax_layer.o obj/data.o obj/matrix.o obj/network.o obj/connected_layer.o obj/cost_layer.o obj/parser.o obj/option_list.o obj/darknet.o obj/detection_layer.o obj/captcha.o obj/route_layer.o obj/writing.o obj/box.o obj/nightmare.o obj/normalization_layer.o obj/avgpool_layer.o obj/coco.o obj/dice.o obj/yolo.o obj/detector.o obj/layer.o obj/compare.o obj/classifier.o obj/local_layer.o obj/swag.o obj/shortcut_layer.o obj/representation_layer.o obj/activation_layer.o obj/rnn_layer.o obj/gru_layer.o obj/rnn.o obj/rnn_vid.o obj/crnn_layer.o obj/demo.o obj/tag.o obj/cifar.o obj/go.o obj/batchnorm_layer.o obj/art.o obj/region_layer.o obj/reorg_layer.o obj/reorg_old_layer.o obj/super.o obj/voxel.o obj/tree.o obj/yolo_layer.o obj/gaussian_yolo_layer.o obj/upsample_layer.o obj/lstm_layer.o obj/conv_lstm_layer.o obj/scale_channels_layer.o obj/sam_layer.o -o darknet -lm -pthread\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!./darknet" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IM282uWWfW2n", + "outputId": "3123df19-cfb2-46f1-f709-e631518642e1" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "usage: ./darknet \n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!wget https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "kGMG34IBfqAO", + "outputId": "dbcfea6b-3e02-466a-d890-37a26a885651" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2023-07-13 09:05:10-- https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights\n", + "Resolving github.com (github.com)... 140.82.121.3\n", + "Connecting to github.com (github.com)|140.82.121.3|:443... connected.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: https://objects.githubusercontent.com/github-production-release-asset-2e65be/75388965/ba4b6380-889c-11ea-9751-f994f5961796?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20230713%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230713T090510Z&X-Amz-Expires=300&X-Amz-Signature=cea41ca1934f8fb97456ef5d2cc519bde7b60d1b7bec35fd51b2eb42dc06aca4&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=75388965&response-content-disposition=attachment%3B%20filename%3Dyolov4.weights&response-content-type=application%2Foctet-stream [following]\n", + "--2023-07-13 09:05:10-- https://objects.githubusercontent.com/github-production-release-asset-2e65be/75388965/ba4b6380-889c-11ea-9751-f994f5961796?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20230713%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230713T090510Z&X-Amz-Expires=300&X-Amz-Signature=cea41ca1934f8fb97456ef5d2cc519bde7b60d1b7bec35fd51b2eb42dc06aca4&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=75388965&response-content-disposition=attachment%3B%20filename%3Dyolov4.weights&response-content-type=application%2Foctet-stream\n", + "Resolving objects.githubusercontent.com (objects.githubusercontent.com)... 185.199.109.133, 185.199.111.133, 185.199.110.133, ...\n", + "Connecting to objects.githubusercontent.com (objects.githubusercontent.com)|185.199.109.133|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 257717640 (246M) [application/octet-stream]\n", + "Saving to: ‘yolov4.weights’\n", + "\n", + "yolov4.weights 100%[===================>] 245.78M 46.7MB/s in 5.3s \n", + "\n", + "2023-07-13 09:05:15 (46.7 MB/s) - ‘yolov4.weights’ saved [257717640/257717640]\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!./darknet detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights data/eagle.jpg" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "OKjVBw3hfwZ1", + "outputId": "0bdae556-9094-434c-e163-fd4308d17c0b" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " GPU isn't used \n", + " OpenCV isn't used - data augmentation will be slow \n", + "mini_batch = 1, batch = 8, time_steps = 1, train = 0 \n", + " layer filters size/strd(dil) input output\n", + " 0 conv 32 3 x 3/ 1 608 x 608 x 3 -> 608 x 608 x 32 0.639 BF\n", + " 1 conv 64 3 x 3/ 2 608 x 608 x 32 -> 304 x 304 x 64 3.407 BF\n", + " 2 conv 64 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 64 0.757 BF\n", + " 3 route 1 \t\t -> 304 x 304 x 64 \n", + " 4 conv 64 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 64 0.757 BF\n", + " 5 conv 32 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 32 0.379 BF\n", + " 6 conv 64 3 x 3/ 1 304 x 304 x 32 -> 304 x 304 x 64 3.407 BF\n", + " 7 Shortcut Layer: 4, wt = 0, wn = 0, outputs: 304 x 304 x 64 0.006 BF\n", + " 8 conv 64 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 64 0.757 BF\n", + " 9 route 8 2 \t -> 304 x 304 x 128 \n", + " 10 conv 64 1 x 1/ 1 304 x 304 x 128 -> 304 x 304 x 64 1.514 BF\n", + " 11 conv 128 3 x 3/ 2 304 x 304 x 64 -> 152 x 152 x 128 3.407 BF\n", + " 12 conv 64 1 x 1/ 1 152 x 152 x 128 -> 152 x 152 x 64 0.379 BF\n", + " 13 route 11 \t\t -> 152 x 152 x 128 \n", + " 14 conv 64 1 x 1/ 1 152 x 152 x 128 -> 152 x 152 x 64 0.379 BF\n", + " 15 conv 64 1 x 1/ 1 152 x 152 x 64 -> 152 x 152 x 64 0.189 BF\n", + " 16 conv 64 3 x 3/ 1 152 x 152 x 64 -> 152 x 152 x 64 1.703 BF\n", + " 17 Shortcut Layer: 14, wt = 0, wn = 0, outputs: 152 x 152 x 64 0.001 BF\n", + " 18 conv 64 1 x 1/ 1 152 x 152 x 64 -> 152 x 152 x 64 0.189 BF\n", + " 19 conv 64 3 x 3/ 1 152 x 152 x 64 -> 152 x 152 x 64 1.703 BF\n", + " 20 Shortcut Layer: 17, wt = 0, wn = 0, outputs: 152 x 152 x 64 0.001 BF\n", + " 21 conv 64 1 x 1/ 1 152 x 152 x 64 -> 152 x 152 x 64 0.189 BF\n", + " 22 route 21 12 \t -> 152 x 152 x 128 \n", + " 23 conv 128 1 x 1/ 1 152 x 152 x 128 -> 152 x 152 x 128 0.757 BF\n", + " 24 conv 256 3 x 3/ 2 152 x 152 x 128 -> 76 x 76 x 256 3.407 BF\n", + " 25 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF\n", + " 26 route 24 \t\t -> 76 x 76 x 256 \n", + " 27 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF\n", + " 28 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF\n", + " 29 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF\n", + " 30 Shortcut Layer: 27, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF\n", + " 31 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF\n", + " 32 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF\n", + " 33 Shortcut Layer: 30, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF\n", + " 34 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF\n", + " 35 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF\n", + " 36 Shortcut Layer: 33, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF\n", + " 37 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF\n", + " 38 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF\n", + " 39 Shortcut Layer: 36, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF\n", + " 40 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF\n", + " 41 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF\n", + " 42 Shortcut Layer: 39, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF\n", + " 43 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF\n", + " 44 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF\n", + " 45 Shortcut Layer: 42, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF\n", + " 46 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF\n", + " 47 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF\n", + " 48 Shortcut Layer: 45, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF\n", + " 49 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF\n", + " 50 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF\n", + " 51 Shortcut Layer: 48, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF\n", + " 52 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF\n", + " 53 route 52 25 \t -> 76 x 76 x 256 \n", + " 54 conv 256 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 256 0.757 BF\n", + " 55 conv 512 3 x 3/ 2 76 x 76 x 256 -> 38 x 38 x 512 3.407 BF\n", + " 56 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF\n", + " 57 route 55 \t\t -> 38 x 38 x 512 \n", + " 58 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF\n", + " 59 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF\n", + " 60 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF\n", + " 61 Shortcut Layer: 58, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF\n", + " 62 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF\n", + " 63 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF\n", + " 64 Shortcut Layer: 61, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF\n", + " 65 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF\n", + " 66 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF\n", + " 67 Shortcut Layer: 64, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF\n", + " 68 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF\n", + " 69 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF\n", + " 70 Shortcut Layer: 67, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF\n", + " 71 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF\n", + " 72 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF\n", + " 73 Shortcut Layer: 70, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF\n", + " 74 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF\n", + " 75 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF\n", + " 76 Shortcut Layer: 73, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF\n", + " 77 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF\n", + " 78 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF\n", + " 79 Shortcut Layer: 76, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF\n", + " 80 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF\n", + " 81 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF\n", + " 82 Shortcut Layer: 79, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF\n", + " 83 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF\n", + " 84 route 83 56 \t -> 38 x 38 x 512 \n", + " 85 conv 512 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 512 0.757 BF\n", + " 86 conv 1024 3 x 3/ 2 38 x 38 x 512 -> 19 x 19 x1024 3.407 BF\n", + " 87 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF\n", + " 88 route 86 \t\t -> 19 x 19 x1024 \n", + " 89 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF\n", + " 90 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF\n", + " 91 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF\n", + " 92 Shortcut Layer: 89, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF\n", + " 93 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF\n", + " 94 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF\n", + " 95 Shortcut Layer: 92, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF\n", + " 96 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF\n", + " 97 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF\n", + " 98 Shortcut Layer: 95, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF\n", + " 99 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF\n", + " 100 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF\n", + " 101 Shortcut Layer: 98, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF\n", + " 102 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF\n", + " 103 route 102 87 \t -> 19 x 19 x1024 \n", + " 104 conv 1024 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x1024 0.757 BF\n", + " 105 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF\n", + " 106 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF\n", + " 107 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF\n", + " 108 max 5x 5/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.005 BF\n", + " 109 route 107 \t\t -> 19 x 19 x 512 \n", + " 110 max 9x 9/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.015 BF\n", + " 111 route 107 \t\t -> 19 x 19 x 512 \n", + " 112 max 13x13/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.031 BF\n", + " 113 route 112 110 108 107 \t -> 19 x 19 x2048 \n", + " 114 conv 512 1 x 1/ 1 19 x 19 x2048 -> 19 x 19 x 512 0.757 BF\n", + " 115 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF\n", + " 116 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF\n", + " 117 conv 256 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 256 0.095 BF\n", + " 118 upsample 2x 19 x 19 x 256 -> 38 x 38 x 256\n", + " 119 route 85 \t\t -> 38 x 38 x 512 \n", + " 120 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF\n", + " 121 route 120 118 \t -> 38 x 38 x 512 \n", + " 122 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF\n", + " 123 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF\n", + " 124 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF\n", + " 125 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF\n", + " 126 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF\n", + " 127 conv 128 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 128 0.095 BF\n", + " 128 upsample 2x 38 x 38 x 128 -> 76 x 76 x 128\n", + " 129 route 54 \t\t -> 76 x 76 x 256 \n", + " 130 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF\n", + " 131 route 130 128 \t -> 76 x 76 x 256 \n", + " 132 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF\n", + " 133 conv 256 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BF\n", + " 134 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF\n", + " 135 conv 256 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BF\n", + " 136 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF\n", + " 137 conv 256 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BF\n", + " 138 conv 255 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 255 0.754 BF\n", + " 139 yolo\n", + "[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.20\n", + "nms_kind: greedynms (1), beta = 0.600000 \n", + " 140 route 136 \t\t -> 76 x 76 x 128 \n", + " 141 conv 256 3 x 3/ 2 76 x 76 x 128 -> 38 x 38 x 256 0.852 BF\n", + " 142 route 141 126 \t -> 38 x 38 x 512 \n", + " 143 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF\n", + " 144 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF\n", + " 145 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF\n", + " 146 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF\n", + " 147 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF\n", + " 148 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF\n", + " 149 conv 255 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 255 0.377 BF\n", + " 150 yolo\n", + "[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.10\n", + "nms_kind: greedynms (1), beta = 0.600000 \n", + " 151 route 147 \t\t -> 38 x 38 x 256 \n", + " 152 conv 512 3 x 3/ 2 38 x 38 x 256 -> 19 x 19 x 512 0.852 BF\n", + " 153 route 152 116 \t -> 19 x 19 x1024 \n", + " 154 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF\n", + " 155 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF\n", + " 156 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF\n", + " 157 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF\n", + " 158 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF\n", + " 159 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF\n", + " 160 conv 255 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 255 0.189 BF\n", + " 161 yolo\n", + "[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.05\n", + "nms_kind: greedynms (1), beta = 0.600000 \n", + "Total BFLOPS 128.459 \n", + "avg_outputs = 1068395 \n", + "Loading weights from yolov4.weights...\n", + " seen 64, trained: 32032 K-images (500 Kilo-batches_64) \n", + "Done! Loaded 162 layers from weights-file \n", + " Detection layer: 139 - type = 28 \n", + " Detection layer: 150 - type = 28 \n", + " Detection layer: 161 - type = 28 \n", + "data/eagle.jpg: Predicted in 26348.319000 milli-seconds.\n", + "bird: 97%\n", + "Not compiled with OpenCV, saving to predictions.png instead\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import cv2\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ], + "metadata": { + "id": "JUrmZwRAgCxz" + }, + "execution_count": 10, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "image=cv2.imread('predictions.jpg')\n", + "fig=plt.figure()\n", + "fig.set_size_inches(12,14)\n", + "plt.imshow(image)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 469 + }, + "id": "lt_Qlr6vgTJR", + "outputId": "70f66fdf-c2d5-4c24-c30e-a3ca7f5a1a8c" + }, + "execution_count": 11, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 11 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ] + } + ] +} \ No newline at end of file diff --git a/machine-learning/week3/Week 3 Image Alignment.ipynb b/machine-learning/week3/Week 3 Image Alignment.ipynb new file mode 100644 index 000000000..49e1dcc9a --- /dev/null +++ b/machine-learning/week3/Week 3 Image Alignment.ipynb @@ -0,0 +1,148 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "8f72856b", + "metadata": {}, + "outputs": [], + "source": [ + "import cv2\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "e21dbb7d", + "metadata": {}, + "outputs": [], + "source": [ + "def align_image(image):\n", + " \n", + " gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n", + "\n", + " \n", + " sift = cv2.SIFT_create()\n", + "\n", + "\n", + " keypoints, descriptors = sift.detectAndCompute(gray, None)\n", + "\n", + "\n", + " matcher = cv2.BFMatcher()\n", + "\n", + "\n", + " matches = matcher.knnMatch(descriptors, descriptors, k=2)\n", + " \n", + " good_matches = []\n", + " for m, n in matches:\n", + " if m.distance < 0.75 * n.distance:\n", + " good_matches.append(m)\n", + "\n", + " src_points = np.float32([keypoints[m.queryIdx].pt for m in good_matches]).reshape(-1, 1, 2)\n", + " dst_points = np.float32([keypoints[m.trainIdx].pt for m in good_matches]).reshape(-1, 1, 2)\n", + "\n", + "\n", + " M, mask = cv2.findHomography(src_points, dst_points, cv2.RANSAC, 5.0)\n", + "\n", + "\n", + " aligned_image = cv2.warpPerspective(image, M, (image.shape[1], image.shape[0]))\n", + "\n", + " return aligned_image" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "5ad7015f", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "image = cv2.imread(\"C:\\\\Users\\\\91740\\\\OneDrive\\\\Desktop\\\\g.jpg\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2361d9d7", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "81e03c86", + "metadata": {}, + "outputs": [], + "source": [ + "clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))\n", + "enhanced_image = cv2.cvtColor(image1, cv2.COLOR_BGR2LAB)\n", + "enhanced_image[:, :, 0] = clahe.apply(enhanced_image1[:, :, 0])\n", + "enhanced_image = cv2.cvtColor(enhanced_image1, cv2.COLOR_LAB2BGR)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "b2a89ab1", + "metadata": {}, + "outputs": [], + "source": [ + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "d8c5e61e", + "metadata": {}, + "outputs": [], + "source": [ + "aligned_image = align_image(enhanced_image)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f2e30048", + "metadata": {}, + "outputs": [], + "source": [ + "cv2.imshow('Aligned Image', aligned_image)\n", + "cv2.waitKey(0)\n", + "cv2.destroyAllWindows()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f9fbe637", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/machine-learning/week4/Chat_Bot Week4.ipynb b/machine-learning/week4/Chat_Bot Week4.ipynb new file mode 100644 index 000000000..1573a27cc --- /dev/null +++ b/machine-learning/week4/Chat_Bot Week4.ipynb @@ -0,0 +1,4365 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "P_QTQHHq4342" + }, + "outputs": [], + "source": [ + "import numpy\n", + "\n", + "import tensorflow\n", + "import random" + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "RFoJ82Lf5K4j" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import nltk\n", + "nltk.download('punkt')\n", + "from nltk.stem.lancaster import LancasterStemmer\n", + "stemmer = LancasterStemmer()" + ], + "metadata": { + "id": "FO6ClwGd46Ht", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "575b971d-f3f3-43c2-ecc5-d077eddde95d" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "[nltk_data] Downloading package punkt to /root/nltk_data...\n", + "[nltk_data] Unzipping tokenizers/punkt.zip.\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import json\n", + "\n", + "\n", + "json_file_path = \"/content/intents.json\"\n", + "\n", + "with open(json_file_path) as json_file:\n", + " data = json.load(json_file)\n" + ], + "metadata": { + "id": "Dp6FwiiO4-dq" + }, + "execution_count": 10, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "words = []\n", + "labels = []\n", + "docs_x = []\n", + "docs_y = []" + ], + "metadata": { + "id": "RIYdjslF5Qcx" + }, + "execution_count": 11, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "for intent in data['intents']:\n", + " for pattern in intent['patterns']:\n", + " wrds = nltk.word_tokenize(pattern)\n", + " words.extend(wrds)\n", + " docs_x.append(wrds)\n", + " docs_y.append(intent[\"tag\"])\n", + "\n", + " if intent['tag'] not in labels:\n", + " labels.append(intent['tag'])" + ], + "metadata": { + "id": "aetDZr5l7n9T" + }, + "execution_count": 12, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "words = [stemmer.stem(w.lower()) for w in words if w != \"?\"]\n", + "words = sorted(list(set(words)))\n", + "\n", + "labels = sorted(labels)" + ], + "metadata": { + "id": "nIXu0XZc8DfO" + }, + "execution_count": 13, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "training = []\n", + "output = []\n", + "\n", + "out_empty = [0 for _ in range(len(labels))]\n", + "\n", + "for x, doc in enumerate(docs_x):\n", + " bag = []\n", + "\n", + " wrds = [stemmer.stem(w.lower()) for w in doc]\n", + "\n", + " for w in words:\n", + " if w in wrds:\n", + " bag.append(1)\n", + " else:\n", + " bag.append(0)\n", + "\n", + " output_row = out_empty[:]\n", + " output_row[labels.index(docs_y[x])] = 1\n", + "\n", + " training.append(bag)\n", + " output.append(output_row)\n", + "\n", + "\n", + "training = numpy.array(training)\n", + "output = numpy.array(output)" + ], + "metadata": { + "id": "mCZmk41W8k-5" + }, + "execution_count": 14, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "!pip install tflearn" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "U7wwe4hZ8pJu", + "outputId": "1e1b9419-d937-4250-c654-cb8e83cf4dd0" + }, + "execution_count": 16, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting tflearn\n", + " Downloading tflearn-0.5.0.tar.gz (107 kB)\n", + "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/107.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m107.3/107.3 kB\u001b[0m \u001b[31m2.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from tflearn) (1.22.4)\n", + "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from tflearn) (1.16.0)\n", + "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from tflearn) (8.4.0)\n", + "Building wheels for collected packages: tflearn\n", + " Building wheel for tflearn (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for tflearn: filename=tflearn-0.5.0-py3-none-any.whl size=127283 sha256=a6f381c5e8399b87389ed4ee68a1595b8e5a28699b1ac506fe7833af19ae0709\n", + " Stored in directory: /root/.cache/pip/wheels/55/fb/7b/e06204a0ceefa45443930b9a250cb5ebe31def0e4e8245a465\n", + "Successfully built tflearn\n", + "Installing collected packages: tflearn\n", + "Successfully installed tflearn-0.5.0\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import tflearn" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IDIwBP4w9QzO", + "outputId": "23a5ccdf-2ec4-461e-fb52-889277544b6d" + }, + "execution_count": 17, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "WARNING:tensorflow:From /usr/local/lib/python3.10/dist-packages/tensorflow/python/compat/v2_compat.py:107: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "non-resource variables are not supported in the long term\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import tensorflow as tf\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense\n", + "\n", + "\n", + "model = Sequential([\n", + " Dense(8, input_shape=(len(training[0]),)),\n", + " Dense(8),\n", + " Dense(len(output[0]), activation=\"softmax\")\n", + "])\n", + "\n", + "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "model.fit(training, output, epochs=1000, batch_size=8)\n", + "\n", + "model.save('model.tflearn')\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "834kf2sG9oAs", + "outputId": "0587720f-cab1-42be-a961-5abc13494436" + }, + "execution_count": 25, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Train on 26 samples\n", + "Epoch 1/1000\n", + "26/26 [==============================] - 0s 9ms/sample - loss: 1.7451 - acc: 0.3077\n", + "Epoch 2/1000\n", + "26/26 [==============================] - 0s 547us/sample - loss: 1.7246 - acc: 0.3462\n", + "Epoch 3/1000\n", + "26/26 [==============================] - 0s 701us/sample - loss: 1.7090 - acc: 0.3462\n", + "Epoch 4/1000\n", + "26/26 [==============================] - 0s 455us/sample - loss: 1.6939 - acc: 0.3462\n", + "Epoch 5/1000\n", + "26/26 [==============================] - 0s 455us/sample - loss: 1.6792 - acc: 0.3462\n", + "Epoch 6/1000\n", + "26/26 [==============================] - 0s 431us/sample - loss: 1.6643 - acc: 0.3846\n", + "Epoch 7/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 1.6498 - acc: 0.3846\n", + "Epoch 8/1000\n", + "26/26 [==============================] - 0s 338us/sample - loss: 1.6356 - acc: 0.3846\n", + "Epoch 9/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 1.6215 - acc: 0.3846\n", + "Epoch 10/1000\n", + "26/26 [==============================] - 0s 413us/sample - loss: 1.6080 - acc: 0.4231\n", + "Epoch 11/1000\n", + "26/26 [==============================] - 0s 406us/sample - loss: 1.5936 - acc: 0.4231\n", + "Epoch 12/1000\n", + "26/26 [==============================] - 0s 536us/sample - loss: 1.5813 - acc: 0.4615\n", + "Epoch 13/1000\n", + "26/26 [==============================] - 0s 475us/sample - loss: 1.5654 - acc: 0.5000\n", + "Epoch 14/1000\n", + "26/26 [==============================] - 0s 521us/sample - loss: 1.5518 - acc: 0.5000\n", + "Epoch 15/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 1.5381 - acc: 0.5000\n", + "Epoch 16/1000\n", + "26/26 [==============================] - 0s 515us/sample - loss: 1.5239 - acc: 0.5000\n", + "Epoch 17/1000\n", + "26/26 [==============================] - 0s 395us/sample - loss: 1.5087 - acc: 0.5000\n", + "Epoch 18/1000\n", + "26/26 [==============================] - 0s 409us/sample - loss: 1.4943 - acc: 0.5000\n", + "Epoch 19/1000\n", + "26/26 [==============================] - 0s 453us/sample - loss: 1.4796 - acc: 0.5000\n", + "Epoch 20/1000\n", + "26/26 [==============================] - 0s 412us/sample - loss: 1.4645 - acc: 0.5769\n", + "Epoch 21/1000\n", + "26/26 [==============================] - 0s 416us/sample - loss: 1.4497 - acc: 0.5769\n", + "Epoch 22/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 1.4356 - acc: 0.5385\n", + "Epoch 23/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 1.4206 - acc: 0.5385\n", + "Epoch 24/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 1.4054 - acc: 0.5385\n", + "Epoch 25/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 1.3914 - acc: 0.5385\n", + "Epoch 26/1000\n", + "26/26 [==============================] - 0s 384us/sample - loss: 1.3770 - acc: 0.5385\n", + "Epoch 27/1000\n", + "26/26 [==============================] - 0s 417us/sample - loss: 1.3612 - acc: 0.5385\n", + "Epoch 28/1000\n", + "26/26 [==============================] - 0s 430us/sample - loss: 1.3468 - acc: 0.5385\n", + "Epoch 29/1000\n", + "26/26 [==============================] - 0s 399us/sample - loss: 1.3319 - acc: 0.5385\n", + "Epoch 30/1000\n", + "26/26 [==============================] - 0s 428us/sample - loss: 1.3163 - acc: 0.5385\n", + "Epoch 31/1000\n", + "26/26 [==============================] - 0s 402us/sample - loss: 1.3021 - acc: 0.5769\n", + "Epoch 32/1000\n", + "26/26 [==============================] - 0s 445us/sample - loss: 1.2863 - acc: 0.5769\n", + "Epoch 33/1000\n", + "26/26 [==============================] - 0s 360us/sample - loss: 1.2712 - acc: 0.5769\n", + "Epoch 34/1000\n", + "26/26 [==============================] - 0s 412us/sample - loss: 1.2558 - acc: 0.5769\n", + "Epoch 35/1000\n", + "26/26 [==============================] - 0s 410us/sample - loss: 1.2400 - acc: 0.5769\n", + "Epoch 36/1000\n", + "26/26 [==============================] - 0s 431us/sample - loss: 1.2245 - acc: 0.5769\n", + "Epoch 37/1000\n", + "26/26 [==============================] - 0s 429us/sample - loss: 1.2090 - acc: 0.5769\n", + "Epoch 38/1000\n", + "26/26 [==============================] - 0s 393us/sample - loss: 1.1923 - acc: 0.6154\n", + "Epoch 39/1000\n", + "26/26 [==============================] - 0s 514us/sample - loss: 1.1767 - acc: 0.6154\n", + "Epoch 40/1000\n", + "26/26 [==============================] - 0s 679us/sample - loss: 1.1609 - acc: 0.6538\n", + "Epoch 41/1000\n", + "26/26 [==============================] - 0s 505us/sample - loss: 1.1455 - acc: 0.6538\n", + "Epoch 42/1000\n", + "26/26 [==============================] - 0s 583us/sample - loss: 1.1291 - acc: 0.6538\n", + "Epoch 43/1000\n", + "26/26 [==============================] - 0s 428us/sample - loss: 1.1143 - acc: 0.6923\n", + "Epoch 44/1000\n", + "26/26 [==============================] - 0s 445us/sample - loss: 1.0985 - acc: 0.6923\n", + "Epoch 45/1000\n", + "26/26 [==============================] - 0s 398us/sample - loss: 1.0829 - acc: 0.6923\n", + "Epoch 46/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 1.0676 - acc: 0.6923\n", + "Epoch 47/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 1.0519 - acc: 0.6923\n", + "Epoch 48/1000\n", + "26/26 [==============================] - 0s 393us/sample - loss: 1.0366 - acc: 0.7308\n", + "Epoch 49/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 1.0210 - acc: 0.7308\n", + "Epoch 50/1000\n", + "26/26 [==============================] - 0s 425us/sample - loss: 1.0059 - acc: 0.7308\n", + "Epoch 51/1000\n", + "26/26 [==============================] - 0s 413us/sample - loss: 0.9903 - acc: 0.7308\n", + "Epoch 52/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 0.9756 - acc: 0.7308\n", + "Epoch 53/1000\n", + "26/26 [==============================] - 0s 554us/sample - loss: 0.9597 - acc: 0.7692\n", + "Epoch 54/1000\n", + "26/26 [==============================] - 0s 420us/sample - loss: 0.9455 - acc: 0.7692\n", + "Epoch 55/1000\n", + "26/26 [==============================] - 0s 413us/sample - loss: 0.9300 - acc: 0.7692\n", + "Epoch 56/1000\n", + "26/26 [==============================] - 0s 469us/sample - loss: 0.9167 - acc: 0.7692\n", + "Epoch 57/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 0.9017 - acc: 0.7692\n", + "Epoch 58/1000\n", + "26/26 [==============================] - 0s 378us/sample - loss: 0.8889 - acc: 0.7692\n", + "Epoch 59/1000\n", + "26/26 [==============================] - 0s 383us/sample - loss: 0.8739 - acc: 0.7692\n", + "Epoch 60/1000\n", + "26/26 [==============================] - 0s 375us/sample - loss: 0.8605 - acc: 0.7692\n", + "Epoch 61/1000\n", + "26/26 [==============================] - 0s 444us/sample - loss: 0.8466 - acc: 0.8077\n", + "Epoch 62/1000\n", + "26/26 [==============================] - 0s 449us/sample - loss: 0.8330 - acc: 0.8462\n", + "Epoch 63/1000\n", + "26/26 [==============================] - 0s 428us/sample - loss: 0.8188 - acc: 0.8462\n", + "Epoch 64/1000\n", + "26/26 [==============================] - 0s 417us/sample - loss: 0.8053 - acc: 0.8462\n", + "Epoch 65/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 0.7920 - acc: 0.8846\n", + "Epoch 66/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 0.7772 - acc: 0.8846\n", + "Epoch 67/1000\n", + "26/26 [==============================] - 0s 415us/sample - loss: 0.7643 - acc: 0.8846\n", + "Epoch 68/1000\n", + "26/26 [==============================] - 0s 393us/sample - loss: 0.7514 - acc: 0.8462\n", + "Epoch 69/1000\n", + "26/26 [==============================] - 0s 389us/sample - loss: 0.7376 - acc: 0.8846\n", + "Epoch 70/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 0.7254 - acc: 0.8846\n", + "Epoch 71/1000\n", + "26/26 [==============================] - 0s 415us/sample - loss: 0.7126 - acc: 0.8846\n", + "Epoch 72/1000\n", + "26/26 [==============================] - 0s 421us/sample - loss: 0.7013 - acc: 0.8846\n", + "Epoch 73/1000\n", + "26/26 [==============================] - 0s 554us/sample - loss: 0.6887 - acc: 0.8846\n", + "Epoch 74/1000\n", + "26/26 [==============================] - 0s 387us/sample - loss: 0.6770 - acc: 0.8846\n", + "Epoch 75/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 0.6653 - acc: 0.8846\n", + "Epoch 76/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 0.6537 - acc: 0.8846\n", + "Epoch 77/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 0.6424 - acc: 0.8846\n", + "Epoch 78/1000\n", + "26/26 [==============================] - 0s 370us/sample - loss: 0.6306 - acc: 0.8846\n", + "Epoch 79/1000\n", + "26/26 [==============================] - 0s 372us/sample - loss: 0.6197 - acc: 0.8846\n", + "Epoch 80/1000\n", + "26/26 [==============================] - 0s 416us/sample - loss: 0.6079 - acc: 0.8846\n", + "Epoch 81/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 0.5966 - acc: 0.8846\n", + "Epoch 82/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 0.5860 - acc: 0.8846\n", + "Epoch 83/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 0.5753 - acc: 0.8846\n", + "Epoch 84/1000\n", + "26/26 [==============================] - 0s 408us/sample - loss: 0.5640 - acc: 0.8846\n", + "Epoch 85/1000\n", + "26/26 [==============================] - 0s 408us/sample - loss: 0.5542 - acc: 0.8846\n", + "Epoch 86/1000\n", + "26/26 [==============================] - 0s 416us/sample - loss: 0.5434 - acc: 0.8846\n", + "Epoch 87/1000\n", + "26/26 [==============================] - 0s 464us/sample - loss: 0.5332 - acc: 0.9231\n", + "Epoch 88/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 0.5229 - acc: 0.9231\n", + "Epoch 89/1000\n", + "26/26 [==============================] - 0s 410us/sample - loss: 0.5125 - acc: 0.9231\n", + "Epoch 90/1000\n", + "26/26 [==============================] - 0s 455us/sample - loss: 0.5027 - acc: 0.9231\n", + "Epoch 91/1000\n", + "26/26 [==============================] - 0s 430us/sample - loss: 0.4932 - acc: 0.9231\n", + "Epoch 92/1000\n", + "26/26 [==============================] - 0s 476us/sample - loss: 0.4835 - acc: 0.9231\n", + "Epoch 93/1000\n", + "26/26 [==============================] - 0s 533us/sample - loss: 0.4742 - acc: 0.9231\n", + "Epoch 94/1000\n", + "26/26 [==============================] - 0s 601us/sample - loss: 0.4656 - acc: 0.9231\n", + "Epoch 95/1000\n", + "26/26 [==============================] - 0s 517us/sample - loss: 0.4566 - acc: 0.9231\n", + "Epoch 96/1000\n", + "26/26 [==============================] - 0s 468us/sample - loss: 0.4478 - acc: 0.9615\n", + "Epoch 97/1000\n", + "26/26 [==============================] - 0s 434us/sample - loss: 0.4399 - acc: 0.9615\n", + "Epoch 98/1000\n", + "26/26 [==============================] - 0s 423us/sample - loss: 0.4313 - acc: 0.9615\n", + "Epoch 99/1000\n", + "26/26 [==============================] - 0s 560us/sample - loss: 0.4220 - acc: 0.9615\n", + "Epoch 100/1000\n", + "26/26 [==============================] - 0s 445us/sample - loss: 0.4149 - acc: 0.9615\n", + "Epoch 101/1000\n", + "26/26 [==============================] - 0s 466us/sample - loss: 0.4072 - acc: 0.9615\n", + "Epoch 102/1000\n", + "26/26 [==============================] - 0s 579us/sample - loss: 0.3992 - acc: 0.9615\n", + "Epoch 103/1000\n", + "26/26 [==============================] - 0s 368us/sample - loss: 0.3911 - acc: 0.9615\n", + "Epoch 104/1000\n", + "26/26 [==============================] - 0s 375us/sample - loss: 0.3836 - acc: 1.0000\n", + "Epoch 105/1000\n", + "26/26 [==============================] - 0s 384us/sample - loss: 0.3763 - acc: 1.0000\n", + "Epoch 106/1000\n", + "26/26 [==============================] - 0s 409us/sample - loss: 0.3682 - acc: 1.0000\n", + "Epoch 107/1000\n", + "26/26 [==============================] - 0s 425us/sample - loss: 0.3611 - acc: 1.0000\n", + "Epoch 108/1000\n", + "26/26 [==============================] - 0s 423us/sample - loss: 0.3541 - acc: 1.0000\n", + "Epoch 109/1000\n", + "26/26 [==============================] - 0s 428us/sample - loss: 0.3475 - acc: 1.0000\n", + "Epoch 110/1000\n", + "26/26 [==============================] - 0s 450us/sample - loss: 0.3410 - acc: 1.0000\n", + "Epoch 111/1000\n", + "26/26 [==============================] - 0s 468us/sample - loss: 0.3349 - acc: 1.0000\n", + "Epoch 112/1000\n", + "26/26 [==============================] - 0s 438us/sample - loss: 0.3283 - acc: 1.0000\n", + "Epoch 113/1000\n", + "26/26 [==============================] - 0s 394us/sample - loss: 0.3219 - acc: 1.0000\n", + "Epoch 114/1000\n", + "26/26 [==============================] - 0s 425us/sample - loss: 0.3165 - acc: 1.0000\n", + "Epoch 115/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 0.3108 - acc: 1.0000\n", + "Epoch 116/1000\n", + "26/26 [==============================] - 0s 449us/sample - loss: 0.3047 - acc: 1.0000\n", + "Epoch 117/1000\n", + "26/26 [==============================] - 0s 509us/sample - loss: 0.2989 - acc: 1.0000\n", + "Epoch 118/1000\n", + "26/26 [==============================] - 0s 578us/sample - loss: 0.2931 - acc: 1.0000\n", + "Epoch 119/1000\n", + "26/26 [==============================] - 0s 563us/sample - loss: 0.2876 - acc: 1.0000\n", + "Epoch 120/1000\n", + "26/26 [==============================] - 0s 384us/sample - loss: 0.2825 - acc: 1.0000\n", + "Epoch 121/1000\n", + "26/26 [==============================] - 0s 408us/sample - loss: 0.2770 - acc: 1.0000\n", + "Epoch 122/1000\n", + "26/26 [==============================] - 0s 370us/sample - loss: 0.2715 - acc: 1.0000\n", + "Epoch 123/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 0.2664 - acc: 1.0000\n", + "Epoch 124/1000\n", + "26/26 [==============================] - 0s 398us/sample - loss: 0.2606 - acc: 1.0000\n", + "Epoch 125/1000\n", + "26/26 [==============================] - 0s 423us/sample - loss: 0.2553 - acc: 1.0000\n", + "Epoch 126/1000\n", + "26/26 [==============================] - 0s 427us/sample - loss: 0.2507 - acc: 1.0000\n", + "Epoch 127/1000\n", + "26/26 [==============================] - 0s 396us/sample - loss: 0.2459 - acc: 1.0000\n", + "Epoch 128/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 0.2411 - acc: 1.0000\n", + "Epoch 129/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 0.2368 - acc: 1.0000\n", + "Epoch 130/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 0.2325 - acc: 1.0000\n", + "Epoch 131/1000\n", + "26/26 [==============================] - 0s 368us/sample - loss: 0.2274 - acc: 1.0000\n", + "Epoch 132/1000\n", + "26/26 [==============================] - 0s 385us/sample - loss: 0.2232 - acc: 1.0000\n", + "Epoch 133/1000\n", + "26/26 [==============================] - 0s 395us/sample - loss: 0.2189 - acc: 1.0000\n", + "Epoch 134/1000\n", + "26/26 [==============================] - 0s 420us/sample - loss: 0.2147 - acc: 1.0000\n", + "Epoch 135/1000\n", + "26/26 [==============================] - 0s 378us/sample - loss: 0.2105 - acc: 1.0000\n", + "Epoch 136/1000\n", + "26/26 [==============================] - 0s 439us/sample - loss: 0.2063 - acc: 1.0000\n", + "Epoch 137/1000\n", + "26/26 [==============================] - 0s 442us/sample - loss: 0.2024 - acc: 1.0000\n", + "Epoch 138/1000\n", + "26/26 [==============================] - 0s 416us/sample - loss: 0.1987 - acc: 1.0000\n", + "Epoch 139/1000\n", + "26/26 [==============================] - 0s 611us/sample - loss: 0.1946 - acc: 1.0000\n", + "Epoch 140/1000\n", + "26/26 [==============================] - 0s 428us/sample - loss: 0.1906 - acc: 1.0000\n", + "Epoch 141/1000\n", + "26/26 [==============================] - 0s 401us/sample - loss: 0.1871 - acc: 1.0000\n", + "Epoch 142/1000\n", + "26/26 [==============================] - 0s 540us/sample - loss: 0.1836 - acc: 1.0000\n", + "Epoch 143/1000\n", + "26/26 [==============================] - 0s 450us/sample - loss: 0.1796 - acc: 1.0000\n", + "Epoch 144/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 0.1764 - acc: 1.0000\n", + "Epoch 145/1000\n", + "26/26 [==============================] - 0s 584us/sample - loss: 0.1726 - acc: 1.0000\n", + "Epoch 146/1000\n", + "26/26 [==============================] - 0s 411us/sample - loss: 0.1693 - acc: 1.0000\n", + "Epoch 147/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 0.1661 - acc: 1.0000\n", + "Epoch 148/1000\n", + "26/26 [==============================] - 0s 412us/sample - loss: 0.1629 - acc: 1.0000\n", + "Epoch 149/1000\n", + "26/26 [==============================] - 0s 391us/sample - loss: 0.1598 - acc: 1.0000\n", + "Epoch 150/1000\n", + "26/26 [==============================] - 0s 385us/sample - loss: 0.1565 - acc: 1.0000\n", + "Epoch 151/1000\n", + "26/26 [==============================] - 0s 395us/sample - loss: 0.1535 - acc: 1.0000\n", + "Epoch 152/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 0.1508 - acc: 1.0000\n", + "Epoch 153/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 0.1478 - acc: 1.0000\n", + "Epoch 154/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 0.1449 - acc: 1.0000\n", + "Epoch 155/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 0.1417 - acc: 1.0000\n", + "Epoch 156/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 0.1391 - acc: 1.0000\n", + "Epoch 157/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 0.1363 - acc: 1.0000\n", + "Epoch 158/1000\n", + "26/26 [==============================] - 0s 393us/sample - loss: 0.1339 - acc: 1.0000\n", + "Epoch 159/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 0.1314 - acc: 1.0000\n", + "Epoch 160/1000\n", + "26/26 [==============================] - 0s 551us/sample - loss: 0.1291 - acc: 1.0000\n", + "Epoch 161/1000\n", + "26/26 [==============================] - 0s 419us/sample - loss: 0.1268 - acc: 1.0000\n", + "Epoch 162/1000\n", + "26/26 [==============================] - 0s 376us/sample - loss: 0.1247 - acc: 1.0000\n", + "Epoch 163/1000\n", + "26/26 [==============================] - 0s 502us/sample - loss: 0.1226 - acc: 1.0000\n", + "Epoch 164/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 0.1205 - acc: 1.0000\n", + "Epoch 165/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 0.1184 - acc: 1.0000\n", + "Epoch 166/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 0.1164 - acc: 1.0000\n", + "Epoch 167/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 0.1144 - acc: 1.0000\n", + "Epoch 168/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 0.1126 - acc: 1.0000\n", + "Epoch 169/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 0.1105 - acc: 1.0000\n", + "Epoch 170/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 0.1087 - acc: 1.0000\n", + "Epoch 171/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 0.1070 - acc: 1.0000\n", + "Epoch 172/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 0.1052 - acc: 1.0000\n", + "Epoch 173/1000\n", + "26/26 [==============================] - 0s 375us/sample - loss: 0.1035 - acc: 1.0000\n", + "Epoch 174/1000\n", + "26/26 [==============================] - 0s 375us/sample - loss: 0.1019 - acc: 1.0000\n", + "Epoch 175/1000\n", + "26/26 [==============================] - 0s 454us/sample - loss: 0.0999 - acc: 1.0000\n", + "Epoch 176/1000\n", + "26/26 [==============================] - 0s 519us/sample - loss: 0.0983 - acc: 1.0000\n", + "Epoch 177/1000\n", + "26/26 [==============================] - 0s 498us/sample - loss: 0.0964 - acc: 1.0000\n", + "Epoch 178/1000\n", + "26/26 [==============================] - 0s 445us/sample - loss: 0.0948 - acc: 1.0000\n", + "Epoch 179/1000\n", + "26/26 [==============================] - 0s 428us/sample - loss: 0.0930 - acc: 1.0000\n", + "Epoch 180/1000\n", + "26/26 [==============================] - 0s 424us/sample - loss: 0.0915 - acc: 1.0000\n", + "Epoch 181/1000\n", + "26/26 [==============================] - 0s 415us/sample - loss: 0.0901 - acc: 1.0000\n", + "Epoch 182/1000\n", + "26/26 [==============================] - 0s 391us/sample - loss: 0.0883 - acc: 1.0000\n", + "Epoch 183/1000\n", + "26/26 [==============================] - 0s 401us/sample - loss: 0.0869 - acc: 1.0000\n", + "Epoch 184/1000\n", + "26/26 [==============================] - 0s 372us/sample - loss: 0.0855 - acc: 1.0000\n", + "Epoch 185/1000\n", + "26/26 [==============================] - 0s 387us/sample - loss: 0.0840 - acc: 1.0000\n", + "Epoch 186/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 0.0826 - acc: 1.0000\n", + "Epoch 187/1000\n", + "26/26 [==============================] - 0s 410us/sample - loss: 0.0814 - acc: 1.0000\n", + "Epoch 188/1000\n", + "26/26 [==============================] - 0s 593us/sample - loss: 0.0801 - acc: 1.0000\n", + "Epoch 189/1000\n", + "26/26 [==============================] - 0s 448us/sample - loss: 0.0789 - acc: 1.0000\n", + "Epoch 190/1000\n", + "26/26 [==============================] - 0s 431us/sample - loss: 0.0777 - acc: 1.0000\n", + "Epoch 191/1000\n", + "26/26 [==============================] - 0s 395us/sample - loss: 0.0764 - acc: 1.0000\n", + "Epoch 192/1000\n", + "26/26 [==============================] - 0s 395us/sample - loss: 0.0753 - acc: 1.0000\n", + "Epoch 193/1000\n", + "26/26 [==============================] - 0s 383us/sample - loss: 0.0739 - acc: 1.0000\n", + "Epoch 194/1000\n", + "26/26 [==============================] - 0s 402us/sample - loss: 0.0728 - acc: 1.0000\n", + "Epoch 195/1000\n", + "26/26 [==============================] - 0s 419us/sample - loss: 0.0717 - acc: 1.0000\n", + "Epoch 196/1000\n", + "26/26 [==============================] - 0s 375us/sample - loss: 0.0704 - acc: 1.0000\n", + "Epoch 197/1000\n", + "26/26 [==============================] - 0s 360us/sample - loss: 0.0694 - acc: 1.0000\n", + "Epoch 198/1000\n", + "26/26 [==============================] - 0s 360us/sample - loss: 0.0684 - acc: 1.0000\n", + "Epoch 199/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 0.0673 - acc: 1.0000\n", + "Epoch 200/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 0.0662 - acc: 1.0000\n", + "Epoch 201/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 0.0652 - acc: 1.0000\n", + "Epoch 202/1000\n", + "26/26 [==============================] - 0s 386us/sample - loss: 0.0641 - acc: 1.0000\n", + "Epoch 203/1000\n", + "26/26 [==============================] - 0s 449us/sample - loss: 0.0632 - acc: 1.0000\n", + "Epoch 204/1000\n", + "26/26 [==============================] - 0s 395us/sample - loss: 0.0623 - acc: 1.0000\n", + "Epoch 205/1000\n", + "26/26 [==============================] - 0s 386us/sample - loss: 0.0613 - acc: 1.0000\n", + "Epoch 206/1000\n", + "26/26 [==============================] - 0s 386us/sample - loss: 0.0604 - acc: 1.0000\n", + "Epoch 207/1000\n", + "26/26 [==============================] - 0s 442us/sample - loss: 0.0594 - acc: 1.0000\n", + "Epoch 208/1000\n", + "26/26 [==============================] - 0s 428us/sample - loss: 0.0586 - acc: 1.0000\n", + "Epoch 209/1000\n", + "26/26 [==============================] - 0s 378us/sample - loss: 0.0577 - acc: 1.0000\n", + "Epoch 210/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 0.0567 - acc: 1.0000\n", + "Epoch 211/1000\n", + "26/26 [==============================] - 0s 579us/sample - loss: 0.0559 - acc: 1.0000\n", + "Epoch 212/1000\n", + "26/26 [==============================] - 0s 490us/sample - loss: 0.0551 - acc: 1.0000\n", + "Epoch 213/1000\n", + "26/26 [==============================] - 0s 594us/sample - loss: 0.0544 - acc: 1.0000\n", + "Epoch 214/1000\n", + "26/26 [==============================] - 0s 432us/sample - loss: 0.0537 - acc: 1.0000\n", + "Epoch 215/1000\n", + "26/26 [==============================] - 0s 417us/sample - loss: 0.0529 - acc: 1.0000\n", + "Epoch 216/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 0.0522 - acc: 1.0000\n", + "Epoch 217/1000\n", + "26/26 [==============================] - 0s 450us/sample - loss: 0.0516 - acc: 1.0000\n", + "Epoch 218/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 0.0508 - acc: 1.0000\n", + "Epoch 219/1000\n", + "26/26 [==============================] - 0s 373us/sample - loss: 0.0500 - acc: 1.0000\n", + "Epoch 220/1000\n", + "26/26 [==============================] - 0s 572us/sample - loss: 0.0493 - acc: 1.0000\n", + "Epoch 221/1000\n", + "26/26 [==============================] - 0s 427us/sample - loss: 0.0486 - acc: 1.0000\n", + "Epoch 222/1000\n", + "26/26 [==============================] - 0s 372us/sample - loss: 0.0479 - acc: 1.0000\n", + "Epoch 223/1000\n", + "26/26 [==============================] - 0s 383us/sample - loss: 0.0473 - acc: 1.0000\n", + "Epoch 224/1000\n", + "26/26 [==============================] - 0s 626us/sample - loss: 0.0467 - acc: 1.0000\n", + "Epoch 225/1000\n", + "26/26 [==============================] - 0s 424us/sample - loss: 0.0460 - acc: 1.0000\n", + "Epoch 226/1000\n", + "26/26 [==============================] - 0s 331us/sample - loss: 0.0454 - acc: 1.0000\n", + "Epoch 227/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 0.0449 - acc: 1.0000\n", + "Epoch 228/1000\n", + "26/26 [==============================] - 0s 340us/sample - loss: 0.0443 - acc: 1.0000\n", + "Epoch 229/1000\n", + "26/26 [==============================] - 0s 387us/sample - loss: 0.0436 - acc: 1.0000\n", + "Epoch 230/1000\n", + "26/26 [==============================] - 0s 383us/sample - loss: 0.0430 - acc: 1.0000\n", + "Epoch 231/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 0.0424 - acc: 1.0000\n", + "Epoch 232/1000\n", + "26/26 [==============================] - 0s 403us/sample - loss: 0.0419 - acc: 1.0000\n", + "Epoch 233/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 0.0414 - acc: 1.0000\n", + "Epoch 234/1000\n", + "26/26 [==============================] - 0s 370us/sample - loss: 0.0408 - acc: 1.0000\n", + "Epoch 235/1000\n", + "26/26 [==============================] - 0s 368us/sample - loss: 0.0403 - acc: 1.0000\n", + "Epoch 236/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 0.0397 - acc: 1.0000\n", + "Epoch 237/1000\n", + "26/26 [==============================] - 0s 394us/sample - loss: 0.0392 - acc: 1.0000\n", + "Epoch 238/1000\n", + "26/26 [==============================] - 0s 353us/sample - loss: 0.0388 - acc: 1.0000\n", + "Epoch 239/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 0.0382 - acc: 1.0000\n", + "Epoch 240/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 0.0377 - acc: 1.0000\n", + "Epoch 241/1000\n", + "26/26 [==============================] - 0s 338us/sample - loss: 0.0373 - acc: 1.0000\n", + "Epoch 242/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 0.0368 - acc: 1.0000\n", + "Epoch 243/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 0.0364 - acc: 1.0000\n", + "Epoch 244/1000\n", + "26/26 [==============================] - 0s 353us/sample - loss: 0.0360 - acc: 1.0000\n", + "Epoch 245/1000\n", + "26/26 [==============================] - 0s 523us/sample - loss: 0.0355 - acc: 1.0000\n", + "Epoch 246/1000\n", + "26/26 [==============================] - 0s 424us/sample - loss: 0.0351 - acc: 1.0000\n", + "Epoch 247/1000\n", + "26/26 [==============================] - 0s 410us/sample - loss: 0.0347 - acc: 1.0000\n", + "Epoch 248/1000\n", + "26/26 [==============================] - 0s 472us/sample - loss: 0.0343 - acc: 1.0000\n", + "Epoch 249/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 0.0339 - acc: 1.0000\n", + "Epoch 250/1000\n", + "26/26 [==============================] - 0s 458us/sample - loss: 0.0334 - acc: 1.0000\n", + "Epoch 251/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 0.0331 - acc: 1.0000\n", + "Epoch 252/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 0.0327 - acc: 1.0000\n", + "Epoch 253/1000\n", + "26/26 [==============================] - 0s 375us/sample - loss: 0.0323 - acc: 1.0000\n", + "Epoch 254/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 0.0319 - acc: 1.0000\n", + "Epoch 255/1000\n", + "26/26 [==============================] - 0s 387us/sample - loss: 0.0315 - acc: 1.0000\n", + "Epoch 256/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 0.0311 - acc: 1.0000\n", + "Epoch 257/1000\n", + "26/26 [==============================] - 0s 375us/sample - loss: 0.0308 - acc: 1.0000\n", + "Epoch 258/1000\n", + "26/26 [==============================] - 0s 492us/sample - loss: 0.0304 - acc: 1.0000\n", + "Epoch 259/1000\n", + "26/26 [==============================] - 0s 459us/sample - loss: 0.0301 - acc: 1.0000\n", + "Epoch 260/1000\n", + "26/26 [==============================] - 0s 408us/sample - loss: 0.0297 - acc: 1.0000\n", + "Epoch 261/1000\n", + "26/26 [==============================] - 0s 383us/sample - loss: 0.0294 - acc: 1.0000\n", + "Epoch 262/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 0.0291 - acc: 1.0000\n", + "Epoch 263/1000\n", + "26/26 [==============================] - 0s 406us/sample - loss: 0.0287 - acc: 1.0000\n", + "Epoch 264/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 0.0284 - acc: 1.0000\n", + "Epoch 265/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 0.0281 - acc: 1.0000\n", + "Epoch 266/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 0.0278 - acc: 1.0000\n", + "Epoch 267/1000\n", + "26/26 [==============================] - 0s 404us/sample - loss: 0.0275 - acc: 1.0000\n", + "Epoch 268/1000\n", + "26/26 [==============================] - 0s 387us/sample - loss: 0.0272 - acc: 1.0000\n", + "Epoch 269/1000\n", + "26/26 [==============================] - 0s 385us/sample - loss: 0.0269 - acc: 1.0000\n", + "Epoch 270/1000\n", + "26/26 [==============================] - 0s 420us/sample - loss: 0.0266 - acc: 1.0000\n", + "Epoch 271/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 0.0263 - acc: 1.0000\n", + "Epoch 272/1000\n", + "26/26 [==============================] - 0s 419us/sample - loss: 0.0260 - acc: 1.0000\n", + "Epoch 273/1000\n", + "26/26 [==============================] - 0s 427us/sample - loss: 0.0257 - acc: 1.0000\n", + "Epoch 274/1000\n", + "26/26 [==============================] - 0s 385us/sample - loss: 0.0255 - acc: 1.0000\n", + "Epoch 275/1000\n", + "26/26 [==============================] - 0s 421us/sample - loss: 0.0252 - acc: 1.0000\n", + "Epoch 276/1000\n", + "26/26 [==============================] - 0s 568us/sample - loss: 0.0249 - acc: 1.0000\n", + "Epoch 277/1000\n", + "26/26 [==============================] - 0s 550us/sample - loss: 0.0246 - acc: 1.0000\n", + "Epoch 278/1000\n", + "26/26 [==============================] - 0s 503us/sample - loss: 0.0244 - acc: 1.0000\n", + "Epoch 279/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 0.0241 - acc: 1.0000\n", + "Epoch 280/1000\n", + "26/26 [==============================] - 0s 384us/sample - loss: 0.0239 - acc: 1.0000\n", + "Epoch 281/1000\n", + "26/26 [==============================] - 0s 370us/sample - loss: 0.0236 - acc: 1.0000\n", + "Epoch 282/1000\n", + "26/26 [==============================] - 0s 377us/sample - loss: 0.0234 - acc: 1.0000\n", + "Epoch 283/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 0.0231 - acc: 1.0000\n", + "Epoch 284/1000\n", + "26/26 [==============================] - 0s 412us/sample - loss: 0.0229 - acc: 1.0000\n", + "Epoch 285/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 0.0227 - acc: 1.0000\n", + "Epoch 286/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 0.0224 - acc: 1.0000\n", + "Epoch 287/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 0.0222 - acc: 1.0000\n", + "Epoch 288/1000\n", + "26/26 [==============================] - 0s 671us/sample - loss: 0.0220 - acc: 1.0000\n", + "Epoch 289/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 0.0217 - acc: 1.0000\n", + "Epoch 290/1000\n", + "26/26 [==============================] - 0s 389us/sample - loss: 0.0215 - acc: 1.0000\n", + "Epoch 291/1000\n", + "26/26 [==============================] - 0s 512us/sample - loss: 0.0213 - acc: 1.0000\n", + "Epoch 292/1000\n", + "26/26 [==============================] - 0s 490us/sample - loss: 0.0211 - acc: 1.0000\n", + "Epoch 293/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 0.0209 - acc: 1.0000\n", + "Epoch 294/1000\n", + "26/26 [==============================] - 0s 483us/sample - loss: 0.0206 - acc: 1.0000\n", + "Epoch 295/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 0.0204 - acc: 1.0000\n", + "Epoch 296/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 0.0202 - acc: 1.0000\n", + "Epoch 297/1000\n", + "26/26 [==============================] - 0s 421us/sample - loss: 0.0200 - acc: 1.0000\n", + "Epoch 298/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 0.0198 - acc: 1.0000\n", + "Epoch 299/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 0.0196 - acc: 1.0000\n", + "Epoch 300/1000\n", + "26/26 [==============================] - 0s 394us/sample - loss: 0.0194 - acc: 1.0000\n", + "Epoch 301/1000\n", + "26/26 [==============================] - 0s 350us/sample - loss: 0.0192 - acc: 1.0000\n", + "Epoch 302/1000\n", + "26/26 [==============================] - 0s 420us/sample - loss: 0.0190 - acc: 1.0000\n", + "Epoch 303/1000\n", + "26/26 [==============================] - 0s 418us/sample - loss: 0.0189 - acc: 1.0000\n", + "Epoch 304/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 0.0187 - acc: 1.0000\n", + "Epoch 305/1000\n", + "26/26 [==============================] - 0s 350us/sample - loss: 0.0185 - acc: 1.0000\n", + "Epoch 306/1000\n", + "26/26 [==============================] - 0s 349us/sample - loss: 0.0183 - acc: 1.0000\n", + "Epoch 307/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 0.0182 - acc: 1.0000\n", + "Epoch 308/1000\n", + "26/26 [==============================] - 0s 356us/sample - loss: 0.0180 - acc: 1.0000\n", + "Epoch 309/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 0.0178 - acc: 1.0000\n", + "Epoch 310/1000\n", + "26/26 [==============================] - 0s 326us/sample - loss: 0.0177 - acc: 1.0000\n", + "Epoch 311/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 0.0175 - acc: 1.0000\n", + "Epoch 312/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 0.0173 - acc: 1.0000\n", + "Epoch 313/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 0.0172 - acc: 1.0000\n", + "Epoch 314/1000\n", + "26/26 [==============================] - 0s 387us/sample - loss: 0.0170 - acc: 1.0000\n", + "Epoch 315/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 0.0169 - acc: 1.0000\n", + "Epoch 316/1000\n", + "26/26 [==============================] - 0s 378us/sample - loss: 0.0167 - acc: 1.0000\n", + "Epoch 317/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 0.0166 - acc: 1.0000\n", + "Epoch 318/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 0.0164 - acc: 1.0000\n", + "Epoch 319/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 0.0163 - acc: 1.0000\n", + "Epoch 320/1000\n", + "26/26 [==============================] - 0s 329us/sample - loss: 0.0161 - acc: 1.0000\n", + "Epoch 321/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 0.0160 - acc: 1.0000\n", + "Epoch 322/1000\n", + "26/26 [==============================] - 0s 348us/sample - loss: 0.0158 - acc: 1.0000\n", + "Epoch 323/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 0.0157 - acc: 1.0000\n", + "Epoch 324/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 0.0155 - acc: 1.0000\n", + "Epoch 325/1000\n", + "26/26 [==============================] - 0s 372us/sample - loss: 0.0154 - acc: 1.0000\n", + "Epoch 326/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 0.0152 - acc: 1.0000\n", + "Epoch 327/1000\n", + "26/26 [==============================] - 0s 421us/sample - loss: 0.0151 - acc: 1.0000\n", + "Epoch 328/1000\n", + "26/26 [==============================] - 0s 445us/sample - loss: 0.0150 - acc: 1.0000\n", + "Epoch 329/1000\n", + "26/26 [==============================] - 0s 425us/sample - loss: 0.0148 - acc: 1.0000\n", + "Epoch 330/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 0.0147 - acc: 1.0000\n", + "Epoch 331/1000\n", + "26/26 [==============================] - 0s 355us/sample - loss: 0.0146 - acc: 1.0000\n", + "Epoch 332/1000\n", + "26/26 [==============================] - 0s 447us/sample - loss: 0.0145 - acc: 1.0000\n", + "Epoch 333/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 0.0143 - acc: 1.0000\n", + "Epoch 334/1000\n", + "26/26 [==============================] - 0s 368us/sample - loss: 0.0142 - acc: 1.0000\n", + "Epoch 335/1000\n", + "26/26 [==============================] - 0s 466us/sample - loss: 0.0141 - acc: 1.0000\n", + "Epoch 336/1000\n", + "26/26 [==============================] - 0s 370us/sample - loss: 0.0140 - acc: 1.0000\n", + "Epoch 337/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 0.0138 - acc: 1.0000\n", + "Epoch 338/1000\n", + "26/26 [==============================] - 0s 384us/sample - loss: 0.0137 - acc: 1.0000\n", + "Epoch 339/1000\n", + "26/26 [==============================] - 0s 386us/sample - loss: 0.0136 - acc: 1.0000\n", + "Epoch 340/1000\n", + "26/26 [==============================] - 0s 393us/sample - loss: 0.0135 - acc: 1.0000\n", + "Epoch 341/1000\n", + "26/26 [==============================] - 0s 391us/sample - loss: 0.0134 - acc: 1.0000\n", + "Epoch 342/1000\n", + "26/26 [==============================] - 0s 401us/sample - loss: 0.0133 - acc: 1.0000\n", + "Epoch 343/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 0.0131 - acc: 1.0000\n", + "Epoch 344/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 0.0130 - acc: 1.0000\n", + "Epoch 345/1000\n", + "26/26 [==============================] - 0s 389us/sample - loss: 0.0129 - acc: 1.0000\n", + "Epoch 346/1000\n", + "26/26 [==============================] - 0s 370us/sample - loss: 0.0128 - acc: 1.0000\n", + "Epoch 347/1000\n", + "26/26 [==============================] - 0s 424us/sample - loss: 0.0127 - acc: 1.0000\n", + "Epoch 348/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 0.0126 - acc: 1.0000\n", + "Epoch 349/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 0.0125 - acc: 1.0000\n", + "Epoch 350/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 0.0124 - acc: 1.0000\n", + "Epoch 351/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 0.0123 - acc: 1.0000\n", + "Epoch 352/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 0.0123 - acc: 1.0000\n", + "Epoch 353/1000\n", + "26/26 [==============================] - 0s 360us/sample - loss: 0.0122 - acc: 1.0000\n", + "Epoch 354/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 0.0121 - acc: 1.0000\n", + "Epoch 355/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 0.0120 - acc: 1.0000\n", + "Epoch 356/1000\n", + "26/26 [==============================] - 0s 349us/sample - loss: 0.0118 - acc: 1.0000\n", + "Epoch 357/1000\n", + "26/26 [==============================] - 0s 393us/sample - loss: 0.0118 - acc: 1.0000\n", + "Epoch 358/1000\n", + "26/26 [==============================] - 0s 395us/sample - loss: 0.0117 - acc: 1.0000\n", + "Epoch 359/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 0.0116 - acc: 1.0000\n", + "Epoch 360/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 0.0115 - acc: 1.0000\n", + "Epoch 361/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 0.0114 - acc: 1.0000\n", + "Epoch 362/1000\n", + "26/26 [==============================] - 0s 398us/sample - loss: 0.0113 - acc: 1.0000\n", + "Epoch 363/1000\n", + "26/26 [==============================] - 0s 611us/sample - loss: 0.0112 - acc: 1.0000\n", + "Epoch 364/1000\n", + "26/26 [==============================] - 0s 522us/sample - loss: 0.0111 - acc: 1.0000\n", + "Epoch 365/1000\n", + "26/26 [==============================] - 0s 637us/sample - loss: 0.0111 - acc: 1.0000\n", + "Epoch 366/1000\n", + "26/26 [==============================] - 0s 716us/sample - loss: 0.0110 - acc: 1.0000\n", + "Epoch 367/1000\n", + "26/26 [==============================] - 0s 605us/sample - loss: 0.0109 - acc: 1.0000\n", + "Epoch 368/1000\n", + "26/26 [==============================] - 0s 495us/sample - loss: 0.0108 - acc: 1.0000\n", + "Epoch 369/1000\n", + "26/26 [==============================] - 0s 521us/sample - loss: 0.0107 - acc: 1.0000\n", + "Epoch 370/1000\n", + "26/26 [==============================] - 0s 620us/sample - loss: 0.0106 - acc: 1.0000\n", + "Epoch 371/1000\n", + "26/26 [==============================] - 0s 686us/sample - loss: 0.0105 - acc: 1.0000\n", + "Epoch 372/1000\n", + "26/26 [==============================] - 0s 676us/sample - loss: 0.0104 - acc: 1.0000\n", + "Epoch 373/1000\n", + "26/26 [==============================] - 0s 675us/sample - loss: 0.0104 - acc: 1.0000\n", + "Epoch 374/1000\n", + "26/26 [==============================] - 0s 453us/sample - loss: 0.0103 - acc: 1.0000\n", + "Epoch 375/1000\n", + "26/26 [==============================] - 0s 469us/sample - loss: 0.0102 - acc: 1.0000\n", + "Epoch 376/1000\n", + "26/26 [==============================] - 0s 456us/sample - loss: 0.0101 - acc: 1.0000\n", + "Epoch 377/1000\n", + "26/26 [==============================] - 0s 459us/sample - loss: 0.0101 - acc: 1.0000\n", + "Epoch 378/1000\n", + "26/26 [==============================] - 0s 395us/sample - loss: 0.0100 - acc: 1.0000\n", + "Epoch 379/1000\n", + "26/26 [==============================] - 0s 499us/sample - loss: 0.0099 - acc: 1.0000\n", + "Epoch 380/1000\n", + "26/26 [==============================] - 0s 446us/sample - loss: 0.0098 - acc: 1.0000\n", + "Epoch 381/1000\n", + "26/26 [==============================] - 0s 438us/sample - loss: 0.0098 - acc: 1.0000\n", + "Epoch 382/1000\n", + "26/26 [==============================] - 0s 554us/sample - loss: 0.0097 - acc: 1.0000\n", + "Epoch 383/1000\n", + "26/26 [==============================] - 0s 538us/sample - loss: 0.0096 - acc: 1.0000\n", + "Epoch 384/1000\n", + "26/26 [==============================] - 0s 517us/sample - loss: 0.0096 - acc: 1.0000\n", + "Epoch 385/1000\n", + "26/26 [==============================] - 0s 605us/sample - loss: 0.0095 - acc: 1.0000\n", + "Epoch 386/1000\n", + "26/26 [==============================] - 0s 394us/sample - loss: 0.0094 - acc: 1.0000\n", + "Epoch 387/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 0.0093 - acc: 1.0000\n", + "Epoch 388/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 0.0093 - acc: 1.0000\n", + "Epoch 389/1000\n", + "26/26 [==============================] - 0s 396us/sample - loss: 0.0092 - acc: 1.0000\n", + "Epoch 390/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 0.0091 - acc: 1.0000\n", + "Epoch 391/1000\n", + "26/26 [==============================] - 0s 396us/sample - loss: 0.0091 - acc: 1.0000\n", + "Epoch 392/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 0.0090 - acc: 1.0000\n", + "Epoch 393/1000\n", + "26/26 [==============================] - 0s 581us/sample - loss: 0.0090 - acc: 1.0000\n", + "Epoch 394/1000\n", + "26/26 [==============================] - 0s 433us/sample - loss: 0.0089 - acc: 1.0000\n", + "Epoch 395/1000\n", + "26/26 [==============================] - 0s 443us/sample - loss: 0.0088 - acc: 1.0000\n", + "Epoch 396/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 0.0088 - acc: 1.0000\n", + "Epoch 397/1000\n", + "26/26 [==============================] - 0s 423us/sample - loss: 0.0087 - acc: 1.0000\n", + "Epoch 398/1000\n", + "26/26 [==============================] - 0s 348us/sample - loss: 0.0087 - acc: 1.0000\n", + "Epoch 399/1000\n", + "26/26 [==============================] - 0s 322us/sample - loss: 0.0086 - acc: 1.0000\n", + "Epoch 400/1000\n", + "26/26 [==============================] - 0s 417us/sample - loss: 0.0085 - acc: 1.0000\n", + "Epoch 401/1000\n", + "26/26 [==============================] - 0s 403us/sample - loss: 0.0085 - acc: 1.0000\n", + "Epoch 402/1000\n", + "26/26 [==============================] - 0s 404us/sample - loss: 0.0084 - acc: 1.0000\n", + "Epoch 403/1000\n", + "26/26 [==============================] - 0s 394us/sample - loss: 0.0084 - acc: 1.0000\n", + "Epoch 404/1000\n", + "26/26 [==============================] - 0s 319us/sample - loss: 0.0083 - acc: 1.0000\n", + "Epoch 405/1000\n", + "26/26 [==============================] - 0s 489us/sample - loss: 0.0082 - acc: 1.0000\n", + "Epoch 406/1000\n", + "26/26 [==============================] - 0s 548us/sample - loss: 0.0082 - acc: 1.0000\n", + "Epoch 407/1000\n", + "26/26 [==============================] - 0s 535us/sample - loss: 0.0081 - acc: 1.0000\n", + "Epoch 408/1000\n", + "26/26 [==============================] - 0s 491us/sample - loss: 0.0081 - acc: 1.0000\n", + "Epoch 409/1000\n", + "26/26 [==============================] - 0s 425us/sample - loss: 0.0080 - acc: 1.0000\n", + "Epoch 410/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 0.0080 - acc: 1.0000\n", + "Epoch 411/1000\n", + "26/26 [==============================] - 0s 464us/sample - loss: 0.0079 - acc: 1.0000\n", + "Epoch 412/1000\n", + "26/26 [==============================] - 0s 557us/sample - loss: 0.0079 - acc: 1.0000\n", + "Epoch 413/1000\n", + "26/26 [==============================] - 0s 597us/sample - loss: 0.0078 - acc: 1.0000\n", + "Epoch 414/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 0.0078 - acc: 1.0000\n", + "Epoch 415/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 0.0077 - acc: 1.0000\n", + "Epoch 416/1000\n", + "26/26 [==============================] - 0s 350us/sample - loss: 0.0077 - acc: 1.0000\n", + "Epoch 417/1000\n", + "26/26 [==============================] - 0s 353us/sample - loss: 0.0076 - acc: 1.0000\n", + "Epoch 418/1000\n", + "26/26 [==============================] - 0s 340us/sample - loss: 0.0076 - acc: 1.0000\n", + "Epoch 419/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 0.0075 - acc: 1.0000\n", + "Epoch 420/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 0.0075 - acc: 1.0000\n", + "Epoch 421/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 0.0074 - acc: 1.0000\n", + "Epoch 422/1000\n", + "26/26 [==============================] - 0s 448us/sample - loss: 0.0074 - acc: 1.0000\n", + "Epoch 423/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 0.0073 - acc: 1.0000\n", + "Epoch 424/1000\n", + "26/26 [==============================] - 0s 450us/sample - loss: 0.0073 - acc: 1.0000\n", + "Epoch 425/1000\n", + "26/26 [==============================] - 0s 589us/sample - loss: 0.0073 - acc: 1.0000\n", + "Epoch 426/1000\n", + "26/26 [==============================] - 0s 702us/sample - loss: 0.0072 - acc: 1.0000\n", + "Epoch 427/1000\n", + "26/26 [==============================] - 0s 765us/sample - loss: 0.0072 - acc: 1.0000\n", + "Epoch 428/1000\n", + "26/26 [==============================] - 0s 722us/sample - loss: 0.0071 - acc: 1.0000\n", + "Epoch 429/1000\n", + "26/26 [==============================] - 0s 429us/sample - loss: 0.0071 - acc: 1.0000\n", + "Epoch 430/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 0.0070 - acc: 1.0000\n", + "Epoch 431/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 0.0070 - acc: 1.0000\n", + "Epoch 432/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 0.0070 - acc: 1.0000\n", + "Epoch 433/1000\n", + "26/26 [==============================] - 0s 499us/sample - loss: 0.0069 - acc: 1.0000\n", + "Epoch 434/1000\n", + "26/26 [==============================] - 0s 594us/sample - loss: 0.0069 - acc: 1.0000\n", + "Epoch 435/1000\n", + "26/26 [==============================] - 0s 470us/sample - loss: 0.0068 - acc: 1.0000\n", + "Epoch 436/1000\n", + "26/26 [==============================] - 0s 571us/sample - loss: 0.0068 - acc: 1.0000\n", + "Epoch 437/1000\n", + "26/26 [==============================] - 0s 491us/sample - loss: 0.0068 - acc: 1.0000\n", + "Epoch 438/1000\n", + "26/26 [==============================] - 0s 383us/sample - loss: 0.0067 - acc: 1.0000\n", + "Epoch 439/1000\n", + "26/26 [==============================] - 0s 550us/sample - loss: 0.0067 - acc: 1.0000\n", + "Epoch 440/1000\n", + "26/26 [==============================] - 0s 439us/sample - loss: 0.0066 - acc: 1.0000\n", + "Epoch 441/1000\n", + "26/26 [==============================] - 0s 356us/sample - loss: 0.0066 - acc: 1.0000\n", + "Epoch 442/1000\n", + "26/26 [==============================] - 0s 383us/sample - loss: 0.0066 - acc: 1.0000\n", + "Epoch 443/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 0.0065 - acc: 1.0000\n", + "Epoch 444/1000\n", + "26/26 [==============================] - 0s 429us/sample - loss: 0.0065 - acc: 1.0000\n", + "Epoch 445/1000\n", + "26/26 [==============================] - 0s 409us/sample - loss: 0.0064 - acc: 1.0000\n", + "Epoch 446/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 0.0064 - acc: 1.0000\n", + "Epoch 447/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 0.0064 - acc: 1.0000\n", + "Epoch 448/1000\n", + "26/26 [==============================] - 0s 334us/sample - loss: 0.0063 - acc: 1.0000\n", + "Epoch 449/1000\n", + "26/26 [==============================] - 0s 326us/sample - loss: 0.0063 - acc: 1.0000\n", + "Epoch 450/1000\n", + "26/26 [==============================] - 0s 328us/sample - loss: 0.0062 - acc: 1.0000\n", + "Epoch 451/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 0.0062 - acc: 1.0000\n", + "Epoch 452/1000\n", + "26/26 [==============================] - 0s 350us/sample - loss: 0.0062 - acc: 1.0000\n", + "Epoch 453/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 0.0061 - acc: 1.0000\n", + "Epoch 454/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 0.0061 - acc: 1.0000\n", + "Epoch 455/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 0.0061 - acc: 1.0000\n", + "Epoch 456/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 0.0060 - acc: 1.0000\n", + "Epoch 457/1000\n", + "26/26 [==============================] - 0s 434us/sample - loss: 0.0060 - acc: 1.0000\n", + "Epoch 458/1000\n", + "26/26 [==============================] - 0s 463us/sample - loss: 0.0060 - acc: 1.0000\n", + "Epoch 459/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 0.0059 - acc: 1.0000\n", + "Epoch 460/1000\n", + "26/26 [==============================] - 0s 340us/sample - loss: 0.0059 - acc: 1.0000\n", + "Epoch 461/1000\n", + "26/26 [==============================] - 0s 337us/sample - loss: 0.0058 - acc: 1.0000\n", + "Epoch 462/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 0.0058 - acc: 1.0000\n", + "Epoch 463/1000\n", + "26/26 [==============================] - 0s 360us/sample - loss: 0.0058 - acc: 1.0000\n", + "Epoch 464/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 0.0057 - acc: 1.0000\n", + "Epoch 465/1000\n", + "26/26 [==============================] - 0s 378us/sample - loss: 0.0057 - acc: 1.0000\n", + "Epoch 466/1000\n", + "26/26 [==============================] - 0s 350us/sample - loss: 0.0057 - acc: 1.0000\n", + "Epoch 467/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 0.0056 - acc: 1.0000\n", + "Epoch 468/1000\n", + "26/26 [==============================] - 0s 386us/sample - loss: 0.0056 - acc: 1.0000\n", + "Epoch 469/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 0.0056 - acc: 1.0000\n", + "Epoch 470/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 0.0055 - acc: 1.0000\n", + "Epoch 471/1000\n", + "26/26 [==============================] - 0s 402us/sample - loss: 0.0055 - acc: 1.0000\n", + "Epoch 472/1000\n", + "26/26 [==============================] - 0s 396us/sample - loss: 0.0055 - acc: 1.0000\n", + "Epoch 473/1000\n", + "26/26 [==============================] - 0s 438us/sample - loss: 0.0054 - acc: 1.0000\n", + "Epoch 474/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 0.0054 - acc: 1.0000\n", + "Epoch 475/1000\n", + "26/26 [==============================] - 0s 385us/sample - loss: 0.0054 - acc: 1.0000\n", + "Epoch 476/1000\n", + "26/26 [==============================] - 0s 563us/sample - loss: 0.0053 - acc: 1.0000\n", + "Epoch 477/1000\n", + "26/26 [==============================] - 0s 461us/sample - loss: 0.0053 - acc: 1.0000\n", + "Epoch 478/1000\n", + "26/26 [==============================] - 0s 476us/sample - loss: 0.0053 - acc: 1.0000\n", + "Epoch 479/1000\n", + "26/26 [==============================] - 0s 454us/sample - loss: 0.0052 - acc: 1.0000\n", + "Epoch 480/1000\n", + "26/26 [==============================] - 0s 372us/sample - loss: 0.0052 - acc: 1.0000\n", + "Epoch 481/1000\n", + "26/26 [==============================] - 0s 472us/sample - loss: 0.0052 - acc: 1.0000\n", + "Epoch 482/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 0.0052 - acc: 1.0000\n", + "Epoch 483/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 0.0051 - acc: 1.0000\n", + "Epoch 484/1000\n", + "26/26 [==============================] - 0s 395us/sample - loss: 0.0051 - acc: 1.0000\n", + "Epoch 485/1000\n", + "26/26 [==============================] - 0s 456us/sample - loss: 0.0051 - acc: 1.0000\n", + "Epoch 486/1000\n", + "26/26 [==============================] - 0s 476us/sample - loss: 0.0050 - acc: 1.0000\n", + "Epoch 487/1000\n", + "26/26 [==============================] - 0s 419us/sample - loss: 0.0050 - acc: 1.0000\n", + "Epoch 488/1000\n", + "26/26 [==============================] - 0s 420us/sample - loss: 0.0050 - acc: 1.0000\n", + "Epoch 489/1000\n", + "26/26 [==============================] - 0s 435us/sample - loss: 0.0049 - acc: 1.0000\n", + "Epoch 490/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 0.0049 - acc: 1.0000\n", + "Epoch 491/1000\n", + "26/26 [==============================] - 0s 394us/sample - loss: 0.0049 - acc: 1.0000\n", + "Epoch 492/1000\n", + "26/26 [==============================] - 0s 418us/sample - loss: 0.0049 - acc: 1.0000\n", + "Epoch 493/1000\n", + "26/26 [==============================] - 0s 418us/sample - loss: 0.0048 - acc: 1.0000\n", + "Epoch 494/1000\n", + "26/26 [==============================] - 0s 405us/sample - loss: 0.0048 - acc: 1.0000\n", + "Epoch 495/1000\n", + "26/26 [==============================] - 0s 348us/sample - loss: 0.0048 - acc: 1.0000\n", + "Epoch 496/1000\n", + "26/26 [==============================] - 0s 335us/sample - loss: 0.0048 - acc: 1.0000\n", + "Epoch 497/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 0.0047 - acc: 1.0000\n", + "Epoch 498/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 0.0047 - acc: 1.0000\n", + "Epoch 499/1000\n", + "26/26 [==============================] - 0s 350us/sample - loss: 0.0047 - acc: 1.0000\n", + "Epoch 500/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 0.0046 - acc: 1.0000\n", + "Epoch 501/1000\n", + "26/26 [==============================] - 0s 376us/sample - loss: 0.0046 - acc: 1.0000\n", + "Epoch 502/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 0.0046 - acc: 1.0000\n", + "Epoch 503/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 0.0046 - acc: 1.0000\n", + "Epoch 504/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 0.0045 - acc: 1.0000\n", + "Epoch 505/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 0.0045 - acc: 1.0000\n", + "Epoch 506/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 0.0045 - acc: 1.0000\n", + "Epoch 507/1000\n", + "26/26 [==============================] - 0s 326us/sample - loss: 0.0045 - acc: 1.0000\n", + "Epoch 508/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 0.0044 - acc: 1.0000\n", + "Epoch 509/1000\n", + "26/26 [==============================] - 0s 340us/sample - loss: 0.0044 - acc: 1.0000\n", + "Epoch 510/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 0.0044 - acc: 1.0000\n", + "Epoch 511/1000\n", + "26/26 [==============================] - 0s 421us/sample - loss: 0.0044 - acc: 1.0000\n", + "Epoch 512/1000\n", + "26/26 [==============================] - 0s 450us/sample - loss: 0.0043 - acc: 1.0000\n", + "Epoch 513/1000\n", + "26/26 [==============================] - 0s 487us/sample - loss: 0.0043 - acc: 1.0000\n", + "Epoch 514/1000\n", + "26/26 [==============================] - 0s 482us/sample - loss: 0.0043 - acc: 1.0000\n", + "Epoch 515/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 0.0043 - acc: 1.0000\n", + "Epoch 516/1000\n", + "26/26 [==============================] - 0s 604us/sample - loss: 0.0043 - acc: 1.0000\n", + "Epoch 517/1000\n", + "26/26 [==============================] - 0s 441us/sample - loss: 0.0042 - acc: 1.0000\n", + "Epoch 518/1000\n", + "26/26 [==============================] - 0s 573us/sample - loss: 0.0042 - acc: 1.0000\n", + "Epoch 519/1000\n", + "26/26 [==============================] - 0s 482us/sample - loss: 0.0042 - acc: 1.0000\n", + "Epoch 520/1000\n", + "26/26 [==============================] - 0s 376us/sample - loss: 0.0042 - acc: 1.0000\n", + "Epoch 521/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 0.0041 - acc: 1.0000\n", + "Epoch 522/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 0.0041 - acc: 1.0000\n", + "Epoch 523/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 0.0041 - acc: 1.0000\n", + "Epoch 524/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 0.0041 - acc: 1.0000\n", + "Epoch 525/1000\n", + "26/26 [==============================] - 0s 360us/sample - loss: 0.0041 - acc: 1.0000\n", + "Epoch 526/1000\n", + "26/26 [==============================] - 0s 343us/sample - loss: 0.0040 - acc: 1.0000\n", + "Epoch 527/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 0.0040 - acc: 1.0000\n", + "Epoch 528/1000\n", + "26/26 [==============================] - 0s 380us/sample - loss: 0.0040 - acc: 1.0000\n", + "Epoch 529/1000\n", + "26/26 [==============================] - 0s 690us/sample - loss: 0.0040 - acc: 1.0000\n", + "Epoch 530/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 0.0040 - acc: 1.0000\n", + "Epoch 531/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 0.0039 - acc: 1.0000\n", + "Epoch 532/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 0.0039 - acc: 1.0000\n", + "Epoch 533/1000\n", + "26/26 [==============================] - 0s 348us/sample - loss: 0.0039 - acc: 1.0000\n", + "Epoch 534/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 0.0039 - acc: 1.0000\n", + "Epoch 535/1000\n", + "26/26 [==============================] - 0s 482us/sample - loss: 0.0039 - acc: 1.0000\n", + "Epoch 536/1000\n", + "26/26 [==============================] - 0s 477us/sample - loss: 0.0038 - acc: 1.0000\n", + "Epoch 537/1000\n", + "26/26 [==============================] - 0s 519us/sample - loss: 0.0038 - acc: 1.0000\n", + "Epoch 538/1000\n", + "26/26 [==============================] - 0s 429us/sample - loss: 0.0038 - acc: 1.0000\n", + "Epoch 539/1000\n", + "26/26 [==============================] - 0s 377us/sample - loss: 0.0038 - acc: 1.0000\n", + "Epoch 540/1000\n", + "26/26 [==============================] - 0s 452us/sample - loss: 0.0038 - acc: 1.0000\n", + "Epoch 541/1000\n", + "26/26 [==============================] - 0s 391us/sample - loss: 0.0037 - acc: 1.0000\n", + "Epoch 542/1000\n", + "26/26 [==============================] - 0s 502us/sample - loss: 0.0037 - acc: 1.0000\n", + "Epoch 543/1000\n", + "26/26 [==============================] - 0s 508us/sample - loss: 0.0037 - acc: 1.0000\n", + "Epoch 544/1000\n", + "26/26 [==============================] - 0s 573us/sample - loss: 0.0037 - acc: 1.0000\n", + "Epoch 545/1000\n", + "26/26 [==============================] - 0s 477us/sample - loss: 0.0037 - acc: 1.0000\n", + "Epoch 546/1000\n", + "26/26 [==============================] - 0s 436us/sample - loss: 0.0036 - acc: 1.0000\n", + "Epoch 547/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 0.0036 - acc: 1.0000\n", + "Epoch 548/1000\n", + "26/26 [==============================] - 0s 396us/sample - loss: 0.0036 - acc: 1.0000\n", + "Epoch 549/1000\n", + "26/26 [==============================] - 0s 425us/sample - loss: 0.0036 - acc: 1.0000\n", + "Epoch 550/1000\n", + "26/26 [==============================] - 0s 457us/sample - loss: 0.0036 - acc: 1.0000\n", + "Epoch 551/1000\n", + "26/26 [==============================] - 0s 402us/sample - loss: 0.0036 - acc: 1.0000\n", + "Epoch 552/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 0.0035 - acc: 1.0000\n", + "Epoch 553/1000\n", + "26/26 [==============================] - 0s 410us/sample - loss: 0.0035 - acc: 1.0000\n", + "Epoch 554/1000\n", + "26/26 [==============================] - 0s 398us/sample - loss: 0.0035 - acc: 1.0000\n", + "Epoch 555/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 0.0035 - acc: 1.0000\n", + "Epoch 556/1000\n", + "26/26 [==============================] - 0s 348us/sample - loss: 0.0035 - acc: 1.0000\n", + "Epoch 557/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 0.0034 - acc: 1.0000\n", + "Epoch 558/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 0.0034 - acc: 1.0000\n", + "Epoch 559/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 0.0034 - acc: 1.0000\n", + "Epoch 560/1000\n", + "26/26 [==============================] - 0s 396us/sample - loss: 0.0034 - acc: 1.0000\n", + "Epoch 561/1000\n", + "26/26 [==============================] - 0s 436us/sample - loss: 0.0034 - acc: 1.0000\n", + "Epoch 562/1000\n", + "26/26 [==============================] - 0s 536us/sample - loss: 0.0034 - acc: 1.0000\n", + "Epoch 563/1000\n", + "26/26 [==============================] - 0s 442us/sample - loss: 0.0033 - acc: 1.0000\n", + "Epoch 564/1000\n", + "26/26 [==============================] - 0s 448us/sample - loss: 0.0033 - acc: 1.0000\n", + "Epoch 565/1000\n", + "26/26 [==============================] - 0s 428us/sample - loss: 0.0033 - acc: 1.0000\n", + "Epoch 566/1000\n", + "26/26 [==============================] - 0s 399us/sample - loss: 0.0033 - acc: 1.0000\n", + "Epoch 567/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 0.0033 - acc: 1.0000\n", + "Epoch 568/1000\n", + "26/26 [==============================] - 0s 389us/sample - loss: 0.0033 - acc: 1.0000\n", + "Epoch 569/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 0.0032 - acc: 1.0000\n", + "Epoch 570/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 0.0032 - acc: 1.0000\n", + "Epoch 571/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 0.0032 - acc: 1.0000\n", + "Epoch 572/1000\n", + "26/26 [==============================] - 0s 462us/sample - loss: 0.0032 - acc: 1.0000\n", + "Epoch 573/1000\n", + "26/26 [==============================] - 0s 506us/sample - loss: 0.0032 - acc: 1.0000\n", + "Epoch 574/1000\n", + "26/26 [==============================] - 0s 487us/sample - loss: 0.0032 - acc: 1.0000\n", + "Epoch 575/1000\n", + "26/26 [==============================] - 0s 444us/sample - loss: 0.0032 - acc: 1.0000\n", + "Epoch 576/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 0.0031 - acc: 1.0000\n", + "Epoch 577/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 0.0031 - acc: 1.0000\n", + "Epoch 578/1000\n", + "26/26 [==============================] - 0s 370us/sample - loss: 0.0031 - acc: 1.0000\n", + "Epoch 579/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 0.0031 - acc: 1.0000\n", + "Epoch 580/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 0.0031 - acc: 1.0000\n", + "Epoch 581/1000\n", + "26/26 [==============================] - 0s 384us/sample - loss: 0.0031 - acc: 1.0000\n", + "Epoch 582/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 0.0030 - acc: 1.0000\n", + "Epoch 583/1000\n", + "26/26 [==============================] - 0s 355us/sample - loss: 0.0030 - acc: 1.0000\n", + "Epoch 584/1000\n", + "26/26 [==============================] - 0s 424us/sample - loss: 0.0030 - acc: 1.0000\n", + "Epoch 585/1000\n", + "26/26 [==============================] - 0s 436us/sample - loss: 0.0030 - acc: 1.0000\n", + "Epoch 586/1000\n", + "26/26 [==============================] - 0s 660us/sample - loss: 0.0030 - acc: 1.0000\n", + "Epoch 587/1000\n", + "26/26 [==============================] - 0s 561us/sample - loss: 0.0030 - acc: 1.0000\n", + "Epoch 588/1000\n", + "26/26 [==============================] - 0s 600us/sample - loss: 0.0030 - acc: 1.0000\n", + "Epoch 589/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 0.0029 - acc: 1.0000\n", + "Epoch 590/1000\n", + "26/26 [==============================] - 0s 460us/sample - loss: 0.0029 - acc: 1.0000\n", + "Epoch 591/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 0.0029 - acc: 1.0000\n", + "Epoch 592/1000\n", + "26/26 [==============================] - 0s 474us/sample - loss: 0.0029 - acc: 1.0000\n", + "Epoch 593/1000\n", + "26/26 [==============================] - 0s 485us/sample - loss: 0.0029 - acc: 1.0000\n", + "Epoch 594/1000\n", + "26/26 [==============================] - 0s 594us/sample - loss: 0.0029 - acc: 1.0000\n", + "Epoch 595/1000\n", + "26/26 [==============================] - 0s 633us/sample - loss: 0.0029 - acc: 1.0000\n", + "Epoch 596/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 0.0029 - acc: 1.0000\n", + "Epoch 597/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 0.0028 - acc: 1.0000\n", + "Epoch 598/1000\n", + "26/26 [==============================] - 0s 372us/sample - loss: 0.0028 - acc: 1.0000\n", + "Epoch 599/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 0.0028 - acc: 1.0000\n", + "Epoch 600/1000\n", + "26/26 [==============================] - 0s 386us/sample - loss: 0.0028 - acc: 1.0000\n", + "Epoch 601/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 0.0028 - acc: 1.0000\n", + "Epoch 602/1000\n", + "26/26 [==============================] - 0s 391us/sample - loss: 0.0028 - acc: 1.0000\n", + "Epoch 603/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 0.0028 - acc: 1.0000\n", + "Epoch 604/1000\n", + "26/26 [==============================] - 0s 408us/sample - loss: 0.0028 - acc: 1.0000\n", + "Epoch 605/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 0.0027 - acc: 1.0000\n", + "Epoch 606/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 0.0027 - acc: 1.0000\n", + "Epoch 607/1000\n", + "26/26 [==============================] - 0s 447us/sample - loss: 0.0027 - acc: 1.0000\n", + "Epoch 608/1000\n", + "26/26 [==============================] - 0s 410us/sample - loss: 0.0027 - acc: 1.0000\n", + "Epoch 609/1000\n", + "26/26 [==============================] - 0s 398us/sample - loss: 0.0027 - acc: 1.0000\n", + "Epoch 610/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 0.0027 - acc: 1.0000\n", + "Epoch 611/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 0.0027 - acc: 1.0000\n", + "Epoch 612/1000\n", + "26/26 [==============================] - 0s 402us/sample - loss: 0.0027 - acc: 1.0000\n", + "Epoch 613/1000\n", + "26/26 [==============================] - 0s 349us/sample - loss: 0.0026 - acc: 1.0000\n", + "Epoch 614/1000\n", + "26/26 [==============================] - 0s 330us/sample - loss: 0.0026 - acc: 1.0000\n", + "Epoch 615/1000\n", + "26/26 [==============================] - 0s 340us/sample - loss: 0.0026 - acc: 1.0000\n", + "Epoch 616/1000\n", + "26/26 [==============================] - 0s 368us/sample - loss: 0.0026 - acc: 1.0000\n", + "Epoch 617/1000\n", + "26/26 [==============================] - 0s 404us/sample - loss: 0.0026 - acc: 1.0000\n", + "Epoch 618/1000\n", + "26/26 [==============================] - 0s 323us/sample - loss: 0.0026 - acc: 1.0000\n", + "Epoch 619/1000\n", + "26/26 [==============================] - 0s 396us/sample - loss: 0.0026 - acc: 1.0000\n", + "Epoch 620/1000\n", + "26/26 [==============================] - 0s 407us/sample - loss: 0.0026 - acc: 1.0000\n", + "Epoch 621/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 0.0026 - acc: 1.0000\n", + "Epoch 622/1000\n", + "26/26 [==============================] - 0s 420us/sample - loss: 0.0025 - acc: 1.0000\n", + "Epoch 623/1000\n", + "26/26 [==============================] - 0s 401us/sample - loss: 0.0025 - acc: 1.0000\n", + "Epoch 624/1000\n", + "26/26 [==============================] - 0s 471us/sample - loss: 0.0025 - acc: 1.0000\n", + "Epoch 625/1000\n", + "26/26 [==============================] - 0s 426us/sample - loss: 0.0025 - acc: 1.0000\n", + "Epoch 626/1000\n", + "26/26 [==============================] - 0s 498us/sample - loss: 0.0025 - acc: 1.0000\n", + "Epoch 627/1000\n", + "26/26 [==============================] - 0s 490us/sample - loss: 0.0025 - acc: 1.0000\n", + "Epoch 628/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 0.0025 - acc: 1.0000\n", + "Epoch 629/1000\n", + "26/26 [==============================] - 0s 540us/sample - loss: 0.0025 - acc: 1.0000\n", + "Epoch 630/1000\n", + "26/26 [==============================] - 0s 548us/sample - loss: 0.0025 - acc: 1.0000\n", + "Epoch 631/1000\n", + "26/26 [==============================] - 0s 444us/sample - loss: 0.0024 - acc: 1.0000\n", + "Epoch 632/1000\n", + "26/26 [==============================] - 0s 435us/sample - loss: 0.0024 - acc: 1.0000\n", + "Epoch 633/1000\n", + "26/26 [==============================] - 0s 