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naotokui/conditional_vae_keras.ipynb

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Conditional VAE in Keras
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conditional_vae_keras.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Conditional Variational Autoencoder in Keras \n",
"\n",
"http://wiseodd.github.io/techblog/2016/12/17/conditional-vae/\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
"Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
"Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
"Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n",
"784 10\n"
]
}
],
"source": [
"from tensorflow.examples.tutorials.mnist import input_data\n",
"from keras.layers import Input, Dense, Lambda, merge\n",
"from keras.models import Model\n",
"from keras.objectives import binary_crossentropy\n",
"from keras.callbacks import LearningRateScheduler\n",
"\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import keras.backend as K\n",
"import tensorflow as tf\n",
"\n",
"\n",
"m = 50 # minibatch size?\n",
"n_z = 2 # dimention of latent space\n",
"n_epoch = 30\n",
"\n",
"mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)\n",
"\n",
"X_train, y_train = mnist.train.images, mnist.train.labels\n",
"X_test, y_test = mnist.test.images, mnist.test.labels\n",
"\n",
"n_x = X_train.shape[1] # 784\n",
"n_y = y_train.shape[1] # 10\n",
"\n",
"print n_x, n_y\n",
"\n",
"# Q(z|X) -- encoder\n",
"X = Input(batch_shape=(m, n_x)) # size of MNIST images\n",
"cond = Input(batch_shape=(m, n_y))\n",
"\n",
"inputs = merge([X, cond], mode='concat', concat_axis=1)\n",
"h_q = Dense(512, activation='relu')(inputs)\n",
"mu = Dense(n_z, activation='linear')(h_q)\n",
"log_sigma = Dense(n_z, activation='linear')(h_q)\n",
"\n",
"def sample_z(args):\n",
" mu, log_sigma = args\n",
" eps = K.random_normal(shape=(m, n_z), mean=0., std=1.)\n",
" return mu + K.exp(log_sigma / 2.) * eps\n",
"\n",
"z = Lambda(sample_z)([mu, log_sigma])\n",
"z_cond = merge([z, cond], mode='concat', concat_axis=1) # <--- NEW!"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# P(X|z) -- decoder\n",
"decoder_hidden = Dense(512, activation='relu')\n",
"decoder_out = Dense(784, activation='sigmoid')\n",
"\n",
"h_p = decoder_hidden(z_cond)\n",
"outputs = decoder_out(h_p)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Overall VAE model, for reconstruction and training\n",
"vae = Model([X, cond], outputs)\n",
"\n",
"# Encoder model, to encode input into latent variable\n",
"# We use the mean as the output as it is the center point, the representative of the gaussian\n",
"encoder = Model([X, cond], mu)\n",
"\n",
"# Generator model, generate new data given latent variable z\n",
"d_cond = Input(shape=(n_y,))\n",
"d_z = Input(shape=(n_z,))\n",
"d_inputs = merge([d_z, d_cond], mode='concat', concat_axis=1)\n",
"d_h = decoder_hidden(d_inputs)\n",
"d_out = decoder_out(d_h)\n",
"decoder = Model([d_z, d_cond], d_out)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def vae_loss(y_true, y_pred):\n",
" \"\"\" Calculate loss = reconstruction loss + KL loss for eatch data in minibatch \"\"\"\n",
" # E[log P(X|z)]\n",
" recon = K.sum(K.binary_crossentropy(y_pred, y_true), axis=1)\n",
" # D_KL(Q(z|X) || P(z|X)); calculate in closed from as both dist. are Gaussian\n",
" kl = 0.5 * K.sum(K.exp(log_sigma) + K.square(mu) - 1. - log_sigma, axis=1)\n",
" \n",
" return recon + kl"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 49500 samples, validate on 5500 samples\n",
"Epoch 1/200\n",
"49500/49500 [==============================] - 15s - loss: 137.9083 - val_loss: 135.6084\n",
"Epoch 2/200\n",
"49500/49500 [==============================] - 14s - loss: 136.5206 - val_loss: 134.5766\n",
"Epoch 3/200\n",
