dlnd_face_generation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Face Generation\n",
    "\n",
    "In this project, you'll define and train a DCGAN on a dataset of faces. Your goal is to get a generator network to generate *new* images of faces that look as realistic as possible!\n",
    "\n",
    "The project will be broken down into a series of tasks from **loading in data to defining and training adversarial networks**. At the end of the notebook, you'll be able to visualize the results of your trained Generator to see how it performs; your generated samples should look like fairly realistic faces with small amounts of noise.\n",
    "\n",
    "### Get the Data\n",
    "\n",
    "You'll be using the [CelebFaces Attributes Dataset (CelebA)](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) to train your adversarial networks.\n",
    "\n",
    "This dataset is more complex than the number datasets (like MNIST or SVHN) you've been working with, and so, you should prepare to define deeper networks and train them for a longer time to get good results. It is suggested that you utilize a GPU for training.\n",
    "\n",
    "### Pre-processed Data\n",
    "\n",
    "Since the project's main focus is on building the GANs, we've done *some* of the pre-processing for you. Each of the CelebA images has been cropped to remove parts of the image that don't include a face, then resized down to 64x64x3 NumPy images. Some sample data is show below.\n",
    "\n",
    "<img src='assets/processed_face_data.png' width=60% />\n",
    "\n",
    "> If you are working locally, you can download this data [by clicking here](https://s3.amazonaws.com/video.udacity-data.com/topher/2018/November/5be7eb6f_processed-celeba-small/processed-celeba-small.zip)\n",
    "\n",
    "This is a zip file that you'll need to extract in the home directory of this notebook for further loading and processing. After extracting the data, you should be left with a directory of data `processed_celeba_small/`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# can comment out after executing\n",
    "#!unzip processed_celeba_small.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = 'processed_celeba_small/'\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL\n",
    "\"\"\"\n",
    "import pickle as pkl\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import problem_unittests as tests\n",
    "#import helper\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize the CelebA Data\n",
    "\n",
    "The [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'll only need the images. Note that these are color images with [3 color channels (RGB)](https://en.wikipedia.org/wiki/Channel_(digital_image)#RGB_Images) each.\n",
    "\n",
    "### Pre-process and Load the Data\n",
    "\n",
    "Since the project's main focus is on building the GANs, we've done *some* of the pre-processing for you. Each of the CelebA images has been cropped to remove parts of the image that don't include a face, then resized down to 64x64x3 NumPy images. This *pre-processed* dataset is a smaller subset of the very large CelebA data.\n",
    "\n",
    "> There are a few other steps that you'll need to **transform** this data and create a **DataLoader**.\n",
    "\n",
    "#### Exercise: Complete the following `get_dataloader` function, such that it satisfies these requirements:\n",
    "\n",
    "* Your images should be square, Tensor images of size `image_size x image_size` in the x and y dimension.\n",
    "* Your function should return a DataLoader that shuffles and batches these Tensor images.\n",
    "\n",
    "#### ImageFolder\n",
    "\n",
    "To create a dataset given a directory of images, it's recommended that you use PyTorch's [ImageFolder](https://pytorch.org/docs/stable/torchvision/datasets.html#imagefolder) wrapper, with a root directory `processed_celeba_small/` and data transformation passed in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# necessary imports\n",
    "import torch\n",
    "from torchvision import datasets\n",