410us/sample - loss: 0.0024 - acc: 1.0000\n", + "Epoch 634/1000\n", + "26/26 [==============================] - 0s 803us/sample - loss: 0.0024 - acc: 1.0000\n", + "Epoch 635/1000\n", + "26/26 [==============================] - 0s 785us/sample - loss: 0.0024 - acc: 1.0000\n", + "Epoch 636/1000\n", + "26/26 [==============================] - 0s 756us/sample - loss: 0.0024 - acc: 1.0000\n", + "Epoch 637/1000\n", + "26/26 [==============================] - 0s 421us/sample - loss: 0.0024 - acc: 1.0000\n", + "Epoch 638/1000\n", + "26/26 [==============================] - 0s 521us/sample - loss: 0.0024 - acc: 1.0000\n", + "Epoch 639/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 0.0023 - acc: 1.0000\n", + "Epoch 640/1000\n", + "26/26 [==============================] - 0s 505us/sample - loss: 0.0023 - acc: 1.0000\n", + "Epoch 641/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 0.0023 - acc: 1.0000\n", + "Epoch 642/1000\n", + "26/26 [==============================] - 0s 458us/sample - loss: 0.0023 - acc: 1.0000\n", + "Epoch 643/1000\n", + "26/26 [==============================] - 0s 515us/sample - loss: 0.0023 - acc: 1.0000\n", + "Epoch 644/1000\n", + "26/26 [==============================] - 0s 538us/sample - loss: 0.0023 - acc: 1.0000\n", + "Epoch 645/1000\n", + "26/26 [==============================] - 0s 416us/sample - loss: 0.0023 - acc: 1.0000\n", + "Epoch 646/1000\n", + "26/26 [==============================] - 0s 356us/sample - loss: 0.0023 - acc: 1.0000\n", + "Epoch 647/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 0.0023 - acc: 1.0000\n", + "Epoch 648/1000\n", + "26/26 [==============================] - 0s 387us/sample - loss: 0.0023 - acc: 1.0000\n", + "Epoch 649/1000\n", + "26/26 [==============================] - 0s 394us/sample - loss: 0.0022 - acc: 1.0000\n", + "Epoch 650/1000\n", + "26/26 [==============================] - 0s 433us/sample - loss: 0.0022 - acc: 1.0000\n", + "Epoch 651/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 0.0022 - acc: 1.0000\n", + "Epoch 652/1000\n", + "26/26 [==============================] - 0s 441us/sample - loss: 0.0022 - acc: 1.0000\n", + "Epoch 653/1000\n", + "26/26 [==============================] - 0s 455us/sample - loss: 0.0022 - acc: 1.0000\n", + "Epoch 654/1000\n", + "26/26 [==============================] - 0s 466us/sample - loss: 0.0022 - acc: 1.0000\n", + "Epoch 655/1000\n", + "26/26 [==============================] - 0s 440us/sample - loss: 0.0022 - acc: 1.0000\n", + "Epoch 656/1000\n", + "26/26 [==============================] - 0s 479us/sample - loss: 0.0022 - acc: 1.0000\n", + "Epoch 657/1000\n", + "26/26 [==============================] - 0s 604us/sample - loss: 0.0022 - acc: 1.0000\n", + "Epoch 658/1000\n", + "26/26 [==============================] - 0s 479us/sample - loss: 0.0022 - acc: 1.0000\n", + "Epoch 659/1000\n", + "26/26 [==============================] - 0s 531us/sample - loss: 0.0021 - acc: 1.0000\n", + "Epoch 660/1000\n", + "26/26 [==============================] - 0s 426us/sample - loss: 0.0021 - acc: 1.0000\n", + "Epoch 661/1000\n", + "26/26 [==============================] - 0s 457us/sample - loss: 0.0021 - acc: 1.0000\n", + "Epoch 662/1000\n", + "26/26 [==============================] - 0s 404us/sample - loss: 0.0021 - acc: 1.0000\n", + "Epoch 663/1000\n", + "26/26 [==============================] - 0s 449us/sample - loss: 0.0021 - acc: 1.0000\n", + "Epoch 664/1000\n", + "26/26 [==============================] - 0s 391us/sample - loss: 0.0021 - acc: 1.0000\n", + "Epoch 665/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 0.0021 - acc: 1.0000\n", + "Epoch 666/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 0.0021 - acc: 1.0000\n", + "Epoch 667/1000\n", + "26/26 [==============================] - 0s 391us/sample - loss: 0.0021 - acc: 1.0000\n", + "Epoch 668/1000\n", + "26/26 [==============================] - 0s 505us/sample - loss: 0.0021 - acc: 1.0000\n", + "Epoch 669/1000\n", + "26/26 [==============================] - 0s 376us/sample - loss: 0.0021 - acc: 1.0000\n", + "Epoch 670/1000\n", + "26/26 [==============================] - 0s 419us/sample - loss: 0.0020 - acc: 1.0000\n", + "Epoch 671/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 0.0020 - acc: 1.0000\n", + "Epoch 672/1000\n", + "26/26 [==============================] - 0s 420us/sample - loss: 0.0020 - acc: 1.0000\n", + "Epoch 673/1000\n", + "26/26 [==============================] - 0s 399us/sample - loss: 0.0020 - acc: 1.0000\n", + "Epoch 674/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 0.0020 - acc: 1.0000\n", + "Epoch 675/1000\n", + "26/26 [==============================] - 0s 349us/sample - loss: 0.0020 - acc: 1.0000\n", + "Epoch 676/1000\n", + "26/26 [==============================] - 0s 603us/sample - loss: 0.0020 - acc: 1.0000\n", + "Epoch 677/1000\n", + "26/26 [==============================] - 0s 509us/sample - loss: 0.0020 - acc: 1.0000\n", + "Epoch 678/1000\n", + "26/26 [==============================] - 0s 386us/sample - loss: 0.0020 - acc: 1.0000\n", + "Epoch 679/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 0.0020 - acc: 1.0000\n", + "Epoch 680/1000\n", + "26/26 [==============================] - 0s 427us/sample - loss: 0.0020 - acc: 1.0000\n", + "Epoch 681/1000\n", + "26/26 [==============================] - 0s 467us/sample - loss: 0.0020 - acc: 1.0000\n", + "Epoch 682/1000\n", + "26/26 [==============================] - 0s 412us/sample - loss: 0.0019 - acc: 1.0000\n", + "Epoch 683/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 0.0019 - acc: 1.0000\n", + "Epoch 684/1000\n", + "26/26 [==============================] - 0s 368us/sample - loss: 0.0019 - acc: 1.0000\n", + "Epoch 685/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 0.0019 - acc: 1.0000\n", + "Epoch 686/1000\n", + "26/26 [==============================] - 0s 483us/sample - loss: 0.0019 - acc: 1.0000\n", + "Epoch 687/1000\n", + "26/26 [==============================] - 0s 433us/sample - loss: 0.0019 - acc: 1.0000\n", + "Epoch 688/1000\n", + "26/26 [==============================] - 0s 438us/sample - loss: 0.0019 - acc: 1.0000\n", + "Epoch 689/1000\n", + "26/26 [==============================] - 0s 464us/sample - loss: 0.0019 - acc: 1.0000\n", + "Epoch 690/1000\n", + "26/26 [==============================] - 0s 630us/sample - loss: 0.0019 - acc: 1.0000\n", + "Epoch 691/1000\n", + "26/26 [==============================] - 0s 573us/sample - loss: 0.0019 - acc: 1.0000\n", + "Epoch 692/1000\n", + "26/26 [==============================] - 0s 546us/sample - loss: 0.0019 - acc: 1.0000\n", + "Epoch 693/1000\n", + "26/26 [==============================] - 0s 505us/sample - loss: 0.0019 - acc: 1.0000\n", + "Epoch 694/1000\n", + "26/26 [==============================] - 0s 378us/sample - loss: 0.0018 - acc: 1.0000\n", + "Epoch 695/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 0.0018 - acc: 1.0000\n", + "Epoch 696/1000\n", + "26/26 [==============================] - 0s 349us/sample - loss: 0.0018 - acc: 1.0000\n", + "Epoch 697/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 0.0018 - acc: 1.0000\n", + "Epoch 698/1000\n", + "26/26 [==============================] - 0s 349us/sample - loss: 0.0018 - acc: 1.0000\n", + "Epoch 699/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 0.0018 - acc: 1.0000\n", + "Epoch 700/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 0.0018 - acc: 1.0000\n", + "Epoch 701/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 0.0018 - acc: 1.0000\n", + "Epoch 702/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 0.0018 - acc: 1.0000\n", + "Epoch 703/1000\n", + "26/26 [==============================] - 0s 522us/sample - loss: 0.0018 - acc: 1.0000\n", + "Epoch 704/1000\n", + "26/26 [==============================] - 0s 576us/sample - loss: 0.0018 - acc: 1.0000\n", + "Epoch 705/1000\n", + "26/26 [==============================] - 0s 383us/sample - loss: 0.0018 - acc: 1.0000\n", + "Epoch 706/1000\n", + "26/26 [==============================] - 0s 355us/sample - loss: 0.0018 - acc: 1.0000\n", + "Epoch 707/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 708/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 709/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 710/1000\n", + "26/26 [==============================] - 0s 410us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 711/1000\n", + "26/26 [==============================] - 0s 583us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 712/1000\n", + "26/26 [==============================] - 0s 562us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 713/1000\n", + "26/26 [==============================] - 0s 586us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 714/1000\n", + "26/26 [==============================] - 0s 665us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 715/1000\n", + "26/26 [==============================] - 0s 827us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 716/1000\n", + "26/26 [==============================] - 0s 499us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 717/1000\n", + "26/26 [==============================] - 0s 478us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 718/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 719/1000\n", + "26/26 [==============================] - 0s 377us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 720/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 721/1000\n", + "26/26 [==============================] - 0s 281us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 722/1000\n", + "26/26 [==============================] - 0s 531us/sample - loss: 0.0017 - acc: 1.0000\n", + "Epoch 723/1000\n", + "26/26 [==============================] - 0s 461us/sample - loss: 0.0016 - acc: 1.0000\n", + "Epoch 724/1000\n", + "26/26 [==============================] - 0s 467us/sample - loss: 0.0016 - acc: 1.0000\n", + "Epoch 725/1000\n", + "26/26 [==============================] - 0s 481us/sample - loss: 0.0016 - acc: 1.0000\n", + "Epoch 726/1000\n", + "26/26 [==============================] - 0s 421us/sample - loss: 0.0016 - acc: 1.0000\n", + "Epoch 727/1000\n", + "26/26 [==============================] - 0s 480us/sample - loss: 0.0016 - acc: 1.0000\n", + "Epoch 728/1000\n", + "26/26 [==============================] - 0s 456us/sample - loss: 0.0016 - acc: 1.0000\n", + "Epoch 729/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 0.0016 - acc: 1.0000\n", + "Epoch 730/1000\n", + "26/26 [==============================] - 0s 478us/sample - loss: 0.0016 - acc: 1.0000\n", + "Epoch 731/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 0.0016 - acc: 1.0000\n", + "Epoch 732/1000\n", + "26/26 [==============================] - 0s 459us/sample - loss: 0.0016 - acc: 1.0000\n", + "Epoch 733/1000\n", + "26/26 [==============================] - 0s 550us/sample - loss: 0.0016 - acc: 1.0000\n", + "Epoch 734/1000\n", + "26/26 [==============================] - 0s 470us/sample - loss: 0.0016 - acc: 1.0000\n", + "Epoch 735/1000\n", + "26/26 [==============================] - 0s 376us/sample - loss: 0.0016 - acc: 1.0000\n", + "Epoch 736/1000\n", + "26/26 [==============================] - 0s 343us/sample - loss: 0.0016 - acc: 1.0000\n", + "Epoch 737/1000\n", + "26/26 [==============================] - 0s 398us/sample - loss: 0.0016 - acc: 1.0000\n", + "Epoch 738/1000\n", + "26/26 [==============================] - 0s 469us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 739/1000\n", + "26/26 [==============================] - 0s 582us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 740/1000\n", + "26/26 [==============================] - 0s 471us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 741/1000\n", + "26/26 [==============================] - 0s 461us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 742/1000\n", + "26/26 [==============================] - 0s 441us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 743/1000\n", + "26/26 [==============================] - 0s 425us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 744/1000\n", + "26/26 [==============================] - 0s 477us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 745/1000\n", + "26/26 [==============================] - 0s 435us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 746/1000\n", + "26/26 [==============================] - 0s 529us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 747/1000\n", + "26/26 [==============================] - 0s 553us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 748/1000\n", + "26/26 [==============================] - 0s 570us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 749/1000\n", + "26/26 [==============================] - 0s 308us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 750/1000\n", + "26/26 [==============================] - 0s 391us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 751/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 752/1000\n", + "26/26 [==============================] - 0s 368us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 753/1000\n", + "26/26 [==============================] - 0s 340us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 754/1000\n", + "26/26 [==============================] - 0s 372us/sample - loss: 0.0015 - acc: 1.0000\n", + "Epoch 755/1000\n", + "26/26 [==============================] - 0s 319us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 756/1000\n", + "26/26 [==============================] - 0s 322us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 757/1000\n", + "26/26 [==============================] - 0s 331us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 758/1000\n", + "26/26 [==============================] - 0s 335us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 759/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 760/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 761/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 762/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 763/1000\n", + "26/26 [==============================] - 0s 380us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 764/1000\n", + "26/26 [==============================] - 0s 401us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 765/1000\n", + "26/26 [==============================] - 0s 399us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 766/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 767/1000\n", + "26/26 [==============================] - 0s 451us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 768/1000\n", + "26/26 [==============================] - 0s 393us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 769/1000\n", + "26/26 [==============================] - 0s 404us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 770/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 771/1000\n", + "26/26 [==============================] - 0s 376us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 772/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 773/1000\n", + "26/26 [==============================] - 0s 370us/sample - loss: 0.0014 - acc: 1.0000\n", + "Epoch 774/1000\n", + "26/26 [==============================] - 0s 370us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 775/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 776/1000\n", + "26/26 [==============================] - 0s 343us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 777/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 778/1000\n", + "26/26 [==============================] - 0s 337us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 779/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 780/1000\n", + "26/26 [==============================] - 0s 373us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 781/1000\n", + "26/26 [==============================] - 0s 316us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 782/1000\n", + "26/26 [==============================] - 0s 329us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 783/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 784/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 785/1000\n", + "26/26 [==============================] - 0s 360us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 786/1000\n", + "26/26 [==============================] - 0s 389us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 787/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 788/1000\n", + "26/26 [==============================] - 0s 325us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 789/1000\n", + "26/26 [==============================] - 0s 334us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 790/1000\n", + "26/26 [==============================] - 0s 355us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 791/1000\n", + "26/26 [==============================] - 0s 505us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 792/1000\n", + "26/26 [==============================] - 0s 573us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 793/1000\n", + "26/26 [==============================] - 0s 511us/sample - loss: 0.0013 - acc: 1.0000\n", + "Epoch 794/1000\n", + "26/26 [==============================] - 0s 511us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 795/1000\n", + "26/26 [==============================] - 0s 506us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 796/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 797/1000\n", + "26/26 [==============================] - 0s 596us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 798/1000\n", + "26/26 [==============================] - 0s 646us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 799/1000\n", + "26/26 [==============================] - 0s 411us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 800/1000\n", + "26/26 [==============================] - 0s 375us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 801/1000\n", + "26/26 [==============================] - 0s 337us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 802/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 803/1000\n", + "26/26 [==============================] - 0s 407us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 804/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 805/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 806/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 807/1000\n", + "26/26 [==============================] - 0s 353us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 808/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 809/1000\n", + "26/26 [==============================] - 0s 355us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 810/1000\n", + "26/26 [==============================] - 0s 459us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 811/1000\n", + "26/26 [==============================] - 0s 577us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 812/1000\n", + "26/26 [==============================] - 0s 549us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 813/1000\n", + "26/26 [==============================] - 0s 391us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 814/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 815/1000\n", + "26/26 [==============================] - 0s 324us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 816/1000\n", + "26/26 [==============================] - 0s 321us/sample - loss: 0.0012 - acc: 1.0000\n", + "Epoch 817/1000\n", + "26/26 [==============================] - 0s 335us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 818/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 819/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 820/1000\n", + "26/26 [==============================] - 0s 348us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 821/1000\n", + "26/26 [==============================] - 0s 333us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 822/1000\n", + "26/26 [==============================] - 0s 335us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 823/1000\n", + "26/26 [==============================] - 0s 335us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 824/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 825/1000\n", + "26/26 [==============================] - 0s 353us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 826/1000\n", + "26/26 [==============================] - 0s 329us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 827/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 828/1000\n", + "26/26 [==============================] - 0s 320us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 829/1000\n", + "26/26 [==============================] - 0s 334us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 830/1000\n", + "26/26 [==============================] - 0s 334us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 831/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 832/1000\n", + "26/26 [==============================] - 0s 333us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 833/1000\n", + "26/26 [==============================] - 0s 425us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 834/1000\n", + "26/26 [==============================] - 0s 330us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 835/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 836/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 837/1000\n", + "26/26 [==============================] - 0s 356us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 838/1000\n", + "26/26 [==============================] - 0s 329us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 839/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 840/1000\n", + "26/26 [==============================] - 0s 315us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 841/1000\n", + "26/26 [==============================] - 0s 328us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 842/1000\n", + "26/26 [==============================] - 0s 313us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 843/1000\n", + "26/26 [==============================] - 0s 334us/sample - loss: 0.0011 - acc: 1.0000\n", + "Epoch 844/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 0.0010 - acc: 1.0000\n", + "Epoch 845/1000\n", + "26/26 [==============================] - 0s 308us/sample - loss: 0.0010 - acc: 1.0000\n", + "Epoch 846/1000\n", + "26/26 [==============================] - 0s 334us/sample - loss: 0.0010 - acc: 1.0000\n", + "Epoch 847/1000\n", + "26/26 [==============================] - 0s 313us/sample - loss: 0.0010 - acc: 1.0000\n", + "Epoch 848/1000\n", + "26/26 [==============================] - 0s 325us/sample - loss: 0.0010 - acc: 1.0000\n", + "Epoch 849/1000\n", + "26/26 [==============================] - 0s 302us/sample - loss: 0.0010 - acc: 1.0000\n", + "Epoch 850/1000\n", + "26/26 [==============================] - 0s 311us/sample - loss: 0.0010 - acc: 1.0000\n", + "Epoch 851/1000\n", + "26/26 [==============================] - 0s 322us/sample - loss: 0.0010 - acc: 1.0000\n", + "Epoch 852/1000\n", + "26/26 [==============================] - 0s 320us/sample - loss: 0.0010 - acc: 1.0000\n", + "Epoch 853/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 0.0010 - acc: 1.0000\n", + "Epoch 854/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 0.0010 - acc: 1.0000\n", + "Epoch 855/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 0.0010 - acc: 1.0000\n", + "Epoch 856/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 0.0010 - acc: 1.0000\n", + "Epoch 857/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 0.0010 - acc: 1.0000\n", + "Epoch 858/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 0.0010 - acc: 1.0000\n", + "Epoch 859/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 9.9701e-04 - acc: 1.0000\n", + "Epoch 860/1000\n", + "26/26 [==============================] - 0s 343us/sample - loss: 9.9342e-04 - acc: 1.0000\n", + "Epoch 861/1000\n", + "26/26 [==============================] - 0s 331us/sample - loss: 9.9023e-04 - acc: 1.0000\n", + "Epoch 862/1000\n", + "26/26 [==============================] - 0s 320us/sample - loss: 9.8631e-04 - acc: 1.0000\n", + "Epoch 863/1000\n", + "26/26 [==============================] - 0s 349us/sample - loss: 9.8267e-04 - acc: 1.0000\n", + "Epoch 864/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 9.7901e-04 - acc: 1.0000\n", + "Epoch 865/1000\n", + "26/26 [==============================] - 0s 328us/sample - loss: 9.7601e-04 - acc: 1.0000\n", + "Epoch 866/1000\n", + "26/26 [==============================] - 0s 396us/sample - loss: 9.7201e-04 - acc: 1.0000\n", + "Epoch 867/1000\n", + "26/26 [==============================] - 0s 409us/sample - loss: 9.6872e-04 - acc: 1.0000\n", + "Epoch 868/1000\n", + "26/26 [==============================] - 0s 348us/sample - loss: 9.6529e-04 - acc: 1.0000\n", + "Epoch 869/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 9.6207e-04 - acc: 1.0000\n", + "Epoch 870/1000\n", + "26/26 [==============================] - 0s 417us/sample - loss: 9.5898e-04 - acc: 1.0000\n", + "Epoch 871/1000\n", + "26/26 [==============================] - 0s 384us/sample - loss: 9.5517e-04 - acc: 1.0000\n", + "Epoch 872/1000\n", + "26/26 [==============================] - 0s 335us/sample - loss: 9.5133e-04 - acc: 1.0000\n", + "Epoch 873/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 9.4806e-04 - acc: 1.0000\n", + "Epoch 874/1000\n", + "26/26 [==============================] - 0s 348us/sample - loss: 9.4486e-04 - acc: 1.0000\n", + "Epoch 875/1000\n", + "26/26 [==============================] - 0s 310us/sample - loss: 9.4108e-04 - acc: 1.0000\n", + "Epoch 876/1000\n", + "26/26 [==============================] - 0s 327us/sample - loss: 9.3826e-04 - acc: 1.0000\n", + "Epoch 877/1000\n", + "26/26 [==============================] - 0s 375us/sample - loss: 9.3489e-04 - acc: 1.0000\n", + "Epoch 878/1000\n", + "26/26 [==============================] - 0s 531us/sample - loss: 9.3196e-04 - acc: 1.0000\n", + "Epoch 879/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 9.2895e-04 - acc: 1.0000\n", + "Epoch 880/1000\n", + "26/26 [==============================] - 0s 601us/sample - loss: 9.2584e-04 - acc: 1.0000\n", + "Epoch 881/1000\n", + "26/26 [==============================] - 0s 360us/sample - loss: 9.2291e-04 - acc: 1.0000\n", + "Epoch 882/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 9.2006e-04 - acc: 1.0000\n", + "Epoch 883/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 9.1699e-04 - acc: 1.0000\n", + "Epoch 884/1000\n", + "26/26 [==============================] - 0s 776us/sample - loss: 9.1408e-04 - acc: 1.0000\n", + "Epoch 885/1000\n", + "26/26 [==============================] - 0s 463us/sample - loss: 9.1137e-04 - acc: 1.0000\n", + "Epoch 886/1000\n", + "26/26 [==============================] - 0s 309us/sample - loss: 9.0805e-04 - acc: 1.0000\n", + "Epoch 887/1000\n", + "26/26 [==============================] - 0s 329us/sample - loss: 9.0477e-04 - acc: 1.0000\n", + "Epoch 888/1000\n", + "26/26 [==============================] - 0s 350us/sample - loss: 9.0106e-04 - acc: 1.0000\n", + "Epoch 889/1000\n", + "26/26 [==============================] - 0s 333us/sample - loss: 8.9827e-04 - acc: 1.0000\n", + "Epoch 890/1000\n", + "26/26 [==============================] - 0s 395us/sample - loss: 8.9475e-04 - acc: 1.0000\n", + "Epoch 891/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 8.9123e-04 - acc: 1.0000\n", + "Epoch 892/1000\n", + "26/26 [==============================] - 0s 355us/sample - loss: 8.8832e-04 - acc: 1.0000\n", + "Epoch 893/1000\n", + "26/26 [==============================] - 0s 310us/sample - loss: 8.8509e-04 - acc: 1.0000\n", + "Epoch 894/1000\n", + "26/26 [==============================] - 0s 312us/sample - loss: 8.8189e-04 - acc: 1.0000\n", + "Epoch 895/1000\n", + "26/26 [==============================] - 0s 309us/sample - loss: 8.7851e-04 - acc: 1.0000\n", + "Epoch 896/1000\n", + "26/26 [==============================] - 0s 329us/sample - loss: 8.7573e-04 - acc: 1.0000\n", + "Epoch 897/1000\n", + "26/26 [==============================] - 0s 572us/sample - loss: 8.7279e-04 - acc: 1.0000\n", + "Epoch 898/1000\n", + "26/26 [==============================] - 0s 416us/sample - loss: 8.7003e-04 - acc: 1.0000\n", + "Epoch 899/1000\n", + "26/26 [==============================] - 0s 389us/sample - loss: 8.6728e-04 - acc: 1.0000\n", + "Epoch 900/1000\n", + "26/26 [==============================] - 0s 510us/sample - loss: 8.6435e-04 - acc: 1.0000\n", + "Epoch 901/1000\n", + "26/26 [==============================] - 0s 378us/sample - loss: 8.6132e-04 - acc: 1.0000\n", + "Epoch 902/1000\n", + "26/26 [==============================] - 0s 375us/sample - loss: 8.5855e-04 - acc: 1.0000\n", + "Epoch 903/1000\n", + "26/26 [==============================] - 0s 551us/sample - loss: 8.5554e-04 - acc: 1.0000\n", + "Epoch 904/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 8.5273e-04 - acc: 1.0000\n", + "Epoch 905/1000\n", + "26/26 [==============================] - 0s 321us/sample - loss: 8.4990e-04 - acc: 1.0000\n", + "Epoch 906/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 8.4710e-04 - acc: 1.0000\n", + "Epoch 907/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 8.4430e-04 - acc: 1.0000\n", + "Epoch 908/1000\n", + "26/26 [==============================] - 0s 404us/sample - loss: 8.4181e-04 - acc: 1.0000\n", + "Epoch 909/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 8.3837e-04 - acc: 1.0000\n", + "Epoch 910/1000\n", + "26/26 [==============================] - 0s 340us/sample - loss: 8.3549e-04 - acc: 1.0000\n", + "Epoch 911/1000\n", + "26/26 [==============================] - 0s 297us/sample - loss: 8.3257e-04 - acc: 1.0000\n", + "Epoch 912/1000\n", + "26/26 [==============================] - 0s 491us/sample - loss: 8.2977e-04 - acc: 1.0000\n", + "Epoch 913/1000\n", + "26/26 [==============================] - 0s 326us/sample - loss: 8.2689e-04 - acc: 1.0000\n", + "Epoch 914/1000\n", + "26/26 [==============================] - 0s 376us/sample - loss: 8.2446e-04 - acc: 1.0000\n", + "Epoch 915/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 8.2171e-04 - acc: 1.0000\n", + "Epoch 916/1000\n", + "26/26 [==============================] - 0s 373us/sample - loss: 8.1915e-04 - acc: 1.0000\n", + "Epoch 917/1000\n", + "26/26 [==============================] - 0s 304us/sample - loss: 8.1657e-04 - acc: 1.0000\n", + "Epoch 918/1000\n", + "26/26 [==============================] - 0s 340us/sample - loss: 8.1368e-04 - acc: 1.0000\n", + "Epoch 919/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 8.1075e-04 - acc: 1.0000\n", + "Epoch 920/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 8.0821e-04 - acc: 1.0000\n", + "Epoch 921/1000\n", + "26/26 [==============================] - 0s 330us/sample - loss: 