{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import os\n",
    "from glob import glob\n",
    "import numpy as np\n",
    "from matplotlib import pyplot\n",
    "from PIL import Image\n",
    "import tensorflow as tf\n",
    "\n",
    "##README : IF output folder already existed in same route, it makes error. change past output folder's name ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Dataset(object):\n",
    "    def __init__(self, data_files):\n",
    "        IMAGE_WIDTH = 25\n",
    "        IMAGE_HEIGHT = 25\n",
    "        self.image_mode = 'RGB'\n",
    "        image_channels = 3\n",
    "        self.data_files = data_files\n",
    "        self.shape = len(data_files), IMAGE_WIDTH, IMAGE_HEIGHT, image_channels\n",
    "    \n",
    "    def get_image(iself,image_path, width, height, mode):\n",
    "        image = Image.open(image_path)\n",
    "        image = image.resize((width,height))\n",
    "        return np.array(image)\n",
    "\n",
    "\n",
    "    def get_batch(self,image_files, width, height, mode):\n",
    "        data_batch = np.array(\n",
    "            [self.get_image(sample_file, width, height, mode) for sample_file in image_files]).astype(np.float32)\n",
    "        \n",
    "        # Make sure the images are in 4 dimensions\n",
    "        if len(data_batch.shape) < 4:\n",
    "            data_batch = data_batch.reshape(data_batch.shape + (1,))\n",
    "        return data_batch\n",
    "\n",
    "    def get_batches(self, batch_size):\n",
    "        IMAGE_MAX_VALUE = 255\n",
    "        current_index = 0\n",
    "        while current_index + batch_size <= self.shape[0]:\n",
    "            data_batch = self.get_batch(\n",
    "                self.data_files[current_index:current_index + batch_size],\n",
    "                self.shape[1],self.shape[2],\n",
    "                self.image_mode)\n",
    "            \n",
    "            current_index += batch_size\n",
    "            \n",
    "            yield data_batch / IMAGE_MAX_VALUE - 0.5\n",
    "\n",
    "\n",
    "def model_inputs(image_width, image_height, image_channels, z_dim):\n",
    "    real_input_images = tf.placeholder(tf.float32, [None, image_width, image_height, image_channels], 'real_input_images')\n",
    "    input_z = tf.placeholder(tf.float32, [None, z_dim], 'input_z')\n",
    "    learning_rate = tf.placeholder(tf.float32, [], 'learning_rate')\n",
    "    return real_input_images, input_z, learning_rate\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def discriminator(images, reuse=False, alpha=0.2, keep_prob=0.5):\n",
    "    with tf.variable_scope('discriminator', reuse=reuse):\n",
    "        # Input layer is 25x25xn\n",
    "        # Convolutional layer, 13x13x64\n",
    "        conv1 = tf.layers.conv2d(images, 64, 5, 2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())\n",
    "        lrelu1 = tf.maximum(alpha * conv1, conv1)\n",
    "        drop1 = tf.layers.dropout(lrelu1, keep_prob)\n",
    "        \n",
    "        # Strided convolutional layer, 7x7x128\n",
    "        conv2 = tf.layers.conv2d(drop1, 128, 5, 2, 'same', use_bias=False)\n",
    "        bn2 = tf.layers.batch_normalization(conv2)\n",
    "        lrelu2 = tf.maximum(alpha * bn2, bn2)\n",
    "        drop2 = tf.layers.dropout(lrelu2, keep_prob)\n",
    "        \n",
    "        # Strided convolutional layer, 4x4x256\n",
    "        conv3 = tf.layers.conv2d(drop2, 256, 5, 2, 'same', use_bias=False)\n",
    "        bn3 = tf.layers.batch_normalization(conv3)\n",
    "        lrelu3 = tf.maximum(alpha * bn3, bn3)\n",
    "        drop3 = tf.layers.dropout(lrelu3, keep_prob)\n",
    "        \n",
    "        # fully connected\n",
    "        flat = tf.reshape(drop3, (-1, 4*4*256))\n",
    "        logits = tf.layers.dense(flat, 1)\n",
    "        out = tf.sigmoid(logits)\n",
    "        \n",
    "        return out, logits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generator(z, out_channel_dim, is_train=True, alpha=0.2, keep_prob=0.5):\n",
    "    # TODO: Implement Function\n",
    "    with tf.variable_scope('generator', reuse=(not is_train)):\n",
    "        # First fully connected layer, 8x4x512\n",
    "        fc = tf.layers.dense(z, 4*4*1024, use_bias=False)\n",
    "        fc = tf.reshape(fc, (-1, 4, 4, 1024))\n",
    "        bn0 = tf.layers.batch_normalization(fc, training=is_train)\n",
    "        lrelu0 = tf.maximum(alpha * bn0, bn0)\n",
    "        drop0 = tf.layers.dropout(lrelu0, keep_prob, training=is_train)\n",
    "        \n",
    "        # Deconvolution, 16x8x256\n",
    "        conv1 = tf.layers.conv2d_transpose(drop0, 512,3, 1, 'valid', use_bias=False)\n",
    "        bn1 = tf.layers.batch_normalization(conv1, training=is_train)\n",
    "        lrelu1 = tf.maximum(alpha * bn1, bn1)\n",
    "        drop1 = tf.layers.dropout(lrelu1, keep_prob, training=is_train)\n",
    "        \n",
    "        # Deconvolution, 32x 128\n",
    "        conv2 = tf.layers.conv2d_transpose(drop1, 256, 3, 2, 'same', use_bias=False)\n",
