dlnd_face_generation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Face Generation\n",
    "In this project, you'll use generative adversarial networks to generate new images of faces.\n",
    "### Get the Data\n",
    "You'll be using two datasets in this project:\n",
    "- MNIST\n",
    "- CelebA\n",
    "\n",
    "Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA.  Running the GANs on MNIST will allow you to see how well your model trains sooner.\n",
    "\n",
    "If you're using [FloydHub](https://www.floydhub.com/), set `data_dir` to \"/input\" and use the [FloydHub data ID](http://docs.floydhub.com/home/using_datasets/) \"R5KrjnANiKVhLWAkpXhNBe\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found mnist Data\n",
      "Found celeba Data\n"
     ]
    }
   ],
   "source": [
    "data_dir = './data'\n",
    "\n",
    "# FloydHub - Use with data ID \"R5KrjnANiKVhLWAkpXhNBe\"\n",
    "#data_dir = '/input'\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL\n",
    "\"\"\"\n",
    "import helper\n",
    "\n",
    "helper.download_extract('mnist', data_dir)\n",
    "helper.download_extract('celeba', data_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore the Data\n",
    "### MNIST\n",
    "As you're aware, the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset contains images of handwritten digits. You can view the first number of examples by changing `show_n_images`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x107cc0128>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CgNLT0zVlD+2w1157LQDglltu0WpAvPrqqwCAtm3bYtOmTQB0E9vgwYMBAPvt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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106c31828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_n_images = 25\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL\n",
    "\"\"\"\n",
    "%matplotlib inline\n",
    "import os\n",
    "from glob import glob\n",
    "from matplotlib import pyplot\n",
    "\n",
    "mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')\n",
    "pyplot.imshow(helper.images_square_grid(mnist_images,'L'), cmap='gray')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CelebA\n",
    "The [CelebFaces Attributes Dataset (CelebA)](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) dataset contains over 200,000 celebrity images with annotations.  Since you're going to be generating faces, you won't need the annotations.  You can view the first number of examples by changing `show_n_images`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1087d57b8>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OmKibrTaOKIube8b3jL2aUF7iF/eGVMJKbKDLZiNo7VlwS0PGmOrApthOLy+s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\nrew+Wy0KK1vZyu6z/wOv+tt2PuYXIAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107cf9160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_n_images = 25\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL\n",
    "\"\"\"\n",
    "mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')\n",
    "pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocess the Data\n",
    "Since the project's main focus is on building the GANs, we'll preprocess the data for you.  The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images.  The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.\n",
    "\n",
    "The MNIST images are black and white images with a single [color channel](https://en.wikipedia.org/wiki/Channel_(digital_image%29) while the CelebA images have [3 color channels (RGB color channel)](https://en.wikipedia.org/wiki/Channel_(digital_image%29#RGB_Images).\n",
    "## Build the Neural Network\n",
    "You'll build the components necessary to build a GANs by implementing the following functions below:\n",
    "- `model_inputs`\n",
    "- `discriminator`\n",
    "- `generator`\n",
    "- `model_loss`\n",
    "- `model_opt`\n",
    "- `train`\n",
    "\n",
    "### Check the Version of TensorFlow and Access to GPU\n",
    "This will check to make sure you have the correct version of TensorFlow and access to a GPU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TensorFlow Version: 1.1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/michalczapski/anaconda/envs/tensorflow121/lib/python3.6/site-packages/ipykernel/__main__.py:14: UserWarning: No GPU found. Please use a GPU to train your neural network.\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL\n",
    "\"\"\"\n",
    "from distutils.version import LooseVersion\n",
    "import warnings\n",
    "import tensorflow as tf\n",
    "\n",
    "# Check TensorFlow Version\n",
    "assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)\n",
    "print('TensorFlow Version: {}'.format(tf.__version__))\n",
    "\n",