8.0514e-04 - acc: 1.0000\n", + "Epoch 922/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 8.0250e-04 - acc: 1.0000\n", + "Epoch 923/1000\n", + "26/26 [==============================] - 0s 496us/sample - loss: 7.9946e-04 - acc: 1.0000\n", + "Epoch 924/1000\n", + "26/26 [==============================] - 0s 429us/sample - loss: 7.9721e-04 - acc: 1.0000\n", + "Epoch 925/1000\n", + "26/26 [==============================] - 0s 349us/sample - loss: 7.9391e-04 - acc: 1.0000\n", + "Epoch 926/1000\n", + "26/26 [==============================] - 0s 560us/sample - loss: 7.9110e-04 - acc: 1.0000\n", + "Epoch 927/1000\n", + "26/26 [==============================] - 0s 336us/sample - loss: 7.8843e-04 - acc: 1.0000\n", + "Epoch 928/1000\n", + "26/26 [==============================] - 0s 465us/sample - loss: 7.8576e-04 - acc: 1.0000\n", + "Epoch 929/1000\n", + "26/26 [==============================] - 0s 427us/sample - loss: 7.8293e-04 - acc: 1.0000\n", + "Epoch 930/1000\n", + "26/26 [==============================] - 0s 337us/sample - loss: 7.8071e-04 - acc: 1.0000\n", + "Epoch 931/1000\n", + "26/26 [==============================] - 0s 444us/sample - loss: 7.7809e-04 - acc: 1.0000\n", + "Epoch 932/1000\n", + "26/26 [==============================] - 0s 299us/sample - loss: 7.7553e-04 - acc: 1.0000\n", + "Epoch 933/1000\n", + "26/26 [==============================] - 0s 325us/sample - loss: 7.7312e-04 - acc: 1.0000\n", + "Epoch 934/1000\n", + "26/26 [==============================] - 0s 318us/sample - loss: 7.7052e-04 - acc: 1.0000\n", + "Epoch 935/1000\n", + "26/26 [==============================] - 0s 442us/sample - loss: 7.6810e-04 - acc: 1.0000\n", + "Epoch 936/1000\n", + "26/26 [==============================] - 0s 463us/sample - loss: 7.6561e-04 - acc: 1.0000\n", + "Epoch 937/1000\n", + "26/26 [==============================] - 0s 350us/sample - loss: 7.6323e-04 - acc: 1.0000\n", + "Epoch 938/1000\n", + "26/26 [==============================] - 0s 430us/sample - loss: 7.6079e-04 - acc: 1.0000\n", + "Epoch 939/1000\n", + "26/26 [==============================] - 0s 515us/sample - loss: 7.5830e-04 - acc: 1.0000\n", + "Epoch 940/1000\n", + "26/26 [==============================] - 0s 440us/sample - loss: 7.5604e-04 - acc: 1.0000\n", + "Epoch 941/1000\n", + "26/26 [==============================] - 0s 318us/sample - loss: 7.5326e-04 - acc: 1.0000\n", + "Epoch 942/1000\n", + "26/26 [==============================] - 0s 491us/sample - loss: 7.5096e-04 - acc: 1.0000\n", + "Epoch 943/1000\n", + "26/26 [==============================] - 0s 440us/sample - loss: 7.4852e-04 - acc: 1.0000\n", + "Epoch 944/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 7.4616e-04 - acc: 1.0000\n", + "Epoch 945/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 7.4382e-04 - acc: 1.0000\n", + "Epoch 946/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 7.4155e-04 - acc: 1.0000\n", + "Epoch 947/1000\n", + "26/26 [==============================] - 0s 448us/sample - loss: 7.3885e-04 - acc: 1.0000\n", + "Epoch 948/1000\n", + "26/26 [==============================] - 0s 441us/sample - loss: 7.3667e-04 - acc: 1.0000\n", + "Epoch 949/1000\n", + "26/26 [==============================] - 0s 525us/sample - loss: 7.3447e-04 - acc: 1.0000\n", + "Epoch 950/1000\n", + "26/26 [==============================] - 0s 405us/sample - loss: 7.3181e-04 - acc: 1.0000\n", + "Epoch 951/1000\n", + "26/26 [==============================] - 0s 421us/sample - loss: 7.2932e-04 - acc: 1.0000\n", + "Epoch 952/1000\n", + "26/26 [==============================] - 0s 368us/sample - loss: 7.2682e-04 - acc: 1.0000\n", + "Epoch 953/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 7.2428e-04 - acc: 1.0000\n", + "Epoch 954/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 7.2192e-04 - acc: 1.0000\n", + "Epoch 955/1000\n", + "26/26 [==============================] - 0s 332us/sample - loss: 7.1955e-04 - acc: 1.0000\n", + "Epoch 956/1000\n", + "26/26 [==============================] - 0s 303us/sample - loss: 7.1718e-04 - acc: 1.0000\n", + "Epoch 957/1000\n", + "26/26 [==============================] - 0s 325us/sample - loss: 7.1498e-04 - acc: 1.0000\n", + "Epoch 958/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 7.1282e-04 - acc: 1.0000\n", + "Epoch 959/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 7.1044e-04 - acc: 1.0000\n", + "Epoch 960/1000\n", + "26/26 [==============================] - 0s 332us/sample - loss: 7.0836e-04 - acc: 1.0000\n", + "Epoch 961/1000\n", + "26/26 [==============================] - 0s 300us/sample - loss: 7.0611e-04 - acc: 1.0000\n", + "Epoch 962/1000\n", + "26/26 [==============================] - 0s 385us/sample - loss: 7.0399e-04 - acc: 1.0000\n", + "Epoch 963/1000\n", + "26/26 [==============================] - 0s 294us/sample - loss: 7.0178e-04 - acc: 1.0000\n", + "Epoch 964/1000\n", + "26/26 [==============================] - 0s 281us/sample - loss: 6.9945e-04 - acc: 1.0000\n", + "Epoch 965/1000\n", + "26/26 [==============================] - 0s 311us/sample - loss: 6.9712e-04 - acc: 1.0000\n", + "Epoch 966/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 6.9452e-04 - acc: 1.0000\n", + "Epoch 967/1000\n", + "26/26 [==============================] - 0s 306us/sample - loss: 6.9264e-04 - acc: 1.0000\n", + "Epoch 968/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 6.9005e-04 - acc: 1.0000\n", + "Epoch 969/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 6.8808e-04 - acc: 1.0000\n", + "Epoch 970/1000\n", + "26/26 [==============================] - 0s 326us/sample - loss: 6.8596e-04 - acc: 1.0000\n", + "Epoch 971/1000\n", + "26/26 [==============================] - 0s 462us/sample - loss: 6.8387e-04 - acc: 1.0000\n", + "Epoch 972/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 6.8177e-04 - acc: 1.0000\n", + "Epoch 973/1000\n", + "26/26 [==============================] - 0s 327us/sample - loss: 6.7971e-04 - acc: 1.0000\n", + "Epoch 974/1000\n", + "26/26 [==============================] - 0s 405us/sample - loss: 6.7773e-04 - acc: 1.0000\n", + "Epoch 975/1000\n", + "26/26 [==============================] - 0s 426us/sample - loss: 6.7538e-04 - acc: 1.0000\n", + "Epoch 976/1000\n", + "26/26 [==============================] - 0s 333us/sample - loss: 6.7323e-04 - acc: 1.0000\n", + "Epoch 977/1000\n", + "26/26 [==============================] - 0s 315us/sample - loss: 6.7110e-04 - acc: 1.0000\n", + "Epoch 978/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 6.6902e-04 - acc: 1.0000\n", + "Epoch 979/1000\n", + "26/26 [==============================] - 0s 420us/sample - loss: 6.6703e-04 - acc: 1.0000\n", + "Epoch 980/1000\n", + "26/26 [==============================] - 0s 419us/sample - loss: 6.6497e-04 - acc: 1.0000\n", + "Epoch 981/1000\n", + "26/26 [==============================] - 0s 452us/sample - loss: 6.6300e-04 - acc: 1.0000\n", + "Epoch 982/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 6.6105e-04 - acc: 1.0000\n", + "Epoch 983/1000\n", + "26/26 [==============================] - 0s 507us/sample - loss: 6.5898e-04 - acc: 1.0000\n", + "Epoch 984/1000\n", + "26/26 [==============================] - 0s 373us/sample - loss: 6.5700e-04 - acc: 1.0000\n", + "Epoch 985/1000\n", + "26/26 [==============================] - 0s 393us/sample - loss: 6.5506e-04 - acc: 1.0000\n", + "Epoch 986/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 6.5288e-04 - acc: 1.0000\n", + "Epoch 987/1000\n", + "26/26 [==============================] - 0s 435us/sample - loss: 6.5099e-04 - acc: 1.0000\n", + "Epoch 988/1000\n", + "26/26 [==============================] - 0s 423us/sample - loss: 6.4897e-04 - acc: 1.0000\n", + "Epoch 989/1000\n", + "26/26 [==============================] - 0s 314us/sample - loss: 6.4694e-04 - acc: 1.0000\n", + "Epoch 990/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 6.4497e-04 - acc: 1.0000\n", + "Epoch 991/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 6.4279e-04 - acc: 1.0000\n", + "Epoch 992/1000\n", + "26/26 [==============================] - 0s 443us/sample - loss: 6.4098e-04 - acc: 1.0000\n", + "Epoch 993/1000\n", + "26/26 [==============================] - 0s 423us/sample - loss: 6.3900e-04 - acc: 1.0000\n", + "Epoch 994/1000\n", + "26/26 [==============================] - 0s 479us/sample - loss: 6.3675e-04 - acc: 1.0000\n", + "Epoch 995/1000\n", + "26/26 [==============================] - 0s 428us/sample - loss: 6.3473e-04 - acc: 1.0000\n", + "Epoch 996/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 6.3240e-04 - acc: 1.0000\n", + "Epoch 997/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 6.3059e-04 - acc: 1.0000\n", + "Epoch 998/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 6.2870e-04 - acc: 1.0000\n", + "Epoch 999/1000\n", + "26/26 [==============================] - 0s 297us/sample - loss: 6.2625e-04 - acc: 1.0000\n", + "Epoch 1000/1000\n", + "26/26 [==============================] - 0s 344us/sample - loss: 6.2455e-04 - acc: 1.0000\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "\n", + "try:\n", + " model.load(\"model.tflearn\")\n", + "except:\n", + " model.fit(training, output, epochs=1000, batch_size=8)\n", + " model.save(\"model.tflearn\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "yMA7TxSt8tuR", + "outputId": "4fe84cc9-a2b1-4a00-a6db-9d577a663bc9" + }, + "execution_count": 28, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Train on 26 samples\n", + "Epoch 1/1000\n", + "26/26 [==============================] - 0s 489us/sample - loss: 6.2248e-04 - acc: 1.0000\n", + "Epoch 2/1000\n", + "26/26 [==============================] - 0s 399us/sample - loss: 6.2066e-04 - acc: 1.0000\n", + "Epoch 3/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 6.1863e-04 - acc: 1.0000\n", + "Epoch 4/1000\n", + "26/26 [==============================] - 0s 493us/sample - loss: 6.1703e-04 - acc: 1.0000\n", + "Epoch 5/1000\n", + "26/26 [==============================] - 0s 429us/sample - loss: 6.1497e-04 - acc: 1.0000\n", + "Epoch 6/1000\n", + "26/26 [==============================] - 0s 447us/sample - loss: 6.1282e-04 - acc: 1.0000\n", + "Epoch 7/1000\n", + "26/26 [==============================] - 0s 419us/sample - loss: 6.1096e-04 - acc: 1.0000\n", + "Epoch 8/1000\n", + "26/26 [==============================] - 0s 419us/sample - loss: 6.0934e-04 - acc: 1.0000\n", + "Epoch 9/1000\n", + "26/26 [==============================] - 0s 412us/sample - loss: 6.0735e-04 - acc: 1.0000\n", + "Epoch 10/1000\n", + "26/26 [==============================] - 0s 435us/sample - loss: 6.0566e-04 - acc: 1.0000\n", + "Epoch 11/1000\n", + "26/26 [==============================] - 0s 423us/sample - loss: 6.0332e-04 - acc: 1.0000\n", + "Epoch 12/1000\n", + "26/26 [==============================] - 0s 438us/sample - loss: 6.0162e-04 - acc: 1.0000\n", + "Epoch 13/1000\n", + "26/26 [==============================] - 0s 421us/sample - loss: 5.9995e-04 - acc: 1.0000\n", + "Epoch 14/1000\n", + "26/26 [==============================] - 0s 418us/sample - loss: 5.9787e-04 - acc: 1.0000\n", + "Epoch 15/1000\n", + "26/26 [==============================] - 0s 524us/sample - loss: 5.9599e-04 - acc: 1.0000\n", + "Epoch 16/1000\n", + "26/26 [==============================] - 0s 412us/sample - loss: 5.9400e-04 - acc: 1.0000\n", + "Epoch 17/1000\n", + "26/26 [==============================] - 0s 403us/sample - loss: 5.9228e-04 - acc: 1.0000\n", + "Epoch 18/1000\n", + "26/26 [==============================] - 0s 432us/sample - loss: 5.9041e-04 - acc: 1.0000\n", + "Epoch 19/1000\n", + "26/26 [==============================] - 0s 427us/sample - loss: 5.8872e-04 - acc: 1.0000\n", + "Epoch 20/1000\n", + "26/26 [==============================] - 0s 537us/sample - loss: 5.8697e-04 - acc: 1.0000\n", + "Epoch 21/1000\n", + "26/26 [==============================] - 0s 609us/sample - loss: 5.8517e-04 - acc: 1.0000\n", + "Epoch 22/1000\n", + "26/26 [==============================] - 0s 427us/sample - loss: 5.8335e-04 - acc: 1.0000\n", + "Epoch 23/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 5.8174e-04 - acc: 1.0000\n", + "Epoch 24/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 5.7976e-04 - acc: 1.0000\n", + "Epoch 25/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 5.7799e-04 - acc: 1.0000\n", + "Epoch 26/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 5.7619e-04 - acc: 1.0000\n", + "Epoch 27/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 5.7449e-04 - acc: 1.0000\n", + "Epoch 28/1000\n", + "26/26 [==============================] - 0s 383us/sample - loss: 5.7280e-04 - acc: 1.0000\n", + "Epoch 29/1000\n", + "26/26 [==============================] - 0s 416us/sample - loss: 5.7085e-04 - acc: 1.0000\n", + "Epoch 30/1000\n", + "26/26 [==============================] - 0s 384us/sample - loss: 5.6909e-04 - acc: 1.0000\n", + "Epoch 31/1000\n", + "26/26 [==============================] - 0s 411us/sample - loss: 5.6732e-04 - acc: 1.0000\n", + "Epoch 32/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 5.6559e-04 - acc: 1.0000\n", + "Epoch 33/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 5.6381e-04 - acc: 1.0000\n", + "Epoch 34/1000\n", + "26/26 [==============================] - 0s 426us/sample - loss: 5.6217e-04 - acc: 1.0000\n", + "Epoch 35/1000\n", + "26/26 [==============================] - 0s 391us/sample - loss: 5.6038e-04 - acc: 1.0000\n", + "Epoch 36/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 5.5837e-04 - acc: 1.0000\n", + "Epoch 37/1000\n", + "26/26 [==============================] - 0s 380us/sample - loss: 5.5687e-04 - acc: 1.0000\n", + "Epoch 38/1000\n", + "26/26 [==============================] - 0s 378us/sample - loss: 5.5503e-04 - acc: 1.0000\n", + "Epoch 39/1000\n", + "26/26 [==============================] - 0s 427us/sample - loss: 5.5335e-04 - acc: 1.0000\n", + "Epoch 40/1000\n", + "26/26 [==============================] - 0s 380us/sample - loss: 5.5153e-04 - acc: 1.0000\n", + "Epoch 41/1000\n", + "26/26 [==============================] - 0s 389us/sample - loss: 5.4990e-04 - acc: 1.0000\n", + "Epoch 42/1000\n", + "26/26 [==============================] - 0s 380us/sample - loss: 5.4792e-04 - acc: 1.0000\n", + "Epoch 43/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 5.4625e-04 - acc: 1.0000\n", + "Epoch 44/1000\n", + "26/26 [==============================] - 0s 401us/sample - loss: 5.4460e-04 - acc: 1.0000\n", + "Epoch 45/1000\n", + "26/26 [==============================] - 0s 398us/sample - loss: 5.4289e-04 - acc: 1.0000\n", + "Epoch 46/1000\n", + "26/26 [==============================] - 0s 401us/sample - loss: 5.4130e-04 - acc: 1.0000\n", + "Epoch 47/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 5.3942e-04 - acc: 1.0000\n", + "Epoch 48/1000\n", + "26/26 [==============================] - 0s 407us/sample - loss: 5.3757e-04 - acc: 1.0000\n", + "Epoch 49/1000\n", + "26/26 [==============================] - 0s 326us/sample - loss: 5.3597e-04 - acc: 1.0000\n", + "Epoch 50/1000\n", + "26/26 [==============================] - 0s 360us/sample - loss: 5.3446e-04 - acc: 1.0000\n", + "Epoch 51/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 5.3264e-04 - acc: 1.0000\n", + "Epoch 52/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 5.3109e-04 - acc: 1.0000\n", + "Epoch 53/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 5.2951e-04 - acc: 1.0000\n", + "Epoch 54/1000\n", + "26/26 [==============================] - 0s 458us/sample - loss: 5.2789e-04 - acc: 1.0000\n", + "Epoch 55/1000\n", + "26/26 [==============================] - 0s 407us/sample - loss: 5.2624e-04 - acc: 1.0000\n", + "Epoch 56/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 5.2468e-04 - acc: 1.0000\n", + "Epoch 57/1000\n", + "26/26 [==============================] - 0s 454us/sample - loss: 5.2304e-04 - acc: 1.0000\n", + "Epoch 58/1000\n", + "26/26 [==============================] - 0s 425us/sample - loss: 5.2157e-04 - acc: 1.0000\n", + "Epoch 59/1000\n", + "26/26 [==============================] - 0s 376us/sample - loss: 5.1991e-04 - acc: 1.0000\n", + "Epoch 60/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 5.1817e-04 - acc: 1.0000\n", + "Epoch 61/1000\n", + "26/26 [==============================] - 0s 416us/sample - loss: 5.1680e-04 - acc: 1.0000\n", + "Epoch 62/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 5.1523e-04 - acc: 1.0000\n", + "Epoch 63/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 5.1350e-04 - acc: 1.0000\n", + "Epoch 64/1000\n", + "26/26 [==============================] - 0s 372us/sample - loss: 5.1207e-04 - acc: 1.0000\n", + "Epoch 65/1000\n", + "26/26 [==============================] - 0s 343us/sample - loss: 5.1046e-04 - acc: 1.0000\n", + "Epoch 66/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 5.0885e-04 - acc: 1.0000\n", + "Epoch 67/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 5.0737e-04 - acc: 1.0000\n", + "Epoch 68/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 5.0597e-04 - acc: 1.0000\n", + "Epoch 69/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 5.0452e-04 - acc: 1.0000\n", + "Epoch 70/1000\n", + "26/26 [==============================] - 0s 344us/sample - loss: 5.0289e-04 - acc: 1.0000\n", + "Epoch 71/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 5.0147e-04 - acc: 1.0000\n", + "Epoch 72/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 5.0005e-04 - acc: 1.0000\n", + "Epoch 73/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 4.9858e-04 - acc: 1.0000\n", + "Epoch 74/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 4.9726e-04 - acc: 1.0000\n", + "Epoch 75/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 4.9565e-04 - acc: 1.0000\n", + "Epoch 76/1000\n", + "26/26 [==============================] - 0s 336us/sample - loss: 4.9422e-04 - acc: 1.0000\n", + "Epoch 77/1000\n", + "26/26 [==============================] - 0s 322us/sample - loss: 4.9277e-04 - acc: 1.0000\n", + "Epoch 78/1000\n", + "26/26 [==============================] - 0s 326us/sample - loss: 4.9121e-04 - acc: 1.0000\n", + "Epoch 79/1000\n", + "26/26 [==============================] - 0s 494us/sample - loss: 4.8976e-04 - acc: 1.0000\n", + "Epoch 80/1000\n", + "26/26 [==============================] - 0s 520us/sample - loss: 4.8833e-04 - acc: 1.0000\n", + "Epoch 81/1000\n", + "26/26 [==============================] - 0s 334us/sample - loss: 4.8673e-04 - acc: 1.0000\n", + "Epoch 82/1000\n", + "26/26 [==============================] - 0s 328us/sample - loss: 4.8530e-04 - acc: 1.0000\n", + "Epoch 83/1000\n", + "26/26 [==============================] - 0s 330us/sample - loss: 4.8383e-04 - acc: 1.0000\n", + "Epoch 84/1000\n", + "26/26 [==============================] - 0s 496us/sample - loss: 4.8240e-04 - acc: 1.0000\n", + "Epoch 85/1000\n", + "26/26 [==============================] - 0s 370us/sample - loss: 4.8099e-04 - acc: 1.0000\n", + "Epoch 86/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 4.7955e-04 - acc: 1.0000\n", + "Epoch 87/1000\n", + "26/26 [==============================] - 0s 411us/sample - loss: 4.7813e-04 - acc: 1.0000\n", + "Epoch 88/1000\n", + "26/26 [==============================] - 0s 405us/sample - loss: 4.7681e-04 - acc: 1.0000\n", + "Epoch 89/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 4.7548e-04 - acc: 1.0000\n", + "Epoch 90/1000\n", + "26/26 [==============================] - 0s 391us/sample - loss: 4.7394e-04 - acc: 1.0000\n", + "Epoch 91/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 4.7261e-04 - acc: 1.0000\n", + "Epoch 92/1000\n", + "26/26 [==============================] - 0s 474us/sample - loss: 4.7114e-04 - acc: 1.0000\n", + "Epoch 93/1000\n", + "26/26 [==============================] - 0s 402us/sample - loss: 4.6956e-04 - acc: 1.0000\n", + "Epoch 94/1000\n", + "26/26 [==============================] - 0s 393us/sample - loss: 4.6812e-04 - acc: 1.0000\n", + "Epoch 95/1000\n", + "26/26 [==============================] - 0s 387us/sample - loss: 4.6673e-04 - acc: 1.0000\n", + "Epoch 96/1000\n", + "26/26 [==============================] - 0s 327us/sample - loss: 4.6538e-04 - acc: 1.0000\n", + "Epoch 97/1000\n", + "26/26 [==============================] - 0s 333us/sample - loss: 4.6409e-04 - acc: 1.0000\n", + "Epoch 98/1000\n", + "26/26 [==============================] - 0s 434us/sample - loss: 4.6280e-04 - acc: 1.0000\n", + "Epoch 99/1000\n", + "26/26 [==============================] - 0s 350us/sample - loss: 4.6143e-04 - acc: 1.0000\n", + "Epoch 100/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 4.6023e-04 - acc: 1.0000\n", + "Epoch 101/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 4.5888e-04 - acc: 1.0000\n", + "Epoch 102/1000\n", + "26/26 [==============================] - 0s 393us/sample - loss: 4.5768e-04 - acc: 1.0000\n", + "Epoch 103/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 4.5639e-04 - acc: 1.0000\n", + "Epoch 104/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 4.5495e-04 - acc: 1.0000\n", + "Epoch 105/1000\n", + "26/26 [==============================] - 0s 380us/sample - loss: 4.5361e-04 - acc: 1.0000\n", + "Epoch 106/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 4.5236e-04 - acc: 1.0000\n", + "Epoch 107/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 4.5087e-04 - acc: 1.0000\n", + "Epoch 108/1000\n", + "26/26 [==============================] - 0s 387us/sample - loss: 4.4956e-04 - acc: 1.0000\n", + "Epoch 109/1000\n", + "26/26 [==============================] - 0s 423us/sample - loss: 4.4813e-04 - acc: 1.0000\n", + "Epoch 110/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 4.4693e-04 - acc: 1.0000\n", + "Epoch 111/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 4.4548e-04 - acc: 1.0000\n", + "Epoch 112/1000\n", + "26/26 [==============================] - 0s 425us/sample - loss: 4.4421e-04 - acc: 1.0000\n", + "Epoch 113/1000\n", + "26/26 [==============================] - 0s 325us/sample - loss: 4.4296e-04 - acc: 1.0000\n", + "Epoch 114/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 4.4168e-04 - acc: 1.0000\n", + "Epoch 115/1000\n", + "26/26 [==============================] - 0s 384us/sample - loss: 4.4049e-04 - acc: 1.0000\n", + "Epoch 116/1000\n", + "26/26 [==============================] - 0s 385us/sample - loss: 4.3917e-04 - acc: 1.0000\n", + "Epoch 117/1000\n", + "26/26 [==============================] - 0s 394us/sample - loss: 4.3786e-04 - acc: 1.0000\n", + "Epoch 118/1000\n", + "26/26 [==============================] - 0s 406us/sample - loss: 4.3642e-04 - acc: 1.0000\n", + "Epoch 119/1000\n", + "26/26 [==============================] - 0s 437us/sample - loss: 4.3526e-04 - acc: 1.0000\n", + "Epoch 120/1000\n", + "26/26 [==============================] - 0s 409us/sample - loss: 4.3361e-04 - acc: 1.0000\n", + "Epoch 121/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 4.3237e-04 - acc: 1.0000\n", + "Epoch 122/1000\n", + "26/26 [==============================] - 0s 389us/sample - loss: 4.3097e-04 - acc: 1.0000\n", + "Epoch 123/1000\n", + "26/26 [==============================] - 0s 375us/sample - loss: 4.2969e-04 - acc: 1.0000\n", + "Epoch 124/1000\n", + "26/26 [==============================] - 0s 373us/sample - loss: 4.2855e-04 - acc: 1.0000\n", + "Epoch 125/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 4.2705e-04 - acc: 1.0000\n", + "Epoch 126/1000\n", + "26/26 [==============================] - 0s 386us/sample - loss: 4.2577e-04 - acc: 1.0000\n", + "Epoch 127/1000\n", + "26/26 [==============================] - 0s 387us/sample - loss: 4.2442e-04 - acc: 1.0000\n", + "Epoch 128/1000\n", + "26/26 [==============================] - 0s 391us/sample - loss: 4.2311e-04 - acc: 1.0000\n", + "Epoch 129/1000\n", + "26/26 [==============================] - 0s 404us/sample - loss: 4.2183e-04 - acc: 1.0000\n", + "Epoch 130/1000\n", + "26/26 [==============================] - 0s 428us/sample - loss: 4.2044e-04 - acc: 1.0000\n", + "Epoch 131/1000\n", + "26/26 [==============================] - 0s 404us/sample - loss: 4.1890e-04 - acc: 1.0000\n", + "Epoch 132/1000\n", + "26/26 [==============================] - 0s 454us/sample - loss: 4.1774e-04 - acc: 1.0000\n", + "Epoch 133/1000\n", + "26/26 [==============================] - 0s 393us/sample - loss: 4.1641e-04 - acc: 1.0000\n", + "Epoch 134/1000\n", + "26/26 [==============================] - 0s 580us/sample - loss: 4.1510e-04 - acc: 1.0000\n", + "Epoch 135/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 4.1385e-04 - acc: 1.0000\n", + "Epoch 136/1000\n", + "26/26 [==============================] - 0s 368us/sample - loss: 4.1266e-04 - acc: 1.0000\n", + "Epoch 137/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 4.1138e-04 - acc: 1.0000\n", + "Epoch 138/1000\n", + "26/26 [==============================] - 0s 386us/sample - loss: 4.1019e-04 - acc: 1.0000\n", + "Epoch 139/1000\n", + "26/26 [==============================] - 0s 375us/sample - loss: 4.0880e-04 - acc: 1.0000\n", + "Epoch 140/1000\n", + "26/26 [==============================] - 0s 372us/sample - loss: 4.0776e-04 - acc: 1.0000\n", + "Epoch 141/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 4.0666e-04 - acc: 1.0000\n", + "Epoch 142/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 4.0545e-04 - acc: 1.0000\n", + "Epoch 143/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 4.0426e-04 - acc: 1.0000\n", + "Epoch 144/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 4.0318e-04 - acc: 1.0000\n", + "Epoch 145/1000\n", + "26/26 [==============================] - 0s 387us/sample - loss: 4.0199e-04 - acc: 1.0000\n", + "Epoch 146/1000\n", + "26/26 [==============================] - 0s 547us/sample - loss: 4.0076e-04 - acc: 1.0000\n", + "Epoch 147/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 3.9951e-04 - acc: 1.0000\n", + "Epoch 148/1000\n", + "26/26 [==============================] - 0s 405us/sample - loss: 3.9826e-04 - acc: 1.0000\n", + "Epoch 149/1000\n", + "26/26 [==============================] - 0s 597us/sample - loss: 3.9715e-04 - acc: 1.0000\n", + "Epoch 150/1000\n", + "26/26 [==============================] - 0s 534us/sample - loss: 3.9590e-04 - acc: 1.0000\n", + "Epoch 151/1000\n", + "26/26 [==============================] - 0s 645us/sample - loss: 3.9481e-04 - acc: 1.0000\n", + "Epoch 152/1000\n", + "26/26 [==============================] - 0s 496us/sample - loss: 3.9368e-04 - acc: 1.0000\n", + "Epoch 153/1000\n", + "26/26 [==============================] - 0s 398us/sample - loss: 3.9251e-04 - acc: 1.0000\n", + "Epoch 154/1000\n", + "26/26 [==============================] - 0s 441us/sample - loss: 3.9124e-04 - acc: 1.0000\n", + "Epoch 155/1000\n", + "26/26 [==============================] - 0s 408us/sample - loss: 3.9008e-04 - acc: 1.0000\n", + "Epoch 156/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 3.8893e-04 - acc: 1.0000\n", + "Epoch 157/1000\n", + "26/26 [==============================] - 0s 387us/sample - loss: 3.8780e-04 - acc: 1.0000\n", + "Epoch 158/1000\n", + "26/26 [==============================] - 0s 355us/sample - loss: 3.8666e-04 - acc: 1.0000\n", + "Epoch 159/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 3.8555e-04 - acc: 1.0000\n", + "Epoch 160/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 3.8424e-04 - acc: 1.0000\n", + "Epoch 161/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 3.8309e-04 - acc: 1.0000\n", + "Epoch 162/1000\n", + "26/26 [==============================] - 0s 404us/sample - loss: 3.8202e-04 - acc: 1.0000\n", + "Epoch 163/1000\n", + "26/26 [==============================] - 0s 356us/sample - loss: 3.8078e-04 - acc: 1.0000\n", + "Epoch 164/1000\n", + "26/26 [==============================] - 0s 376us/sample - loss: 3.7967e-04 - acc: 1.0000\n", + "Epoch 165/1000\n", + "26/26 [==============================] - 0s 464us/sample - loss: 3.7846e-04 - acc: 1.0000\n", + "Epoch 166/1000\n", + "26/26 [==============================] - 0s 387us/sample - loss: 3.7740e-04 - acc: 1.0000\n", + "Epoch 167/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 3.7616e-04 - acc: 1.0000\n", + "Epoch 168/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 3.7519e-04 - acc: 1.0000\n", + "Epoch 169/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 3.7416e-04 - acc: 1.0000\n", + "Epoch 170/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 3.7314e-04 - acc: 1.0000\n", + "Epoch 171/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 3.7219e-04 - acc: 1.0000\n", + "Epoch 172/1000\n", + "26/26 [==============================] - 0s 377us/sample - loss: 3.7108e-04 - acc: 1.0000\n", + "Epoch 173/1000\n", + "26/26 [==============================] - 0s 421us/sample - loss: 3.7013e-04 - acc: 1.0000\n", + "Epoch 174/1000\n", + "26/26 [==============================] - 0s 408us/sample - loss: 3.6919e-04 - acc: 1.0000\n", + "Epoch 175/1000\n", + "26/26 [==============================] - 0s 353us/sample - loss: 3.6812e-04 - acc: 1.0000\n", + "Epoch 176/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 3.6692e-04 - acc: 1.0000\n", + "Epoch 177/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 3.6580e-04 - acc: 1.0000\n", + "Epoch 178/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 3.6477e-04 - acc: 1.0000\n", + "Epoch 179/1000\n", + "26/26 [==============================] - 0s 356us/sample - loss: 3.6375e-04 - acc: 1.0000\n", + "Epoch 180/1000\n", + "26/26 [==============================] - 0s 370us/sample - loss: 3.6264e-04 - acc: 1.0000\n", + "Epoch 181/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 3.6147e-04 - acc: 1.0000\n", + "Epoch 182/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 3.6052e-04 - acc: 1.0000\n", + "Epoch 183/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 3.5941e-04 - acc: 1.0000\n", + "Epoch 184/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 3.5835e-04 - acc: 1.0000\n", + "Epoch 185/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 3.5740e-04 - acc: 1.0000\n", + "Epoch 186/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 3.5625e-04 - acc: 1.0000\n", + "Epoch 187/1000\n", + "26/26 [==============================] - 0s 406us/sample - loss: 3.5537e-04 - acc: 1.0000\n", + "Epoch 188/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 3.5412e-04 - acc: 1.0000\n", + "Epoch 189/1000\n", + "26/26 [==============================] - 0s 383us/sample - loss: 3.5301e-04 - acc: 1.0000\n", + "Epoch 190/1000\n", + "26/26 [==============================] - 0s 353us/sample - loss: 3.5195e-04 - acc: 1.0000\n", + "Epoch 191/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 3.5090e-04 - acc: 1.0000\n", + "Epoch 192/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 3.4978e-04 - acc: 1.0000\n", + "Epoch 193/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 3.4898e-04 - acc: 1.0000\n", + "Epoch 194/1000\n", + "26/26 [==============================] - 0s 331us/sample - loss: 3.4778e-04 - acc: 1.0000\n", + "Epoch 195/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 