"49500/49500 [==============================] - 14s - loss: 135.6138 - val_loss: 134.0674\n",
"Epoch 4/200\n",
"49500/49500 [==============================] - 14s - loss: 134.8819 - val_loss: 133.3267\n",
"Epoch 5/200\n",
"49500/49500 [==============================] - 14s - loss: 134.3215 - val_loss: 132.8428\n",
"Epoch 6/200\n",
"49500/49500 [==============================] - 14s - loss: 133.8154 - val_loss: 132.5298\n",
"Epoch 7/200\n",
"49500/49500 [==============================] - 14s - loss: 133.4274 - val_loss: 132.2938\n",
"Epoch 8/200\n",
"49500/49500 [==============================] - 15s - loss: 133.1127 - val_loss: 131.8600\n",
"Epoch 9/200\n",
"49500/49500 [==============================] - 15s - loss: 132.7737 - val_loss: 131.6924\n",
"Epoch 10/200\n",
"49500/49500 [==============================] - 15s - loss: 132.5264 - val_loss: 131.5100\n",
"Epoch 11/200\n",
"49500/49500 [==============================] - 15s - loss: 132.2754 - val_loss: 131.4352\n",
"Epoch 12/200\n",
"49500/49500 [==============================] - 15s - loss: 132.0826 - val_loss: 131.2213\n",
"Epoch 13/200\n",
"49500/49500 [==============================] - 14s - loss: 131.8900 - val_loss: 131.2285\n",
"Epoch 14/200\n",
"49500/49500 [==============================] - 15s - loss: 131.6712 - val_loss: 130.7224\n",
"Epoch 15/200\n",
"49500/49500 [==============================] - 15s - loss: 131.5225 - val_loss: 130.9763\n",
"Epoch 16/200\n",
"49500/49500 [==============================] - 15s - loss: 131.3861 - val_loss: 130.7968\n",
"Epoch 17/200\n",
"49500/49500 [==============================] - 15s - loss: 131.2142 - val_loss: 130.8915\n",
"Epoch 18/200\n",
"49500/49500 [==============================] - 15s - loss: 131.1074 - val_loss: 130.5177\n",
"Epoch 19/200\n",
"49500/49500 [==============================] - 15s - loss: 130.9767 - val_loss: 130.7766\n",
"Epoch 20/200\n",
"49500/49500 [==============================] - 17s - loss: 130.8435 - val_loss: 130.7809\n",
"Epoch 21/200\n",
"49500/49500 [==============================] - 17s - loss: 130.7425 - val_loss: 130.4382\n",
"Epoch 22/200\n",
"49500/49500 [==============================] - 15s - loss: 130.6048 - val_loss: 130.5166\n",
"Epoch 23/200\n",
"49500/49500 [==============================] - 16s - loss: 130.5164 - val_loss: 130.0970\n",
"Epoch 24/200\n",
"49500/49500 [==============================] - 16s - loss: 130.3904 - val_loss: 130.3945\n",
"Epoch 25/200\n",
"49500/49500 [==============================] - 16s - loss: 130.2870 - val_loss: 130.3284\n",
"Epoch 26/200\n",
"49500/49500 [==============================] - 16s - loss: 130.1616 - val_loss: 130.3388\n",
"Epoch 27/200\n",
"49500/49500 [==============================] - 16s - loss: 130.1057 - val_loss: 130.1869\n",
"Epoch 28/200\n",
"49500/49500 [==============================] - 16s - loss: 130.0076 - val_loss: 130.0874\n",
"Epoch 29/200\n",
"49500/49500 [==============================] - 16s - loss: 129.9216 - val_loss: 130.1133\n",
"Epoch 30/200\n",
"49500/49500 [==============================] - 16s - loss: 129.8405 - val_loss: 130.2667\n",
"Epoch 31/200\n",
"49500/49500 [==============================] - 16s - loss: 129.7317 - val_loss: 129.9085\n",
"Epoch 32/200\n",
"49500/49500 [==============================] - 16s - loss: 129.6628 - val_loss: 130.1768\n",
"Epoch 33/200\n",
"49500/49500 [==============================] - 16s - loss: 129.6188 - val_loss: 130.0617\n",
"Epoch 34/200\n",
"49500/49500 [==============================] - 17s - loss: 129.5200 - val_loss: 130.3366\n",
"Epoch 35/200\n",
"49500/49500 [==============================] - 16s - loss: 129.4461 - val_loss: 129.9790\n",
"Epoch 36/200\n",
"49500/49500 [==============================] - 15s - loss: 129.3573 - val_loss: 129.8545\n",
"Epoch 37/200\n",
