DcGAN_Tensorflow_Demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Adapted from: https://github.com/lazyprogrammer/machine_learning_examples/blob/master/unsupervised_class3/\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.utils import shuffle\n",
    "import imageio as im\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "LEARNING_RATE = 0.0002\n",
    "BETA1 = 0.5\n",
    "BATCH_SIZE = 64\n",
    "EPOCHS = 2\n",
    "SAVE_SAMPLE_PERIOD = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lrelu(x, alpha=0.2):\n",
    "  return tf.maximum(alpha*x, x)\n",
    "\n",
    "def get_mnist(limit=None):\n",
    "  print(\"Reading in and transforming data...\")\n",
    "  df = pd.read_csv('large_files/train.csv')\n",
    "  data = df.values\n",
    "  # np.random.shuffle(data)\n",
    "  X = data[:, 1:] / 255.0 # data is from 0..255\n",
    "  Y = data[:, 0]\n",
    "  X, Y = shuffle(X, Y)\n",
    "  if limit is not None:\n",
    "    X, Y = X[:limit], Y[:limit]\n",
    "  return X, Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ConvLayer:\n",
    "  def __init__(self, name, mi, mo, apply_batch_norm, filtersz=5, stride=2, f=tf.nn.relu):\n",
    "    # mi = input feature map size\n",
    "    # mo = output feature map size\n",
    "    # self.W = tf.Variable(0.02*tf.random_normal(shape=(filtersz, filtersz, mi, mo)))\n",
    "    # self.b = tf.Variable(np.zeros(mo, dtype=np.float32))\n",
    "    self.W = tf.get_variable(\n",
    "      \"W_%s\" % name,\n",
    "      shape=(filtersz, filtersz, mi, mo),\n",
    "      # initializer=tf.contrib.layers.xavier_initializer(),\n",
    "      initializer=tf.truncated_normal_initializer(stddev=0.02),\n",
    "    )\n",
    "    self.b = tf.get_variable(\n",
    "      \"b_%s\" % name,\n",
    "      shape=(mo,),\n",
    "      initializer=tf.zeros_initializer(),\n",
    "    )\n",
    "    self.name = name\n",
    "    self.f = f\n",
    "    self.stride = stride\n",
    "    self.apply_batch_norm = apply_batch_norm\n",
    "    self.params = [self.W, self.b]\n",
    "\n",
    "  def forward(self, X, reuse, is_training):\n",
    "    # print(\"**************** reuse:\", reuse)\n",
    "    conv_out = tf.nn.conv2d(\n",
    "      X,\n",
    "      self.W,\n",
    "      strides=[1, self.stride, self.stride, 1],\n",
    "      padding='SAME'\n",
    "    )\n",
    "    conv_out = tf.nn.bias_add(conv_out, self.b)\n",
    "\n",
    "    # apply batch normalization\n",
    "    if self.apply_batch_norm:\n",
    "      conv_out = tf.contrib.layers.batch_norm(\n",
    "        conv_out,\n",
    "        decay=0.9, \n",
    "        updates_collections=None,\n",
    "        epsilon=1e-5,\n",
    "        scale=True,\n",
    "        is_training=is_training,\n",
    "        reuse=reuse,\n",
    "        scope=self.name,\n",
    "      )\n",
    "    return self.f(conv_out)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class FractionallyStridedConvLayer:\n",
    "  def __init__(self, name, mi, mo, output_shape, apply_batch_norm, filtersz=5, stride=2, f=tf.nn.relu):\n",
    "    # mi = input feature map size\n",
    "    # mo = output feature map size\n",
    "    # NOTE!!! shape is specified in the OPPOSITE way from regular conv\n",
    "    # self.W = tf.Variable(0.02*tf.random_normal(shape=(filtersz, filtersz, mo, mi)))\n",
    "    # self.b = tf.Variable(np.zeros(mo, dtype=np.float32))\n",
    "    self.W = tf.get_variable(\n",
    "      \"W_%s\" % name,\n",
    "      shape=(filtersz, filtersz, mo, mi),\n",
    "      # initializer=tf.contrib.layers.xavier_initializer(),\n",
    "      initializer=tf.random_normal_initializer(stddev=0.02),\n",
    "    )\n",
    "    self.b = tf.get_variable(\n",
    "      \"b_%s\" % name,\n",
    "      shape=(mo,),\n",
    "      initializer=tf.zeros_initializer(),\n",
    "    )\n",
    "    self.f = f\n",
    "    self.stride = stride\n",
    "    self.name = name\n",
    "    self.output_shape = output_shape\n",
    "    self.apply_batch_norm = apply_batch_norm\n",
    "    self.params = [self.W, self.b]\n",