3.4682e-04 - acc: 1.0000\n", + "Epoch 196/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 3.4591e-04 - acc: 1.0000\n", + "Epoch 197/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 3.4486e-04 - acc: 1.0000\n", + "Epoch 198/1000\n", + "26/26 [==============================] - 0s 435us/sample - loss: 3.4399e-04 - acc: 1.0000\n", + "Epoch 199/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 3.4315e-04 - acc: 1.0000\n", + "Epoch 200/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 3.4212e-04 - acc: 1.0000\n", + "Epoch 201/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 3.4122e-04 - acc: 1.0000\n", + "Epoch 202/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 3.4026e-04 - acc: 1.0000\n", + "Epoch 203/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 3.3921e-04 - acc: 1.0000\n", + "Epoch 204/1000\n", + "26/26 [==============================] - 0s 368us/sample - loss: 3.3838e-04 - acc: 1.0000\n", + "Epoch 205/1000\n", + "26/26 [==============================] - 0s 370us/sample - loss: 3.3737e-04 - acc: 1.0000\n", + "Epoch 206/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 3.3644e-04 - acc: 1.0000\n", + "Epoch 207/1000\n", + "26/26 [==============================] - 0s 356us/sample - loss: 3.3533e-04 - acc: 1.0000\n", + "Epoch 208/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 3.3433e-04 - acc: 1.0000\n", + "Epoch 209/1000\n", + "26/26 [==============================] - 0s 398us/sample - loss: 3.3337e-04 - acc: 1.0000\n", + "Epoch 210/1000\n", + "26/26 [==============================] - 0s 445us/sample - loss: 3.3237e-04 - acc: 1.0000\n", + "Epoch 211/1000\n", + "26/26 [==============================] - 0s 598us/sample - loss: 3.3145e-04 - acc: 1.0000\n", + "Epoch 212/1000\n", + "26/26 [==============================] - 0s 427us/sample - loss: 3.3054e-04 - acc: 1.0000\n", + "Epoch 213/1000\n", + "26/26 [==============================] - 0s 427us/sample - loss: 3.2956e-04 - acc: 1.0000\n", + "Epoch 214/1000\n", + "26/26 [==============================] - 0s 454us/sample - loss: 3.2869e-04 - acc: 1.0000\n", + "Epoch 215/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 3.2783e-04 - acc: 1.0000\n", + "Epoch 216/1000\n", + "26/26 [==============================] - 0s 380us/sample - loss: 3.2686e-04 - acc: 1.0000\n", + "Epoch 217/1000\n", + "26/26 [==============================] - 0s 399us/sample - loss: 3.2591e-04 - acc: 1.0000\n", + "Epoch 218/1000\n", + "26/26 [==============================] - 0s 376us/sample - loss: 3.2486e-04 - acc: 1.0000\n", + "Epoch 219/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 3.2388e-04 - acc: 1.0000\n", + "Epoch 220/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 3.2296e-04 - acc: 1.0000\n", + "Epoch 221/1000\n", + "26/26 [==============================] - 0s 333us/sample - loss: 3.2212e-04 - acc: 1.0000\n", + "Epoch 222/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 3.2120e-04 - acc: 1.0000\n", + "Epoch 223/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 3.2033e-04 - acc: 1.0000\n", + "Epoch 224/1000\n", + "26/26 [==============================] - 0s 353us/sample - loss: 3.1947e-04 - acc: 1.0000\n", + "Epoch 225/1000\n", + "26/26 [==============================] - 0s 356us/sample - loss: 3.1854e-04 - acc: 1.0000\n", + "Epoch 226/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 3.1776e-04 - acc: 1.0000\n", + "Epoch 227/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 3.1695e-04 - acc: 1.0000\n", + "Epoch 228/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 3.1607e-04 - acc: 1.0000\n", + "Epoch 229/1000\n", + "26/26 [==============================] - 0s 385us/sample - loss: 3.1524e-04 - acc: 1.0000\n", + "Epoch 230/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 3.1406e-04 - acc: 1.0000\n", + "Epoch 231/1000\n", + "26/26 [==============================] - 0s 408us/sample - loss: 3.1306e-04 - acc: 1.0000\n", + "Epoch 232/1000\n", + "26/26 [==============================] - 0s 373us/sample - loss: 3.1218e-04 - acc: 1.0000\n", + "Epoch 233/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 3.1125e-04 - acc: 1.0000\n", + "Epoch 234/1000\n", + "26/26 [==============================] - 0s 376us/sample - loss: 3.1037e-04 - acc: 1.0000\n", + "Epoch 235/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 3.0947e-04 - acc: 1.0000\n", + "Epoch 236/1000\n", + "26/26 [==============================] - 0s 375us/sample - loss: 3.0859e-04 - acc: 1.0000\n", + "Epoch 237/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 3.0773e-04 - acc: 1.0000\n", + "Epoch 238/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 3.0669e-04 - acc: 1.0000\n", + "Epoch 239/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 3.0594e-04 - acc: 1.0000\n", + "Epoch 240/1000\n", + "26/26 [==============================] - 0s 330us/sample - loss: 3.0485e-04 - acc: 1.0000\n", + "Epoch 241/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 3.0411e-04 - acc: 1.0000\n", + "Epoch 242/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 3.0312e-04 - acc: 1.0000\n", + "Epoch 243/1000\n", + "26/26 [==============================] - 0s 329us/sample - loss: 3.0236e-04 - acc: 1.0000\n", + "Epoch 244/1000\n", + "26/26 [==============================] - 0s 326us/sample - loss: 3.0151e-04 - acc: 1.0000\n", + "Epoch 245/1000\n", + "26/26 [==============================] - 0s 348us/sample - loss: 3.0056e-04 - acc: 1.0000\n", + "Epoch 246/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 2.9972e-04 - acc: 1.0000\n", + "Epoch 247/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 2.9881e-04 - acc: 1.0000\n", + "Epoch 248/1000\n", + "26/26 [==============================] - 0s 356us/sample - loss: 2.9802e-04 - acc: 1.0000\n", + "Epoch 249/1000\n", + "26/26 [==============================] - 0s 337us/sample - loss: 2.9726e-04 - acc: 1.0000\n", + "Epoch 250/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 2.9638e-04 - acc: 1.0000\n", + "Epoch 251/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 2.9573e-04 - acc: 1.0000\n", + "Epoch 252/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 2.9484e-04 - acc: 1.0000\n", + "Epoch 253/1000\n", + "26/26 [==============================] - 0s 380us/sample - loss: 2.9405e-04 - acc: 1.0000\n", + "Epoch 254/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 2.9316e-04 - acc: 1.0000\n", + "Epoch 255/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 2.9238e-04 - acc: 1.0000\n", + "Epoch 256/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 2.9157e-04 - acc: 1.0000\n", + "Epoch 257/1000\n", + "26/26 [==============================] - 0s 589us/sample - loss: 2.9080e-04 - acc: 1.0000\n", + "Epoch 258/1000\n", + "26/26 [==============================] - 0s 455us/sample - loss: 2.9004e-04 - acc: 1.0000\n", + "Epoch 259/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 2.8929e-04 - acc: 1.0000\n", + "Epoch 260/1000\n", + "26/26 [==============================] - 0s 452us/sample - loss: 2.8852e-04 - acc: 1.0000\n", + "Epoch 261/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 2.8779e-04 - acc: 1.0000\n", + "Epoch 262/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 2.8697e-04 - acc: 1.0000\n", + "Epoch 263/1000\n", + "26/26 [==============================] - 0s 449us/sample - loss: 2.8615e-04 - acc: 1.0000\n", + "Epoch 264/1000\n", + "26/26 [==============================] - 0s 372us/sample - loss: 2.8541e-04 - acc: 1.0000\n", + "Epoch 265/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 2.8456e-04 - acc: 1.0000\n", + "Epoch 266/1000\n", + "26/26 [==============================] - 0s 419us/sample - loss: 2.8375e-04 - acc: 1.0000\n", + "Epoch 267/1000\n", + "26/26 [==============================] - 0s 368us/sample - loss: 2.8294e-04 - acc: 1.0000\n", + "Epoch 268/1000\n", + "26/26 [==============================] - 0s 380us/sample - loss: 2.8213e-04 - acc: 1.0000\n", + "Epoch 269/1000\n", + "26/26 [==============================] - 0s 405us/sample - loss: 2.8135e-04 - acc: 1.0000\n", + "Epoch 270/1000\n", + "26/26 [==============================] - 0s 417us/sample - loss: 2.8050e-04 - acc: 1.0000\n", + "Epoch 271/1000\n", + "26/26 [==============================] - 0s 370us/sample - loss: 2.7965e-04 - acc: 1.0000\n", + "Epoch 272/1000\n", + "26/26 [==============================] - 0s 370us/sample - loss: 2.7887e-04 - acc: 1.0000\n", + "Epoch 273/1000\n", + "26/26 [==============================] - 0s 331us/sample - loss: 2.7803e-04 - acc: 1.0000\n", + "Epoch 274/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 2.7721e-04 - acc: 1.0000\n", + "Epoch 275/1000\n", + "26/26 [==============================] - 0s 404us/sample - loss: 2.7646e-04 - acc: 1.0000\n", + "Epoch 276/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 2.7566e-04 - acc: 1.0000\n", + "Epoch 277/1000\n", + "26/26 [==============================] - 0s 442us/sample - loss: 2.7489e-04 - acc: 1.0000\n", + "Epoch 278/1000\n", + "26/26 [==============================] - 0s 494us/sample - loss: 2.7412e-04 - acc: 1.0000\n", + "Epoch 279/1000\n", + "26/26 [==============================] - 0s 494us/sample - loss: 2.7339e-04 - acc: 1.0000\n", + "Epoch 280/1000\n", + "26/26 [==============================] - 0s 542us/sample - loss: 2.7262e-04 - acc: 1.0000\n", + "Epoch 281/1000\n", + "26/26 [==============================] - 0s 413us/sample - loss: 2.7190e-04 - acc: 1.0000\n", + "Epoch 282/1000\n", + "26/26 [==============================] - 0s 409us/sample - loss: 2.7113e-04 - acc: 1.0000\n", + "Epoch 283/1000\n", + "26/26 [==============================] - 0s 636us/sample - loss: 2.7037e-04 - acc: 1.0000\n", + "Epoch 284/1000\n", + "26/26 [==============================] - 0s 504us/sample - loss: 2.6961e-04 - acc: 1.0000\n", + "Epoch 285/1000\n", + "26/26 [==============================] - 0s 594us/sample - loss: 2.6897e-04 - acc: 1.0000\n", + "Epoch 286/1000\n", + "26/26 [==============================] - 0s 410us/sample - loss: 2.6817e-04 - acc: 1.0000\n", + "Epoch 287/1000\n", + "26/26 [==============================] - 0s 470us/sample - loss: 2.6757e-04 - acc: 1.0000\n", + "Epoch 288/1000\n", + "26/26 [==============================] - 0s 378us/sample - loss: 2.6677e-04 - acc: 1.0000\n", + "Epoch 289/1000\n", + "26/26 [==============================] - 0s 344us/sample - loss: 2.6610e-04 - acc: 1.0000\n", + "Epoch 290/1000\n", + "26/26 [==============================] - 0s 638us/sample - loss: 2.6534e-04 - acc: 1.0000\n", + "Epoch 291/1000\n", + "26/26 [==============================] - 0s 378us/sample - loss: 2.6457e-04 - acc: 1.0000\n", + "Epoch 292/1000\n", + "26/26 [==============================] - 0s 332us/sample - loss: 2.6381e-04 - acc: 1.0000\n", + "Epoch 293/1000\n", + "26/26 [==============================] - 0s 329us/sample - loss: 2.6301e-04 - acc: 1.0000\n", + "Epoch 294/1000\n", + "26/26 [==============================] - 0s 430us/sample - loss: 2.6217e-04 - acc: 1.0000\n", + "Epoch 295/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 2.6147e-04 - acc: 1.0000\n", + "Epoch 296/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 2.6074e-04 - acc: 1.0000\n", + "Epoch 297/1000\n", + "26/26 [==============================] - 0s 543us/sample - loss: 2.6007e-04 - acc: 1.0000\n", + "Epoch 298/1000\n", + "26/26 [==============================] - 0s 324us/sample - loss: 2.5931e-04 - acc: 1.0000\n", + "Epoch 299/1000\n", + "26/26 [==============================] - 0s 323us/sample - loss: 2.5868e-04 - acc: 1.0000\n", + "Epoch 300/1000\n", + "26/26 [==============================] - 0s 303us/sample - loss: 2.5799e-04 - acc: 1.0000\n", + "Epoch 301/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 2.5726e-04 - acc: 1.0000\n", + "Epoch 302/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 2.5665e-04 - acc: 1.0000\n", + "Epoch 303/1000\n", + "26/26 [==============================] - 0s 322us/sample - loss: 2.5590e-04 - acc: 1.0000\n", + "Epoch 304/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 2.5522e-04 - acc: 1.0000\n", + "Epoch 305/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 2.5457e-04 - acc: 1.0000\n", + "Epoch 306/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 2.5383e-04 - acc: 1.0000\n", + "Epoch 307/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 2.5306e-04 - acc: 1.0000\n", + "Epoch 308/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 2.5241e-04 - acc: 1.0000\n", + "Epoch 309/1000\n", + "26/26 [==============================] - 0s 407us/sample - loss: 2.5162e-04 - acc: 1.0000\n", + "Epoch 310/1000\n", + "26/26 [==============================] - 0s 432us/sample - loss: 2.5088e-04 - acc: 1.0000\n", + "Epoch 311/1000\n", + "26/26 [==============================] - 0s 368us/sample - loss: 2.5025e-04 - acc: 1.0000\n", + "Epoch 312/1000\n", + "26/26 [==============================] - 0s 331us/sample - loss: 2.4951e-04 - acc: 1.0000\n", + "Epoch 313/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 2.4893e-04 - acc: 1.0000\n", + "Epoch 314/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 2.4823e-04 - acc: 1.0000\n", + "Epoch 315/1000\n", + "26/26 [==============================] - 0s 325us/sample - loss: 2.4755e-04 - acc: 1.0000\n", + "Epoch 316/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 2.4690e-04 - acc: 1.0000\n", + "Epoch 317/1000\n", + "26/26 [==============================] - 0s 336us/sample - loss: 2.4608e-04 - acc: 1.0000\n", + "Epoch 318/1000\n", + "26/26 [==============================] - 0s 511us/sample - loss: 2.4566e-04 - acc: 1.0000\n", + "Epoch 319/1000\n", + "26/26 [==============================] - 0s 391us/sample - loss: 2.4475e-04 - acc: 1.0000\n", + "Epoch 320/1000\n", + "26/26 [==============================] - 0s 350us/sample - loss: 2.4399e-04 - acc: 1.0000\n", + "Epoch 321/1000\n", + "26/26 [==============================] - 0s 325us/sample - loss: 2.4329e-04 - acc: 1.0000\n", + "Epoch 322/1000\n", + "26/26 [==============================] - 0s 373us/sample - loss: 2.4260e-04 - acc: 1.0000\n", + "Epoch 323/1000\n", + "26/26 [==============================] - 0s 328us/sample - loss: 2.4190e-04 - acc: 1.0000\n", + "Epoch 324/1000\n", + "26/26 [==============================] - 0s 340us/sample - loss: 2.4122e-04 - acc: 1.0000\n", + "Epoch 325/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 2.4058e-04 - acc: 1.0000\n", + "Epoch 326/1000\n", + "26/26 [==============================] - 0s 356us/sample - loss: 2.3995e-04 - acc: 1.0000\n", + "Epoch 327/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 2.3926e-04 - acc: 1.0000\n", + "Epoch 328/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 2.3859e-04 - acc: 1.0000\n", + "Epoch 329/1000\n", + "26/26 [==============================] - 0s 323us/sample - loss: 2.3790e-04 - acc: 1.0000\n", + "Epoch 330/1000\n", + "26/26 [==============================] - 0s 309us/sample - loss: 2.3726e-04 - acc: 1.0000\n", + "Epoch 331/1000\n", + "26/26 [==============================] - 0s 315us/sample - loss: 2.3659e-04 - acc: 1.0000\n", + "Epoch 332/1000\n", + "26/26 [==============================] - 0s 298us/sample - loss: 2.3590e-04 - acc: 1.0000\n", + "Epoch 333/1000\n", + "26/26 [==============================] - 0s 310us/sample - loss: 2.3523e-04 - acc: 1.0000\n", + "Epoch 334/1000\n", + "26/26 [==============================] - 0s 293us/sample - loss: 2.3464e-04 - acc: 1.0000\n", + "Epoch 335/1000\n", + "26/26 [==============================] - 0s 286us/sample - loss: 2.3400e-04 - acc: 1.0000\n", + "Epoch 336/1000\n", + "26/26 [==============================] - 0s 344us/sample - loss: 2.3331e-04 - acc: 1.0000\n", + "Epoch 337/1000\n", + "26/26 [==============================] - 0s 317us/sample - loss: 2.3267e-04 - acc: 1.0000\n", + "Epoch 338/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 2.3206e-04 - acc: 1.0000\n", + "Epoch 339/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 2.3138e-04 - acc: 1.0000\n", + "Epoch 340/1000\n", + "26/26 [==============================] - 0s 325us/sample - loss: 2.3071e-04 - acc: 1.0000\n", + "Epoch 341/1000\n", + "26/26 [==============================] - 0s 320us/sample - loss: 2.3019e-04 - acc: 1.0000\n", + "Epoch 342/1000\n", + "26/26 [==============================] - 0s 520us/sample - loss: 2.2946e-04 - acc: 1.0000\n", + "Epoch 343/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 2.2875e-04 - acc: 1.0000\n", + "Epoch 344/1000\n", + "26/26 [==============================] - 0s 344us/sample - loss: 2.2818e-04 - acc: 1.0000\n", + "Epoch 345/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 2.2755e-04 - acc: 1.0000\n", + "Epoch 346/1000\n", + "26/26 [==============================] - 0s 461us/sample - loss: 2.2696e-04 - acc: 1.0000\n", + "Epoch 347/1000\n", + "26/26 [==============================] - 0s 368us/sample - loss: 2.2631e-04 - acc: 1.0000\n", + "Epoch 348/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 2.2572e-04 - acc: 1.0000\n", + "Epoch 349/1000\n", + "26/26 [==============================] - 0s 433us/sample - loss: 2.2508e-04 - acc: 1.0000\n", + "Epoch 350/1000\n", + "26/26 [==============================] - 0s 431us/sample - loss: 2.2452e-04 - acc: 1.0000\n", + "Epoch 351/1000\n", + "26/26 [==============================] - 0s 396us/sample - loss: 2.2401e-04 - acc: 1.0000\n", + "Epoch 352/1000\n", + "26/26 [==============================] - 0s 385us/sample - loss: 2.2339e-04 - acc: 1.0000\n", + "Epoch 353/1000\n", + "26/26 [==============================] - 0s 429us/sample - loss: 2.2286e-04 - acc: 1.0000\n", + "Epoch 354/1000\n", + "26/26 [==============================] - 0s 333us/sample - loss: 2.2235e-04 - acc: 1.0000\n", + "Epoch 355/1000\n", + "26/26 [==============================] - 0s 373us/sample - loss: 2.2176e-04 - acc: 1.0000\n", + "Epoch 356/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 2.2122e-04 - acc: 1.0000\n", + "Epoch 357/1000\n", + "26/26 [==============================] - 0s 410us/sample - loss: 2.2063e-04 - acc: 1.0000\n", + "Epoch 358/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 2.1996e-04 - acc: 1.0000\n", + "Epoch 359/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 2.1935e-04 - acc: 1.0000\n", + "Epoch 360/1000\n", + "26/26 [==============================] - 0s 406us/sample - loss: 2.1873e-04 - acc: 1.0000\n", + "Epoch 361/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 2.1814e-04 - acc: 1.0000\n", + "Epoch 362/1000\n", + "26/26 [==============================] - 0s 470us/sample - loss: 2.1755e-04 - acc: 1.0000\n", + "Epoch 363/1000\n", + "26/26 [==============================] - 0s 432us/sample - loss: 2.1690e-04 - acc: 1.0000\n", + "Epoch 364/1000\n", + "26/26 [==============================] - 0s 494us/sample - loss: 2.1640e-04 - acc: 1.0000\n", + "Epoch 365/1000\n", + "26/26 [==============================] - 0s 463us/sample - loss: 2.1583e-04 - acc: 1.0000\n", + "Epoch 366/1000\n", + "26/26 [==============================] - 0s 417us/sample - loss: 2.1532e-04 - acc: 1.0000\n", + "Epoch 367/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 2.1482e-04 - acc: 1.0000\n", + "Epoch 368/1000\n", + "26/26 [==============================] - 0s 386us/sample - loss: 2.1423e-04 - acc: 1.0000\n", + "Epoch 369/1000\n", + "26/26 [==============================] - 0s 407us/sample - loss: 2.1367e-04 - acc: 1.0000\n", + "Epoch 370/1000\n", + "26/26 [==============================] - 0s 469us/sample - loss: 2.1316e-04 - acc: 1.0000\n", + "Epoch 371/1000\n", + "26/26 [==============================] - 0s 545us/sample - loss: 2.1259e-04 - acc: 1.0000\n", + "Epoch 372/1000\n", + "26/26 [==============================] - 0s 411us/sample - loss: 2.1207e-04 - acc: 1.0000\n", + "Epoch 373/1000\n", + "26/26 [==============================] - 0s 380us/sample - loss: 2.1153e-04 - acc: 1.0000\n", + "Epoch 374/1000\n", + "26/26 [==============================] - 0s 355us/sample - loss: 2.1096e-04 - acc: 1.0000\n", + "Epoch 375/1000\n", + "26/26 [==============================] - 0s 327us/sample - loss: 2.1042e-04 - acc: 1.0000\n", + "Epoch 376/1000\n", + "26/26 [==============================] - 0s 375us/sample - loss: 2.0986e-04 - acc: 1.0000\n", + "Epoch 377/1000\n", + "26/26 [==============================] - 0s 372us/sample - loss: 2.0933e-04 - acc: 1.0000\n", + "Epoch 378/1000\n", + "26/26 [==============================] - 0s 336us/sample - loss: 2.0871e-04 - acc: 1.0000\n", + "Epoch 379/1000\n", + "26/26 [==============================] - 0s 353us/sample - loss: 2.0823e-04 - acc: 1.0000\n", + "Epoch 380/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 2.0757e-04 - acc: 1.0000\n", + "Epoch 381/1000\n", + "26/26 [==============================] - 0s 320us/sample - loss: 2.0711e-04 - acc: 1.0000\n", + "Epoch 382/1000\n", + "26/26 [==============================] - 0s 334us/sample - loss: 2.0659e-04 - acc: 1.0000\n", + "Epoch 383/1000\n", + "26/26 [==============================] - 0s 360us/sample - loss: 2.0599e-04 - acc: 1.0000\n", + "Epoch 384/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 2.0552e-04 - acc: 1.0000\n", + "Epoch 385/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 2.0501e-04 - acc: 1.0000\n", + "Epoch 386/1000\n", + "26/26 [==============================] - 0s 333us/sample - loss: 2.0444e-04 - acc: 1.0000\n", + "Epoch 387/1000\n", + "26/26 [==============================] - 0s 315us/sample - loss: 2.0394e-04 - acc: 1.0000\n", + "Epoch 388/1000\n", + "26/26 [==============================] - 0s 340us/sample - loss: 2.0331e-04 - acc: 1.0000\n", + "Epoch 389/1000\n", + "26/26 [==============================] - 0s 356us/sample - loss: 2.0276e-04 - acc: 1.0000\n", + "Epoch 390/1000\n", + "26/26 [==============================] - 0s 334us/sample - loss: 2.0224e-04 - acc: 1.0000\n", + "Epoch 391/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 2.0169e-04 - acc: 1.0000\n", + "Epoch 392/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 2.0109e-04 - acc: 1.0000\n", + "Epoch 393/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 2.0056e-04 - acc: 1.0000\n", + "Epoch 394/1000\n", + "26/26 [==============================] - 0s 389us/sample - loss: 1.9998e-04 - acc: 1.0000\n", + "Epoch 395/1000\n", + "26/26 [==============================] - 0s 353us/sample - loss: 1.9946e-04 - acc: 1.0000\n", + "Epoch 396/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 1.9895e-04 - acc: 1.0000\n", + "Epoch 397/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 1.9837e-04 - acc: 1.0000\n", + "Epoch 398/1000\n", + "26/26 [==============================] - 0s 335us/sample - loss: 1.9782e-04 - acc: 1.0000\n", + "Epoch 399/1000\n", + "26/26 [==============================] - 0s 337us/sample - loss: 1.9733e-04 - acc: 1.0000\n", + "Epoch 400/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 1.9670e-04 - acc: 1.0000\n", + "Epoch 401/1000\n", + "26/26 [==============================] - 0s 353us/sample - loss: 1.9611e-04 - acc: 1.0000\n", + "Epoch 402/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 1.9560e-04 - acc: 1.0000\n", + "Epoch 403/1000\n", + "26/26 [==============================] - 0s 376us/sample - loss: 1.9508e-04 - acc: 1.0000\n", + "Epoch 404/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 1.9454e-04 - acc: 1.0000\n", + "Epoch 405/1000\n", + "26/26 [==============================] - 0s 333us/sample - loss: 1.9402e-04 - acc: 1.0000\n", + "Epoch 406/1000\n", + "26/26 [==============================] - 0s 323us/sample - loss: 1.9357e-04 - acc: 1.0000\n", + "Epoch 407/1000\n", + "26/26 [==============================] - 0s 329us/sample - loss: 1.9299e-04 - acc: 1.0000\n", + "Epoch 408/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 1.9245e-04 - acc: 1.0000\n", + "Epoch 409/1000\n", + "26/26 [==============================] - 0s 386us/sample - loss: 1.9186e-04 - acc: 1.0000\n", + "Epoch 410/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 1.9137e-04 - acc: 1.0000\n", + "Epoch 411/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 1.9081e-04 - acc: 1.0000\n", + "Epoch 412/1000\n", + "26/26 [==============================] - 0s 445us/sample - loss: 1.9030e-04 - acc: 1.0000\n", + "Epoch 413/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 1.8973e-04 - acc: 1.0000\n", + "Epoch 414/1000\n", + "26/26 [==============================] - 0s 378us/sample - loss: 1.8921e-04 - acc: 1.0000\n", + "Epoch 415/1000\n", + "26/26 [==============================] - 0s 335us/sample - loss: 1.8865e-04 - acc: 1.0000\n", + "Epoch 416/1000\n", + "26/26 [==============================] - 0s 336us/sample - loss: 1.8814e-04 - acc: 1.0000\n", + "Epoch 417/1000\n", + "26/26 [==============================] - 0s 348us/sample - loss: 1.8760e-04 - acc: 1.0000\n", + "Epoch 418/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 1.8708e-04 - acc: 1.0000\n", + "Epoch 419/1000\n", + "26/26 [==============================] - 0s 340us/sample - loss: 1.8656e-04 - acc: 1.0000\n", + "Epoch 420/1000\n", + "26/26 [==============================] - 0s 349us/sample - loss: 1.8609e-04 - acc: 1.0000\n", + "Epoch 421/1000\n", + "26/26 [==============================] - 0s 325us/sample - loss: 1.8557e-04 - acc: 1.0000\n", + "Epoch 422/1000\n", + "26/26 [==============================] - 0s 349us/sample - loss: 1.8506e-04 - acc: 1.0000\n", + "Epoch 423/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 1.8460e-04 - acc: 1.0000\n", + "Epoch 424/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 1.8412e-04 - acc: 1.0000\n", + "Epoch 425/1000\n", + "26/26 [==============================] - 0s 337us/sample - loss: 1.8361e-04 - acc: 1.0000\n", + "Epoch 426/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 1.8308e-04 - acc: 1.0000\n", + "Epoch 427/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 1.8252e-04 - acc: 1.0000\n", + "Epoch 428/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 1.8204e-04 - acc: 1.0000\n", + "Epoch 429/1000\n", + "26/26 [==============================] - 0s 360us/sample - loss: 1.8153e-04 - acc: 1.0000\n", + "Epoch 430/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 1.8104e-04 - acc: 1.0000\n", + "Epoch 431/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 1.8052e-04 - acc: 1.0000\n", + "Epoch 432/1000\n", + "26/26 [==============================] - 0s 306us/sample - loss: 1.8003e-04 - acc: 1.0000\n", + "Epoch 433/1000\n", + "26/26 [==============================] - 0s 330us/sample - loss: 1.7958e-04 - acc: 1.0000\n", + "Epoch 434/1000\n", + "26/26 [==============================] - 0s 325us/sample - loss: 1.7909e-04 - acc: 1.0000\n", + "Epoch 435/1000\n", + "26/26 [==============================] - 0s 329us/sample - loss: 1.7865e-04 - acc: 1.0000\n", + "Epoch 436/1000\n", + "26/26 [==============================] - 0s 430us/sample - loss: 1.7820e-04 - acc: 1.0000\n", + "Epoch 437/1000\n", + "26/26 [==============================] - 0s 494us/sample - loss: 1.7773e-04 - acc: 1.0000\n", + "Epoch 438/1000\n", + "26/26 [==============================] - 0s 355us/sample - loss: 1.7730e-04 - acc: 1.0000\n", + "Epoch 439/1000\n", + "26/26 [==============================] - 0s 498us/sample - loss: 1.7684e-04 - acc: 1.0000\n", + "Epoch 440/1000\n", + "26/26 [==============================] - 0s 472us/sample - loss: 1.7636e-04 - acc: 1.0000\n", + "Epoch 441/1000\n", + "26/26 [==============================] - 0s 566us/sample - loss: 1.7584e-04 - acc: 1.0000\n", + "Epoch 442/1000\n", + "26/26 [==============================] - 0s 498us/sample - loss: 1.7542e-04 - acc: 1.0000\n", + "Epoch 443/1000\n", + "26/26 [==============================] - 0s 608us/sample - loss: 1.7497e-04 - acc: 1.0000\n", + "Epoch 444/1000\n", + "26/26 [==============================] - 0s 445us/sample - loss: 1.7453e-04 - acc: 1.0000\n", + "Epoch 445/1000\n", + "26/26 [==============================] - 0s 348us/sample - loss: 1.7408e-04 - acc: 1.0000\n", + "Epoch 446/1000\n", + "26/26 [==============================] - 0s 507us/sample - loss: 1.7364e-04 - acc: 1.0000\n", + "Epoch 447/1000\n", + "26/26 [==============================] - 0s 407us/sample - loss: 1.7321e-04 - acc: 1.0000\n", + "Epoch 448/1000\n", + "26/26 [==============================] - 0s 542us/sample - loss: 1.7278e-04 - acc: 1.0000\n", + "Epoch 449/1000\n", + "26/26 [==============================] - 0s 468us/sample - loss: 1.7233e-04 - acc: 1.0000\n", + "Epoch 450/1000\n", + "26/26 [==============================] - 0s 380us/sample - loss: 1.7177e-04 - acc: 1.0000\n", + "Epoch 451/1000\n", + "26/26 [==============================] - 0s 545us/sample - loss: 1.7130e-04 - acc: 1.0000\n", + "Epoch 452/1000\n", + "26/26 [==============================] - 0s 430us/sample - loss: 1.7095e-04 - acc: 1.0000\n", + "Epoch 453/1000\n", + "26/26 [==============================] - 0s 555us/sample - loss: 1.7051e-04 - acc: 1.0000\n", + "Epoch 454/1000\n", + "26/26 [==============================] - 0s 610us/sample - loss: 1.7007e-04 - acc: 1.0000\n", + "Epoch 455/1000\n", + "26/26 [==============================] - 0s 449us/sample - loss: 1.6958e-04 - acc: 1.0000\n", + "Epoch 456/1000\n", + "26/26 [==============================] - 0s 431us/sample - loss: 1.6918e-04 - acc: 1.0000\n", + "Epoch 457/1000\n", + "26/26 [==============================] - 0s 513us/sample - loss: 1.6872e-04 - acc: 1.0000\n", + "Epoch 458/1000\n", + "26/26 [==============================] - 0s 437us/sample - loss: 1.6825e-04 - acc: 1.0000\n", + "Epoch 459/1000\n", + "26/26 [==============================] - 0s 446us/sample - loss: 1.6780e-04 - acc: 1.0000\n", + "Epoch 460/1000\n", + "26/26 [==============================] - 0s 372us/sample - loss: 1.6739e-04 - acc: 1.0000\n", + "Epoch 461/1000\n", + "26/26 [==============================] - 0s 384us/sample - loss: 1.6693e-04 - acc: 1.0000\n", + "Epoch 462/1000\n", + "26/26 [==============================] - 0s 621us/sample - loss: 1.6645e-04 - acc: 1.0000\n", + "Epoch 463/1000\n", + "26/26 [==============================] - 0s 484us/sample - loss: 1.6606e-04 - acc: 1.0000\n", + "Epoch 464/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 1.6560e-04 - acc: 1.0000\n", + "Epoch 465/1000\n", + "26/26 [==============================] - 0s 525us/sample - loss: 1.6518e-04 - acc: 1.0000\n", + "Epoch 466/1000\n", + "26/26 [==============================] - 0s 504us/sample - loss: 1.6475e-04 - acc: 1.0000\n", + "Epoch 467/1000\n", + "26/26 [==============================] - 0s 398us/sample - loss: 1.6428e-04 - acc: 1.0000\n", + "Epoch 468/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 1.6377e-04 - acc: 1.0000\n", + "Epoch 469/1000\n", + "26/26 [==============================] - 0s 424us/sample - loss: 1.6341e-04 - acc: 1.0000\n", + "Epoch 470/1000\n", + "26/26 [==============================] - 0s 618us/sample - loss: 1.6292e-04 - acc: 1.0000\n", + "Epoch 471/1000\n", + "26/26 [==============================] - 0s 482us/sample - loss: 1.6249e-04 - acc: 