    "# Check for a GPU\n",
    "if not tf.test.gpu_device_name():\n",
    "    warnings.warn('No GPU found. Please use a GPU to train your neural network.')\n",
    "else:\n",
    "    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Input\n",
    "Implement the `model_inputs` function to create TF Placeholders for the Neural Network. It should create the following placeholders:\n",
    "- Real input images placeholder with rank 4 using `image_width`, `image_height`, and `image_channels`.\n",
    "- Z input placeholder with rank 2 using `z_dim`.\n",
    "- Learning rate placeholder with rank 0.\n",
    "\n",
    "Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "import problem_unittests as tests\n",
    "\n",
    "def model_inputs(image_width, image_height, image_channels, z_dim):\n",
    "    \"\"\"\n",
    "    Create the model inputs\n",
    "    :param image_width: The input image width\n",
    "    :param image_height: The input image height\n",
    "    :param image_channels: The number of image channels\n",
    "    :param z_dim: The dimension of Z\n",
    "    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)\n",
    "    \"\"\"\n",
    "    # TODO: Implement Function\n",
    "    inputs_real=tf.placeholder(tf.float32, (None,image_width,image_height,image_channels),name='input_real')\n",
    "    inputs_z=tf.placeholder(tf.float32,(None,z_dim),name='input_z')\n",
    "    lr=tf.placeholder(tf.float32)\n",
    "    \n",
    "\n",
    "    return inputs_real, inputs_z, lr\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "tests.test_model_inputs(model_inputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discriminator\n",
    "Implement `discriminator` to create a discriminator neural network that discriminates on `images`.  This function should be able to reuse the variables in the neural network.  Use [`tf.variable_scope`](https://www.tensorflow.org/api_docs/python/tf/variable_scope) with a scope name of \"discriminator\" to allow the variables to be reused.  The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "def discriminator(images, reuse=False):\n",
    "    \"\"\"\n",
    "    Create the discriminator network\n",
    "    :param images: Tensor of input image(s)\n",
    "    :param reuse: Boolean if the weights should be reused\n",
    "    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)\n",
    "    \"\"\"\n",
    "    # TODO: Implement Function\n",
    "    with tf.variable_scope('discriminator', reuse=reuse):\n",
    "        alpha = 0.01\n",
    "        # Input layer is 28x28x1\n",
    "        x1 = tf.layers.conv2d(images, 64, 5, 2, 'same', activation=None)\n",
    "        x1 = tf.maximum(alpha * x1, x1)\n",
    "        #now is 14x14x64\n",
    "        x2 = tf.layers.conv2d(x1, 128, 5, 2, 'same', activation=None)\n",
    "        x2 = tf.layers.batch_normalization(x2, training=True)\n",
    "        x2 = tf.maximum(alpha * x2, x2)\n",
    "        #now is 7x7x128\n",
    "        \n",
    "        flat=tf.reshape(x2,(-1,7*7*128))\n",
    "        \n",
    "        logits = tf.layers.dense(flat,1)\n",
    "        out = tf.sigmoid(logits)\n",
    "    \n",
    "        \n",
    "        return out, logits\n",
    "\n",
    "    \n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "tests.test_discriminator(discriminator, tf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generator\n",
    "Implement `generator` to generate an image using `z`. This function should be able to reuse the variables in the neural network.  Use [`tf.variable_scope`](https://www.tensorflow.org/api_docs/python/tf/variable_scope) with a scope name of \"generator\" to allow the variables to be reused. The function should return the generated 28 x 28 x `out_channel_dim` images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "def generator(z, out_channel_dim, is_train=True):\n",
    "    \"\"\"\n",
    "    Create the generator network\n",
    "    :param z: Input z\n",
    "    :param out_channel_dim: The number of channels in the output image\n",
    "    :param is_train: Boolean if generator is being used for training\n",
    "    :return: The tensor output of the generator\n",
    "    \"\"\"\n",
    "    # TODO: Implement Function\n",
    "    with tf.variable_scope('generator', reuse=(not is_train)):\n",
    "        \n",
    "        alpha=0.01\n",
    "        \n",
    "        # First fully connected layer\n",
    "        x1 = tf.layers.dense(z, 7*7*512)\n",
    "        # covolution\n",
    "        x1 = tf.reshape(x1,(-1,7,7,512))\n",
    "        x1 = tf.layers.batch_normalization(x1, training=is_train)\n",
    "        x1 = tf.maximum(alpha * x1, x1)\n",
    "        #print(x1.get_shape().as_list())\n",
    "        #now is 7x7x512\n",