    "        bn2 = tf.layers.batch_normalization(conv2, training=is_train)\n",
    "        lrelu2 = tf.maximum(alpha * bn2, bn2)\n",
    "        drop2 = tf.layers.dropout(lrelu2, keep_prob, training=is_train)\n",
    "        \n",
    "        # Output layer, 28x28xn\n",
    "        logits = tf.layers.conv2d_transpose(drop2, out_channel_dim, 3, 2, 'valid')\n",
    "        \n",
    "        out = tf.tanh(logits)\n",
    "        \n",
    "        print(fc.shape)\n",
    "        print(drop1.shape)\n",
    "        print(drop2.shape)\n",
    "        print(logits.shape)\n",
    "        \n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_loss(input_real, input_z, out_channel_dim, alpha=0.2, smooth_factor=0.1):\n",
    "    d_model_real, d_logits_real = discriminator(input_real, alpha=alpha)\n",
    "    \n",
    "    d_loss_real = tf.reduce_mean(\n",
    "        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real,\n",
    "                                                labels=tf.ones_like(d_model_real) * (1 - smooth_factor)))\n",
    "    \n",
    "    input_fake = generator(input_z, out_channel_dim, alpha=alpha)\n",
    "    d_model_fake, d_logits_fake = discriminator(input_fake, reuse=True, alpha=alpha)\n",
    "    \n",
    "    d_loss_fake = tf.reduce_mean(\n",
    "        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))\n",
    "    \n",
    "    g_loss = tf.reduce_mean(\n",
    "        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))\n",
    "\n",
    "    return d_loss_real + d_loss_fake, g_loss\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_opt(d_loss, g_loss, learning_rate, beta1):\n",
    "    # Get weights and bias to update\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",
    "    # Optimize\n",
    "    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):\n",
    "        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)\n",
    "        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)\n",
    "\n",
    "    return d_train_opt, g_train_opt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):\n",
    "    cmap = None if image_mode == 'RGB' else 'gray'\n",
    "    z_dim = input_z.get_shape().as_list()[-1]\n",
    "    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])\n",
    "\n",
    "    samples = sess.run(\n",
    "        generator(input_z, out_channel_dim, False),\n",
    "        feed_dict={input_z: example_z})\n",
    "    \n",
    "    # pyplot.show()\n",
    "    return samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode,\n",
    "          print_every=10, show_every=10):\n",
    "    # TODO: Build Model\n",
    "    input_real, input_z, _ = model_inputs(data_shape[2], data_shape[1], data_shape[3], z_dim)\n",
    "    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3], alpha=0.2)\n",
    "    d_train_opt, g_train_opt = model_opt(d_loss, g_loss, learning_rate, beta1)\n",
    "    \n",
    "    saver = tf.train.Saver()\n",
    "    sample_z = np.random.uniform(-1, 1, size=(72, z_dim))\n",
    "    \n",
    "    samples, losses = [], []\n",
    "    \n",
    "    steps = 0\n",
    "    count = 0\n",
    "    \n",
    "    with tf.Session() as sess:\n",
    "        saver = tf.train.Saver()\n",
    "        sess.run(tf.global_variables_initializer())\n",
    "        \n",
    "        # continue training\n",
    "        save_path = saver.save(sess, \"/tmp/model.ckpt\")\n",
    "        ckpt = tf.train.latest_checkpoint('./model/')\n",
    "        saver.restore(sess, save_path)\n",
    "        \n",
    "        #newsaver = tf.train.import_meta_graph('./model/70.meta')\n",
    "        #newsaver.restore(sess, tf.train.latest_checkpoint('./model/'))\n",
    "        \n",
    "        coord = tf.train.Coordinator()\n",
    "        threads = tf.train.start_queue_runners(sess=sess, coord=coord)\n",
    "\n",
    "        os.mkdir('output')\n",
    "        for epoch_i in range(epoch_count):\n",
    "            for batch_images in get_batches(batch_size):\n",
    "                # Train Model\n",
    "                steps += 1\n",
    "                batch_images *= 2.0\n",
    "                \n",
    "                # Sample random noise for G\n",
    "                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))\n",
    "                \n",
    "                # Run optimizers\n",
    "                sess.run(d_train_opt, feed_dict={input_real: batch_images, input_z: batch_z})\n",
    "                sess.run(g_train_opt, feed_dict={input_z: batch_z})\n",
    "                \n",
    "                if steps % print_every == 0:\n",
    "                    os.mkdir('output/'+ str(steps))\n",
    "                    # At the end of each epoch, get the losses and print them out\n",
    "                    train_loss_d = d_loss.eval({input_real: batch_images, input_z: batch_z})\n",
    "                    train_loss_g = g_loss.eval({input_z: batch_z})\n",
    "                    print(\"Epoch {}/{} Step {}...\".format(epoch_i+1, epoch_count, steps),\n",
    "                      \"Discriminator Loss: {:.4f}...\".format(train_loss_d),\n",