"49500/49500 [==============================] - 15s - loss: 129.3132 - val_loss: 129.8702\n",
"Epoch 38/200\n",
"49500/49500 [==============================] - 15s - loss: 129.2232 - val_loss: 129.8541\n",
"Epoch 39/200\n",
"49500/49500 [==============================] - 16s - loss: 129.1453 - val_loss: 130.1594\n",
"Epoch 40/200\n",
"49500/49500 [==============================] - 14s - loss: 129.0752 - val_loss: 129.9074\n",
"Epoch 41/200\n",
"49500/49500 [==============================] - 15s - loss: 129.0437 - val_loss: 129.8531\n",
"Epoch 42/200\n",
"49500/49500 [==============================] - 15s - loss: 128.9666 - val_loss: 129.8445\n",
"Epoch 43/200\n",
"49500/49500 [==============================] - 16s - loss: 128.9105 - val_loss: 130.0421\n",
"Epoch 44/200\n",
"49500/49500 [==============================] - 16s - loss: 128.8636 - val_loss: 129.6990\n",
"Epoch 45/200\n",
"49500/49500 [==============================] - 14s - loss: 128.7981 - val_loss: 129.7255\n",
"Epoch 46/200\n",
"49500/49500 [==============================] - 15s - loss: 128.7616 - val_loss: 129.9555\n",
"Epoch 47/200\n",
"49500/49500 [==============================] - 14s - loss: 128.6786 - val_loss: 129.8646\n",
"Epoch 48/200\n",
"49500/49500 [==============================] - 14s - loss: 128.6151 - val_loss: 129.7133\n",
"Epoch 49/200\n",
"49500/49500 [==============================] - 14s - loss: 128.6035 - val_loss: 129.8153\n",
"Epoch 50/200\n",
"49500/49500 [==============================] - 14s - loss: 128.5407 - val_loss: 130.0285\n",
"Epoch 51/200\n",
"49500/49500 [==============================] - 14s - loss: 128.5274 - val_loss: 130.0896\n",
"Epoch 52/200\n",
"49500/49500 [==============================] - 14s - loss: 128.4374 - val_loss: 129.8709\n",
"Epoch 53/200\n",
"49500/49500 [==============================] - 14s - loss: 128.4081 - val_loss: 129.8762\n",
"Epoch 54/200\n",
"49500/49500 [==============================] - 14s - loss: 128.3629 - val_loss: 129.9238\n",
"Epoch 55/200\n",
"49500/49500 [==============================] - 14s - loss: 128.3252 - val_loss: 129.9790\n",
"Epoch 56/200\n",
"49500/49500 [==============================] - 14s - loss: 128.2698 - val_loss: 129.8766\n",
"Epoch 57/200\n",
"49500/49500 [==============================] - 15s - loss: 128.2244 - val_loss: 130.0016\n",
"Epoch 58/200\n",
"49500/49500 [==============================] - 14s - loss: 128.1858 - val_loss: 129.8597\n",
"Epoch 59/200\n",
"49500/49500 [==============================] - 15s - loss: 128.1625 - val_loss: 130.1001\n",
"Epoch 60/200\n",
"49500/49500 [==============================] - 15s - loss: 128.0951 - val_loss: 129.7517\n",
"Epoch 61/200\n",
"49500/49500 [==============================] - 14s - loss: 128.1078 - val_loss: 129.9192\n",
"Epoch 62/200\n",
"49500/49500 [==============================] - 15s - loss: 128.0159 - val_loss: 129.8667\n",
"Epoch 63/200\n",
"49500/49500 [==============================] - 15s - loss: 127.9952 - val_loss: 130.0722\n",
"Epoch 64/200\n",
"49500/49500 [==============================] - 14s - loss: 127.9285 - val_loss: 129.9449\n",
"Epoch 65/200\n",
"49500/49500 [==============================] - 14s - loss: 127.9288 - val_loss: 129.8379\n",
"Epoch 66/200\n",
"49500/49500 [==============================] - 14s - loss: 127.8919 - val_loss: 130.0142\n",
"Epoch 67/200\n",
"49500/49500 [==============================] - 15s - loss: 127.8498 - val_loss: 129.9107\n",
"Epoch 68/200\n",
"49500/49500 [==============================] - 16s - loss: 127.8069 - val_loss: 129.8713\n",
"Epoch 69/200\n",
"49500/49500 [==============================] - 15s - loss: 127.8012 - val_loss: 129.9799\n",
"Epoch 70/200\n",
"49500/49500 [==============================] - 15s - loss: 127.7588 - val_loss: 129.9048\n",
"Epoch 71/200\n",