    "\n",
    "  def forward(self, X, reuse, is_training):\n",
    "    conv_out = tf.nn.conv2d_transpose(\n",
    "      value=X,\n",
    "      filter=self.W,\n",
    "      output_shape=self.output_shape,\n",
    "      strides=[1, self.stride, self.stride, 1],\n",
    "    )\n",
    "    conv_out = tf.nn.bias_add(conv_out, self.b)\n",
    "\n",
    "    # apply batch normalization\n",
    "    if self.apply_batch_norm:\n",
    "      conv_out = tf.contrib.layers.batch_norm(\n",
    "        conv_out,\n",
    "        decay=0.9, \n",
    "        updates_collections=None,\n",
    "        epsilon=1e-5,\n",
    "        scale=True,\n",
    "        is_training=is_training,\n",
    "        reuse=reuse,\n",
    "        scope=self.name,\n",
    "      )\n",
    "\n",
    "    return self.f(conv_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DenseLayer(object):\n",
    "  def __init__(self, name, M1, M2, apply_batch_norm, f=tf.nn.relu):\n",
    "    self.W = tf.get_variable(\n",
    "      \"W_%s\" % name,\n",
    "      shape=(M1, M2),\n",
    "      initializer=tf.random_normal_initializer(stddev=0.02),\n",
    "    )\n",
    "    self.b = tf.get_variable(\n",
    "      \"b_%s\" % name,\n",
    "      shape=(M2,),\n",
    "      initializer=tf.zeros_initializer(),\n",
    "    )\n",
    "    self.f = f\n",
    "    self.name = name\n",
    "    self.apply_batch_norm = apply_batch_norm\n",
    "    self.params = [self.W, self.b]\n",
    "\n",
    "  def forward(self, X, reuse, is_training):\n",
    "    a = tf.matmul(X, self.W) + self.b\n",
    "\n",
    "    # apply batch normalization\n",
    "    if self.apply_batch_norm:\n",
    "      a = tf.contrib.layers.batch_norm(\n",
    "        a,\n",
    "        decay=0.9, \n",
    "        updates_collections=None,\n",
    "        epsilon=1e-5,\n",
    "        scale=True,\n",
    "        is_training=is_training,\n",
    "        reuse=reuse,\n",
    "        scope=self.name,\n",
    "      )\n",
    "    return self.f(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DCGAN:\n",
    "  def __init__(self, img_length, num_colors, d_sizes, g_sizes):\n",
    "\n",
    "    # save for later\n",
    "    self.img_length = img_length\n",
    "    self.num_colors = num_colors\n",
    "    self.latent_dims = g_sizes['z']\n",
    "\n",
    "    # define the input data\n",
    "    self.X = tf.placeholder(\n",
    "      tf.float32,\n",
    "      shape=(None, img_length, img_length, num_colors),\n",
    "      name='X'\n",
    "    )\n",
    "    self.Z = tf.placeholder(\n",
    "      tf.float32,\n",
    "      shape=(None, self.latent_dims),\n",
    "      name='Z'\n",
    "    )\n",
    "\n",
    "    # note: by making batch_sz a placeholder, we can specify a variable\n",
    "    # number of samples in the FS-conv operation where we are required\n",
    "    # to pass in output_shape\n",
    "    # we need only pass in the batch size via feed_dict\n",
    "    self.batch_sz = tf.placeholder(tf.int32, shape=(), name='batch_sz')\n",
    "\n",
    "    # build the discriminator\n",
    "    logits = self.build_discriminator(self.X, d_sizes)\n",
    "\n",
    "    # build generator\n",
    "    self.sample_images = self.build_generator(self.Z, g_sizes)\n",
    "\n",
    "    # get sample logits\n",
    "    with tf.variable_scope(\"discriminator\") as scope:\n",
    "      scope.reuse_variables()\n",
    "      sample_logits = self.d_forward(self.sample_images, True)\n",
    "\n",
    "    # get sample images for test time (batch norm is different)\n",
    "    with tf.variable_scope(\"generator\") as scope:\n",
    "      scope.reuse_variables()\n",
    "      self.sample_images_test = self.g_forward(\n",
    "        self.Z, reuse=True, is_training=False\n",
    "      )\n",
    "\n",
    "    # build costs\n",
    "    self.d_cost_real = tf.nn.sigmoid_cross_entropy_with_logits(\n",
    "      logits=logits,\n",
    "      labels=tf.ones_like(logits)\n",
    "    )\n",
    "    self.d_cost_fake = tf.nn.sigmoid_cross_entropy_with_logits(\n",
