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"source": [
"# Generative Adversarial Networks\n",
"\n",
"GANs are a novel Neural Network architecture which are designed to emulate the distribution of a given dataset.\n",
"\n",
"The key concept behind these networks is that there are two neural nets employed as opposed to the usual one. **The first network** is a generator network, which takes in a random vector, and converts that into a different distribution. It is designed such that if $z\\sim Unif(-1,1)$ and $z\\in\\mathcal{R}^d$, $G(z)\\sim p(X)$ where $p(X)$ is the data distribution and $G(\\cdot)$ is the generator network.\n",
"\n",
"**The second network** is a binary classifier network that we are used to. It takes in a vector and classifies if the data is 'real' or 'fake'. At a certain point if the generator is powerful enough it will not be able to distinguish between the two classes and give each a probability of 0.5.\n",
"\n",
"It is trained in a cat and mouse kind of way in the sense that we train the generator and then the discriminator and keep repeating. It can be rather unstable hence we employ batch normalisation and leaky relu rather heavily. Finally the GAN paper shows that in theory GANs do reach equilibrium and will converge to the true data distribution.\n",
"\n",
"Personally what I find fascinating is that it is such a simple idea to use two networks (atleast when written down), but yet so powerful.\n",
"\n",
"## Reference\n",
"1. GAN paper: https://arxiv.org/pdf/1406.2661.pdf"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: tqdm in /root/miniconda3/lib/python3.6/site-packages\r\n"
]
}
],
"source": [
"!pip install tqdm"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": [
"IPython.OutputArea.auto_scroll_threshold = 9999;"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%javascript\n",
"IPython.OutputArea.auto_scroll_threshold = 9999;"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"from keras.models import Sequential, Model\n",
"from keras.layers import Dense, Conv2D, Conv2DTranspose, BatchNormalization, Flatten, Reshape, LeakyReLU, Activation\n",
"from keras.activations import sigmoid\n",
"import tensorflow as tf\n",
"\n",
"from utilities import show_graph\n",
"\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"from tensorflow.examples.tutorials.mnist import input_data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Extracting ./data/train-images-idx3-ubyte.gz\n",
"Extracting ./data/train-labels-idx1-ubyte.gz\n",
"Extracting ./data/t10k-images-idx3-ubyte.gz\n",
"Extracting ./data/t10k-labels-idx1-ubyte.gz\n"
]
}
],
"source": [
"mnist = input_data.read_data_sets('./data', one_hot=False)\n",
"image_size = (28, 28, 1)\n",
"def plot_sample(imgs):\n",
" sample_imgs = imgs[:9]\n",
" plt.figure(figsize=(12,12))\n",
" for i in range(9):\n",
" plt.subplot(3,3,i+1)\n",
" plt.imshow(sample_imgs[i].squeeze(), cmap='gray')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"batch_size = 128\n",
"z_dim = 100\n",
"image_height = 28\n",
"image_width = 28\n",
"n_channels = 1"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"leaky_relu = lambda x: tf.maximum(x, 0.1 * x)\n",
"\n",
"def upsample(x, depth, is_train, strides=2, activation=leaky_relu):\n",
" x = tf.layers.conv2d_transpose(x, depth, 5, padding='same', strides=strides)\n",
" x = tf.layers.batch_normalization(x, training=is_train)\n",
" x = activation(x)\n",
" return x, int(depth/2)\n",