    "                      \"Generator Loss: {:.4f}\".format(train_loss_g))\n",
    "                    # Save losses for viewing after training\n",
    "                    #losses.append((train_loss_d, train_loss_g))\n",
    "                            \n",
    "                if steps % show_every == 0:\n",
    "                    count = count +1\n",
    "                    iterr = count*show_every\n",
    "                    # Show example output for the generator # 25 number for 1 time\n",
    "                    images_grid = show_generator_output(sess, 25, input_z, data_shape[3], data_image_mode)\n",
    "                    x = 0\n",
    "                    for image_grid in images_grid : \n",
    "                        x = x+1\n",
    "                        dst = os.path.join(\"output\", str(steps),str(iterr)+str(x)+\".png\")\n",
    "                        pyplot.imsave(dst, image_grid)\n",
    "                        \n",
    "                 # saving the model         \n",
    "                if epoch_i % 10 == 0:\n",
    "                    if not os.path.exists('./model/'):\n",
    "                        os.makedirs('./model')\n",
    "                    saver.save(sess, './model/' + str(epoch_i))      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5004\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "INFO:tensorflow:Restoring parameters from /tmp/model.ckpt\n",
      "Epoch 1/200 Step 10... Discriminator Loss: 0.7986... Generator Loss: 2.7782\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 2/200 Step 20... Discriminator Loss: 0.7019... Generator Loss: 1.2096\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 2/200 Step 30... Discriminator Loss: 0.6407... Generator Loss: 1.7675\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 3/200 Step 40... Discriminator Loss: 0.9732... Generator Loss: 0.9018\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 3/200 Step 50... Discriminator Loss: 1.2455... Generator Loss: 2.2003\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 4/200 Step 60... Discriminator Loss: 0.9650... Generator Loss: 1.1981\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 4/200 Step 70... Discriminator Loss: 0.9376... Generator Loss: 1.6022\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 5/200 Step 80... Discriminator Loss: 0.9873... Generator Loss: 0.9408\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 5/200 Step 90... Discriminator Loss: 1.1370... Generator Loss: 2.2449\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 6/200 Step 100... Discriminator Loss: 0.9307... Generator Loss: 1.1019\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 6/200 Step 110... Discriminator Loss: 0.9045... Generator Loss: 1.3023\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 7/200 Step 120... Discriminator Loss: 1.4306... Generator Loss: 3.0811\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 7/200 Step 130... Discriminator Loss: 0.8306... Generator Loss: 1.4418\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 8/200 Step 140... Discriminator Loss: 1.0130... Generator Loss: 0.9772\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 8/200 Step 150... Discriminator Loss: 1.1253... Generator Loss: 2.7651\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 9/200 Step 160... Discriminator Loss: 1.2028... Generator Loss: 0.5614\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 9/200 Step 170... Discriminator Loss: 1.1864... Generator Loss: 0.6131\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 10/200 Step 180... Discriminator Loss: 0.8613... Generator Loss: 1.1399\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 10/200 Step 190... Discriminator Loss: 0.7570... Generator Loss: 1.9568\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 11/200 Step 200... Discriminator Loss: 0.8872... Generator Loss: 1.3420\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 12/200 Step 210... Discriminator Loss: 0.7758... Generator Loss: 1.3705\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 12/200 Step 220... Discriminator Loss: 0.9375... Generator Loss: 2.3697\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 13/200 Step 230... Discriminator Loss: 1.0274... Generator Loss: 2.6057\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 13/200 Step 240... Discriminator Loss: 0.8219... Generator Loss: 1.2095\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 14/200 Step 250... Discriminator Loss: 0.8607... Generator Loss: 1.8890\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 14/200 Step 260... Discriminator Loss: 0.8661... Generator Loss: 1.4806\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 15/200 Step 270... Discriminator Loss: 0.8005... Generator Loss: 1.6766\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 15/200 Step 280... Discriminator Loss: 0.8658... Generator Loss: 1.6609\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 16/200 Step 290... Discriminator Loss: 1.3357... Generator Loss: 0.5010\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 16/200 Step 