    "        x2 = tf.layers.conv2d_transpose(x1, 256, 5, strides=2, padding='same', activation=None)\n",
    "        x2 = tf.layers.batch_normalization(x2, training=is_train)\n",
    "        x2 = tf.maximum(alpha * x2, x2)\n",
    "        #print(x2.get_shape().as_list())\n",
    "        # now is 14x14x256\n",
    "\n",
    "        # Output layer\n",
    "        logits = tf.layers.conv2d_transpose(x2, out_channel_dim, 5,strides=2, padding='same')\n",
    "        #now is 28x28x out_channel_dim\n",
    "        out = tf.tanh(logits)\n",
    "    return out\n",
    "    print(out)\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "tests.test_generator(generator, tf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loss\n",
    "Implement `model_loss` to build the GANs for training and calculate the loss.  The function should return a tuple of (discriminator loss, generator loss).  Use the following functions you implemented:\n",
    "- `discriminator(images, reuse=False)`\n",
    "- `generator(z, out_channel_dim, is_train=True)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "def model_loss(input_real, input_z, out_channel_dim):\n",
    "    \"\"\"\n",
    "    Get the loss for the discriminator and generator\n",
    "    :param input_real: Images from the real dataset\n",
    "    :param input_z: Z input\n",
    "    :param out_channel_dim: The number of channels in the output image\n",
    "    :return: A tuple of (discriminator loss, generator loss)\n",
    "    \"\"\"\n",
    "    # TODO: Implement Function\n",
    "\n",
    "\n",
    "    g_model = generator(input_z, out_channel_dim,is_train=True)\n",
    "    d_model_real, d_logits_real = discriminator(input_real)\n",
    "    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)\n",
    "\n",
    "    d_loss_real = tf.reduce_mean(\n",
    "        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))\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",
    "    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",
    "    d_loss = d_loss_real + d_loss_fake\n",
    "\n",
    "    return d_loss, g_loss\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "tests.test_model_loss(model_loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimization\n",
    "Implement `model_opt` to create the optimization operations for the GANs. Use [`tf.trainable_variables`](https://www.tensorflow.org/api_docs/python/tf/trainable_variables) to get all the trainable variables.  Filter the variables with names that are in the discriminator and generator scope names.  The function should return a tuple of (discriminator training operation, generator training operation)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "def model_opt(d_loss, g_loss, learning_rate, beta1):\n",
    "    \"\"\"\n",
    "    Get optimization operations\n",
    "    :param d_loss: Discriminator loss Tensor\n",
    "    :param g_loss: Generator loss Tensor\n",
    "    :param learning_rate: Learning Rate Placeholder\n",
    "    :param beta1: The exponential decay rate for the 1st moment in the optimizer\n",
    "    :return: A tuple of (discriminator training operation, generator training operation)\n",
    "    \"\"\"\n",
    "    # TODO: Implement Function\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",
    "    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",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "tests.test_model_opt(model_opt, tf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neural Network Training\n",
    "### Show Output\n",
    "Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL\n",
    "\"\"\"\n",
    "import numpy as np\n",
    "\n",
    "def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):\n",
    "    \"\"\"\n",
    "    Show example output for the generator\n",
    "    :param sess: TensorFlow session\n",
    "    :param n_images: Number of Images to display\n",
    "    :param input_z: Input Z Tensor\n",
    "    :param out_channel_dim: The number of channels in the output image\n",
    "    :param image_mode: The mode to use for images (\"RGB\" or \"L\")\n",
    "    \"\"\"\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",
    "    images_grid = helper.images_square_grid(samples, image_mode)\n",
    "    pyplot.imshow(images_grid, cmap=cmap)\n",
    "    pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train\n",
    "Implement `train` to build and train the GANs.  Use the following functions you implemented:\n",
    "- `model_inputs(image_width, image_height, image_channels, z_dim)`\n",
    "- `model_loss(input_real, input_z, out_channel_dim)`\n",
    "- `model_opt(d_loss, g_loss, learning_rate, beta1)`\n",
    "\n",