"49500/49500 [==============================] - 14s - loss: 127.7215 - val_loss: 129.9788\n",
"Epoch 72/200\n",
"49500/49500 [==============================] - 14s - loss: 127.6983 - val_loss: 129.7742\n",
"Epoch 73/200\n",
"49500/49500 [==============================] - 14s - loss: 127.6471 - val_loss: 129.9289\n",
"Epoch 74/200\n",
"49500/49500 [==============================] - 14s - loss: 127.6232 - val_loss: 130.1457\n",
"Epoch 75/200\n",
"49500/49500 [==============================] - 14s - loss: 127.5908 - val_loss: 129.9356\n",
"Epoch 76/200\n",
"49500/49500 [==============================] - 14s - loss: 127.5661 - val_loss: 129.9611\n",
"Epoch 77/200\n",
"49500/49500 [==============================] - 14s - loss: 127.5602 - val_loss: 129.9345\n",
"Epoch 78/200\n",
"49500/49500 [==============================] - 14s - loss: 127.5018 - val_loss: 130.1664\n",
"Epoch 79/200\n",
"49500/49500 [==============================] - 14s - loss: 127.5071 - val_loss: 129.9555\n",
"Epoch 80/200\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"49500/49500 [==============================] - 14s - loss: 127.4751 - val_loss: 129.9477\n",
"Epoch 81/200\n",
"49500/49500 [==============================] - 14s - loss: 127.4536 - val_loss: 130.0634\n",
"Epoch 82/200\n",
"49500/49500 [==============================] - 14s - loss: 127.3850 - val_loss: 130.2444\n",
"Epoch 83/200\n",
"49500/49500 [==============================] - 14s - loss: 127.3811 - val_loss: 130.0860\n",
"Epoch 84/200\n",
"49500/49500 [==============================] - 14s - loss: 127.3428 - val_loss: 130.0622\n",
"Epoch 85/200\n",
"49500/49500 [==============================] - 15s - loss: 127.3158 - val_loss: 129.9395\n",
"Epoch 86/200\n",
"49500/49500 [==============================] - 14s - loss: 127.2645 - val_loss: 130.0771\n",
"Epoch 87/200\n",
"49500/49500 [==============================] - 14s - loss: 127.2725 - val_loss: 130.0237\n",
"Epoch 88/200\n",
"49500/49500 [==============================] - 14s - loss: 127.2277 - val_loss: 130.0852\n",
"Epoch 89/200\n",
"49500/49500 [==============================] - 14s - loss: 127.2238 - val_loss: 130.0281\n",
"Epoch 90/200\n",
"49500/49500 [==============================] - 15s - loss: 127.1641 - val_loss: 130.1119\n",
"Epoch 91/200\n",
"49500/49500 [==============================] - 14s - loss: 127.1886 - val_loss: 130.1220\n",
"Epoch 92/200\n",
"49500/49500 [==============================] - 14s - loss: 127.1556 - val_loss: 130.0911\n",
"Epoch 93/200\n",
"49500/49500 [==============================] - 15s - loss: 127.1075 - val_loss: 130.3982\n",
"Epoch 94/200\n",
"49500/49500 [==============================] - 15s - loss: 127.0875 - val_loss: 130.1775\n",
"Epoch 95/200\n",
"49500/49500 [==============================] - 15s - loss: 127.0752 - val_loss: 130.3740\n",
"Epoch 96/200\n",
"49500/49500 [==============================] - 14s - loss: 127.0443 - val_loss: 130.3412\n",
"Epoch 97/200\n",
"49500/49500 [==============================] - 15s - loss: 127.0311 - val_loss: 130.2326\n",
"Epoch 98/200\n",
"49500/49500 [==============================] - 16s - loss: 127.0161 - val_loss: 130.1827\n",
"Epoch 99/200\n",
"49500/49500 [==============================] - 15s - loss: 126.9732 - val_loss: 130.0954\n",
"Epoch 100/200\n",
"49500/49500 [==============================] - 15s - loss: 126.9634 - val_loss: 130.2200\n",
"Epoch 101/200\n",
"49500/49500 [==============================] - 15s - loss: 126.9400 - val_loss: 130.3350\n",
"Epoch 102/200\n",
"49500/49500 [==============================] - 15s - loss: 126.9115 - val_loss: 130.3507\n",
"Epoch 103/200\n",
"49500/49500 [==============================] - 16s - loss: 126.8871 - val_loss: 130.3811\n",
"Epoch 104/200\n",
"49500/49500 [==============================] - 15s - loss: 126.8767 - val_loss: 130.1845\n",