    "      logits=sample_logits,\n",
    "      labels=tf.zeros_like(sample_logits)\n",
    "    )\n",
    "    self.d_cost = tf.reduce_mean(self.d_cost_real) + tf.reduce_mean(self.d_cost_fake)\n",
    "    self.g_cost = tf.reduce_mean(\n",
    "      tf.nn.sigmoid_cross_entropy_with_logits(\n",
    "        logits=sample_logits,\n",
    "        labels=tf.ones_like(sample_logits)\n",
    "      )\n",
    "    )\n",
    "    real_predictions = tf.cast(logits > 0, tf.float32)\n",
    "    fake_predictions = tf.cast(sample_logits < 0, tf.float32)\n",
    "    num_predictions = 2.0*BATCH_SIZE\n",
    "    num_correct = tf.reduce_sum(real_predictions) + tf.reduce_sum(fake_predictions)\n",
    "    self.d_accuracy = num_correct / num_predictions\n",
    "\n",
    "\n",
    "    # optimizers\n",
    "    self.d_params = [t for t in tf.trainable_variables() if t.name.startswith('d')]\n",
    "    self.g_params = [t for t in tf.trainable_variables() if t.name.startswith('g')]\n",
    "\n",
    "    self.d_train_op = tf.train.AdamOptimizer(\n",
    "      LEARNING_RATE, beta1=BETA1\n",
    "    ).minimize(\n",
    "      self.d_cost, var_list=self.d_params\n",
    "    )\n",
    "    self.g_train_op = tf.train.AdamOptimizer(\n",
    "      LEARNING_RATE, beta1=BETA1\n",
    "    ).minimize(\n",
    "      self.g_cost, var_list=self.g_params\n",
    "    )\n",
    "\n",
    "    # show_all_variables()\n",
    "    # exit()\n",
    "\n",
    "    # set up session and variables for later\n",
    "    self.init_op = tf.global_variables_initializer()\n",
    "    self.sess = tf.InteractiveSession()\n",
    "    self.sess.run(self.init_op)\n",
    "\n",
    "\n",
    "  def build_discriminator(self, X, d_sizes):\n",
    "    with tf.variable_scope(\"discriminator\") as scope:\n",
    "\n",
    "      # build conv layers\n",
    "      self.d_convlayers = []\n",
    "      mi = self.num_colors\n",
    "      dim = self.img_length\n",
    "      count = 0\n",
    "      for mo, filtersz, stride, apply_batch_norm in d_sizes['conv_layers']:\n",
    "        # make up a name - used for get_variable\n",
    "        name = \"convlayer_%s\" % count\n",
    "        count += 1\n",
    "\n",
    "        layer = ConvLayer(name, mi, mo, apply_batch_norm, filtersz, stride, lrelu)\n",
    "        self.d_convlayers.append(layer)\n",
    "        mi = mo\n",
    "        print(\"dim:\", dim)\n",
    "        dim = int(np.ceil(float(dim) / stride))\n",
    "\n",
    "\n",
    "      mi = mi * dim * dim\n",
    "      # build dense layers\n",
    "      self.d_denselayers = []\n",
    "      for mo, apply_batch_norm in d_sizes['dense_layers']:\n",
    "        name = \"denselayer_%s\" % count\n",
    "        count += 1\n",
    "\n",
    "        layer = DenseLayer(name, mi, mo, apply_batch_norm, lrelu)\n",
    "        mi = mo\n",
    "        self.d_denselayers.append(layer)\n",
    "\n",
    "\n",
    "      # final logistic layer\n",
    "      name = \"denselayer_%s\" % count\n",
    "      self.d_finallayer = DenseLayer(name, mi, 1, False, lambda x: x)\n",
    "\n",
    "      # get the logits\n",
    "      logits = self.d_forward(X)\n",
    "\n",
    "      # build the cost later\n",
    "      return logits\n",
    "\n",
    "\n",
    "  def d_forward(self, X, reuse=None, is_training=True):\n",
    "    # encapsulate this because we use it twice\n",
    "    output = X\n",
    "    for layer in self.d_convlayers:\n",
    "      output = layer.forward(output, reuse, is_training)\n",
    "    output = tf.contrib.layers.flatten(output)\n",
    "    for layer in self.d_denselayers:\n",
    "      output = layer.forward(output, reuse, is_training)\n",
    "    logits = self.d_finallayer.forward(output, reuse, is_training)\n",
    "    return logits\n",
    "\n",
    "\n",
    "  def build_generator(self, Z, g_sizes):\n",
    "    with tf.variable_scope(\"generator\") as scope:\n",
    "\n",
    "      # determine the size of the data at each step\n",
    "      dims = [self.img_length]\n",
    "      dim = self.img_length\n",
