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@akelleh
Created March 21, 2018 04:13
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import keras.datasets.mnist as mnist"
]
},
{
"cell_type": "code",
"execution_count": 175,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"60000 train samples\n",
"10000 test samples\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"input_25 (InputLayer) (None, 784) 0 \n",
"_________________________________________________________________\n",
"dense_99 (Dense) (None, 64) 50240 \n",
"_________________________________________________________________\n",
"dense_100 (Dense) (None, 32) 2080 \n",
"_________________________________________________________________\n",
"dropout_74 (Dropout) (None, 32) 0 \n",
"_________________________________________________________________\n",
"dense_101 (Dense) (None, 64) 2112 \n",
"_________________________________________________________________\n",
"dropout_75 (Dropout) (None, 64) 0 \n",
"_________________________________________________________________\n",
"dense_102 (Dense) (None, 784) 50960 \n",
"=================================================================\n",
"Total params: 105,392\n",
"Trainable params: 105,392\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n",
"Train on 60000 samples, validate on 10000 samples\n",
"Epoch 1/3\n",
"60000/60000 [==============================] - 9s 148us/step - loss: 0.0449 - val_loss: 0.0295\n",
"Epoch 2/3\n",
"60000/60000 [==============================] - 8s 126us/step - loss: 0.0352 - val_loss: 0.0278\n",
"Epoch 3/3\n",
"60000/60000 [==============================] - 8s 129us/step - loss: 0.0338 - val_loss: 0.0254\n"
]
}
],
"source": [
"from __future__ import print_function\n",
"\n",
"import keras\n",
"from keras.datasets import mnist\n",
"from keras.models import Sequential, Model\n",
"from keras.layers import Dense, Dropout, Input\n",
"from keras.optimizers import RMSprop\n",
"\n",
"batch_size = 128\n",
"num_classes = 10\n",
"epochs = 3\n",
"\n",
"# the data, split between train and test sets\n",
"(x_train_original, y_train_original), (x_test_original, y_test_original) = mnist.load_data()\n",
"\n",
"x_train = x_train_original.reshape(60000, 784)\n",
"x_test = x_test_original.reshape(10000, 784)\n",
"x_train = x_train.astype('float32')\n",
"x_test = x_test.astype('float32')\n",
"x_train /= 255\n",
"x_test /= 255\n",
"print(x_train.shape[0], 'train samples')\n",
"print(x_test.shape[0], 'test samples')\n",
"\n",
"# convert class vectors to binary class matrices\n",
"y_train = keras.utils.to_categorical(y_train_original, num_classes)\n",
"y_test = keras.utils.to_categorical(y_test_original, num_classes)\n",
"\n",
"image_in = Input(shape=(784,))\n",
"h1 = Dense(64, activation='tanh', input_shape=(784,))(image_in)\n",
"d1 = Dropout(0.2)(h1)\n",
"h = Dense(32, activation='tanh')(h1)\n",
"d2 = Dropout(0.2)(h)\n",
"h3 = Dense(64, activation='tanh')(d2)\n",
"d3 = Dropout(0.2)(h3)\n",
"y_out = Dense(784, activation='tanh')(d3)\n",
"\n",
"model = Model(inputs=image_in, outputs=y_out)\n",
"model.summary()\n",
"\n",
"encoder = Model(inputs=image_in, outputs=h)\n",
"\n",
"\n",
"model.compile(loss='mean_squared_error',\n",
" optimizer=RMSprop())\n",
"\n",
"history = model.fit(x_train, x_train,\n",
" batch_size=batch_size,\n",
" epochs=epochs,\n",
" verbose=1,\n",
" validation_data=(x_test, x_test))\n",
"score = model.evaluate(x_test, x_test, verbose=0)\n",
"#print('Test loss:', score[0])\n",
"#print('Test accuracy:', score[1])"
]
},
{
"cell_type": "code",
"execution_count": 176,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.30881899, -0.37014669, -0.48614194, -0.37480015, -0.04873336,\n",
" -0.16111036, -0.30602831, 0.47493639, 0.58977368, -0.15715472,\n",
" 0.47912705, -0.60411532, 0.20357904, 0.1706616 , -0.31207395,\n",
" 0.38645902, -0.52485531, 0.34079817, -0.34794383, -0.70057532,\n",
" -0.1572147 , 0.21118663, 0.25580769, -0.23430494, 0.04501505,\n",
" -0.0660134 , -0.57826233, 0.08206075, -0.36830798, -0.64459981,\n",
" 0.10428348, 0.03839779])"
]
},
"execution_count": 176,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"encoder.predict(x_test)[0]"
]
},
{
"cell_type": "code",
"execution_count": 177,
"metadata": {},
"outputs": [],
"source": [
"x_pred = encoder.predict(x_test)\n",
"reduced_dim = TSNE(n_components=2).fit_transform(x_pred[:2000])"
]
},
{
"cell_type": "code",
"execution_count": 178,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"df = pd.DataFrame(reduced_dim)\n",
"df[\"target\"] = y_test_original[:2000]"
]
},
{
"cell_type": "code",
"execution_count": 179,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.PairGrid at 0x1573791d0>"
]
},
"execution_count": 179,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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JSk0od2GyZIlGxVnLhHcT8IjQ2qWxIJwVye62tYomRVGorq7WvYvVq1fHeBeh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\nEk20fInWE7Li9ojnUf6CMB6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W9kQLtFOWuX+XSJxvumow99UcSphXWVyYR4pJJk2TRlFV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50fVdBQ7cJkFkGApM7GWInZKmnVxYlUh8OViN8XRBAEJJNIWztz3vDcFsm4vRbnu/wxQ3Q4LIZV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\nKNqEMiAKhUKhaBP/HzdBCCaPA+uXAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x157379750>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# you can see the same number ends up clustered; similar numbers (4 and 9) are close together!\n",
"from seaborn import pairplot\n",
"\n",
"pairplot(x_vars=[0], y_vars=[1], data=df, hue=\"target\", size=5)"
]
},
{
"cell_type": "code",
"execution_count": 180,
"metadata": {},
"outputs": [],
"source": [
"# check out how well the image is reconstructed from just a few floats!\n",
"\n",
"from PIL import Image\n",
"import numpy as np\n",
"\n",
"image_number = 10\n",
"\n",
"width, height = 28, 28\n",
"data = np.zeros((height, width, 1), dtype=np.uint8)\n",
"# show the original image\n",
"img = Image.fromarray(255*x_test[image_number].reshape(28,28))\n",
"img.show()\n",
"# show the decoded image\n",
"img = Image.fromarray(255*model.predict(x_test)[image_number].reshape(28,28))\n",
"img.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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