    "from torchvision import transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_dataloader(batch_size, image_size, data_dir='processed_celeba_small/'):\n",
    "    \"\"\"\n",
    "    Batch the neural network data using DataLoader\n",
    "    :param batch_size: The size of each batch; the number of images in a batch\n",
    "    :param img_size: The square size of the image data (x, y)\n",
    "    :param data_dir: Directory where image data is located\n",
    "    :return: DataLoader with batched data\n",
    "    \"\"\"\n",
    "    \n",
    "    image_transforms = transforms.Compose([transforms.Resize(image_size),\n",
    "                                           transforms.ToTensor(),\n",
    "                                          ])\n",
    "    dataloader = torch.utils.data.DataLoader(datasets.ImageFolder(data_dir, transform=image_transforms)\n",
    "                                             , shuffle=True, batch_size=batch_size)\n",
    "    \n",
    "    return dataloader\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a DataLoader\n",
    "\n",
    "#### Exercise: Create a DataLoader `celeba_train_loader` with appropriate hyperparameters.\n",
    "\n",
    "Call the above function and create a dataloader to view images. \n",
    "* You can decide on any reasonable `batch_size` parameter\n",
    "* Your `image_size` **must be** `32`. Resizing the data to a smaller size will make for faster training, while still creating convincing images of faces!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define function hyperparameters\n",
    "batch_size = 20\n",
    "img_size = 32\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "# Call your function and get a dataloader\n",
    "celeba_train_loader = get_dataloader(batch_size, img_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, you can view some images! You should seen square images of somewhat-centered faces.\n",
    "\n",
    "Note: You'll need to convert the Tensor images into a NumPy type and transpose the dimensions to correctly display an image, suggested `imshow` code is below, but it may not be perfect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd2857adeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# helper display function\n",
    "def imshow(img):\n",
    "    npimg = img.numpy()\n",
    "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "# obtain one batch of training images\n",
    "dataiter = iter(celeba_train_loader)\n",
    "images, _ = dataiter.next() # _ for no labels\n",
    "\n",
    "# plot the images in the batch, along with the corresponding labels\n",
    "fig = plt.figure(figsize=(20, 4))\n",
    "plot_size=20\n",
    "for idx in np.arange(plot_size):\n",
    "    ax = fig.add_subplot(2, plot_size/2, idx+1, xticks=[], yticks=[])\n",
    "    imshow(images[idx])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise: Pre-process your image data and scale it to a pixel range of -1 to 1\n",
    "\n",
    "You need to do a bit of pre-processing; you know that the output of a `tanh` activated generator will contain pixel values in a range from -1 to 1, and so, we need to rescale our training images to a range of -1 to 1. (Right now, they are in a range from 0-1.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Complete the scale function\n",
    "def scale(x, feature_range=(-1, 1)):\n",
    "    ''' Scale takes in an image x and returns that image, scaled\n",
    "       with a feature_range of pixel values from -1 to 1. \n",
    "       This function assumes that the input x is already scaled from 0-1.'''\n",
    "    # assume x is scaled to (0, 1)\n",
    "    # scale to feature_range and return scaled x\n",
    "    \n",
    "    return x*(feature_range[1] - feature_range[0]) + feature_range[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min:  tensor(-0.9765)\n",
      "Max:  tensor(0.9843)\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "# check scaled range\n",
    "# should be close to -1 to 1\n",
    "img = images[0]\n",
    "scaled_img = scale(img)\n",
    "\n",
    "print('Min: ', scaled_img.min())\n",
    "print('Max: ', scaled_img.max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Define the Model\n",