    "      for _, _, stride, _ in reversed(g_sizes['conv_layers']):\n",
    "        dim = int(np.ceil(float(dim) / stride))\n",
    "        dims.append(dim)\n",
    "\n",
    "      # note: dims is actually backwards\n",
    "      # the first layer of the generator is actually last\n",
    "      # so let's reverse it\n",
    "      dims = list(reversed(dims))\n",
    "      print(\"dims:\", dims)\n",
    "      self.g_dims = dims\n",
    "\n",
    "\n",
    "      # dense layers\n",
    "      mi = self.latent_dims\n",
    "      self.g_denselayers = []\n",
    "      count = 0\n",
    "      for mo, apply_batch_norm in g_sizes['dense_layers']:\n",
    "        name = \"g_denselayer_%s\" % count\n",
    "        count += 1\n",
    "\n",
    "        layer = DenseLayer(name, mi, mo, apply_batch_norm)\n",
    "        self.g_denselayers.append(layer)\n",
    "        mi = mo\n",
    "\n",
    "      # final dense layer\n",
    "      mo = g_sizes['projection'] * dims[0] * dims[0]\n",
    "      name = \"g_denselayer_%s\" % count\n",
    "      layer = DenseLayer(name, mi, mo, not g_sizes['bn_after_project'])\n",
    "      self.g_denselayers.append(layer)\n",
    "\n",
    "\n",
    "      # fs-conv layers\n",
    "      mi = g_sizes['projection']\n",
    "      self.g_convlayers = []\n",
    "\n",
    "      # output may use tanh or sigmoid\n",
    "      num_relus = len(g_sizes['conv_layers']) - 1\n",
    "      activation_functions = [tf.nn.relu]*num_relus + [g_sizes['output_activation']]\n",
    "\n",
    "      for i in range(len(g_sizes['conv_layers'])):\n",
    "        name = \"fs_convlayer_%s\" % i\n",
    "        mo, filtersz, stride, apply_batch_norm = g_sizes['conv_layers'][i]\n",
    "        f = activation_functions[i]\n",
    "        output_shape = [self.batch_sz, dims[i+1], dims[i+1], mo]\n",
    "        print(\"mi:\", mi, \"mo:\", mo, \"outp shape:\", output_shape)\n",
    "        layer = FractionallyStridedConvLayer(\n",
    "          name, mi, mo, output_shape, apply_batch_norm, filtersz, stride, f\n",
    "        )\n",
    "        self.g_convlayers.append(layer)\n",
    "        mi = mo\n",
    "\n",
    "      # get the output\n",
    "      self.g_sizes = g_sizes\n",
    "      return self.g_forward(Z)\n",
    "\n",
    "\n",
    "  def g_forward(self, Z, reuse=None, is_training=True):\n",
    "    # dense layers\n",
    "    output = Z\n",
    "    for layer in self.g_denselayers:\n",
    "      output = layer.forward(output, reuse, is_training)\n",
    "\n",
    "    # project and reshape\n",
    "    output = tf.reshape(\n",
    "      output,\n",
    "      [-1, self.g_dims[0], self.g_dims[0], self.g_sizes['projection']],\n",
    "    )\n",
    "\n",
    "    # apply batch norm\n",
    "    if self.g_sizes['bn_after_project']:\n",
    "      output = tf.contrib.layers.batch_norm(\n",
    "        output,\n",
    "        decay=0.9, \n",
    "        updates_collections=None,\n",
    "        epsilon=1e-5,\n",
    "        scale=True,\n",
    "        is_training=is_training,\n",
    "        reuse=reuse,\n",
    "        scope='bn_after_project'\n",
    "      )\n",
    "\n",
    "    # pass through fs-conv layers\n",
    "    for layer in self.g_convlayers:\n",
    "      output = layer.forward(output, reuse, is_training)\n",
    "\n",
    "    return output\n",
    "\n",
    "\n",
    "  def fit(self, X):\n",
    "    d_costs = []\n",
    "    g_costs = []\n",
    "\n",
    "    N = len(X)\n",
    "    n_batches = N // BATCH_SIZE\n",
    "    total_iters = 0\n",
    "    for i in range(EPOCHS):\n",
    "      print(\"epoch:\", i)\n",
    "      np.random.shuffle(X)\n",
    "      for j in range(n_batches):\n",
    "        t0 = datetime.now()\n",
    "        batch = X[j*BATCH_SIZE:(j+1)*BATCH_SIZE]\n",
    "\n",
    "        Z = np.random.uniform(-1, 1, size=(BATCH_SIZE, self.latent_dims))\n",
    "\n",
    "        # train the discriminator\n",
    "        _, d_cost, d_acc = self.sess.run(\n",
    "          (self.d_train_op, self.d_cost, self.d_accuracy),\n",
