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Anastasia
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Minor modifications
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‎DenoisingAutoencoder/Denoising-Autoencoder-using-Tensorflow.ipynb‎

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@@ -7,18 +7,22 @@
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import sys\n",
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"import tensorflow as tf\n",
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"import matplotlib.pyplot as plt\n",
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"from tensorflow.keras import Sequential, Model\n",
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"\n",
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"from tensorflow.keras.layers import (\n",
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" Conv2D,\n",
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" MaxPooling2D,\n",
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" Conv2DTranspose,\n",
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" Flatten,\n",
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" Dense\n",
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")\n",
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"from tensorflow.keras import Sequential, Model\n",
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"from tensorflow.keras.utils import to_categorical\n",
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"\n",
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"import os\n",
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"import random\n",
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"import tensorflow as tf\n",
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"\n",
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"%matplotlib inline"
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]
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},
@@ -35,27 +39,13 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import random\n",
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"\n",
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"# settings for reproducibility\n",
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"seed = 42\n",
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"# reproducibility on CPU\n",
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"random.seed(seed)\n",
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"tf.random.set_seed(seed)\n",
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"np.random.seed(seed)\n",
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"\n",
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"# reproducibility on GPU\n",
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"os.environ['TF_DETERMINISTIC_OPS'] = '1'\n",
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"\n",
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"is_use_gpu = True\n",
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"\n",
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"if is_use_gpu:\n",
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" gpu_devices = tf.config.list_physical_devices('GPU')\n",
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" tf.config.experimental.set_visible_devices(gpu_devices[0], 'GPU')\n",
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" tf.config.experimental.set_memory_growth(gpu_devices[0], True)\n",
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" os.environ['TF_USE_CUDNN'] = '1'\n",
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"else:\n",
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" os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"-1\""
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"os.environ['TF_DETERMINISTIC_OPS'] = '1'"
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]
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},
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{
@@ -366,55 +356,55 @@
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"text": [
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"Train on 60000 samples, validate on 10000 samples\n",
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"Epoch 1/25\n",
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"60000/60000 [==============================] - 8s 126us/sample - loss: 0.5725 - val_loss: 0.4929\n",
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"60000/60000 [==============================] - 8s 130us/sample - loss: 0.5725 - val_loss: 0.4929\n",
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"Epoch 2/25\n",
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"60000/60000 [==============================] - 5s 89us/sample - loss: 0.4393 - val_loss: 0.3462\n",
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"60000/60000 [==============================] - 6s 93us/sample - loss: 0.4393 - val_loss: 0.3462\n",
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"Epoch 3/25\n",
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"60000/60000 [==============================] - 5s 89us/sample - loss: 0.2448 - val_loss: 0.2063\n",
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"60000/60000 [==============================] - 6s 93us/sample - loss: 0.2448 - val_loss: 0.2063\n",
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"Epoch 4/25\n",
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"60000/60000 [==============================] - 5s 89us/sample - loss: 0.1895 - val_loss: 0.1741\n",
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"60000/60000 [==============================] - 6s 92us/sample - loss: 0.1895 - val_loss: 0.1741\n",
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"Epoch 5/25\n",
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"60000/60000 [==============================] - 5s 90us/sample - loss: 0.1671 - val_loss: 0.1590\n",
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"60000/60000 [==============================] - 6s 95us/sample - loss: 0.1671 - val_loss: 0.1590\n",
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"Epoch 6/25\n",
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"60000/60000 [==============================] - 5s 90us/sample - loss: 0.1548 - val_loss: 0.1485\n",
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"60000/60000 [==============================] - 6s 93us/sample - loss: 0.1548 - val_loss: 0.1485\n",
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"Epoch 7/25\n",
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"60000/60000 [==============================] - 5s 90us/sample - loss: 0.1457 - val_loss: 0.1407\n",
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"60000/60000 [==============================] - 5s 91us/sample - loss: 0.1457 - val_loss: 0.1407\n",