"Epoch 105/200\n",
"49500/49500 [==============================] - 15s - loss: 126.8616 - val_loss: 130.2252\n",
"Epoch 106/200\n",
"49500/49500 [==============================] - 15s - loss: 126.8411 - val_loss: 130.3802\n",
"Epoch 107/200\n",
"49500/49500 [==============================] - 15s - loss: 126.8037 - val_loss: 130.3490\n",
"Epoch 108/200\n",
"49500/49500 [==============================] - 15s - loss: 126.7857 - val_loss: 130.2897\n",
"Epoch 109/200\n",
"49500/49500 [==============================] - 15s - loss: 126.7673 - val_loss: 130.2429\n",
"Epoch 110/200\n",
"49500/49500 [==============================] - 15s - loss: 126.7633 - val_loss: 130.3046\n",
"Epoch 111/200\n",
"49500/49500 [==============================] - 15s - loss: 126.7203 - val_loss: 130.4216\n",
"Epoch 112/200\n",
"49500/49500 [==============================] - 15s - loss: 126.7174 - val_loss: 130.2226\n",
"Epoch 113/200\n",
"49500/49500 [==============================] - 15s - loss: 126.7012 - val_loss: 130.3504\n",
"Epoch 114/200\n",
"49500/49500 [==============================] - 15s - loss: 126.6773 - val_loss: 130.3839\n",
"Epoch 115/200\n",
"49500/49500 [==============================] - 15s - loss: 126.6464 - val_loss: 130.3921\n",
"Epoch 116/200\n",
"49500/49500 [==============================] - 15s - loss: 126.6225 - val_loss: 130.3353\n",
"Epoch 117/200\n",
"49500/49500 [==============================] - 15s - loss: 126.6221 - val_loss: 130.4594\n",
"Epoch 118/200\n",
"49500/49500 [==============================] - 15s - loss: 126.6162 - val_loss: 130.3237\n",
"Epoch 119/200\n",
"49500/49500 [==============================] - 15s - loss: 126.5903 - val_loss: 130.5590\n",
"Epoch 120/200\n",
"49500/49500 [==============================] - 15s - loss: 126.5570 - val_loss: 130.2775\n",
"Epoch 121/200\n",
"49500/49500 [==============================] - 15s - loss: 126.5403 - val_loss: 130.3838\n",
"Epoch 122/200\n",
"49500/49500 [==============================] - 15s - loss: 126.5445 - val_loss: 130.6507\n",
"Epoch 123/200\n",
"49500/49500 [==============================] - 15s - loss: 126.5314 - val_loss: 130.5058\n",
"Epoch 124/200\n",
"49500/49500 [==============================] - 15s - loss: 126.5043 - val_loss: 130.5718\n",
"Epoch 125/200\n",
"49500/49500 [==============================] - 15s - loss: 126.4382 - val_loss: 130.5603\n",
"Epoch 126/200\n",
"49500/49500 [==============================] - 15s - loss: 126.4612 - val_loss: 130.5488\n",
"Epoch 127/200\n",
"49500/49500 [==============================] - 15s - loss: 126.4386 - val_loss: 130.3601\n",
"Epoch 128/200\n",
"49500/49500 [==============================] - 15s - loss: 126.4406 - val_loss: 130.5757\n",
"Epoch 129/200\n",
"49500/49500 [==============================] - 15s - loss: 126.4149 - val_loss: 130.5338\n",
"Epoch 130/200\n",
"49500/49500 [==============================] - 15s - loss: 126.4155 - val_loss: 130.4577\n",
"Epoch 131/200\n",
"49500/49500 [==============================] - 15s - loss: 126.3904 - val_loss: 130.7210\n",
"Epoch 132/200\n",
"49500/49500 [==============================] - 15s - loss: 126.3567 - val_loss: 130.6464\n",
"Epoch 133/200\n",
"49500/49500 [==============================] - 15s - loss: 126.3357 - val_loss: 130.5121\n",
"Epoch 134/200\n",
"49500/49500 [==============================] - 15s - loss: 126.3417 - val_loss: 130.4337\n",
"Epoch 135/200\n",
"49500/49500 [==============================] - 15s - loss: 126.3198 - val_loss: 130.6353\n",
"Epoch 136/200\n",
"49500/49500 [==============================] - 15s - loss: 126.2896 - val_loss: 130.7004\n",
"Epoch 137/200\n",
"49500/49500 [==============================] - 15s - loss: 126.3019 - val_loss: 130.5491\n",
"Epoch 138/200\n",