1.0000\n", + "Epoch 472/1000\n", + "26/26 [==============================] - 0s 444us/sample - loss: 1.6202e-04 - acc: 1.0000\n", + "Epoch 473/1000\n", + "26/26 [==============================] - 0s 408us/sample - loss: 1.6154e-04 - acc: 1.0000\n", + "Epoch 474/1000\n", + "26/26 [==============================] - 0s 376us/sample - loss: 1.6110e-04 - acc: 1.0000\n", + "Epoch 475/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 1.6077e-04 - acc: 1.0000\n", + "Epoch 476/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 1.6025e-04 - acc: 1.0000\n", + "Epoch 477/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 1.5984e-04 - acc: 1.0000\n", + "Epoch 478/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 1.5945e-04 - acc: 1.0000\n", + "Epoch 479/1000\n", + "26/26 [==============================] - 0s 429us/sample - loss: 1.5904e-04 - acc: 1.0000\n", + "Epoch 480/1000\n", + "26/26 [==============================] - 0s 476us/sample - loss: 1.5862e-04 - acc: 1.0000\n", + "Epoch 481/1000\n", + "26/26 [==============================] - 0s 385us/sample - loss: 1.5816e-04 - acc: 1.0000\n", + "Epoch 482/1000\n", + "26/26 [==============================] - 0s 344us/sample - loss: 1.5771e-04 - acc: 1.0000\n", + "Epoch 483/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 1.5731e-04 - acc: 1.0000\n", + "Epoch 484/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 1.5691e-04 - acc: 1.0000\n", + "Epoch 485/1000\n", + "26/26 [==============================] - 0s 335us/sample - loss: 1.5646e-04 - acc: 1.0000\n", + "Epoch 486/1000\n", + "26/26 [==============================] - 0s 336us/sample - loss: 1.5604e-04 - acc: 1.0000\n", + "Epoch 487/1000\n", + "26/26 [==============================] - 0s 323us/sample - loss: 1.5558e-04 - acc: 1.0000\n", + "Epoch 488/1000\n", + "26/26 [==============================] - 0s 343us/sample - loss: 1.5520e-04 - acc: 1.0000\n", + "Epoch 489/1000\n", + "26/26 [==============================] - 0s 398us/sample - loss: 1.5482e-04 - acc: 1.0000\n", + "Epoch 490/1000\n", + "26/26 [==============================] - 0s 437us/sample - loss: 1.5439e-04 - acc: 1.0000\n", + "Epoch 491/1000\n", + "26/26 [==============================] - 0s 408us/sample - loss: 1.5402e-04 - acc: 1.0000\n", + "Epoch 492/1000\n", + "26/26 [==============================] - 0s 609us/sample - loss: 1.5363e-04 - acc: 1.0000\n", + "Epoch 493/1000\n", + "26/26 [==============================] - 0s 498us/sample - loss: 1.5326e-04 - acc: 1.0000\n", + "Epoch 494/1000\n", + "26/26 [==============================] - 0s 542us/sample - loss: 1.5288e-04 - acc: 1.0000\n", + "Epoch 495/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 1.5252e-04 - acc: 1.0000\n", + "Epoch 496/1000\n", + "26/26 [==============================] - 0s 373us/sample - loss: 1.5210e-04 - acc: 1.0000\n", + "Epoch 497/1000\n", + "26/26 [==============================] - 0s 403us/sample - loss: 1.5170e-04 - acc: 1.0000\n", + "Epoch 498/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 1.5132e-04 - acc: 1.0000\n", + "Epoch 499/1000\n", + "26/26 [==============================] - 0s 577us/sample - loss: 1.5093e-04 - acc: 1.0000\n", + "Epoch 500/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 1.5052e-04 - acc: 1.0000\n", + "Epoch 501/1000\n", + "26/26 [==============================] - 0s 351us/sample - loss: 1.5010e-04 - acc: 1.0000\n", + "Epoch 502/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 1.4964e-04 - acc: 1.0000\n", + "Epoch 503/1000\n", + "26/26 [==============================] - 0s 418us/sample - loss: 1.4927e-04 - acc: 1.0000\n", + "Epoch 504/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 1.4883e-04 - acc: 1.0000\n", + "Epoch 505/1000\n", + "26/26 [==============================] - 0s 470us/sample - loss: 1.4845e-04 - acc: 1.0000\n", + "Epoch 506/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 1.4800e-04 - acc: 1.0000\n", + "Epoch 507/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 1.4762e-04 - acc: 1.0000\n", + "Epoch 508/1000\n", + "26/26 [==============================] - 0s 447us/sample - loss: 1.4727e-04 - acc: 1.0000\n", + "Epoch 509/1000\n", + "26/26 [==============================] - 0s 446us/sample - loss: 1.4687e-04 - acc: 1.0000\n", + "Epoch 510/1000\n", + "26/26 [==============================] - 0s 453us/sample - loss: 1.4644e-04 - acc: 1.0000\n", + "Epoch 511/1000\n", + "26/26 [==============================] - 0s 577us/sample - loss: 1.4611e-04 - acc: 1.0000\n", + "Epoch 512/1000\n", + "26/26 [==============================] - 0s 527us/sample - loss: 1.4567e-04 - acc: 1.0000\n", + "Epoch 513/1000\n", + "26/26 [==============================] - 0s 493us/sample - loss: 1.4531e-04 - acc: 1.0000\n", + "Epoch 514/1000\n", + "26/26 [==============================] - 0s 484us/sample - loss: 1.4491e-04 - acc: 1.0000\n", + "Epoch 515/1000\n", + "26/26 [==============================] - 0s 404us/sample - loss: 1.4457e-04 - acc: 1.0000\n", + "Epoch 516/1000\n", + "26/26 [==============================] - 0s 391us/sample - loss: 1.4421e-04 - acc: 1.0000\n", + "Epoch 517/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 1.4387e-04 - acc: 1.0000\n", + "Epoch 518/1000\n", + "26/26 [==============================] - 0s 474us/sample - loss: 1.4346e-04 - acc: 1.0000\n", + "Epoch 519/1000\n", + "26/26 [==============================] - 0s 485us/sample - loss: 1.4308e-04 - acc: 1.0000\n", + "Epoch 520/1000\n", + "26/26 [==============================] - 0s 433us/sample - loss: 1.4269e-04 - acc: 1.0000\n", + "Epoch 521/1000\n", + "26/26 [==============================] - 0s 409us/sample - loss: 1.4230e-04 - acc: 1.0000\n", + "Epoch 522/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 1.4188e-04 - acc: 1.0000\n", + "Epoch 523/1000\n", + "26/26 [==============================] - 0s 416us/sample - loss: 1.4151e-04 - acc: 1.0000\n", + "Epoch 524/1000\n", + "26/26 [==============================] - 0s 408us/sample - loss: 1.4117e-04 - acc: 1.0000\n", + "Epoch 525/1000\n", + "26/26 [==============================] - 0s 439us/sample - loss: 1.4076e-04 - acc: 1.0000\n", + "Epoch 526/1000\n", + "26/26 [==============================] - 0s 496us/sample - loss: 1.4040e-04 - acc: 1.0000\n", + "Epoch 527/1000\n", + "26/26 [==============================] - 0s 583us/sample - loss: 1.4004e-04 - acc: 1.0000\n", + "Epoch 528/1000\n", + "26/26 [==============================] - 0s 403us/sample - loss: 1.3971e-04 - acc: 1.0000\n", + "Epoch 529/1000\n", + "26/26 [==============================] - 0s 539us/sample - loss: 1.3935e-04 - acc: 1.0000\n", + "Epoch 530/1000\n", + "26/26 [==============================] - 0s 766us/sample - loss: 1.3896e-04 - acc: 1.0000\n", + "Epoch 531/1000\n", + "26/26 [==============================] - 0s 596us/sample - loss: 1.3864e-04 - acc: 1.0000\n", + "Epoch 532/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 1.3830e-04 - acc: 1.0000\n", + "Epoch 533/1000\n", + "26/26 [==============================] - 0s 377us/sample - loss: 1.3795e-04 - acc: 1.0000\n", + "Epoch 534/1000\n", + "26/26 [==============================] - 0s 355us/sample - loss: 1.3762e-04 - acc: 1.0000\n", + "Epoch 535/1000\n", + "26/26 [==============================] - 0s 344us/sample - loss: 1.3729e-04 - acc: 1.0000\n", + "Epoch 536/1000\n", + "26/26 [==============================] - 0s 335us/sample - loss: 1.3692e-04 - acc: 1.0000\n", + "Epoch 537/1000\n", + "26/26 [==============================] - 0s 325us/sample - loss: 1.3657e-04 - acc: 1.0000\n", + "Epoch 538/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 1.3626e-04 - acc: 1.0000\n", + "Epoch 539/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 1.3589e-04 - acc: 1.0000\n", + "Epoch 540/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 1.3551e-04 - acc: 1.0000\n", + "Epoch 541/1000\n", + "26/26 [==============================] - 0s 349us/sample - loss: 1.3522e-04 - acc: 1.0000\n", + "Epoch 542/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 1.3488e-04 - acc: 1.0000\n", + "Epoch 543/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 1.3453e-04 - acc: 1.0000\n", + "Epoch 544/1000\n", + "26/26 [==============================] - 0s 372us/sample - loss: 1.3423e-04 - acc: 1.0000\n", + "Epoch 545/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 1.3395e-04 - acc: 1.0000\n", + "Epoch 546/1000\n", + "26/26 [==============================] - 0s 316us/sample - loss: 1.3360e-04 - acc: 1.0000\n", + "Epoch 547/1000\n", + "26/26 [==============================] - 0s 332us/sample - loss: 1.3329e-04 - acc: 1.0000\n", + "Epoch 548/1000\n", + "26/26 [==============================] - 0s 334us/sample - loss: 1.3296e-04 - acc: 1.0000\n", + "Epoch 549/1000\n", + "26/26 [==============================] - 0s 343us/sample - loss: 1.3260e-04 - acc: 1.0000\n", + "Epoch 550/1000\n", + "26/26 [==============================] - 0s 389us/sample - loss: 1.3228e-04 - acc: 1.0000\n", + "Epoch 551/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 1.3184e-04 - acc: 1.0000\n", + "Epoch 552/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 1.3157e-04 - acc: 1.0000\n", + "Epoch 553/1000\n", + "26/26 [==============================] - 0s 437us/sample - loss: 1.3115e-04 - acc: 1.0000\n", + "Epoch 554/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 1.3081e-04 - acc: 1.0000\n", + "Epoch 555/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 1.3044e-04 - acc: 1.0000\n", + "Epoch 556/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 1.3007e-04 - acc: 1.0000\n", + "Epoch 557/1000\n", + "26/26 [==============================] - 0s 411us/sample - loss: 1.2974e-04 - acc: 1.0000\n", + "Epoch 558/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 1.2944e-04 - acc: 1.0000\n", + "Epoch 559/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 1.2912e-04 - acc: 1.0000\n", + "Epoch 560/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 1.2880e-04 - acc: 1.0000\n", + "Epoch 561/1000\n", + "26/26 [==============================] - 0s 373us/sample - loss: 1.2849e-04 - acc: 1.0000\n", + "Epoch 562/1000\n", + "26/26 [==============================] - 0s 343us/sample - loss: 1.2816e-04 - acc: 1.0000\n", + "Epoch 563/1000\n", + "26/26 [==============================] - 0s 404us/sample - loss: 1.2785e-04 - acc: 1.0000\n", + "Epoch 564/1000\n", + "26/26 [==============================] - 0s 403us/sample - loss: 1.2754e-04 - acc: 1.0000\n", + "Epoch 565/1000\n", + "26/26 [==============================] - 0s 410us/sample - loss: 1.2720e-04 - acc: 1.0000\n", + "Epoch 566/1000\n", + "26/26 [==============================] - 0s 404us/sample - loss: 1.2689e-04 - acc: 1.0000\n", + "Epoch 567/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 1.2657e-04 - acc: 1.0000\n", + "Epoch 568/1000\n", + "26/26 [==============================] - 0s 355us/sample - loss: 1.2629e-04 - acc: 1.0000\n", + "Epoch 569/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 1.2596e-04 - acc: 1.0000\n", + "Epoch 570/1000\n", + "26/26 [==============================] - 0s 377us/sample - loss: 1.2563e-04 - acc: 1.0000\n", + "Epoch 571/1000\n", + "26/26 [==============================] - 0s 332us/sample - loss: 1.2531e-04 - acc: 1.0000\n", + "Epoch 572/1000\n", + "26/26 [==============================] - 0s 472us/sample - loss: 1.2496e-04 - acc: 1.0000\n", + "Epoch 573/1000\n", + "26/26 [==============================] - 0s 384us/sample - loss: 1.2461e-04 - acc: 1.0000\n", + "Epoch 574/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 1.2428e-04 - acc: 1.0000\n", + "Epoch 575/1000\n", + "26/26 [==============================] - 0s 343us/sample - loss: 1.2394e-04 - acc: 1.0000\n", + "Epoch 576/1000\n", + "26/26 [==============================] - 0s 395us/sample - loss: 1.2363e-04 - acc: 1.0000\n", + "Epoch 577/1000\n", + "26/26 [==============================] - 0s 370us/sample - loss: 1.2325e-04 - acc: 1.0000\n", + "Epoch 578/1000\n", + "26/26 [==============================] - 0s 403us/sample - loss: 1.2295e-04 - acc: 1.0000\n", + "Epoch 579/1000\n", + "26/26 [==============================] - 0s 592us/sample - loss: 1.2262e-04 - acc: 1.0000\n", + "Epoch 580/1000\n", + "26/26 [==============================] - 0s 653us/sample - loss: 1.2233e-04 - acc: 1.0000\n", + "Epoch 581/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 1.2201e-04 - acc: 1.0000\n", + "Epoch 582/1000\n", + "26/26 [==============================] - 0s 557us/sample - loss: 1.2173e-04 - acc: 1.0000\n", + "Epoch 583/1000\n", + "26/26 [==============================] - 0s 492us/sample - loss: 1.2142e-04 - acc: 1.0000\n", + "Epoch 584/1000\n", + "26/26 [==============================] - 0s 469us/sample - loss: 1.2113e-04 - acc: 1.0000\n", + "Epoch 585/1000\n", + "26/26 [==============================] - 0s 477us/sample - loss: 1.2083e-04 - acc: 1.0000\n", + "Epoch 586/1000\n", + "26/26 [==============================] - 0s 626us/sample - loss: 1.2053e-04 - acc: 1.0000\n", + "Epoch 587/1000\n", + "26/26 [==============================] - 0s 502us/sample - loss: 1.2026e-04 - acc: 1.0000\n", + "Epoch 588/1000\n", + "26/26 [==============================] - 0s 550us/sample - loss: 1.1996e-04 - acc: 1.0000\n", + "Epoch 589/1000\n", + "26/26 [==============================] - 0s 419us/sample - loss: 1.1971e-04 - acc: 1.0000\n", + "Epoch 590/1000\n", + "26/26 [==============================] - 0s 401us/sample - loss: 1.1942e-04 - acc: 1.0000\n", + "Epoch 591/1000\n", + "26/26 [==============================] - 0s 428us/sample - loss: 1.1911e-04 - acc: 1.0000\n", + "Epoch 592/1000\n", + "26/26 [==============================] - 0s 597us/sample - loss: 1.1884e-04 - acc: 1.0000\n", + "Epoch 593/1000\n", + "26/26 [==============================] - 0s 491us/sample - loss: 1.1854e-04 - acc: 1.0000\n", + "Epoch 594/1000\n", + "26/26 [==============================] - 0s 551us/sample - loss: 1.1822e-04 - acc: 1.0000\n", + "Epoch 595/1000\n", + "26/26 [==============================] - 0s 513us/sample - loss: 1.1798e-04 - acc: 1.0000\n", + "Epoch 596/1000\n", + "26/26 [==============================] - 0s 455us/sample - loss: 1.1766e-04 - acc: 1.0000\n", + "Epoch 597/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 1.1740e-04 - acc: 1.0000\n", + "Epoch 598/1000\n", + "26/26 [==============================] - 0s 465us/sample - loss: 1.1711e-04 - acc: 1.0000\n", + "Epoch 599/1000\n", + "26/26 [==============================] - 0s 452us/sample - loss: 1.1685e-04 - acc: 1.0000\n", + "Epoch 600/1000\n", + "26/26 [==============================] - 0s 492us/sample - loss: 1.1655e-04 - acc: 1.0000\n", + "Epoch 601/1000\n", + "26/26 [==============================] - 0s 480us/sample - loss: 1.1628e-04 - acc: 1.0000\n", + "Epoch 602/1000\n", + "26/26 [==============================] - 0s 420us/sample - loss: 1.1595e-04 - acc: 1.0000\n", + "Epoch 603/1000\n", + "26/26 [==============================] - 0s 385us/sample - loss: 1.1568e-04 - acc: 1.0000\n", + "Epoch 604/1000\n", + "26/26 [==============================] - 0s 343us/sample - loss: 1.1539e-04 - acc: 1.0000\n", + "Epoch 605/1000\n", + "26/26 [==============================] - 0s 321us/sample - loss: 1.1510e-04 - acc: 1.0000\n", + "Epoch 606/1000\n", + "26/26 [==============================] - 0s 314us/sample - loss: 1.1475e-04 - acc: 1.0000\n", + "Epoch 607/1000\n", + "26/26 [==============================] - 0s 333us/sample - loss: 1.1447e-04 - acc: 1.0000\n", + "Epoch 608/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 1.1417e-04 - acc: 1.0000\n", + "Epoch 609/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 1.1387e-04 - acc: 1.0000\n", + "Epoch 610/1000\n", + "26/26 [==============================] - 0s 337us/sample - loss: 1.1356e-04 - acc: 1.0000\n", + "Epoch 611/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 1.1324e-04 - acc: 1.0000\n", + "Epoch 612/1000\n", + "26/26 [==============================] - 0s 377us/sample - loss: 1.1295e-04 - acc: 1.0000\n", + "Epoch 613/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 1.1267e-04 - acc: 1.0000\n", + "Epoch 614/1000\n", + "26/26 [==============================] - 0s 334us/sample - loss: 1.1238e-04 - acc: 1.0000\n", + "Epoch 615/1000\n", + "26/26 [==============================] - 0s 326us/sample - loss: 1.1208e-04 - acc: 1.0000\n", + "Epoch 616/1000\n", + "26/26 [==============================] - 0s 325us/sample - loss: 1.1180e-04 - acc: 1.0000\n", + "Epoch 617/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 1.1152e-04 - acc: 1.0000\n", + "Epoch 618/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 1.1125e-04 - acc: 1.0000\n", + "Epoch 619/1000\n", + "26/26 [==============================] - 0s 515us/sample - loss: 1.1094e-04 - acc: 1.0000\n", + "Epoch 620/1000\n", + "26/26 [==============================] - 0s 389us/sample - loss: 1.1066e-04 - acc: 1.0000\n", + "Epoch 621/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 1.1039e-04 - acc: 1.0000\n", + "Epoch 622/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 1.1013e-04 - acc: 1.0000\n", + "Epoch 623/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 1.0985e-04 - acc: 1.0000\n", + "Epoch 624/1000\n", + "26/26 [==============================] - 0s 384us/sample - loss: 1.0956e-04 - acc: 1.0000\n", + "Epoch 625/1000\n", + "26/26 [==============================] - 0s 423us/sample - loss: 1.0925e-04 - acc: 1.0000\n", + "Epoch 626/1000\n", + "26/26 [==============================] - 0s 720us/sample - loss: 1.0895e-04 - acc: 1.0000\n", + "Epoch 627/1000\n", + "26/26 [==============================] - 0s 488us/sample - loss: 1.0867e-04 - acc: 1.0000\n", + "Epoch 628/1000\n", + "26/26 [==============================] - 0s 489us/sample - loss: 1.0834e-04 - acc: 1.0000\n", + "Epoch 629/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 1.0807e-04 - acc: 1.0000\n", + "Epoch 630/1000\n", + "26/26 [==============================] - 0s 451us/sample - loss: 1.0780e-04 - acc: 1.0000\n", + "Epoch 631/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 1.0754e-04 - acc: 1.0000\n", + "Epoch 632/1000\n", + "26/26 [==============================] - 0s 405us/sample - loss: 1.0725e-04 - acc: 1.0000\n", + "Epoch 633/1000\n", + "26/26 [==============================] - 0s 389us/sample - loss: 1.0699e-04 - acc: 1.0000\n", + "Epoch 634/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 1.0674e-04 - acc: 1.0000\n", + "Epoch 635/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 1.0650e-04 - acc: 1.0000\n", + "Epoch 636/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 1.0620e-04 - acc: 1.0000\n", + "Epoch 637/1000\n", + "26/26 [==============================] - 0s 599us/sample - loss: 1.0597e-04 - acc: 1.0000\n", + "Epoch 638/1000\n", + "26/26 [==============================] - 0s 447us/sample - loss: 1.0574e-04 - acc: 1.0000\n", + "Epoch 639/1000\n", + "26/26 [==============================] - 0s 413us/sample - loss: 1.0541e-04 - acc: 1.0000\n", + "Epoch 640/1000\n", + "26/26 [==============================] - 0s 409us/sample - loss: 1.0511e-04 - acc: 1.0000\n", + "Epoch 641/1000\n", + "26/26 [==============================] - 0s 467us/sample - loss: 1.0485e-04 - acc: 1.0000\n", + "Epoch 642/1000\n", + "26/26 [==============================] - 0s 447us/sample - loss: 1.0459e-04 - acc: 1.0000\n", + "Epoch 643/1000\n", + "26/26 [==============================] - 0s 486us/sample - loss: 1.0434e-04 - acc: 1.0000\n", + "Epoch 644/1000\n", + "26/26 [==============================] - 0s 453us/sample - loss: 1.0408e-04 - acc: 1.0000\n", + "Epoch 645/1000\n", + "26/26 [==============================] - 0s 487us/sample - loss: 1.0381e-04 - acc: 1.0000\n", + "Epoch 646/1000\n", + "26/26 [==============================] - 0s 410us/sample - loss: 1.0355e-04 - acc: 1.0000\n", + "Epoch 647/1000\n", + "26/26 [==============================] - 0s 438us/sample - loss: 1.0326e-04 - acc: 1.0000\n", + "Epoch 648/1000\n", + "26/26 [==============================] - 0s 477us/sample - loss: 1.0304e-04 - acc: 1.0000\n", + "Epoch 649/1000\n", + "26/26 [==============================] - 0s 398us/sample - loss: 1.0280e-04 - acc: 1.0000\n", + "Epoch 650/1000\n", + "26/26 [==============================] - 0s 540us/sample - loss: 1.0254e-04 - acc: 1.0000\n", + "Epoch 651/1000\n", + "26/26 [==============================] - 0s 409us/sample - loss: 1.0230e-04 - acc: 1.0000\n", + "Epoch 652/1000\n", + "26/26 [==============================] - 0s 494us/sample - loss: 1.0203e-04 - acc: 1.0000\n", + "Epoch 653/1000\n", + "26/26 [==============================] - 0s 469us/sample - loss: 1.0174e-04 - acc: 1.0000\n", + "Epoch 654/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 1.0150e-04 - acc: 1.0000\n", + "Epoch 655/1000\n", + "26/26 [==============================] - 0s 499us/sample - loss: 1.0121e-04 - acc: 1.0000\n", + "Epoch 656/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 1.0091e-04 - acc: 1.0000\n", + "Epoch 657/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 1.0067e-04 - acc: 1.0000\n", + "Epoch 658/1000\n", + "26/26 [==============================] - 0s 478us/sample - loss: 1.0036e-04 - acc: 1.0000\n", + "Epoch 659/1000\n", + "26/26 [==============================] - 0s 401us/sample - loss: 1.0011e-04 - acc: 1.0000\n", + "Epoch 660/1000\n", + "26/26 [==============================] - 0s 416us/sample - loss: 9.9874e-05 - acc: 1.0000\n", + "Epoch 661/1000\n", + "26/26 [==============================] - 0s 444us/sample - loss: 9.9617e-05 - acc: 1.0000\n", + "Epoch 662/1000\n", + "26/26 [==============================] - 0s 463us/sample - loss: 9.9379e-05 - acc: 1.0000\n", + "Epoch 663/1000\n", + "26/26 [==============================] - 0s 399us/sample - loss: 9.9113e-05 - acc: 1.0000\n", + "Epoch 664/1000\n", + "26/26 [==============================] - 0s 393us/sample - loss: 9.8879e-05 - acc: 1.0000\n", + "Epoch 665/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 9.8627e-05 - acc: 1.0000\n", + "Epoch 666/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 9.8348e-05 - acc: 1.0000\n", + "Epoch 667/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 9.8082e-05 - acc: 1.0000\n", + "Epoch 668/1000\n", + "26/26 [==============================] - 0s 350us/sample - loss: 9.7843e-05 - acc: 1.0000\n", + "Epoch 669/1000\n", + "26/26 [==============================] - 0s 332us/sample - loss: 9.7550e-05 - acc: 1.0000\n", + "Epoch 670/1000\n", + "26/26 [==============================] - 0s 360us/sample - loss: 9.7321e-05 - acc: 1.0000\n", + "Epoch 671/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 9.7087e-05 - acc: 1.0000\n", + "Epoch 672/1000\n", + "26/26 [==============================] - 0s 377us/sample - loss: 9.6844e-05 - acc: 1.0000\n", + "Epoch 673/1000\n", + "26/26 [==============================] - 0s 350us/sample - loss: 9.6619e-05 - acc: 1.0000\n", + "Epoch 674/1000\n", + "26/26 [==============================] - 0s 360us/sample - loss: 9.6395e-05 - acc: 1.0000\n", + "Epoch 675/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 9.6138e-05 - acc: 1.0000\n", + "Epoch 676/1000\n", + "26/26 [==============================] - 0s 348us/sample - loss: 9.5927e-05 - acc: 1.0000\n", + "Epoch 677/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 9.5652e-05 - acc: 1.0000\n", + "Epoch 678/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 9.5405e-05 - acc: 1.0000\n", + "Epoch 679/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 9.5153e-05 - acc: 1.0000\n", + "Epoch 680/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 9.4914e-05 - acc: 1.0000\n", + "Epoch 681/1000\n", + "26/26 [==============================] - 0s 335us/sample - loss: 9.4667e-05 - acc: 1.0000\n", + "Epoch 682/1000\n", + "26/26 [==============================] - 0s 373us/sample - loss: 9.4447e-05 - acc: 1.0000\n", + "Epoch 683/1000\n", + "26/26 [==============================] - 0s 328us/sample - loss: 9.4217e-05 - acc: 1.0000\n", + "Epoch 684/1000\n", + "26/26 [==============================] - 0s 328us/sample - loss: 9.3984e-05 - acc: 1.0000\n", + "Epoch 685/1000\n", + "26/26 [==============================] - 0s 329us/sample - loss: 9.3759e-05 - acc: 1.0000\n", + "Epoch 686/1000\n", + "26/26 [==============================] - 0s 321us/sample - loss: 9.3488e-05 - acc: 1.0000\n", + "Epoch 687/1000\n", + "26/26 [==============================] - 0s 317us/sample - loss: 9.3287e-05 - acc: 1.0000\n", + "Epoch 688/1000\n", + "26/26 [==============================] - 0s 312us/sample - loss: 9.3016e-05 - acc: 1.0000\n", + "Epoch 689/1000\n", + "26/26 [==============================] - 0s 325us/sample - loss: 9.2783e-05 - acc: 1.0000\n", + "Epoch 690/1000\n", + "26/26 [==============================] - 0s 319us/sample - loss: 9.2549e-05 - acc: 1.0000\n", + "Epoch 691/1000\n", + "26/26 [==============================] - 0s 328us/sample - loss: 9.2315e-05 - acc: 1.0000\n", + "Epoch 692/1000\n", + "26/26 [==============================] - 0s 325us/sample - loss: 9.2081e-05 - acc: 1.0000\n", + "Epoch 693/1000\n", + "26/26 [==============================] - 0s 389us/sample - loss: 9.1866e-05 - acc: 1.0000\n", + "Epoch 694/1000\n", + "26/26 [==============================] - 0s 355us/sample - loss: 9.1636e-05 - acc: 1.0000\n", + "Epoch 695/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 9.1416e-05 - acc: 1.0000\n", + "Epoch 696/1000\n", + "26/26 [==============================] - 0s 337us/sample - loss: 9.1187e-05 - acc: 1.0000\n", + "Epoch 697/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 9.0976e-05 - acc: 1.0000\n", + "Epoch 698/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 9.0752e-05 - acc: 1.0000\n", + "Epoch 699/1000\n", + "26/26 [==============================] - 0s 335us/sample - loss: 9.0518e-05 - acc: 1.0000\n", + "Epoch 700/1000\n", + "26/26 [==============================] - 0s 339us/sample - loss: 9.0280e-05 - acc: 1.0000\n", + "Epoch 701/1000\n", + "26/26 [==============================] - 0s 428us/sample - loss: 9.0096e-05 - acc: 1.0000\n", + "Epoch 702/1000\n", + "26/26 [==============================] - 0s 386us/sample - loss: 8.9817e-05 - acc: 1.0000\n", + "Epoch 703/1000\n", + "26/26 [==============================] - 0s 345us/sample - loss: 8.9592e-05 - acc: 1.0000\n", + "Epoch 704/1000\n", + "26/26 [==============================] - 0s 329us/sample - loss: 8.9372e-05 - acc: 1.0000\n", + "Epoch 705/1000\n", + "26/26 [==============================] - 0s 1ms/sample - loss: 8.9111e-05 - acc: 1.0000\n", + "Epoch 706/1000\n", + "26/26 [==============================] - 0s 766us/sample - loss: 8.8840e-05 - acc: 1.0000\n", + "Epoch 707/1000\n", + "26/26 [==============================] - 0s 564us/sample - loss: 8.8643e-05 - acc: 1.0000\n", + "Epoch 708/1000\n", + "26/26 [==============================] - 0s 475us/sample - loss: 8.8428e-05 - acc: 1.0000\n", + "Epoch 709/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 8.8189e-05 - acc: 1.0000\n", + "Epoch 710/1000\n", + "26/26 [==============================] - 0s 530us/sample - loss: 8.7983e-05 - acc: 1.0000\n", + "Epoch 711/1000\n", + "26/26 [==============================] - 0s 449us/sample - loss: 8.7754e-05 - acc: 1.0000\n", + "Epoch 712/1000\n", + "26/26 [==============================] - 0s 449us/sample - loss: 8.7529e-05 - acc: 1.0000\n", + "Epoch 713/1000\n", + "26/26 [==============================] - 0s 754us/sample - loss: 8.7300e-05 - acc: 1.0000\n", + "Epoch 714/1000\n", + "26/26 [==============================] - 0s 425us/sample - loss: 8.7098e-05 - acc: 1.0000\n", + "Epoch 715/1000\n", + "26/26 [==============================] - 0s 396us/sample - loss: 8.6873e-05 - acc: 1.0000\n", + "Epoch 716/1000\n", + "26/26 [==============================] - 0s 403us/sample - loss: 8.6658e-05 - acc: 1.0000\n", + "Epoch 717/1000\n", + "26/26 [==============================] - 0s 401us/sample - loss: 8.6461e-05 - acc: 1.0000\n", + "Epoch 718/1000\n", + "26/26 [==============================] - 0s 336us/sample - loss: 8.6250e-05 - acc: 1.0000\n", + "Epoch 719/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 8.6012e-05 - acc: 1.0000\n", + "Epoch 720/1000\n", + "26/26 [==============================] - 0s 383us/sample - loss: 8.5778e-05 - acc: 1.0000\n", + "Epoch 721/1000\n", + "26/26 [==============================] - 0s 329us/sample - loss: 8.5562e-05 - acc: 1.0000\n", + "Epoch 722/1000\n", + "26/26 [==============================] - 0s 320us/sample - loss: 8.5319e-05 - acc: 1.0000\n", + "Epoch 723/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 8.5063e-05 - acc: 1.0000\n", + "Epoch 724/1000\n", + "26/26 [==============================] - 0s 355us/sample - loss: 8.4861e-05 - acc: 1.0000\n", + "Epoch 725/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 8.4632e-05 - acc: 1.0000\n", + "Epoch 726/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 8.4398e-05 - acc: 1.0000\n", + "Epoch 727/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 8.4183e-05 - acc: 1.0000\n", + "Epoch 728/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 8.3995e-05 - acc: 1.0000\n", + "Epoch 729/1000\n", + "26/26 [==============================] - 0s 372us/sample - loss: 8.3752e-05 - acc: 1.0000\n", + "Epoch 730/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 8.3573e-05 - acc: 1.0000\n", + "Epoch 731/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 8.3348e-05 - acc: 1.0000\n", + "Epoch 732/1000\n", + "26/26 [==============================] - 0s 423us/sample - loss: 8.3119e-05 - acc: 1.0000\n", + "Epoch 733/1000\n", + "26/26 [==============================] - 0s 469us/sample - loss: 8.2913e-05 - acc: 1.0000\n", + "Epoch 734/1000\n", + "26/26 [==============================] - 0s 451us/sample - loss: 8.2693e-05 - acc: 1.0000\n", + "Epoch 735/1000\n", + "26/26 [==============================] - 0s 438us/sample - loss: 8.2473e-05 - acc: 1.0000\n", + "Epoch 736/1000\n", + "26/26 [==============================] - 0s 502us/sample - loss: 8.2262e-05 - acc: 1.0000\n", + "Epoch 737/1000\n", + "26/26 [==============================] - 0s 446us/sample - loss: 8.2065e-05 - acc: 1.0000\n", + "Epoch 738/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 8.1886e-05 - acc: 1.0000\n", + "Epoch 739/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 8.1675e-05 - acc: 1.0000\n", + "Epoch 740/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 8.1492e-05 - acc: 1.0000\n", + "Epoch 741/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 8.1290e-05 - acc: 1.0000\n", + "Epoch 742/1000\n", + "26/26 [==============================] - 0s 414us/sample - loss: 8.1107e-05 - acc: 1.0000\n", + "Epoch 743/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 8.0900e-05 - acc: 1.0000\n", + "Epoch 744/1000\n", + "26/26 [==============================] - 0s 468us/sample - loss: 8.0671e-05 - acc: 1.0000\n", + "Epoch 745/1000\n", + "26/26 [==============================] - 0s 420us/sample - loss: 8.0465e-05 - acc: 1.0000\n", + "Epoch 