1. Vanilla GAN TensorFlow.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/diego/.pyenv/versions/miniconda-latest/lib/python2.7/site-packages/subprocess32.py:472: RuntimeWarning: The _posixsubprocess module is not being used. Child process reliability may suffer if your program uses threads.\n",
      "  \"program uses threads.\", RuntimeWarning)\n"
     ]
    }
   ],
   "source": [
    "from IPython import display\n",
    "\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision import transforms, datasets\n",
    "\n",
    "from utils import Logger\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_FOLDER = './tf_data/VGAN/MNIST'\n",
    "IMAGE_PIXELS = 28*28\n",
    "NOISE_SIZE = 100\n",
    "BATCH_SIZE = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def noise(n_rows, n_cols):\n",
    "    return np.random.normal(size=(n_rows, n_cols))\n",
    "\n",
    "def xavier_init(size):\n",
    "    in_dim = size[0] if len(size) == 1 else size[1]\n",
    "    stddev = 1. / np.sqrt(float(in_dim))\n",
    "    return tf.random_uniform(shape=size, minval=-stddev, maxval=stddev)\n",
    "\n",
    "def images_to_vectors(images):\n",
    "    return images.reshape(images.shape[0], 784)\n",
    "\n",
    "def vectors_to_images(vectors):\n",
    "    return vectors.reshape(vectors.shape[0], 28, 28, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def mnist_data():\n",
    "    compose = transforms.Compose(\n",
    "        [transforms.ToTensor(),\n",
    "         transforms.Normalize((.5, .5, .5), (.5, .5, .5))\n",
    "        ])\n",
    "    out_dir = '{}/dataset'.format(DATA_FOLDER)\n",
    "    return datasets.MNIST(root=out_dir, train=True, transform=compose, download=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data\n",
    "data = mnist_data()\n",
    "# Create loader with data, so that we can iterate over it\n",
    "data_loader = DataLoader(data, batch_size=BATCH_SIZE, shuffle=True)\n",
    "# Num batches\n",
    "num_batches = len(data_loader)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Discriminator\n",
    "\n",
    "# Input\n",
    "X = tf.placeholder(tf.float32, shape=(None, IMAGE_PIXELS))\n",
    "\n",
    "# Layer 1 Variables\n",
    "D_W1 = tf.Variable(xavier_init([784, 1024]))\n",
    "D_B1 = tf.Variable(xavier_init([1024]))\n",
    "\n",
    "# Layer 2 Variables\n",
    "D_W2 = tf.Variable(xavier_init([1024, 512]))\n",
    "D_B2 = tf.Variable(xavier_init([512]))\n",
    "\n",
    "# Layer 3 Variables\n",
    "D_W3 = tf.Variable(xavier_init([512, 256]))\n",
    "D_B3 = tf.Variable(xavier_init([256]))\n",
    "\n",
    "# Out Layer Variables\n",
    "D_W4 = tf.Variable(xavier_init([256, 1]))\n",
    "D_B4 = tf.Variable(xavier_init([1]))\n",
    "\n",
    "# Store Variables in list\n",
    "D_var_list = [D_W1, D_B1, D_W2, D_B2, D_W3, D_B3, D_W4, D_B4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Generator\n",
    "\n",
    "# Input\n",
    "Z = tf.placeholder(tf.float32, shape=(None, NOISE_SIZE))\n",
    "\n",
    "# Layer 1 Variables\n",
    "G_W1 = tf.Variable(xavier_init([100, 256]))\n",
    "G_B1 = tf.Variable(xavier_init([256]))\n",
    "\n",
    "# Layer 2 Variables\n",
    "G_W2 = tf.Variable(xavier_init([256, 512]))\n",
    "G_B2 = tf.Variable(xavier_init([512]))\n",
    "\n",
    "# Layer 3 Variables\n",
    "G_W3 = tf.Variable(xavier_init([512, 1024]))\n",
    "G_B3 = tf.Variable(xavier_init([1024]))\n",
    "\n",
    "# Out Layer Variables\n",
    "G_W4 = tf.Variable(xavier_init([1024, 784]))\n",
    "G_B4 = tf.Variable(xavier_init([784]))\n",
    "\n",
    "# Store Variables in list\n",
    "G_var_list = [G_W1, G_B1, G_W2, G_B2, G_W3, G_B3, G_W4, G_B4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def discriminator(x):\n",