    "\n",
    "A GAN is comprised of two adversarial networks, a discriminator and a generator.\n",
    "\n",
    "## Discriminator\n",
    "\n",
    "Your first task will be to define the discriminator. This is a convolutional classifier like you've built before, only without any maxpooling layers. To deal with this complex data, it's suggested you use a deep network with **normalization**. You are also allowed to create any helper functions that may be useful.\n",
    "\n",
    "#### Exercise: Complete the Discriminator class\n",
    "* The inputs to the discriminator are 32x32x3 tensor images\n",
    "* The output should be a single value that will indicate whether a given image is real or fake\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "class Discriminator(nn.Module):\n",
    "\n",
    "    def __init__(self, conv_dim):\n",
    "        \"\"\"\n",
    "        Initialize the Discriminator Module\n",
    "        :param conv_dim: The depth of the first convolutional layer\n",
    "        \"\"\"\n",
    "        super(Discriminator, self).__init__()\n",
    "\n",
    "        # complete init function\n",
    "        self.conv_dim = conv_dim\n",
    "        self.conv1 = nn.Conv2d(3, conv_dim, kernel_size=4, stride=2, padding=1, bias=False)\n",
    "        self.batch_norm1 = nn.BatchNorm2d(conv_dim)\n",
    "        self.conv2 = nn.Conv2d(conv_dim, conv_dim*2,kernel_size=4, stride=2, padding=1, bias=False)\n",
    "        self.batch_norm2 = nn.BatchNorm2d(conv_dim*2)\n",
    "        self.conv3 = nn.Conv2d(conv_dim*2, conv_dim*4, kernel_size=4, stride=2, padding=1, bias=False)\n",
    "        self.batch_norm3 = nn.BatchNorm2d(conv_dim*4)\n",
    "        self.conv4 = nn.Conv2d(conv_dim*4, conv_dim*8, kernel_size=4, stride=2, padding=1, bias=False)\n",
    "        self.batch_norm4 = nn.BatchNorm2d(conv_dim*8)\n",
    "        self.conv5 = nn.Conv2d(conv_dim*8, conv_dim*16, kernel_size=4, stride=2, padding=1, bias=False)\n",
    "        self.fc = nn.Linear(conv_dim*4*4, 1)\n",
    "        \n",
    "\n",
    "    def forward(self, x):\n",
    "        \"\"\"\n",
    "        Forward propagation of the neural network\n",
    "        :param x: The input to the neural network     \n",
    "        :return: Discriminator logits; the output of the neural network\n",
    "        \"\"\"\n",
    "        # define feedforward behavior\n",
    "        x = F.leaky_relu(self.batch_norm1(self.conv1(x)), 0.2)\n",
    "        x = F.leaky_relu(self.batch_norm2(self.conv2(x)), 0.2)\n",
    "        x = F.leaky_relu(self.batch_norm3(self.conv3(x)), 0.2)\n",
    "        x = F.leaky_relu(self.batch_norm4(self.conv4(x)), 0.2)\n",
    "        x = self.conv5(x)\n",
    "        # flatten\n",
    "        x = x.view(-1, self.conv_dim*4*4)\n",
    "        # final output layer\n",
    "        x = F.sigmoid(self.fc(x))\n",
    "        \n",
    "        return x\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "tests.test_discriminator(Discriminator)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generator\n",
    "\n",
    "The generator should upsample an input and generate a *new* image of the same size as our training data `32x32x3`. This should be mostly transpose convolutional layers with normalization applied to the outputs.\n",
    "\n",
    "#### Exercise: Complete the Generator class\n",
    "* The inputs to the generator are vectors of some length `z_size`\n",
    "* The output should be a image of shape `32x32x3`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "z_size 25\n",
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "class Generator(nn.Module):\n",
    "    \n",
    "    def __init__(self, z_size, conv_dim):\n",
    "        \"\"\"\n",
    "        Initialize the Generator Module\n",
    "        :param z_size: The length of the input latent vector, z\n",
    "        :param conv_dim: The depth of the inputs to the *last* transpose convolutional layer\n",
    "        \"\"\"\n",
    "        super(Generator, self).__init__()\n",
    "\n",
    "        # complete init function\n",
    "        self.conv_dim = conv_dim        \n",