746/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 8.0281e-05 - acc: 1.0000\n", + "Epoch 747/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 8.0103e-05 - acc: 1.0000\n", + "Epoch 748/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 7.9896e-05 - acc: 1.0000\n", + "Epoch 749/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 7.9704e-05 - acc: 1.0000\n", + "Epoch 750/1000\n", + "26/26 [==============================] - 0s 335us/sample - loss: 7.9497e-05 - acc: 1.0000\n", + "Epoch 751/1000\n", + "26/26 [==============================] - 0s 321us/sample - loss: 7.9273e-05 - acc: 1.0000\n", + "Epoch 752/1000\n", + "26/26 [==============================] - 0s 296us/sample - loss: 7.9089e-05 - acc: 1.0000\n", + "Epoch 753/1000\n", + "26/26 [==============================] - 0s 287us/sample - loss: 7.8874e-05 - acc: 1.0000\n", + "Epoch 754/1000\n", + "26/26 [==============================] - 0s 280us/sample - loss: 7.8681e-05 - acc: 1.0000\n", + "Epoch 755/1000\n", + "26/26 [==============================] - 0s 277us/sample - loss: 7.8493e-05 - acc: 1.0000\n", + "Epoch 756/1000\n", + "26/26 [==============================] - 0s 296us/sample - loss: 7.8301e-05 - acc: 1.0000\n", + "Epoch 757/1000\n", + "26/26 [==============================] - 0s 304us/sample - loss: 7.8145e-05 - acc: 1.0000\n", + "Epoch 758/1000\n", + "26/26 [==============================] - 0s 310us/sample - loss: 7.7971e-05 - acc: 1.0000\n", + "Epoch 759/1000\n", + "26/26 [==============================] - 0s 327us/sample - loss: 7.7742e-05 - acc: 1.0000\n", + "Epoch 760/1000\n", + "26/26 [==============================] - 0s 243us/sample - loss: 7.7586e-05 - acc: 1.0000\n", + "Epoch 761/1000\n", + "26/26 [==============================] - 0s 336us/sample - loss: 7.7384e-05 - acc: 1.0000\n", + "Epoch 762/1000\n", + "26/26 [==============================] - 0s 287us/sample - loss: 7.7210e-05 - acc: 1.0000\n", + "Epoch 763/1000\n", + "26/26 [==============================] - 0s 288us/sample - loss: 7.7017e-05 - acc: 1.0000\n", + "Epoch 764/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 7.6843e-05 - acc: 1.0000\n", + "Epoch 765/1000\n", + "26/26 [==============================] - 0s 329us/sample - loss: 7.6600e-05 - acc: 1.0000\n", + "Epoch 766/1000\n", + "26/26 [==============================] - 0s 326us/sample - loss: 7.6412e-05 - acc: 1.0000\n", + "Epoch 767/1000\n", + "26/26 [==============================] - 0s 296us/sample - loss: 7.6210e-05 - acc: 1.0000\n", + "Epoch 768/1000\n", + "26/26 [==============================] - 0s 335us/sample - loss: 7.6036e-05 - acc: 1.0000\n", + "Epoch 769/1000\n", + "26/26 [==============================] - 0s 292us/sample - loss: 7.5853e-05 - acc: 1.0000\n", + "Epoch 770/1000\n", + "26/26 [==============================] - 0s 300us/sample - loss: 7.5670e-05 - acc: 1.0000\n", + "Epoch 771/1000\n", + "26/26 [==============================] - 0s 291us/sample - loss: 7.5472e-05 - acc: 1.0000\n", + "Epoch 772/1000\n", + "26/26 [==============================] - 0s 283us/sample - loss: 7.5307e-05 - acc: 1.0000\n", + "Epoch 773/1000\n", + "26/26 [==============================] - 0s 286us/sample - loss: 7.5087e-05 - acc: 1.0000\n", + "Epoch 774/1000\n", + "26/26 [==============================] - 0s 287us/sample - loss: 7.4890e-05 - acc: 1.0000\n", + "Epoch 775/1000\n", + "26/26 [==============================] - 0s 327us/sample - loss: 7.4743e-05 - acc: 1.0000\n", + "Epoch 776/1000\n", + "26/26 [==============================] - 0s 432us/sample - loss: 7.4533e-05 - acc: 1.0000\n", + "Epoch 777/1000\n", + "26/26 [==============================] - 0s 373us/sample - loss: 7.4354e-05 - acc: 1.0000\n", + "Epoch 778/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 7.4166e-05 - acc: 1.0000\n", + "Epoch 779/1000\n", + "26/26 [==============================] - 0s 317us/sample - loss: 7.3992e-05 - acc: 1.0000\n", + "Epoch 780/1000\n", + "26/26 [==============================] - 0s 430us/sample - loss: 7.3822e-05 - acc: 1.0000\n", + "Epoch 781/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 7.3625e-05 - acc: 1.0000\n", + "Epoch 782/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 7.3446e-05 - acc: 1.0000\n", + "Epoch 783/1000\n", + "26/26 [==============================] - 0s 384us/sample - loss: 7.3272e-05 - acc: 1.0000\n", + "Epoch 784/1000\n", + "26/26 [==============================] - 0s 324us/sample - loss: 7.3066e-05 - acc: 1.0000\n", + "Epoch 785/1000\n", + "26/26 [==============================] - 0s 340us/sample - loss: 7.2882e-05 - acc: 1.0000\n", + "Epoch 786/1000\n", + "26/26 [==============================] - 0s 404us/sample - loss: 7.2722e-05 - acc: 1.0000\n", + "Epoch 787/1000\n", + "26/26 [==============================] - 0s 407us/sample - loss: 7.2529e-05 - acc: 1.0000\n", + "Epoch 788/1000\n", + "26/26 [==============================] - 0s 489us/sample - loss: 7.2346e-05 - acc: 1.0000\n", + "Epoch 789/1000\n", + "26/26 [==============================] - 0s 456us/sample - loss: 7.2153e-05 - acc: 1.0000\n", + "Epoch 790/1000\n", + "26/26 [==============================] - 0s 458us/sample - loss: 7.2002e-05 - acc: 1.0000\n", + "Epoch 791/1000\n", + "26/26 [==============================] - 0s 493us/sample - loss: 7.1814e-05 - acc: 1.0000\n", + "Epoch 792/1000\n", + "26/26 [==============================] - 0s 520us/sample - loss: 7.1649e-05 - acc: 1.0000\n", + "Epoch 793/1000\n", + "26/26 [==============================] - 0s 471us/sample - loss: 7.1429e-05 - acc: 1.0000\n", + "Epoch 794/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 7.1246e-05 - acc: 1.0000\n", + "Epoch 795/1000\n", + "26/26 [==============================] - 0s 424us/sample - loss: 7.1094e-05 - acc: 1.0000\n", + "Epoch 796/1000\n", + "26/26 [==============================] - 0s 443us/sample - loss: 7.0920e-05 - acc: 1.0000\n", + "Epoch 797/1000\n", + "26/26 [==============================] - 0s 432us/sample - loss: 7.0741e-05 - acc: 1.0000\n", + "Epoch 798/1000\n", + "26/26 [==============================] - 0s 443us/sample - loss: 7.0576e-05 - acc: 1.0000\n", + "Epoch 799/1000\n", + "26/26 [==============================] - 0s 417us/sample - loss: 7.0430e-05 - acc: 1.0000\n", + "Epoch 800/1000\n", + "26/26 [==============================] - 0s 310us/sample - loss: 7.0210e-05 - acc: 1.0000\n", + "Epoch 801/1000\n", + "26/26 [==============================] - 0s 444us/sample - loss: 7.0045e-05 - acc: 1.0000\n", + "Epoch 802/1000\n", + "26/26 [==============================] - 0s 385us/sample - loss: 6.9884e-05 - acc: 1.0000\n", + "Epoch 803/1000\n", + "26/26 [==============================] - 0s 467us/sample - loss: 6.9728e-05 - acc: 1.0000\n", + "Epoch 804/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 6.9527e-05 - acc: 1.0000\n", + "Epoch 805/1000\n", + "26/26 [==============================] - 0s 452us/sample - loss: 6.9357e-05 - acc: 1.0000\n", + "Epoch 806/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 6.9206e-05 - acc: 1.0000\n", + "Epoch 807/1000\n", + "26/26 [==============================] - 0s 442us/sample - loss: 6.9018e-05 - acc: 1.0000\n", + "Epoch 808/1000\n", + "26/26 [==============================] - 0s 424us/sample - loss: 6.8848e-05 - acc: 1.0000\n", + "Epoch 809/1000\n", + "26/26 [==============================] - 0s 435us/sample - loss: 6.8692e-05 - acc: 1.0000\n", + "Epoch 810/1000\n", + "26/26 [==============================] - 0s 348us/sample - loss: 6.8513e-05 - acc: 1.0000\n", + "Epoch 811/1000\n", + "26/26 [==============================] - 0s 343us/sample - loss: 6.8344e-05 - acc: 1.0000\n", + "Epoch 812/1000\n", + "26/26 [==============================] - 0s 353us/sample - loss: 6.8192e-05 - acc: 1.0000\n", + "Epoch 813/1000\n", + "26/26 [==============================] - 0s 457us/sample - loss: 6.8000e-05 - acc: 1.0000\n", + "Epoch 814/1000\n", + "26/26 [==============================] - 0s 417us/sample - loss: 6.7817e-05 - acc: 1.0000\n", + "Epoch 815/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 6.7670e-05 - acc: 1.0000\n", + "Epoch 816/1000\n", + "26/26 [==============================] - 0s 492us/sample - loss: 6.7519e-05 - acc: 1.0000\n", + "Epoch 817/1000\n", + "26/26 [==============================] - 0s 429us/sample - loss: 6.7344e-05 - acc: 1.0000\n", + "Epoch 818/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 6.7161e-05 - acc: 1.0000\n", + "Epoch 819/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 6.6991e-05 - acc: 1.0000\n", + "Epoch 820/1000\n", + "26/26 [==============================] - 0s 340us/sample - loss: 6.6817e-05 - acc: 1.0000\n", + "Epoch 821/1000\n", + "26/26 [==============================] - 0s 329us/sample - loss: 6.6657e-05 - acc: 1.0000\n", + "Epoch 822/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 6.6473e-05 - acc: 1.0000\n", + "Epoch 823/1000\n", + "26/26 [==============================] - 0s 396us/sample - loss: 6.6322e-05 - acc: 1.0000\n", + "Epoch 824/1000\n", + "26/26 [==============================] - 0s 429us/sample - loss: 6.6175e-05 - acc: 1.0000\n", + "Epoch 825/1000\n", + "26/26 [==============================] - 0s 396us/sample - loss: 6.6024e-05 - acc: 1.0000\n", + "Epoch 826/1000\n", + "26/26 [==============================] - 0s 434us/sample - loss: 6.5873e-05 - acc: 1.0000\n", + "Epoch 827/1000\n", + "26/26 [==============================] - 0s 432us/sample - loss: 6.5703e-05 - acc: 1.0000\n", + "Epoch 828/1000\n", + "26/26 [==============================] - 0s 421us/sample - loss: 6.5529e-05 - acc: 1.0000\n", + "Epoch 829/1000\n", + "26/26 [==============================] - 0s 440us/sample - loss: 6.5378e-05 - acc: 1.0000\n", + "Epoch 830/1000\n", + "26/26 [==============================] - 0s 405us/sample - loss: 6.5190e-05 - acc: 1.0000\n", + "Epoch 831/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 6.5034e-05 - acc: 1.0000\n", + "Epoch 832/1000\n", + "26/26 [==============================] - 0s 474us/sample - loss: 6.4869e-05 - acc: 1.0000\n", + "Epoch 833/1000\n", + "26/26 [==============================] - 0s 415us/sample - loss: 6.4722e-05 - acc: 1.0000\n", + "Epoch 834/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 6.4575e-05 - acc: 1.0000\n", + "Epoch 835/1000\n", + "26/26 [==============================] - 0s 433us/sample - loss: 6.4429e-05 - acc: 1.0000\n", + "Epoch 836/1000\n", + "26/26 [==============================] - 0s 332us/sample - loss: 6.4241e-05 - acc: 1.0000\n", + "Epoch 837/1000\n", + "26/26 [==============================] - 0s 323us/sample - loss: 6.4103e-05 - acc: 1.0000\n", + "Epoch 838/1000\n", + "26/26 [==============================] - 0s 377us/sample - loss: 6.3961e-05 - acc: 1.0000\n", + "Epoch 839/1000\n", + "26/26 [==============================] - 0s 373us/sample - loss: 6.3814e-05 - acc: 1.0000\n", + "Epoch 840/1000\n", + "26/26 [==============================] - 0s 445us/sample - loss: 6.3672e-05 - acc: 1.0000\n", + "Epoch 841/1000\n", + "26/26 [==============================] - 0s 371us/sample - loss: 6.3507e-05 - acc: 1.0000\n", + "Epoch 842/1000\n", + "26/26 [==============================] - 0s 328us/sample - loss: 6.3361e-05 - acc: 1.0000\n", + "Epoch 843/1000\n", + "26/26 [==============================] - 0s 431us/sample - loss: 6.3205e-05 - acc: 1.0000\n", + "Epoch 844/1000\n", + "26/26 [==============================] - 0s 478us/sample - loss: 6.3035e-05 - acc: 1.0000\n", + "Epoch 845/1000\n", + "26/26 [==============================] - 0s 356us/sample - loss: 6.2911e-05 - acc: 1.0000\n", + "Epoch 846/1000\n", + "26/26 [==============================] - 0s 395us/sample - loss: 6.2751e-05 - acc: 1.0000\n", + "Epoch 847/1000\n", + "26/26 [==============================] - 0s 354us/sample - loss: 6.2595e-05 - acc: 1.0000\n", + "Epoch 848/1000\n", + "26/26 [==============================] - 0s 510us/sample - loss: 6.2448e-05 - acc: 1.0000\n", + "Epoch 849/1000\n", + "26/26 [==============================] - 0s 432us/sample - loss: 6.2302e-05 - acc: 1.0000\n", + "Epoch 850/1000\n", + "26/26 [==============================] - 0s 376us/sample - loss: 6.2159e-05 - acc: 1.0000\n", + "Epoch 851/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 6.1985e-05 - acc: 1.0000\n", + "Epoch 852/1000\n", + "26/26 [==============================] - 0s 439us/sample - loss: 6.1861e-05 - acc: 1.0000\n", + "Epoch 853/1000\n", + "26/26 [==============================] - 0s 496us/sample - loss: 6.1715e-05 - acc: 1.0000\n", + "Epoch 854/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 6.1568e-05 - acc: 1.0000\n", + "Epoch 855/1000\n", + "26/26 [==============================] - 0s 464us/sample - loss: 6.1403e-05 - acc: 1.0000\n", + "Epoch 856/1000\n", + "26/26 [==============================] - 0s 398us/sample - loss: 6.1252e-05 - acc: 1.0000\n", + "Epoch 857/1000\n", + "26/26 [==============================] - 0s 446us/sample - loss: 6.1110e-05 - acc: 1.0000\n", + "Epoch 858/1000\n", + "26/26 [==============================] - 0s 454us/sample - loss: 6.0972e-05 - acc: 1.0000\n", + "Epoch 859/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 6.0825e-05 - acc: 1.0000\n", + "Epoch 860/1000\n", + "26/26 [==============================] - 0s 415us/sample - loss: 6.0669e-05 - acc: 1.0000\n", + "Epoch 861/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 6.0532e-05 - acc: 1.0000\n", + "Epoch 862/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 6.0404e-05 - acc: 1.0000\n", + "Epoch 863/1000\n", + "26/26 [==============================] - 0s 394us/sample - loss: 6.0261e-05 - acc: 1.0000\n", + "Epoch 864/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 6.0106e-05 - acc: 1.0000\n", + "Epoch 865/1000\n", + "26/26 [==============================] - 0s 355us/sample - loss: 5.9977e-05 - acc: 1.0000\n", + "Epoch 866/1000\n", + "26/26 [==============================] - 0s 321us/sample - loss: 5.9817e-05 - acc: 1.0000\n", + "Epoch 867/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 5.9670e-05 - acc: 1.0000\n", + "Epoch 868/1000\n", + "26/26 [==============================] - 0s 376us/sample - loss: 5.9537e-05 - acc: 1.0000\n", + "Epoch 869/1000\n", + "26/26 [==============================] - 0s 410us/sample - loss: 5.9418e-05 - acc: 1.0000\n", + "Epoch 870/1000\n", + "26/26 [==============================] - 0s 528us/sample - loss: 5.9262e-05 - acc: 1.0000\n", + "Epoch 871/1000\n", + "26/26 [==============================] - 0s 588us/sample - loss: 5.9102e-05 - acc: 1.0000\n", + "Epoch 872/1000\n", + "26/26 [==============================] - 0s 368us/sample - loss: 5.8941e-05 - acc: 1.0000\n", + "Epoch 873/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 5.8804e-05 - acc: 1.0000\n", + "Epoch 874/1000\n", + "26/26 [==============================] - 0s 335us/sample - loss: 5.8675e-05 - acc: 1.0000\n", + "Epoch 875/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 5.8492e-05 - acc: 1.0000\n", + "Epoch 876/1000\n", + "26/26 [==============================] - 0s 365us/sample - loss: 5.8363e-05 - acc: 1.0000\n", + "Epoch 877/1000\n", + "26/26 [==============================] - 0s 394us/sample - loss: 5.8235e-05 - acc: 1.0000\n", + "Epoch 878/1000\n", + "26/26 [==============================] - 0s 402us/sample - loss: 5.8111e-05 - acc: 1.0000\n", + "Epoch 879/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 5.7960e-05 - acc: 1.0000\n", + "Epoch 880/1000\n", + "26/26 [==============================] - 0s 405us/sample - loss: 5.7818e-05 - acc: 1.0000\n", + "Epoch 881/1000\n", + "26/26 [==============================] - 0s 462us/sample - loss: 5.7699e-05 - acc: 1.0000\n", + "Epoch 882/1000\n", + "26/26 [==============================] - 0s 459us/sample - loss: 5.7557e-05 - acc: 1.0000\n", + "Epoch 883/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 5.7419e-05 - acc: 1.0000\n", + "Epoch 884/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 5.7300e-05 - acc: 1.0000\n", + "Epoch 885/1000\n", + "26/26 [==============================] - 0s 333us/sample - loss: 5.7135e-05 - acc: 1.0000\n", + "Epoch 886/1000\n", + "26/26 [==============================] - 0s 400us/sample - loss: 5.7002e-05 - acc: 1.0000\n", + "Epoch 887/1000\n", + "26/26 [==============================] - 0s 445us/sample - loss: 5.6869e-05 - acc: 1.0000\n", + "Epoch 888/1000\n", + "26/26 [==============================] - 0s 406us/sample - loss: 5.6713e-05 - acc: 1.0000\n", + "Epoch 889/1000\n", + "26/26 [==============================] - 0s 412us/sample - loss: 5.6557e-05 - acc: 1.0000\n", + "Epoch 890/1000\n", + "26/26 [==============================] - 0s 441us/sample - loss: 5.6429e-05 - acc: 1.0000\n", + "Epoch 891/1000\n", + "26/26 [==============================] - 0s 396us/sample - loss: 5.6259e-05 - acc: 1.0000\n", + "Epoch 892/1000\n", + "26/26 [==============================] - 0s 315us/sample - loss: 5.6131e-05 - acc: 1.0000\n", + "Epoch 893/1000\n", + "26/26 [==============================] - 0s 408us/sample - loss: 5.5993e-05 - acc: 1.0000\n", + "Epoch 894/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 5.5842e-05 - acc: 1.0000\n", + "Epoch 895/1000\n", + "26/26 [==============================] - 0s 324us/sample - loss: 5.5704e-05 - acc: 1.0000\n", + "Epoch 896/1000\n", + "26/26 [==============================] - 0s 377us/sample - loss: 5.5572e-05 - acc: 1.0000\n", + "Epoch 897/1000\n", + "26/26 [==============================] - 0s 397us/sample - loss: 5.5434e-05 - acc: 1.0000\n", + "Epoch 898/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 5.5287e-05 - acc: 1.0000\n", + "Epoch 899/1000\n", + "26/26 [==============================] - 0s 382us/sample - loss: 5.5150e-05 - acc: 1.0000\n", + "Epoch 900/1000\n", + "26/26 [==============================] - 0s 524us/sample - loss: 5.4998e-05 - acc: 1.0000\n", + "Epoch 901/1000\n", + "26/26 [==============================] - 0s 404us/sample - loss: 5.4847e-05 - acc: 1.0000\n", + "Epoch 902/1000\n", + "26/26 [==============================] - 0s 325us/sample - loss: 5.4705e-05 - acc: 1.0000\n", + "Epoch 903/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 5.4554e-05 - acc: 1.0000\n", + "Epoch 904/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 5.4430e-05 - acc: 1.0000\n", + "Epoch 905/1000\n", + "26/26 [==============================] - 0s 290us/sample - loss: 5.4274e-05 - acc: 1.0000\n", + "Epoch 906/1000\n", + "26/26 [==============================] - 0s 446us/sample - loss: 5.4146e-05 - acc: 1.0000\n", + "Epoch 907/1000\n", + "26/26 [==============================] - 0s 419us/sample - loss: 5.4013e-05 - acc: 1.0000\n", + "Epoch 908/1000\n", + "26/26 [==============================] - 0s 413us/sample - loss: 5.3903e-05 - acc: 1.0000\n", + "Epoch 909/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 5.3770e-05 - acc: 1.0000\n", + "Epoch 910/1000\n", + "26/26 [==============================] - 0s 607us/sample - loss: 5.3632e-05 - acc: 1.0000\n", + "Epoch 911/1000\n", + "26/26 [==============================] - 0s 527us/sample - loss: 5.3495e-05 - acc: 1.0000\n", + "Epoch 912/1000\n", + "26/26 [==============================] - 0s 527us/sample - loss: 5.3376e-05 - acc: 1.0000\n", + "Epoch 913/1000\n", + "26/26 [==============================] - 0s 359us/sample - loss: 5.3261e-05 - acc: 1.0000\n", + "Epoch 914/1000\n", + "26/26 [==============================] - 0s 513us/sample - loss: 5.3128e-05 - acc: 1.0000\n", + "Epoch 915/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 5.2990e-05 - acc: 1.0000\n", + "Epoch 916/1000\n", + "26/26 [==============================] - 0s 662us/sample - loss: 5.2871e-05 - acc: 1.0000\n", + "Epoch 917/1000\n", + "26/26 [==============================] - 0s 432us/sample - loss: 5.2738e-05 - acc: 1.0000\n", + "Epoch 918/1000\n", + "26/26 [==============================] - 0s 403us/sample - loss: 5.2610e-05 - acc: 1.0000\n", + "Epoch 919/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 5.2472e-05 - acc: 1.0000\n", + "Epoch 920/1000\n", + "26/26 [==============================] - 0s 390us/sample - loss: 5.2367e-05 - acc: 1.0000\n", + "Epoch 921/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 5.2225e-05 - acc: 1.0000\n", + "Epoch 922/1000\n", + "26/26 [==============================] - 0s 349us/sample - loss: 5.2096e-05 - acc: 1.0000\n", + "Epoch 923/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 5.1973e-05 - acc: 1.0000\n", + "Epoch 924/1000\n", + "26/26 [==============================] - 0s 478us/sample - loss: 5.1881e-05 - acc: 1.0000\n", + "Epoch 925/1000\n", + "26/26 [==============================] - 0s 494us/sample - loss: 5.1734e-05 - acc: 1.0000\n", + "Epoch 926/1000\n", + "26/26 [==============================] - 0s 442us/sample - loss: 5.1629e-05 - acc: 1.0000\n", + "Epoch 927/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 5.1491e-05 - acc: 1.0000\n", + "Epoch 928/1000\n", + "26/26 [==============================] - 0s 423us/sample - loss: 5.1363e-05 - acc: 1.0000\n", + "Epoch 929/1000\n", + "26/26 [==============================] - 0s 431us/sample - loss: 5.1221e-05 - acc: 1.0000\n", + "Epoch 930/1000\n", + "26/26 [==============================] - 0s 373us/sample - loss: 5.1111e-05 - acc: 1.0000\n", + "Epoch 931/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 5.0982e-05 - acc: 1.0000\n", + "Epoch 932/1000\n", + "26/26 [==============================] - 0s 327us/sample - loss: 5.0859e-05 - acc: 1.0000\n", + "Epoch 933/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 5.0721e-05 - acc: 1.0000\n", + "Epoch 934/1000\n", + "26/26 [==============================] - 0s 337us/sample - loss: 5.0588e-05 - acc: 1.0000\n", + "Epoch 935/1000\n", + "26/26 [==============================] - 0s 353us/sample - loss: 5.0446e-05 - acc: 1.0000\n", + "Epoch 936/1000\n", + "26/26 [==============================] - 0s 358us/sample - loss: 5.0327e-05 - acc: 1.0000\n", + "Epoch 937/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 5.0180e-05 - acc: 1.0000\n", + "Epoch 938/1000\n", + "26/26 [==============================] - 0s 356us/sample - loss: 5.0079e-05 - acc: 1.0000\n", + "Epoch 939/1000\n", + "26/26 [==============================] - 0s 519us/sample - loss: 4.9928e-05 - acc: 1.0000\n", + "Epoch 940/1000\n", + "26/26 [==============================] - 0s 393us/sample - loss: 4.9823e-05 - acc: 1.0000\n", + "Epoch 941/1000\n", + "26/26 [==============================] - 0s 334us/sample - loss: 4.9694e-05 - acc: 1.0000\n", + "Epoch 942/1000\n", + "26/26 [==============================] - 0s 423us/sample - loss: 4.9570e-05 - acc: 1.0000\n", + "Epoch 943/1000\n", + "26/26 [==============================] - 0s 417us/sample - loss: 4.9433e-05 - acc: 1.0000\n", + "Epoch 944/1000\n", + "26/26 [==============================] - 0s 396us/sample - loss: 4.9323e-05 - acc: 1.0000\n", + "Epoch 945/1000\n", + "26/26 [==============================] - 0s 556us/sample - loss: 4.9231e-05 - acc: 1.0000\n", + "Epoch 946/1000\n", + "26/26 [==============================] - 0s 343us/sample - loss: 4.9117e-05 - acc: 1.0000\n", + "Epoch 947/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 4.9002e-05 - acc: 1.0000\n", + "Epoch 948/1000\n", + "26/26 [==============================] - 0s 342us/sample - loss: 4.8874e-05 - acc: 1.0000\n", + "Epoch 949/1000\n", + "26/26 [==============================] - 0s 379us/sample - loss: 4.8754e-05 - acc: 1.0000\n", + "Epoch 950/1000\n", + "26/26 [==============================] - 0s 429us/sample - loss: 4.8626e-05 - acc: 1.0000\n", + "Epoch 951/1000\n", + "26/26 [==============================] - 0s 402us/sample - loss: 4.8498e-05 - acc: 1.0000\n", + "Epoch 952/1000\n", + "26/26 [==============================] - 0s 405us/sample - loss: 4.8388e-05 - acc: 1.0000\n", + "Epoch 953/1000\n", + "26/26 [==============================] - 0s 364us/sample - loss: 4.8268e-05 - acc: 1.0000\n", + "Epoch 954/1000\n", + "26/26 [==============================] - 0s 363us/sample - loss: 4.8131e-05 - acc: 1.0000\n", + "Epoch 955/1000\n", + "26/26 [==============================] - 0s 362us/sample - loss: 4.8035e-05 - acc: 1.0000\n", + "Epoch 956/1000\n", + "26/26 [==============================] - 0s 381us/sample - loss: 4.7915e-05 - acc: 1.0000\n", + "Epoch 957/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 4.7810e-05 - acc: 1.0000\n", + "Epoch 958/1000\n", + "26/26 [==============================] - 0s 328us/sample - loss: 4.7714e-05 - acc: 1.0000\n", + "Epoch 959/1000\n", + "26/26 [==============================] - 0s 343us/sample - loss: 4.7599e-05 - acc: 1.0000\n", + "Epoch 960/1000\n", + "26/26 [==============================] - 0s 357us/sample - loss: 4.7475e-05 - acc: 1.0000\n", + "Epoch 961/1000\n", + "26/26 [==============================] - 0s 439us/sample - loss: 4.7356e-05 - acc: 1.0000\n", + "Epoch 962/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 4.7260e-05 - acc: 1.0000\n", + "Epoch 963/1000\n", + "26/26 [==============================] - 0s 557us/sample - loss: 4.7159e-05 - acc: 1.0000\n", + "Epoch 964/1000\n", + "26/26 [==============================] - 0s 522us/sample - loss: 4.7017e-05 - acc: 1.0000\n", + "Epoch 965/1000\n", + "26/26 [==============================] - 0s 449us/sample - loss: 4.6898e-05 - acc: 1.0000\n", + "Epoch 966/1000\n", + "26/26 [==============================] - 0s 402us/sample - loss: 4.6792e-05 - acc: 1.0000\n", + "Epoch 967/1000\n", + "26/26 [==============================] - 0s 388us/sample - loss: 4.6668e-05 - acc: 1.0000\n", + "Epoch 968/1000\n", + "26/26 [==============================] - 0s 389us/sample - loss: 4.6535e-05 - acc: 1.0000\n", + "Epoch 969/1000\n", + "26/26 [==============================] - 0s 431us/sample - loss: 4.6448e-05 - acc: 1.0000\n", + "Epoch 970/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 4.6343e-05 - acc: 1.0000\n", + "Epoch 971/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 4.6233e-05 - acc: 1.0000\n", + "Epoch 972/1000\n", + "26/26 [==============================] - 0s 368us/sample - loss: 4.6095e-05 - acc: 1.0000\n", + "Epoch 973/1000\n", + "26/26 [==============================] - 0s 332us/sample - loss: 4.5985e-05 - acc: 1.0000\n", + "Epoch 974/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 4.5866e-05 - acc: 1.0000\n", + "Epoch 975/1000\n", + "26/26 [==============================] - 0s 396us/sample - loss: 4.5761e-05 - acc: 1.0000\n", + "Epoch 976/1000\n", + "26/26 [==============================] - 0s 432us/sample - loss: 4.5664e-05 - acc: 1.0000\n", + "Epoch 977/1000\n", + "26/26 [==============================] - 0s 372us/sample - loss: 4.5554e-05 - acc: 1.0000\n", + "Epoch 978/1000\n", + "26/26 [==============================] - 0s 346us/sample - loss: 4.5444e-05 - acc: 1.0000\n", + "Epoch 979/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 4.5316e-05 - acc: 1.0000\n", + "Epoch 980/1000\n", + "26/26 [==============================] - 0s 374us/sample - loss: 4.5215e-05 - acc: 1.0000\n", + "Epoch 981/1000\n", + "26/26 [==============================] - 0s 383us/sample - loss: 4.5100e-05 - acc: 1.0000\n", + "Epoch 982/1000\n", + "26/26 [==============================] - 0s 367us/sample - loss: 4.5004e-05 - acc: 1.0000\n", + "Epoch 983/1000\n", + "26/26 [==============================] - 0s 352us/sample - loss: 4.4890e-05 - acc: 1.0000\n", + "Epoch 984/1000\n", + "26/26 [==============================] - 0s 341us/sample - loss: 4.4761e-05 - acc: 1.0000\n", + "Epoch 985/1000\n", + "26/26 [==============================] - 0s 326us/sample - loss: 4.4679e-05 - acc: 1.0000\n", + "Epoch 986/1000\n", + "26/26 [==============================] - 0s 356us/sample - loss: 4.4546e-05 - acc: 1.0000\n", + "Epoch 987/1000\n", + "26/26 [==============================] - 0s 366us/sample - loss: 4.4440e-05 - acc: 1.0000\n", + "Epoch 988/1000\n", + "26/26 [==============================] - 0s 369us/sample - loss: 4.4335e-05 - acc: 1.0000\n", + "Epoch 989/1000\n", + "26/26 [==============================] - 0s 323us/sample - loss: 4.4229e-05 - acc: 1.0000\n", + "Epoch 990/1000\n", + "26/26 [==============================] - 0s 361us/sample - loss: 4.4129e-05 - acc: 1.0000\n", + "Epoch 991/1000\n", + "26/26 [==============================] - 0s 353us/sample - loss: 4.4009e-05 - acc: 1.0000\n", + "Epoch 992/1000\n", + "26/26 [==============================] - 0s 394us/sample - loss: 4.3890e-05 - acc: 1.0000\n", + "Epoch 993/1000\n", + "26/26 [==============================] - 0s 399us/sample - loss: 4.3766e-05 - acc: 1.0000\n", + "Epoch 994/1000\n", + "26/26 [==============================] - 0s 347us/sample - loss: 4.3661e-05 - acc: 1.0000\n", + "Epoch 995/1000\n", + "26/26 [==============================] - 0s 439us/sample - loss: 4.3565e-05 - acc: 1.0000\n", + "Epoch 996/1000\n", + "26/26 [==============================] - 0s 422us/sample - loss: 4.3464e-05 - acc: 1.0000\n", + "Epoch 997/1000\n", + "26/26 [==============================] - 0s 436us/sample - loss: 4.3358e-05 - acc: 1.0000\n", + "Epoch 998/1000\n", + "26/26 [==============================] - 0s 508us/sample - loss: 4.3248e-05 - acc: 1.0000\n", + "Epoch 999/1000\n", + "26/26 [==============================] - 0s 392us/sample - loss: 4.3157e-05 - acc: 1.0000\n", + "Epoch 1000/1000\n", + "26/26 [==============================] - 0s 360us/sample - loss: 4.3065e-05 - acc: 1.0000\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "def bag_of_words(s, words):\n", + " bag = [0 for _ in range(len(words))]\n", + "\n", + " s_words = nltk.word_tokenize(s)\n", + " s_words = [stemmer.stem(word.lower()) for word in s_words]\n", + "\n", + " for se in s_words:\n", + " for i, w in enumerate(words):\n", + " if w == se:\n", + " bag[i] = 1\n", + "\n", + " return numpy.array(bag)\n", + "\n", + "import tensorflow as tf\n", + "import numpy as np\n", + "import random\n", + "\n", + "\n", + "model = tf.keras.models.load_model('model.tflearn')\n", + "\n", + "def chat():\n", + " print(\"Start talking with the bot (type quit to stop)!\")\n", + " while True:\n", + " inp = input(\"You: \")\n", + " if inp.lower() == \"quit\":\n", + " break\n", + "\n", + " processed_input = bag_of_words(inp, words)\n", + " processed_input = np.array([processed_input])\n", + "\n", + "\n", + " results = model.predict(processed_input)\n", + " results_index = np.argmax(results)\n", + " tag = labels[results_index]\n", + "\n", + " for tg in data[\"intents\"]:\n", + " if tg['tag'] == tag:\n", + " responses = tg['responses']\n", + "\n", + " print(random.choice(responses))\n", + "\n", + "chat()\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "blSmwDWNAHRM", + "outputId": "a1ecc1d6-41bb-4541-aba6-ae31b2eb81a0" + }, + "execution_count": 35, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Start talking with the bot (type quit to stop)!\n", + "You: hi\n", + "Good to see you again!\n", + "You: hello\n", + "Hello!\n", + "You: what you doing\n", + "Hello!\n", + "You: quit\n" + ] + } + ] + } + ] +} \ No newline at end of file