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Run custom build h5 models #2

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71fbcc5
run custom build .h5 model
arjunmohnot Oct 30, 2019
dd121ff
run custom build keras model through h5 models
arjunmohnot Oct 30, 2019
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310 changes: 310 additions & 0 deletions Run_H5_custom_models.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Demo to create, save, load and run the custom built keras models"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"# Importing Libraries\n",
"from keras.models import Model\n",
"from keras.layers import Input,Dense,Dropout\n",
"from keras.callbacks import ModelCheckpoint\n",
"from keras import backend as K\n",
"import tensorflow as tf"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"#Loading mnist data\n",
"mnist=tf.keras.datasets.mnist"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
"11493376/11490434 [==============================] - 12s 1us/step\n"
]
}
],
"source": [
"(x_train,y_train),(x_test,y_test)=mnist.load_data()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"x_train,x_test=x_train/255.0,x_test/255.0"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Creating the model"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:From c:\\python36\\lib\\site-packages\\tensorflow\\python\\keras\\layers\\core.py:143: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
"WARNING:tensorflow:From c:\\python36\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Use tf.cast instead.\n",
"Epoch 1/5\n",
"60000/60000 [==============================] - 23s 390us/sample - loss: 102.5759 - acc: 0.0880\n",
"Epoch 2/5\n",
"60000/60000 [==============================] - 21s 350us/sample - loss: 102.5556 - acc: 0.1142\n",
"Epoch 3/5\n",
"60000/60000 [==============================] - 21s 346us/sample - loss: 102.5556 - acc: 0.1277\n",
"Epoch 4/5\n",
"60000/60000 [==============================] - 21s 350us/sample - loss: 102.5556 - acc: 0.1095\n",
"Epoch 5/5\n",
"60000/60000 [==============================] - 21s 347us/sample - loss: 102.5556 - acc: 0.1332\n",
"10000/10000 [==============================] - 1s 77us/sample - loss: 102.3131 - acc: 0.9808\n"
]
},
{
"data": {
"text/plain": [
"[102.31307451171875, 0.9808]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model=tf.keras.models.Sequential([\n",
" tf.keras.layers.Flatten(),\n",
" tf.keras.layers.Dense(512,activation=tf.nn.relu),\n",
" tf.keras.layers.Dropout(0.2),\n",
" tf.keras.layers.Dense(10,activation=tf.nn.softmax)\n",
"])\n",
"\n",
"model.compile(optimizer='adam', loss=\"categorical_crossentropy\", metrics=['accuracy'])\n",
"\n",
"model.fit(x_train,y_train,epochs=5)\n",
"model.evaluate(x_test,y_test)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"flatten_1 (Flatten) multiple 0 \n",
"_________________________________________________________________\n",
"dense_2 (Dense) multiple 401920 \n",
"_________________________________________________________________\n",
"dropout_1 (Dropout) multiple 0 \n",
"_________________________________________________________________\n",
"dense_3 (Dense) multiple 5130 \n",
"=================================================================\n",
"Total params: 407,050\n",
"Trainable params: 407,050\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"model.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Storing the model architecture and its weight"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Saved model to disk\n"
]
}
],
"source": [
"\n",
"model_json = model.to_json()\n",
"\n",
"# Write the file name of the model\n",
"\n",
"with open(\"mnist.json\", \"w\") as json_file:\n",
" json_file.write(model_json)\n",
" \n",
"# serialize weights to HDF5\n",
"# Write the file name of the weights\n",
"\n",
"model.save_weights(\"mnist.h5\")\n",
"print(\"Saved model to disk\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Loading the model and running it."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loaded model from disk\n"
]
}
],
"source": [
"\n",
"import time\n",
"import keras\n",
"from keras.models import load_model\n",
"from tensorflow.keras.models import model_from_json\n",
"import json\n",
"\n",
"import pandas as pd\n",
"\n",
"json_file = open('mnist.json', 'r')\n",
"\n",
"loaded_model_json = json_file.read()\n",
"json_file.close()\n",
"loaded_model = model_from_json(loaded_model_json)\n",
"loaded_model.load_weights(\"mnist.h5\")\n",
"print(\"Loaded model from disk\")\n",
"\n",
"#prediction = loaded_model.predict(p)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Doing the prediction"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"prediction = loaded_model.predict(x_test)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"loaded_model.compile(optimizer='adam', loss=\"categorical_crossentropy\", metrics=['accuracy'])"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10000/10000 [==============================] - 1s 79us/sample - loss: 102.3131 - acc: 0.9808\n",
"Accuracy: 98.08\n"
]
}
],
"source": [
"_, accuracy = loaded_model.evaluate(x_test, y_test)\n",
"print('Accuracy: %.2f' % (accuracy*100))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}