    "    l1 = tf.nn.dropout(tf.nn.leaky_relu(tf.matmul(x,   D_W1) + D_B1, .2), .3)\n",
    "    l2 = tf.nn.dropout(tf.nn.leaky_relu(tf.matmul(l1,  D_W2) + D_B2, .2), .3)\n",
    "    l3 = tf.nn.dropout(tf.nn.leaky_relu(tf.matmul(l2,  D_W3) + D_B3, .2), .3)\n",
    "    out = tf.matmul(l3, D_W4) + D_B4\n",
    "    return out\n",
    "\n",
    "def generator(z):\n",
    "    l1 = tf.nn.leaky_relu(tf.matmul(z,  G_W1) + G_B1, .2)\n",
    "    l2 = tf.nn.leaky_relu(tf.matmul(l1, G_W2) + G_B2, .2)\n",
    "    l3 = tf.nn.leaky_relu(tf.matmul(l2, G_W3) + G_B3, .2)\n",
    "    out = tf.nn.tanh(tf.matmul(l3, G_W4) + G_B4)\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "G_sample = generator(Z)\n",
    "D_real = discriminator(X)\n",
    "D_fake = discriminator(G_sample)\n",
    "\n",
    "# Losses\n",
    "D_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_real, labels=tf.ones_like(D_real)))\n",
    "D_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_fake, labels=tf.zeros_like(D_fake)))\n",
    "D_loss = D_loss_real + D_loss_fake\n",
    "G_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_fake, labels=tf.ones_like(D_fake)))\n",
    "\n",
    "# Optimizers\n",
    "D_opt = tf.train.AdamOptimizer(2e-4).minimize(D_loss, var_list=D_var_list)\n",
    "G_opt = tf.train.AdamOptimizer(2e-4).minimize(G_loss, var_list=G_var_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_test_samples = 16\n",
    "test_noise = noise(num_test_samples, NOISE_SIZE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Inits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_epochs = 200\n",
    "\n",
    "# Start interactive session\n",
    "session = tf.InteractiveSession()\n",
    "# Init Variables\n",
    "tf.global_variables_initializer().run()\n",
    "# Init Logger\n",
    "logger = Logger(model_name='DCGAN1', data_name='CIFAR10')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1294aeb90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: [0/200], Batch Num: [0/600]\n",
      "(4.7568736, 1.1068091)\n",
      "Discriminator Loss: 4.7569, Generator Loss: 1.1068\n",
      "D(x): -3.0133, D(G(z)): -0.8286\n"
     ]
    }
   ],
   "source": [
    "# Iterate through epochs\n",
    "for epoch in range(num_epochs):\n",
    "    for n_batch, (batch,_) in enumerate(data_loader):\n",
    "        \n",
    "        # 1. Train Discriminator\n",
    "        X_batch = images_to_vectors(batch.permute(0, 2, 3, 1).numpy())\n",
    "        feed_dict = {X: X_batch, Z: noise(BATCH_SIZE, NOISE_SIZE)}\n",
    "        _, d_error, d_pred_real, d_pred_fake = session.run(\n",
    "            [D_opt, D_loss, D_real, D_fake], feed_dict=feed_dict\n",
    "        )\n",
    "\n",
    "        # 2. Train Generator\n",
    "        feed_dict = {Z: noise(BATCH_SIZE, NOISE_SIZE)}\n",
    "        _, g_error = session.run(\n",
    "            [G_opt, G_loss], feed_dict=feed_dict\n",
    "        )\n",
    "\n",
    "        if n_batch % 100 == 0:\n",
    "            display.clear_output(True)\n",
    "            # Generate images from test noise\n",
    "            test_images = session.run(\n",
    "                G_sample, feed_dict={Z: test_noise}\n",
    "            )\n",
    "            test_images = vectors_to_images(test_images)\n",
    "            # Log Images\n",
    "            logger.log_images(test_images, num_test_samples, epoch, n_batch, num_batches, format='NHWC');\n",
    "            # Log Status\n",
    "            logger.display_status(\n",
    "                epoch, num_epochs, n_batch, num_batches,\n",
    "                d_error, g_error, d_pred_real, d_pred_fake\n",
    "            )"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
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 },
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