    "          feed_dict={self.X: batch, self.Z: Z, self.batch_sz: BATCH_SIZE},\n",
    "        )\n",
    "        d_costs.append(d_cost)\n",
    "\n",
    "        # train the generator\n",
    "        _, g_cost1 = self.sess.run(\n",
    "          (self.g_train_op, self.g_cost),\n",
    "          feed_dict={self.Z: Z, self.batch_sz: BATCH_SIZE},\n",
    "        )\n",
    "        # g_costs.append(g_cost1)\n",
    "        _, g_cost2 = self.sess.run(\n",
    "          (self.g_train_op, self.g_cost),\n",
    "          feed_dict={self.Z: Z, self.batch_sz: BATCH_SIZE},\n",
    "        )\n",
    "        g_costs.append((g_cost1 + g_cost2)/2) # just use the avg\n",
    "\n",
    "        print(\"  batch: %d/%d  -  dt: %s - d_acc: %.2f\" % (j+1, n_batches, datetime.now() - t0, d_acc))\n",
    "\n",
    "\n",
    "        # save samples periodically\n",
    "        total_iters += 1\n",
    "        if total_iters % SAVE_SAMPLE_PERIOD == 0:\n",
    "          print(\"saving a sample...\")\n",
    "          samples = self.sample(64) # shape is (64, D, D, color)\n",
    "\n",
    "          # for convenience\n",
    "          d = self.img_length\n",
    "          \n",
    "          if samples.shape[-1] == 1:\n",
    "            # if color == 1, we want a 2-D image (N x N)\n",
    "            samples = samples.reshape(64, d, d)\n",
    "            flat_image = np.empty((8*d, 8*d))\n",
    "\n",
    "            k = 0\n",
    "            for i in range(8):\n",
    "              for j in range(8):\n",
    "                flat_image[i*d:(i+1)*d, j*d:(j+1)*d] = samples[k].reshape(d, d)\n",
    "                k += 1\n",
    "\n",
    "            # plt.imshow(flat_image, cmap='gray')\n",
    "          else:\n",
    "            # if color == 3, we want a 3-D image (N x N x 3)\n",
    "            flat_image = np.empty((8*d, 8*d, 3))\n",
    "            k = 0\n",
    "            for i in range(8):\n",
    "              for j in range(8):\n",
    "                flat_image[i*d:(i+1)*d, j*d:(j+1)*d] = samples[k]\n",
    "                k += 1\n",
    "            # plt.imshow(flat_image)\n",
    "                    \n",
    "          # plt.savefig('dcgan_samples/samples_at_iter_%d.png' % total_iters)\n",
    "          im.imwrite(\n",
    "            'dcgan_samples/samples_at_iter_%d.png' % total_iters,\n",
    "            flat_image,\n",
    "          )\n",
    "            \n",
    "\n",
    "    # save a plot of the costs\n",
    "    plt.clf()\n",
    "    plt.plot(d_costs, label='discriminator cost')\n",
    "    plt.plot(g_costs, label='generator cost')\n",
    "    plt.legend()\n",
    "    plt.savefig('cost_vs_iteration.png')\n",
    "\n",
    "  def sample(self, n):\n",
    "    Z = np.random.uniform(-1, 1, size=(n, self.latent_dims))\n",
    "    samples = self.sess.run(self.sample_images_test, feed_dict={self.Z: Z, self.batch_sz: n})\n",
    "    return samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading in and transforming data...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dim: 28\ndim: 14\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dims: [7, 14, 28]\nmi: 128 mo: 128 outp shape: [<tf.Tensor 'batch_sz:0' shape=() dtype=int32>, 14, 14, 128]\nmi: 128 mo: 1 outp shape: [<tf.Tensor 'batch_sz:0' shape=() dtype=int32>, 28, 28, 1]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "  batch: 1/656  -  dt: 0:00:00.551357 - d_acc: 0.45\n"
     ]
    },
    {
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     "output_type": "stream",
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     ]
    },
    {
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    },
    {
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    },
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    },
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    },
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     ]
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "WARNING:root:Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