"\n",
"def generator(z, n_channels, is_train):\n",
" with tf.variable_scope('generator', reuse=not is_train):\n",
" depth = 64\n",
"\n",
" generator = tf.layers.dense(z, 7*7*depth)\n",
" generator = tf.layers.batch_normalization(generator, training=is_train)\n",
" generator = leaky_relu(generator)\n",
"\n",
" generator = tf.reshape(generator, (-1, 7, 7, depth))\n",
" generator, depth = upsample(generator, depth, is_train, strides=2)\n",
" generator, depth = upsample(generator, depth, is_train, strides=1)\n",
" generator, depth = upsample(generator, depth, is_train, strides=1)\n",
" generator, _ = upsample(generator, n_channels, is_train, strides=2, activation=tf.tanh)\n",
" \n",
" return generator"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def conv_layer(x, depth, is_train, is_bn=True, strides=2, activation=leaky_relu):\n",
" x = tf.layers.conv2d(x, filters=depth, kernel_size=5, strides=strides, padding='same')\n",
" if is_bn:\n",
" x = tf.layers.batch_normalization(x, training=is_train)\n",
" x = activation(x)\n",
" return x, depth*2\n",
"\n",
"def discriminator(images, is_train, reuse=False):\n",
" with tf.variable_scope('discriminator', reuse=reuse):\n",
" depth = 32\n",
" \n",
" discriminator, depth = conv_layer(images, depth, is_train, is_bn=False)\n",
" discriminator, _ = conv_layer(discriminator, depth, is_train)\n",
" \n",
" discriminator = tf.reshape(discriminator, (-1, 7*7*depth))\n",
" logits = tf.layers.dense(discriminator, 1)\n",
" \n",
" return logits"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Network:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def network(input_z, input_real, n_channels, is_train):\n",
" d_labels = tf.stack([tf.zeros((batch_size, 1)), tf.ones((batch_size, 1))])\n",
" learning_rate = 1e-3\n",
"\n",
" inputs_fake = generator(input_z, n_channels, is_train)\n",
" d_logits_fake = discriminator(inputs_fake, is_train)\n",
" d_logits_real = discriminator(input_real, is_train, reuse=True)\n",
"\n",
" d_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=tf.stack([d_logits_fake, d_logits_real]), \n",
" labels=d_labels))\n",
" g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,\n",
" labels=tf.ones_like(d_logits_fake))) \n",
" \n",
" return d_loss, g_loss\n",
"\n",
"def optimization(d_loss, g_loss):\n",
" learning_rate = 1e-3\n",
" t_vars = tf.trainable_variables()\n",
" d_vars = [var for var in t_vars if var.name.startswith('discriminator')]\n",
" g_vars = [var for var in t_vars if var.name.startswith('generator')]\n",
"\n",
" with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):\n",
" d_trainer = tf.train.AdamOptimizer(learning_rate).minimize(d_loss, var_list=d_vars)\n",
" g_trainer = tf.train.AdamOptimizer(learning_rate).minimize(g_loss, var_list=g_vars)\n",
"\n",
" return d_trainer, g_trainer"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tf.reset_default_graph()\n",
"sess = tf.InteractiveSession()\n",
"\n",
"# Tensor where we will feed the data into graph\n",
"z = tf.placeholder(tf.float32, (None, z_dim), 'z')\n",
"images = tf.placeholder(tf.float32, (None, image_width, image_height, n_channels), 'images')\n",
"\n",
"d_loss, g_loss = network(z, images, 1, is_train=True)\n",
"d_trainer, g_trainer = optimization(d_loss, g_loss)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