    "        self.t_conv1 = nn.ConvTranspose2d(conv_dim, conv_dim*8, kernel_size=4, stride=2, padding=1, bias=False)\n",
    "        self.batch_norm1 = nn.BatchNorm2d(conv_dim*8)\n",
    "        self.t_conv2 = nn.ConvTranspose2d(conv_dim*8, conv_dim*4, kernel_size=4, stride=2, padding=1, bias=False)\n",
    "        self.batch_norm2 = nn.BatchNorm2d(conv_dim*4)\n",
    "        self.t_conv3 = nn.ConvTranspose2d(conv_dim*4, conv_dim*2, kernel_size=4, stride=2, padding=1, bias=False)\n",
    "        self.batch_norm3 = nn.BatchNorm2d(conv_dim*2)\n",
    "        self.t_conv4 = nn.ConvTranspose2d(conv_dim*2, 3, kernel_size=4, stride=2, padding=1, bias=False)\n",
    "        self.fc = nn.Linear(z_size, conv_dim*4)\n",
    "        print('z_size', z_size)\n",
    "        \n",
    "\n",
    "    def forward(self, x):\n",
    "        \"\"\"\n",
    "        Forward propagation of the neural network\n",
    "        :param x: The input to the neural network     \n",
    "        :return: A 32x32x3 Tensor image as output\n",
    "        \"\"\"\n",
    "        # define feedforward behavior\n",
    "        batch_s = x.shape[0]\n",
    "        x = self.fc(x)\n",
    "        x = x.view(batch_s, self.conv_dim, 2, 2)        \n",
    "        x = F.relu(self.batch_norm1(self.t_conv1(x)))\n",
    "        x = F.relu(self.batch_norm2(self.t_conv2(x)))\n",
    "        x = F.relu(self.batch_norm3(self.t_conv3(x)))\n",
    "        x = self.t_conv4(x)\n",
    "        x = F.tanh(x)\n",
    "        \n",
    "        return x\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "tests.test_generator(Generator)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize the weights of your networks\n",
    "\n",
    "To help your models converge, you should initialize the weights of the convolutional and linear layers in your model. From reading the [original DCGAN paper](https://arxiv.org/pdf/1511.06434.pdf), they say:\n",
    "> All weights were initialized from a zero-centered Normal distribution with standard deviation 0.02.\n",
    "\n",
    "So, your next task will be to define a weight initialization function that does just this!\n",
    "\n",
    "You can refer back to the lesson on weight initialization or even consult existing model code, such as that from [the `networks.py` file in CycleGAN Github repository](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py) to help you complete this function.\n",
    "\n",
    "#### Exercise: Complete the weight initialization function\n",
    "\n",
    "* This should initialize only **convolutional** and **linear** layers\n",
    "* Initialize the weights to a normal distribution, centered around 0, with a standard deviation of 0.02.\n",
    "* The bias terms, if they exist, may be left alone or set to 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def weights_init_normal(m):\n",
    "    \"\"\"\n",
    "    Applies initial weights to certain layers in a model .\n",
    "    The weights are taken from a normal distribution \n",
    "    with mean = 0, std dev = 0.02.\n",
    "    :param m: A module or layer in a network    \n",
    "    \"\"\"\n",
    "    # classname will be something like:\n",
    "    # `Conv`, `BatchNorm2d`, `Linear`, etc.\n",
    "    classname = m.__class__.__name__\n",
    "    \n",
    "    # TODO: Apply initial weights to convolutional and linear layers\n",
    "    if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1):\n",
    "        nn.init.normal_(m.weight.data, 0.0, 0.02)\n",
    "        if  hasattr(m.bias, 'data'):\n",
    "            nn.init.constant_(m.bias.data, 0.0)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Build complete network\n",
    "\n",
    "Define your models' hyperparameters and instantiate the discriminator and generator from the classes defined above. Make sure you've passed in the correct input arguments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "def build_network(d_conv_dim, g_conv_dim, z_size):\n",
    "    # define discriminator and generator\n",
    "    D = Discriminator(d_conv_dim)\n",
    "    G = Generator(z_size=z_size, conv_dim=g_conv_dim)\n",