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    },
    {
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     "text": [
      "WARNING:root:Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "  batch: 642/656  -  dt: 0:00:00.563896 - d_acc: 0.59\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  batch: 643/656  -  dt: 0:00:00.539340 - d_acc: 0.59\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  batch: 644/656  -  dt: 0:00:00.550032 - d_acc: 0.59\nsaving a sample...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  batch: 645/656  -  dt: 0:00:00.535796 - d_acc: 0.64\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  batch: 646/656  -  dt: 0:00:00.560710 - d_acc: 0.60\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  batch: 647/656  -  dt: 0:00:00.563076 - d_acc: 0.52\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  batch: 648/656  -  dt: 0:00:00.549440 - d_acc: 0.59\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  batch: 649/656  -  dt: 0:00:00.552370 - d_acc: 0.55\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  batch: 650/656  -  dt: 0:00:00.554998 - d_acc: 0.59\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  batch: 651/656  -  dt: 0:00:00.559817 - d_acc: 0.62\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  batch: 652/656  -  dt: 0:00:00.531460 - d_acc: 0.55\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  batch: 653/656  -  dt: 0:00:00.557673 - d_acc: 0.60\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  batch: 654/656  -  dt: 0:00:00.533640 - d_acc: 0.59\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  batch: 655/656  -  dt: 0:00:00.549805 - d_acc: 0.62\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  batch: 656/656  -  dt: 0:00:00.526322 - d_acc: 0.62\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'model' is not defined",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-12-c215f943b2a2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     32\u001b[0m     \u001b[0mi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m     \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 34\u001b[0;31m     \u001b[0mim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m28\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m28\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     35\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m28\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m28\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'gray'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
     ],
     "output_type": "error"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if __name__ == '__main__':\n",
    "  X, Y = get_mnist()\n",
    "  X = X.reshape(len(X), 28, 28, 1)\n",
    "  dim = X.shape[1]\n",
    "  colors = X.shape[-1]\n",
    "\n",
    "  # for mnist\n",
    "  d_sizes = {\n",
    "    'conv_layers': [(2, 5, 2, False), (64, 5, 2, True)],\n",
    "    'dense_layers': [(1024, True)],\n",
    "  }\n",
    "  g_sizes = {\n",
    "    'z': 100,\n",
    "    'projection': 128,\n",
    "    'bn_after_project': False,\n",
    "    'conv_layers': [(128, 5, 2, True), (colors, 5, 2, False)],\n",
    "    'dense_layers': [(1024, True)],\n",
    "    'output_activation': tf.sigmoid,\n",
    "  }\n",
    "\n",
    "\n",
    "  # setup gan\n",
    "  # note: assume square images, so only need 1 dim\n",
    "  gan = DCGAN(dim, colors, d_sizes, g_sizes)\n",
    "  gan.fit(X)\n",
    "  # samples = gan.sample(1) # just making sure it works\n",
    "\n",
    "  # plot reconstruction\n",
    "  counter=0\n",
    "  while counter<10:\n",
    "    counter+=1\n",
    "    i = np.random.choice(len(X))\n",
    "    x = X[i]\n",
    "    im = model.predict([x]).reshape(28, 28)\n",
    "    plt.subplot(1,2,1)\n",
    "    plt.imshow(x.reshape(28, 28), cmap='gray')\n",
    "    plt.title(\"Original\")\n",
    "    plt.subplot(1,2,2)\n",
    "    plt.imshow(im, cmap='gray')\n",
    "    plt.title(\"Reconstruction\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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