    "Use the `show_generator_output` to show `generator` output while you train. Running `show_generator_output` for every batch will drastically increase training time and increase the size of the notebook.  It's recommended to print the `generator` output every 100 batches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):\n",
    "    \"\"\"\n",
    "    Train the GAN\n",
    "    :!param epoch_count: Number of epochs\n",
    "    :!param batch_size: Batch Size\n",
    "    :!param z_dim: Z dimension\n",
    "    :!param learning_rate: Learning Rate\n",
    "    :!param beta1: The exponential decay rate for the 1st moment in the optimizer\n",
    "    :!param get_batches: Function to get batches\n",
    "    :!param data_shape: Shape of the data\n",
    "    :xparam data_image_mode: The image mode to use for images (\"RGB\" or \"L\")\n",
    "    \"\"\"\n",
    " \n",
    "    # TODO: Build Model\n",
    "    out_channel_dim = data_shape[3]\n",
    "    n_images = 10\n",
    "    losses=[]\n",
    "    samples=[]\n",
    "    print(data_shape) \n",
    "    #inputs_real, input_z, lr = model_inputs(28.,28.,1., z_dim)\n",
    "    inputs_real, input_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)\n",
    "    \n",
    "    d_loss, g_loss = model_loss(inputs_real, input_z, out_channel_dim)\n",
    "    d_train_opt, g_train_opt = model_opt(d_loss, g_loss, lr, beta1)\n",
    "   \n",
    "    steps=0\n",
    "    show_every=100\n",
    "    with tf.Session() as sess:\n",
    "        sess.run(tf.global_variables_initializer())\n",
    "        for epoch_i in range(epoch_count):\n",
    "            for batch_images in get_batches(batch_size):\n",
    "                # TODO: Train Model\n",
    "                steps+=1\n",
    "                # Sample random noise for G\n",
    "                #print(batch_images)\n",
    "                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))\n",
    "                # Run optimizers\n",
    "                _ = sess.run(d_train_opt, feed_dict={inputs_real: batch_images, input_z: batch_z, lr: learning_rate})\n",
    "                _ = sess.run(g_train_opt, feed_dict={inputs_real: batch_images, input_z: batch_z, lr: learning_rate})\n",
    "        \n",
    "        # At the end of each epoch, get the losses and print them out\n",
    "        train_loss_d = sess.run(d_loss, {input_z: batch_z, inputs_real: batch_images})\n",
    "        train_loss_g = g_loss.eval({input_z: batch_z}) \n",
    "         \n",
    "        print(\"Epoch {}/{}...\".format(e+1, epochs),\n",
    "              \"Discriminator Loss: {:.4f}...\".format(train_loss_d),\n",
    "              \"Generator Loss: {:.4f}\".format(train_loss_g)) \n",
    "               \n",
    "        if steps % show_every == 0:\n",
    "            show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode) \n",
    "        \n",
    "       \n",
    "        \n",
    "        \n",
    "\n",
    "                "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MNIST\n",
    "Test your GANs architecture on MNIST.  After 2 epochs, the GANs should be able to generate images that look like handwritten digits.  Make sure the loss of the generator is lower than the loss of the discriminator or close to 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "batch_size = 128\n",
    "z_dim = 100\n",
    "learning_rate = 0.2\n",
    "beta1 = 0.5\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "epochs = 2\n",
    "\n",
    "mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))\n",
    "with tf.Graph().as_default():\n",
    "    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,\n",
    "          mnist_dataset.shape, mnist_dataset.image_mode)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CelebA\n",
    "Run your GANs on CelebA.  It will take around 20 minutes on the average GPU to run one epoch.  You can run the whole epoch or stop when it starts to generate realistic faces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "batch_size = None\n",
    "z_dim = None\n",
    "learning_rate = None\n",
    "beta1 = None\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "epochs = 1\n",
    "\n",
    "celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))\n",
    "with tf.Graph().as_default():\n",
    "    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,\n",
    "          celeba_dataset.shape, celeba_dataset.image_mode)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Submitting This Project\n",
    "When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as \"dlnd_face_generation.ipynb\" and save it as a HTML file under \"File\" -> \"Download as\". Include the \"helper.py\" and \"problem_unittests.py\" files in your submission."
   ]
  }
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