    "\n",
    "    # initialize model weights\n",
    "    D.apply(weights_init_normal)\n",
    "    G.apply(weights_init_normal)\n",
    "\n",
    "    print(D)\n",
    "    print()\n",
    "    print(G)\n",
    "    \n",
    "    return D, G"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise: Define model hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "z_size 100\n",
      "Discriminator(\n",
      "  (conv1): Conv2d(3, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "  (batch_norm1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (conv2): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "  (batch_norm2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (conv3): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "  (batch_norm3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (conv4): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "  (batch_norm4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (conv5): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "  (fc): Linear(in_features=512, out_features=1, bias=True)\n",
      ")\n",
      "\n",
      "Generator(\n",
      "  (t_conv1): ConvTranspose2d(32, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "  (batch_norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (t_conv2): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "  (batch_norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (t_conv3): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "  (batch_norm3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (t_conv4): ConvTranspose2d(64, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "  (fc): Linear(in_features=100, out_features=128, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# Define model hyperparams\n",
    "d_conv_dim = 32\n",
    "g_conv_dim = 32\n",
    "z_size = 100\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "D, G = build_network(d_conv_dim, g_conv_dim, z_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training on GPU\n",
    "\n",
    "Check if you can train on GPU. Here, we'll set this as a boolean variable `train_on_gpu`. Later, you'll be responsible for making sure that \n",
    ">* Models,\n",
    "* Model inputs, and\n",
    "* Loss function arguments\n",
    "\n",
    "Are moved to GPU, where appropriate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training on GPU!\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL\n",
    "\"\"\"\n",
    "import torch\n",
    "\n",
    "# Check for a GPU\n",
    "train_on_gpu = torch.cuda.is_available()\n",
    "if not train_on_gpu:\n",
    "    print('No GPU found. Please use a GPU to train your neural network.')\n",
    "else:\n",
    "    print('Training on GPU!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Discriminator and Generator Losses\n",
    "\n",
    "Now we need to calculate the losses for both types of adversarial networks.\n",
    "\n",
    "### Discriminator Losses\n",
    "\n",
    "> * For the discriminator, the total loss is the sum of the losses for real and fake images, `d_loss = d_real_loss + d_fake_loss`. \n",
    "* Remember that we want the discriminator to output 1 for real images and 0 for fake images, so we need to set up the losses to reflect that.\n",
    "\n",
    "\n",
    "### Generator Loss\n",
    "\n",
    "The generator loss will look similar only with flipped labels. The generator's goal is to get the discriminator to *think* its generated images are *real*.\n",
    "\n",
    "#### Exercise: Complete real and fake loss functions\n",
    "\n",
    "**You may choose to use either cross entropy or a least squares error loss to complete the following `real_loss` and `fake_loss` functions.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def real_loss(D_out, smooth=False):\n",
    "    '''Calculates how close discriminator outputs are to being real.\n",
    "       param, D_out: discriminator logits\n",
    "       return: real loss'''\n",
    "    batch_size = D_out.size(0)\n",
    "    # label smoothing\n",
    "    if smooth:\n",
    "        # smooth, real labels = 0.9\n",
    "        labels = torch.ones(batch_size)*0.9\n",