"49500/49500 [==============================] - 15s - loss: 126.2924 - val_loss: 130.5523\n",
"Epoch 139/200\n",
"49500/49500 [==============================] - 15s - loss: 126.2377 - val_loss: 130.6235\n",
"Epoch 140/200\n",
"49500/49500 [==============================] - 15s - loss: 126.2384 - val_loss: 130.6960\n",
"Epoch 141/200\n",
"49500/49500 [==============================] - 15s - loss: 126.1968 - val_loss: 130.4749\n",
"Epoch 142/200\n",
"49500/49500 [==============================] - 15s - loss: 126.2006 - val_loss: 130.6351\n",
"Epoch 143/200\n",
"49500/49500 [==============================] - 15s - loss: 126.1851 - val_loss: 130.5211\n",
"Epoch 144/200\n",
"49500/49500 [==============================] - 15s - loss: 126.1806 - val_loss: 130.6569\n",
"Epoch 145/200\n",
"49500/49500 [==============================] - 15s - loss: 126.1508 - val_loss: 130.8266\n",
"Epoch 146/200\n",
"49500/49500 [==============================] - 15s - loss: 126.1379 - val_loss: 130.8512\n",
"Epoch 147/200\n",
"49500/49500 [==============================] - 15s - loss: 126.1392 - val_loss: 130.8475\n",
"Epoch 148/200\n",
"49500/49500 [==============================] - 15s - loss: 126.1260 - val_loss: 130.8358\n",
"Epoch 149/200\n",
"49500/49500 [==============================] - 15s - loss: 126.0972 - val_loss: 131.0119\n",
"Epoch 150/200\n",
"49500/49500 [==============================] - 15s - loss: 126.0803 - val_loss: 130.7999\n",
"Epoch 151/200\n",
"49500/49500 [==============================] - 15s - loss: 126.0808 - val_loss: 130.8113\n",
"Epoch 152/200\n",
"49500/49500 [==============================] - 15s - loss: 126.0522 - val_loss: 130.9507\n",
"Epoch 153/200\n",
"49500/49500 [==============================] - 15s - loss: 126.0474 - val_loss: 130.8012\n",
"Epoch 154/200\n",
"49500/49500 [==============================] - 15s - loss: 126.0287 - val_loss: 131.2575\n",
"Epoch 155/200\n",
"49500/49500 [==============================] - 15s - loss: 126.0168 - val_loss: 130.7993\n",
"Epoch 156/200\n",
"49500/49500 [==============================] - 15s - loss: 126.0020 - val_loss: 130.7898\n",
"Epoch 157/200\n",
"49500/49500 [==============================] - 15s - loss: 125.9813 - val_loss: 130.8477\n",
"Epoch 158/200\n",
"49500/49500 [==============================] - 15s - loss: 125.9738 - val_loss: 130.6328\n",
"Epoch 159/200\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"49500/49500 [==============================] - 15s - loss: 125.9728 - val_loss: 130.7757\n",
"Epoch 160/200\n",
"49500/49500 [==============================] - 14s - loss: 125.9602 - val_loss: 130.9115\n",
"Epoch 161/200\n",
"49500/49500 [==============================] - 14s - loss: 125.9162 - val_loss: 131.0202\n",
"Epoch 162/200\n",
"49500/49500 [==============================] - 14s - loss: 125.9007 - val_loss: 131.1456\n",
"Epoch 163/200\n",
"49500/49500 [==============================] - 14s - loss: 125.9119 - val_loss: 131.1405\n",
"Epoch 164/200\n",
"49500/49500 [==============================] - 14s - loss: 125.9154 - val_loss: 130.8137\n",
"Epoch 165/200\n",
"49500/49500 [==============================] - 14s - loss: 125.9073 - val_loss: 131.0335\n",
"Epoch 166/200\n",
"49500/49500 [==============================] - 14s - loss: 125.8582 - val_loss: 131.1667\n",
"Epoch 167/200\n",
"49500/49500 [==============================] - 14s - loss: 125.8601 - val_loss: 131.3358\n",
"Epoch 168/200\n",
"49500/49500 [==============================] - 14s - loss: 125.8280 - val_loss: 131.1046\n",
"Epoch 169/200\n",
"49500/49500 [==============================] - 14s - loss: 125.8515 - val_loss: 131.3299\n",
"Epoch 170/200\n",
"49500/49500 [==============================] - 14s - loss: 125.8236 - val_loss: 130.9938\n",
"Epoch 171/200\n",