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"^Adam_1/update_generator/conv2d_transpose_3/kernel/ApplyAdam"\\n input: "^Adam_1/update_generator/conv2d_transpose_3/bias/ApplyAdam"\\n input: "^Adam_1/update_generator/batch_normalization_4/beta/ApplyAdam"\\n input: "^Adam_1/update_generator/batch_normalization_4/gamma/ApplyAdam"\\n input: "^Adam_1/Assign"\\n input: "^Adam_1/Assign_1"\\n}\\n';\n",
" }\n",
" </script>\n",
" <link rel="import" href="https://tensorboard.appspot.com/tf-graph-basic.build.html" onload=load()>\n",
" <div style="height:600px">\n",
" <tf-graph-basic id="graph0.7156902690640853"></tf-graph-basic>\n",
" </div>\n",
" \"></iframe>\n",
" "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"show_graph(tf.get_default_graph().as_graph_def())"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def show_images(gen_im):\n",
" plt.figure(figsize=(3,3))\n",
" for i in range(9):\n",
" plt.subplot(3,3,i+1)\n",
" plt.imshow(gen_im[i].squeeze())\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
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PqhiWSLhobS+mZLmHJ78sK9BHviYF98xjtOF/ObaK4HQFVVo7tYiFu2GoUYek5mJtqqEH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\n1Nup+PZVzL/8619oX867fSsAYNdxLqS8wZjm7r9KDxcHkLaXEqXzq3Q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dT5wDAAhPplRM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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f81ccfc70b8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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CA7+GuJZYoyiyRLz1W8GvJa5fBCSj4CtEdm3XWl+9zee/Bdq2UfDztNY//LSx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\nsB+JOmDZGKHUt+cSqpxsZ4jpPvHq8ThmZcZbG4VXkeixlBc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KxkxLJBCukhC639YsWeiY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\nbhaXXEqT/guDSviwyPzaGAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f81da326668>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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+6zo8SQnvjt10XnurzTmzeMblsqvtiOWjAQD8BIw0Izo4B16NBkol8lmiqP43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\nMXP2WRYeRb9QpM5fEhnhhzne8106Vtmoydg4FOc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gdwBOMvMvaja9CuAbAH6mv/+05Nl0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\nxP8A8Wc81CVXsUcAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f81cce1f1d0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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MaV8u/2VMFXyC6mhLEOfFmoyfhULdMYca58SmaNWVStUCUy9D/W5pTY+RF6Fa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\nLCrf3gYsbYhWpW6wq1fNbmjPrZSTHdC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RThBZJOofdBk8d8yX/0Ut49euCvxxVO65vGSS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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f81cc3c6128>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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Gfrdix10mqUSdXsIvvlSew/Tbe2mopvNPpLhAPML1MyPSuMws9v/mJuKxQlfQLZ4W0dZt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\nAaVUG3C/eYjnALdqrS8xpdQ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ElbS/z+G/W8157dYjQeb3JybeD8BbVp7B2X8+H4B7T/om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\n5YWGS7vRsfRtk17ML7fyiY2SOGLBfw9Qjfp4sPp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"text/plain": [