    "    else:\n",
    "        labels = torch.ones(batch_size) # real labels = 1\n",
    "    # move labels to GPU if available     \n",
    "    if train_on_gpu:\n",
    "        labels = labels.cuda()\n",
    "    # binary cross entropy with logits loss\n",
    "    criterion = nn.BCELoss()\n",
    "    # calculate loss\n",
    "    loss = criterion(D_out.squeeze(), labels)\n",
    "    return loss\n",
    "\n",
    "def fake_loss(D_out):\n",
    "    '''Calculates how close discriminator outputs are to being fake.\n",
    "       param, D_out: discriminator logits\n",
    "       return: fake loss'''\n",
    "    batch_size = D_out.size(0)\n",
    "    labels = torch.zeros(batch_size)\n",
    "    if train_on_gpu:\n",
    "        labels = labels.cuda()\n",
    "    criterion = nn.BCELoss()\n",
    "    # calculate loss\n",
    "    loss = criterion(D_out.squeeze(), labels)\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimizers\n",
    "\n",
    "#### Exercise: Define optimizers for your Discriminator (D) and Generator (G)\n",
    "\n",
    "Define optimizers for your models with appropriate hyperparameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.optim as optim\n",
    "lr=0.0005\n",
    "g_optimizer = optim.Adam(G.parameters(), lr=lr, betas=(0.3, 0.999))\n",
    "d_optimizer = optim.Adam(D.parameters(), lr=lr, betas=(0.3, 0.999))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Training\n",
    "\n",
    "Training will involve alternating between training the discriminator and the generator. You'll use your functions `real_loss` and `fake_loss` to help you calculate the discriminator losses.\n",
    "\n",
    "* You should train the discriminator by alternating on real and fake images\n",
    "* Then the generator, which tries to trick the discriminator and should have an opposing loss function\n",
    "\n",
    "\n",
    "#### Saving Samples\n",
    "\n",
    "You've been given some code to print out some loss statistics and save some generated \"fake\" samples."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise: Complete the training function\n",
    "\n",
    "Keep in mind that, if you've moved your models to GPU, you'll also have to move any model inputs to GPU."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(D, G, n_epochs, print_every=50):\n",
    "    '''Trains adversarial networks for some number of epochs\n",
    "       param, D: the discriminator network\n",
    "       param, G: the generator network\n",
    "       param, n_epochs: number of epochs to train for\n",
    "       param, print_every: when to print and record the models' losses\n",
    "       return: D and G losses'''\n",
    "    \n",
    "    # move models to GPU\n",
    "    if train_on_gpu:\n",
    "        D.cuda()\n",
    "        G.cuda()\n",
    "\n",
    "    # keep track of loss and generated, \"fake\" samples\n",
    "    samples = []\n",
    "    losses = []\n",
    "\n",
    "    # Get some fixed data for sampling. These are images that are held\n",
    "    # constant throughout training, and allow us to inspect the model's performance\n",
    "    sample_size=16\n",
    "    fixed_z = np.random.uniform(-1, 1, size=(sample_size, z_size))\n",
    "    fixed_z = torch.from_numpy(fixed_z).float()\n",
    "    # move z to GPU if available\n",
    "    if train_on_gpu:\n",
    "        fixed_z = fixed_z.cuda()\n",
    "\n",
    "    # epoch training loop\n",
    "    for epoch in range(n_epochs):\n",
    "\n",
    "        # batch training loop\n",
    "        for batch_i, (real_images, _) in enumerate(celeba_train_loader):\n",
    "\n",
    "            batch_size = real_images.size(0)\n",
    "            real_images = scale(real_images)\n",
    "\n",
    "            # ===============================================\n",
    "            #         YOUR CODE HERE: TRAIN THE NETWORKS\n",
    "            # ===============================================\n",
    "            \n",
    "            # 1. Train the discriminator on real and fake images\n",
    "            if train_on_gpu:\n",
    "                real_images = real_images.cuda()\n",