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"Epoch 8/25\n",
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"60000/60000 [==============================] - 5s 90us/sample - loss: 0.1389 - val_loss: 0.1349\n",
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"60000/60000 [==============================] - 5s 91us/sample - loss: 0.1389 - val_loss: 0.1349\n",
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"Epoch 9/25\n",
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"60000/60000 [==============================] - 5s 90us/sample - loss: 0.1342 - val_loss: 0.1310\n",
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"60000/60000 [==============================] - 5s 91us/sample - loss: 0.1342 - val_loss: 0.1310\n",
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"Epoch 10/25\n",
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"60000/60000 [==============================] - 5s 90us/sample - loss: 0.1308 - val_loss: 0.1281\n",
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"60000/60000 [==============================] - 5s 91us/sample - loss: 0.1308 - val_loss: 0.1281\n",
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"Epoch 11/25\n",
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"60000/60000 [==============================] - 5s 90us/sample - loss: 0.1284 - val_loss: 0.1259\n",
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"60000/60000 [==============================] - 5s 91us/sample - loss: 0.1284 - val_loss: 0.1259\n",
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"Epoch 12/25\n",
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"60000/60000 [==============================] - 5s 92us/sample - loss: 0.1265 - val_loss: 0.1243\n",
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"60000/60000 [==============================] - 5s 91us/sample - loss: 0.1265 - val_loss: 0.1243\n",
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"Epoch 13/25\n",
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"60000/60000 [==============================] - 5s 89us/sample - loss: 0.1250 - val_loss: 0.1229\n",
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"60000/60000 [==============================] - 5s 91us/sample - loss: 0.1250 - val_loss: 0.1229\n",
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"Epoch 14/25\n",
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"60000/60000 [==============================] - 5s 89us/sample - loss: 0.1238 - val_loss: 0.1218\n",
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"60000/60000 [==============================] - 5s 91us/sample - loss: 0.1238 - val_loss: 0.1218\n",
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"Epoch 15/25\n",
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"60000/60000 [==============================] - 5s 90us/sample - loss: 0.1227 - val_loss: 0.1208\n",
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"60000/60000 [==============================] - 5s 91us/sample - loss: 0.1227 - val_loss: 0.1208\n",
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"Epoch 16/25\n",
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"60000/60000 [==============================] - 5s 90us/sample - loss: 0.1217 - val_loss: 0.1199\n",
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"60000/60000 [==============================] - 5s 91us/sample - loss: 0.1217 - val_loss: 0.1199\n",
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"Epoch 17/25\n",
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"60000/60000 [==============================] - 5s 90us/sample - loss: 0.1209 - val_loss: 0.1192\n",
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"60000/60000 [==============================] - 5s 91us/sample - loss: 0.1209 - val_loss: 0.1192\n",
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"Epoch 18/25\n",
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"60000/60000 [==============================] - 5s 90us/sample - loss: 0.1202 - val_loss: 0.1185\n",
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"60000/60000 [==============================] - 5s 91us/sample - loss: 0.1202 - val_loss: 0.1185\n",
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"Epoch 19/25\n",
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"60000/60000 [==============================] - 5s 90us/sample - loss: 0.1195 - val_loss: 0.1179\n",
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"60000/60000 [==============================] - 5s 91us/sample - loss: 0.1195 - val_loss: 0.1179\n",
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"Epoch 20/25\n",
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"60000/60000 [==============================] - 5s 89us/sample - loss: 0.1190 - val_loss: 0.1173\n",
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"60000/60000 [==============================] - 5s 91us/sample - loss: 0.1190 - val_loss: 0.1173\n",
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"Epoch 21/25\n",
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"60000/60000 [==============================] - 5s 91us/sample - loss: 0.1184 - val_loss: 0.1168\n",
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"Epoch 22/25\n",
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"60000/60000 [==============================] - 5s 90us/sample - loss: 0.1179 - val_loss: 0.1163\n",
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"60000/60000 [==============================] - 5s 91us/sample - loss: 0.1179 - val_loss: 0.1163\n",
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"Epoch 23/25\n",
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"60000/60000 [==============================] - 5s 90us/sample - loss: 0.1175 - val_loss: 0.1159\n",
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"60000/60000 [==============================] - 5s 91us/sample - loss: 0.1175 - val_loss: 0.1159\n",
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"Epoch 24/25\n",
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"60000/60000 [==============================] - 5s 90us/sample - loss: 0.1170 - val_loss: 0.1155\n",
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"60000/60000 [==============================] - 5s 91us/sample - loss: 0.1170 - val_loss: 0.1155\n",
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"Epoch 25/25\n",
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"60000/60000 [==============================] - 5s 90us/sample - loss: 0.1166 - val_loss: 0.1152\n"
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"60000/60000 [==============================] - 6s 93us/sample - loss: 0.1166 - val_loss: 0.1152\n"
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]
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},
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{

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