"49500/49500 [==============================] - 14s - loss: 125.7874 - val_loss: 131.0089\n",
"Epoch 172/200\n",
"49500/49500 [==============================] - 14s - loss: 125.7951 - val_loss: 131.2747\n",
"Epoch 173/200\n",
"49500/49500 [==============================] - 14s - loss: 125.7880 - val_loss: 130.8101\n",
"Epoch 174/200\n",
"49500/49500 [==============================] - 14s - loss: 125.7886 - val_loss: 131.1559\n",
"Epoch 175/200\n",
"49500/49500 [==============================] - 14s - loss: 125.7457 - val_loss: 131.3588\n",
"Epoch 176/200\n",
"49500/49500 [==============================] - 14s - loss: 125.7478 - val_loss: 131.1672\n",
"Epoch 177/200\n",
"49500/49500 [==============================] - 14s - loss: 125.7188 - val_loss: 131.2322\n",
"Epoch 178/200\n",
"49500/49500 [==============================] - 14s - loss: 125.7317 - val_loss: 130.9684\n",
"Epoch 179/200\n",
"49500/49500 [==============================] - 14s - loss: 125.7075 - val_loss: 131.3059\n",
"Epoch 180/200\n",
"49500/49500 [==============================] - 14s - loss: 125.7257 - val_loss: 131.0354\n",
"Epoch 181/200\n",
"49500/49500 [==============================] - 14s - loss: 125.6730 - val_loss: 131.4749\n",
"Epoch 182/200\n",
"49500/49500 [==============================] - 14s - loss: 125.6688 - val_loss: 131.1839\n",
"Epoch 183/200\n",
"49500/49500 [==============================] - 14s - loss: 125.6584 - val_loss: 131.1881\n",
"Epoch 184/200\n",
"49500/49500 [==============================] - 14s - loss: 125.6756 - val_loss: 131.1026\n",
"Epoch 185/200\n",
"49500/49500 [==============================] - 14s - loss: 125.6439 - val_loss: 131.1152\n",
"Epoch 186/200\n",
"49500/49500 [==============================] - 14s - loss: 125.6138 - val_loss: 131.4445\n",
"Epoch 187/200\n",
"49500/49500 [==============================] - 14s - loss: 125.6156 - val_loss: 131.2416\n",
"Epoch 188/200\n",
"49500/49500 [==============================] - 14s - loss: 125.6120 - val_loss: 131.3990\n",
"Epoch 189/200\n",
"49500/49500 [==============================] - 14s - loss: 125.5913 - val_loss: 131.4464\n",
"Epoch 190/200\n",
"49500/49500 [==============================] - 14s - loss: 125.5896 - val_loss: 131.5294\n",
"Epoch 191/200\n",
"49500/49500 [==============================] - 14s - loss: 125.5689 - val_loss: 131.3547\n",
"Epoch 192/200\n",
"49500/49500 [==============================] - 14s - loss: 125.5519 - val_loss: 131.3071\n",
"Epoch 193/200\n",
"49500/49500 [==============================] - 14s - loss: 125.5675 - val_loss: 131.2121\n",
"Epoch 194/200\n",
"49500/49500 [==============================] - 14s - loss: 125.5201 - val_loss: 131.3717\n",
"Epoch 195/200\n",
"49500/49500 [==============================] - 14s - loss: 125.5310 - val_loss: 131.3917\n",
"Epoch 196/200\n",
"49500/49500 [==============================] - 14s - loss: 125.5270 - val_loss: 131.5196\n",
"Epoch 197/200\n",
"49500/49500 [==============================] - 14s - loss: 125.5053 - val_loss: 131.4068\n",
"Epoch 198/200\n",
"49500/49500 [==============================] - 15s - loss: 125.5077 - val_loss: 131.5644\n",
"Epoch 199/200\n",
"49500/49500 [==============================] - 14s - loss: 125.5075 - val_loss: 131.4557\n",
"Epoch 200/200\n",
"49500/49500 [==============================] - 14s - loss: 125.4559 - val_loss: 131.5269\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x1267efc90>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vae.compile(optimizer='adam', loss=vae_loss)\n",
"vae.fit([X_train, y_train], X_train, batch_size=m, nb_epoch=200, validation_split=0.1)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training history\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x126b1e390>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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68Dpwz1GP/xL490AFFB3hAKBB12gpFfIcgQ7gVGUlROrUoVKqW8aY5SKSe9RYrd+3HsC3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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x126fae2d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"print \"Training history\"\n",