300... Discriminator Loss: 0.8518... Generator Loss: 1.4408\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 17/200 Step 310... Discriminator Loss: 0.9052... Generator Loss: 1.2558\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 17/200 Step 320... Discriminator Loss: 0.9011... Generator Loss: 1.2468\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 18/200 Step 330... Discriminator Loss: 0.9880... Generator Loss: 0.8800\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 18/200 Step 340... Discriminator Loss: 0.9066... Generator Loss: 2.0460\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 19/200 Step 350... Discriminator Loss: 0.9169... Generator Loss: 1.7369\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 19/200 Step 360... Discriminator Loss: 0.9111... Generator Loss: 1.5251\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 20/200 Step 370... Discriminator Loss: 0.9466... Generator Loss: 1.0476\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 20/200 Step 380... Discriminator Loss: 1.0600... Generator Loss: 1.6264\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 21/200 Step 390... Discriminator Loss: 1.1503... Generator Loss: 0.9095\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 22/200 Step 400... Discriminator Loss: 1.1989... Generator Loss: 1.2204\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 22/200 Step 410... Discriminator Loss: 1.1530... Generator Loss: 0.8920\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 23/200 Step 420... Discriminator Loss: 1.2206... Generator Loss: 0.8665\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 23/200 Step 430... Discriminator Loss: 1.1357... Generator Loss: 1.0771\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 24/200 Step 440... Discriminator Loss: 1.5018... Generator Loss: 0.4140\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 24/200 Step 450... Discriminator Loss: 1.1407... Generator Loss: 0.9182\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 25/200 Step 460... Discriminator Loss: 1.1208... Generator Loss: 1.0497\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 25/200 Step 470... Discriminator Loss: 1.2283... Generator Loss: 1.3409\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 26/200 Step 480... Discriminator Loss: 1.1401... Generator Loss: 0.8807\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 26/200 Step 490... Discriminator Loss: 1.1839... Generator Loss: 0.7198\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 27/200 Step 500... Discriminator Loss: 1.5919... Generator Loss: 0.3560\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 27/200 Step 510... Discriminator Loss: 1.2166... Generator Loss: 1.4234\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 28/200 Step 520... Discriminator Loss: 1.1838... Generator Loss: 1.2357\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 28/200 Step 530... Discriminator Loss: 1.2062... Generator Loss: 1.4508\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 29/200 Step 540... Discriminator Loss: 1.2600... Generator Loss: 1.5470\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 29/200 Step 550... Discriminator Loss: 1.1592... Generator Loss: 0.9399\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 30/200 Step 560... Discriminator Loss: 1.1941... Generator Loss: 1.0776\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 30/200 Step 570... Discriminator Loss: 1.5479... Generator Loss: 2.1296\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 31/200 Step 580... Discriminator Loss: 1.3233... Generator Loss: 0.8222\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 32/200 Step 590... Discriminator Loss: 1.1821... Generator Loss: 0.9809\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 32/200 Step 600... Discriminator Loss: 1.1763... Generator Loss: 0.7344\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 33/200 Step 610... Discriminator Loss: 1.1730... Generator Loss: 1.3747\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 33/200 Step 620... Discriminator Loss: 1.5791... Generator Loss: 0.3566\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 34/200 Step 630... Discriminator Loss: 1.4445... Generator Loss: 0.4481\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 34/200 Step 640... Discriminator Loss: 1.1244... Generator Loss: 1.1338\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 35/200 Step 650... Discriminator Loss: 1.1750... Generator Loss: 0.9281\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 35/200 Step 660... Discriminator Loss: 1.2072... Generator Loss: 1.1870\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 36/200 Step 670... Discriminator Loss: 1.2960... Generator Loss: 0.5793\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n",
      "Epoch 36/200 Step 680... Discriminator Loss: 1.1635... Generator Loss: 1.0436\n",
      "(?, 4, 4, 1024)\n",
      "(?, 6, 6, 512)\n",
      "(?, 12, 12, 256)\n",