"<matplotlib.figure.Figure at 0x7f81cc9a2d30>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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UeRMlG272xlPlmbLo+rsBOPeu43FMBhNP1ap1bp6wMPVjMdA5Yk3uxLASuX0r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\nT5YYMSIiniwxYkREPFlixIiI/w+X4FbB3v0MDgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f81c1e01c18>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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vV6WObkEIUGa4mryUsflqbMTeoyfB3tN11ronfn6rr6qvn3UvAHO+4I+65GhO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\nGSwJEsREMlgSJIiJ/w8DlBoRjPrzjQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f81cc0566d8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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QFZPXUsvdvusMAC6oX8WJZXLiv6ZLghf8ZPn5nDrmXSDOURzsjrWyIypkXzZX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\nZAkRIiDCyRIiRECEkyVEiID4/4W2y0L4Gm7JAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f81cc8ed630>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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BAvjEYcNZPGtO0wj9brH/j79gLQf+qJ61G/5DN+2NCL/Q6foGK+NK\npCR3k2K+Okt3C3XdtmetVI51+yElv6WXyfiaVVQ6ylF//nW1LFU/sCS//nZoOw9k2+UYDvHcKT+3\nf3RjYijXTF4pVruGo6sBeGTifQDEzQB+vT/HwPg50tpHmb/yyLpUrje4ok4fSK11rrt+xHxu+6WK\nHU8c/R1bOmm/93yIarzVakeSJtNiK7g9hKTCGkMGqMFCokr/5bUvsNeudFf+yfrLFeOFz7eKLowZ\na+4YytBQx927u+K5+euFhw3FtKi3xj3fuxuAakfvx2NNlSy8Yrot+Ya/CgMxLECAnsFhI4bF61XJ\nbbP5OaOiXT+91OHp4x60xzqvCHsoteXX//w4jv7PN7XOgwe7LN8vkMYhJOQgXuCJ4eqtEBKXT224\n2Jbf1qvdy4q0vt926pOdipRbk3+3sDTvOWciV96qG3OPL9FrPVHu9tXnMWr7zoL62B0CzhIggE/0\nb86SKvceNxmAT1+pM0uqcuh5GMc91xFc6l01H9dY5bjcKsEvvH8uZ/3iOgDGX7qihwkoLowxONW6\nQLn1LPWzqmsbwqEf6saxARSBsxSSPjtFHxW7Ec3DlRV1pO9PKXNK2PyV0/T3Ln125Vv0uZXsb6Pu\nPLsXyTKgdVf8OPHcm+zzve+AmpBHXrGJeEva5q9u+5ubztK/B4uFREp4+2KVv24YZD0IU5ji4lZV\nHJc1TQRgfeMwVuwZCcCCGb/vUNfocDkvn/YzAC7jtB7stGgklSIYEbytChgXM1AV2hMvWwXAOQM3\n8LPBagAYUHBLBUAcNUTYTWmhoSomHjx5FACHzLOUZdjMteb6n3RZpTcJJn3Jks/8O3tVmV/80akA\nuC3v5O7jFohhAQL0DPo1ZwkN0g0+MqiSz53/NwBiNuTo0hadFe7afC7tV1jTqjUrSkkJQ1rUpf++\nheMBuKqqDoCoRCh3OooI3SJfc2p3URJzhInH2X2qbq/+45jfAFAfh0HrfcWI6xopEWYS7vv5ODqE\nnMTmLFOmYm/lMvVzO+WpzzH3vX8A4PyBTb7qS/VO9uBt9Fs0w/JRU2e/84gfEJiOAwToGfQ+Z8lB\nrvSilKy/s5qT21Vn+fOh4QD8/rzZWmh3Pa6NTuJ5ojoDSjE20sfdf7gAgHM+/W0AJkUiODbnglOq\ns5/b2tpzUSGLgFTP4QM2WGrEruA3GYjs0D0dBWtHxsXzDMjr8lgssTnL1HUM8jjttla+ec4nADj1\nW98DoNoppdX6+HlbwrOt6rebOKd9/0YARhhvY1iGOGM9hG4Hi4iMAe4HhqEMa54xZq6I1AAPAOOB\nOuASY8y+nutqz6PFNLGapbTRAgijmMBYmUK7aWOlPpzjRORpjjRajTBKJlpaW1kRXwRHEK3Fgh/O\nEgO+aIx5VUQqgFfsTfwUMN8Yc5dNj3Yrmq+jaHjnX9W69YtTfsbKFvX78jhKfP1bWig1NlSKzOp5\n6MaPV0/URtOZVC8el4RCmFgMQZgiJ1Ip1cTcNpYwnxozjO1sokaGUW92rQLm9wSt2ZCIn+YIN5zv\nmc6V1mtv/DzlW17PsUJBcJiCpZU4S9x/UCPD2R6vo4Zh1LMzZ1o7eEWneUjH99ZT9aCOuYvalDsY\nR6hcrwvD0qSSwKjf7gBg7qh/dqr/oguuYuQq3ZeUbxKuBPKIj9btYDHGbAe2298NIrIWTWR0ATDb\nFvsN8BxFfoEmnacD4j2lMY4r0Y1NfzpxDgDl3mDJwIZNWzuO9aG6Z+YfATihJLmd2GP1df81E4CJ\nc9cRr99HVMqI2igjYYlQZipopZndbGUms9nASt+0CiAiBXv1QzJ+Wtu7p3Fu+XwAWmx4l4qFG3G7\nCPLddYUOURlIqaMeD2HXoYxKpdVsYabMZgMrIJ/n2uVLnBTxmmv0/jcPEyrXWQfXVnVunT1I/bqi\nEsa1d2+9dXx16rYTb++8/aK3kJPOIiLjgRnAYmCYHUgAO1AxLdM11wDXAJRmCHfTX9FsGmlgP1XU\n0EYrURngWU380SqHT9q4JK2D86P1MHquhcD3YBGRcuBh4PPGmIOSKvIYY0Qk45RijJkHzAOolJqc\nJtoVm+yC1qRWKqyX8UdufwaA+W+fqoVeX5dQ7D3v3HVzp/Ohd+nq/AfKut5WW273HbUfNw7nxQOJ\n4zHTzgpeYirTCUukg2nRL61VzuCiaZsHzj8BAPnULqqsafc1uxAb37krN5FCpEPin5hpZ4W7kKnO\nDKU1BTk9Vy/VQxZ4z2ngLhXRhixtxIQ7KvSDQ4cSvy9/W+OFLX9ZrRqT9r6Uv3k447ncjMG+SotI\nBB0ovzfG/MUe3ikiI+z5EcCunFrup3CNywp3EcMZy1DRwVpClFajaxlHHK3xhQyXcQwVdZk5Umkt\nBvxYwwS4F1hrjPl+yqm/Ap8E