"fig = plt.figure(figsize=(10,4))\n",
"ax1 = fig.add_subplot(1, 2, 1)\n",
"plt.plot(vae.history.history['loss'])\n",
"ax1.set_title('loss')\n",
"ax2 = fig.add_subplot(1, 2, 2)\n",
"plt.plot(vae.history.history['val_loss'])\n",
"ax2.set_title('validation loss')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## random Z / conditioning with 0-9"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x123ef1110>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from keras.utils import np_utils\n",
"digit_size = 28\n",
"\n",
"z_sample = np.random.rand(1, 2) # random \n",
"\n",
"plt.figure(figsize=(20, 2))\n",
"for i in range(10):\n",
" c = np_utils.to_categorical(i,n_y)\n",
" x_decoded = decoder.predict([z_sample, c])\n",
" digit = x_decoded[0].reshape(digit_size, digit_size)\n",
"\n",
" plt.subplot(1, n_y, i+1)\n",
" plt.axis('off')\n",
" plt.imshow(digit, cmap='Greys_r',)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## conditioning with numbers (0-9) / interpolating Z"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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UrA2rDyPyGsW26rQrDz30UPp51qxZzaZktfqxK02a8J7hXh0Rsf766zf7iCOO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\nbB/+9Kc/bfZmm23W7OnTp6d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"text/plain": [
"<matplotlib.figure.Figure at 0x12c5f0c90>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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"text/plain": [
"<matplotlib.figure.Figure at 0x12b1e84d0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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"text/plain": [
"<matplotlib.figure.Figure at 0x12b9e2a90>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x12644e910>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x126edfd50>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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LkIicxzoW+ZrMI99nEX3lkdeIc+jVV1+dzuOcuXbt2nTssccea/HWrVtbXD/r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\nkCRJkiRJUgf8EkiSJEmSJKkD/wvZa2TwkleaEgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x12b0e2fd0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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"text/plain": [
"<matplotlib.figure.Figure at 0x12c87a390>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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"text/plain": [
"<matplotlib.figure.Figure at 0x126f19e90>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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uWEfJthSL9EWuOUmfDh06tONn0B+5xhrjj+tprr3XDWso2dZikX7k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\nj85edjhEAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x12f1acdd0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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"text/plain": [
"<matplotlib.figure.Figure at 0x12d1d2250>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for j in range(10):\n",
" c = np_utils.to_categorical(j,n_y)\n",
"\n",
" plt.figure(figsize=(20, 2))\n",
" for i in range(10):\n",
" z_sample = np.array([[0.1 * i, 0.1*i]])\n",
" x_decoded = decoder.predict([z_sample, c])\n",
" digit = x_decoded[0].reshape(digit_size, digit_size)\n",
"\n",
" plt.subplot(1, n_y, i+1)\n",
" plt.axis('off')\n",
" plt.imshow(digit, cmap='Greys_r',)\n",
"plt.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": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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