      "(?, 25, 25, 3)\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-bbe3447e21dd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mceleba_dataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataset\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mglob\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'./smallone/*.png'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGraph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_default\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m     \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mz_dim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlearning_rate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbeta1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mceleba_dataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_batches\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mceleba_dataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mceleba_dataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimage_mode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-8-2e8656e87584>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode, print_every, show_every)\u001b[0m\n\u001b[1;32m     41\u001b[0m                 \u001b[0;31m# Run optimizers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m                 \u001b[0msess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md_train_opt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0minput_real\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbatch_images\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_z\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbatch_z\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m                 \u001b[0msess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg_train_opt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0minput_z\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbatch_z\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     44\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0msteps\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mprint_every\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda2/envs/actionGAN/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    875\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    876\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 877\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    878\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    879\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda2/envs/actionGAN/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1098\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1099\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1100\u001b[0;31m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m   1101\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1102\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda2/envs/actionGAN/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1270\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1271\u001b[0m       return self._do_call(_run_fn, feeds, fetches, targets, options,\n\u001b[0;32m-> 1272\u001b[0;31m                            run_metadata)\n\u001b[0m\u001b[1;32m   1273\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1274\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda2/envs/actionGAN/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1276\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1277\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1278\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1279\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1280\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda2/envs/actionGAN/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1261\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_extend_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1262\u001b[0m       return self._call_tf_sessionrun(\n\u001b[0;32m-> 1263\u001b[0;31m           options, feed_dict, fetch_list, target_list, run_metadata)\n\u001b[0m\u001b[1;32m   1264\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1265\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda2/envs/actionGAN/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_call_tf_sessionrun\u001b[0;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\u001b[0m\n\u001b[1;32m   1348\u001b[0m     return tf_session.TF_SessionRun_wrapper(\n\u001b[1;32m   1349\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1350\u001b[0;31m         run_metadata)\n\u001b[0m\u001b[1;32m   1351\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1352\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_call_tf_sessionprun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "batch_size = 256\n",
    "z_dim = 100\n",
    "learning_rate = 0.00025\n",
    "beta1 = 0.45\n",
    "\n",
    "epochs = 200\n",
    "print(len(glob('./smallone/*.png')))\n",
    "celeba_dataset = Dataset( glob('./smallone/*.png'))\n",
    "with tf.Graph().as_default():\n",
    "    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches, celeba_dataset.shape, celeba_dataset.image_mode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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