7rLfjxa7c1M/o7Frz/3wF/jb97TpG6vVY/jGv+p3q4mxJa7cwwti\nUeZ0vfkpblxW2qAPVRtVFo7saMCNhDHGsKbtZQZKBeNE3SgwhiGMZDuJgAq+aDUUQQm1iO5TOX3u\n0X+g3qonV7/0SQAm81puJlNjMK6mql5tFlNGBeNkSsKkP0RGsZ3EVt/iPVdj8DazVbym0rspTS4O\n7ztVJ6YzS3UZICQlbJ6nHGXygxrrzSTq6Rv4EcNOBz4OrBQRL2rzl9FB8qCIXA1sAi7x1WIOm7+8\niPnlDy1m1klfBOCNj/8YSCrpZVLCUU7XAaS/sUdf+t89pOFLx/9obcJSE2myMbjCYUx7jP1mNzvM\nJsqp4mWj+9ony/GMk6NZaV4GOA7Y75vWfJG2cWruz38EwLGREqb8WZ1Aj75d42vltbZiXA6YPZ1p\ndU5gnExjpaumY4pNq6Unvs260jtC+xnHAfD+2zTmmbf1otW0M+h3L9vLeieZa3fwYw1bAHQl+J2V\nc4v9GINkCGeHLtU/aYunM+W9POM+tMoY0z/zdeeIQVLL2XJxx4P2BZoZeh/PxB84YmgtFvq1b1gq\ny518h4YynRq6HoDll/8AUNd7zzvVW5nfFW9iW1y5zcLzNFbV2K0qmsVd0ymzrZsS0jRjVuM+ghfM\n/NhIcnV7zDM6iOMHD3V5XZ8gB4kh4ZEgDiW7dWtxbaShQ5lNsbZ+51UR+IYFCOATfcNZMkUByRSt\nMeWYaz2KJ92sPkEfu+0MPRcKJXQQL4uUlA9M/I7tshuiUsQqL3d6pmMJDlOgo1XOi5IpcYe9GGkt\nZ0wDICTKFdtNnAFPWmU33/jEIl3K6+JIfpxVvGv9lk+5/2+pD9mjXzwHgIofPAbA3F9exIhu4h13\nXX9KhuNQR1+31GWGXGMiB5wlQACf6Ndex1mrsd6tnpcrJH2TnNRcjTYNnWluts2brH0ols5iMNrH\nbLG0UmdAT49KWRiMD9C57B0bafH2bR+CkI155vUzVxZoTJfmmu7uTdd12mu7y+GYlnIDkhbP6Hy1\nTP7mtvMBGL1uN/FssaH9ds3jJOl7i4ybM619I4ZlDIzt4yanlsuyMmva2iFkk5d6LuOJlyvlBqXW\n1VMxxLJtSspAs4nHMU2qAJf9TUWua1/S4HnmwMHE1oOCEqX2VArs7urJ8uw8pb/sSR00bqby3b0j\niRC4Kdel58JJBBbP3tVMCMSwAAF8QnpkwaerxkR2A43Anl5rtDDU0rGv44wxQ/xcGNDar5EXrb06\nWABEZJkxJsegtH2DQvsa0No/kW9fAzEsQACfCAZLgAA+0ReDZV4ftJkvCu1rQGv/RF597XWdJUCA\nwxWBGBYggE8EgyVAAJ/otcEiInNEZJ2IbLChk/oNRGSMiPxTRNaIyGoR+Zw9foeIbBWR5fZzrs/6\nAlr7AYpNK8aYHv+gMXDeAiaieQdeB47pjbZ99m8EcJL9XQGsB44B7gBuCmgNaDXG9BpnmQVsMMa8\nbYxpA/6Exh3rFzDGbDfGvGp/NwBebLR8ENDaT1BkWnttsIwCUoPfbqGATvck0mKjAdwgIitE5Fci\nUu2jioDWfogi0Boo+KlIj40G/BQVMaajUTm/14fdKyoCWnOntbcGy1ZgTMr/0fZYv0Gm2GjGmJ3G\nmLjRbYm/QMWO7hDQ2o9QRFp7bbAsBaaIyAQRKQEuQ+OO9Qt0FRvNCyJocSGwykd1Aa39BEWmtXc2\nfxljYiLyWeAp1ILyK2PM6t5o2ye6io12uYhMR+O71QH/0V1FAa1HJq0QuLsECOAbgYIfIIBPBIMl\nQACfCAZLgAA+EQyWAAF8IhgsAQL4RDBYAgTwiWCwBAjgE/8f20Ugg1qfiQwAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f81cbedf358>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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dJML0nl3flQrO5/1C/MA+bS7moR98DwDPngWdX8MphF87UbO01V4HUJ8vlo/G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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f81c15f75f8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sess.run(tf.global_variables_initializer())\n",
"epochs = 10\n",
"batches_per_epoch = len(mnist.train.images) // batch_size\n",
"\n",
"g_losses = []\n",
"d_losses = []\n",
"\n",
"for epoch_i in range(epochs):\n",
" batch_z = np.random.uniform(-1, 1, size=(9, z_dim))\n",
" gen_im = sess.run(generator(z, n_channels, is_train=False), feed_dict={z: batch_z})\n",
" show_images(gen_im)\n",
" for _ in range(batches_per_epoch):\n",
" batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))\n",
" real_im = mnist.train.next_batch(batch_size)[0].reshape((-1,28,28, n_channels))\n",
" \n",
" d_l, _ = sess.run([d_loss, d_trainer], feed_dict={images: 2*(real_im-0.5), z: batch_z})\n",
" g_l, _ = sess.run([g_loss, g_trainer], feed_dict={images: 2*(real_im-0.5), z: batch_z})\n",
" \n",
" g_losses.append(g_l)\n",
" d_losses.append(d_l)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"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": "3.6.1"
},
"latex_envs": {
"bibliofile": "biblio.bib",
"cite_by": "apalike",
"current_citInitial": 1,
"eqLabelWithNumbers": true,
"eqNumInitial": 0
}
},
"nbformat": 4,
"nbformat_minor": 2
}