    "            d_optimizer.zero_grad()\n",
    "            D_real = D(real_images)\n",
    "            d_real_loss = real_loss(D_real)\n",
    "            z_flex = np.random.uniform(-1, 1, size=(batch_size, z_size))\n",
    "            z_flex = torch.from_numpy(z_flex).float()\n",
    "            if train_on_gpu:\n",
    "                z_flex = z_flex.cuda()\n",
    "\n",
    "\n",
    "            fake_images = G(z_flex)\n",
    "            D_fake = D(fake_images)\n",
    "            d_fake_loss = fake_loss(D_fake)\n",
    "\n",
    "            d_loss = d_real_loss + d_fake_loss\n",
    "            d_loss.backward()\n",
    "            d_optimizer.step()\n",
    "\n",
    "            # 2. Train the generator with an adversarial loss\n",
    "            g_optimizer.zero_grad()\n",
    "            z_flex = np.random.uniform(-1, 1, size=(batch_size, z_size))\n",
    "            z_flex = torch.from_numpy(z_flex).float()\n",
    "            if train_on_gpu:\n",
    "                z_flex = z_flex.cuda()\n",
    "            fake_images = G(z_flex)\n",
    "            D_fake = D(fake_images)\n",
    "            g_loss = real_loss(D_fake, True) # use real loss to flip labels\n",
    "            g_loss.backward()\n",
    "            g_optimizer.step()\n",
    "            \n",
    "            \n",
    "            # ===============================================\n",
    "            #              END OF YOUR CODE\n",
    "            # ===============================================\n",
    "\n",
    "            # Print some loss stats\n",
    "            if batch_i % print_every == 0:\n",
    "                # append discriminator loss and generator loss\n",
    "                losses.append((d_loss.item(), g_loss.item()))\n",
    "                # print discriminator and generator loss\n",
    "                print('Epoch [{:5d}/{:5d}] | d_loss: {:6.4f} | g_loss: {:6.4f}'.format(\n",
    "                        epoch+1, n_epochs, d_loss.item(), g_loss.item()))\n",
    "\n",
    "\n",
    "        ## AFTER EACH EPOCH##    \n",
    "        # this code assumes your generator is named G, feel free to change the name\n",
    "        # generate and save sample, fake images\n",
    "        G.eval() # for generating samples\n",
    "        samples_z = G(fixed_z)\n",
    "        samples.append(samples_z)\n",
    "        G.train() # back to training mode\n",
    "\n",
    "    # Save training generator samples\n",
    "    with open('train_samples.pkl', 'wb') as f:\n",
    "        pkl.dump(samples, f)\n",
    "    \n",
    "    # finally return losses\n",
    "    return losses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set your number of training epochs and train your GAN!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [    1/   10] | d_loss: 1.3998 | g_loss: 0.8902\n",
      "Epoch [    1/   10] | d_loss: 0.7218 | g_loss: 2.4139\n",
      "Epoch [    1/   10] | d_loss: 1.0146 | g_loss: 1.6081\n",
      "Epoch [    1/   10] | d_loss: 1.2413 | g_loss: 1.2347\n",
      "Epoch [    1/   10] | d_loss: 1.1994 | g_loss: 1.5040\n",
      "Epoch [    1/   10] | d_loss: 1.0633 | g_loss: 1.4995\n",
      "Epoch [    1/   10] | d_loss: 1.4147 | g_loss: 1.8807\n",
      "Epoch [    1/   10] | d_loss: 1.0845 | g_loss: 1.9490\n",
      "Epoch [    1/   10] | d_loss: 1.1487 | g_loss: 1.7474\n",
      "Epoch [    1/   10] | d_loss: 1.2233 | g_loss: 1.2667\n",
      "Epoch [    1/   10] | d_loss: 1.1847 | g_loss: 1.1029\n",
      "Epoch [    1/   10] | d_loss: 1.1388 | g_loss: 1.3839\n",
      "Epoch [    1/   10] | d_loss: 2.0840 | g_loss: 2.1559\n",
      "Epoch [    1/   10] | d_loss: 1.2334 | g_loss: 1.4100\n",
      "Epoch [    1/   10] | d_loss: 1.0986 | g_loss: 1.5371\n",
      "Epoch [    1/   10] | d_loss: 1.0792 | g_loss: 1.2833\n",
      "Epoch [    1/   10] | d_loss: 1.1274 | g_loss: 1.4431\n",
      "Epoch [    1/   10] | d_loss: 1.5053 | g_loss: 1.0890\n",
      "Epoch [    1/   10] | d_loss: 1.2348 | g_loss: 2.4489\n",
      "Epoch [    1/   10] | d_loss: 1.0571 | g_loss: 1.2616\n",
      "Epoch [    1/   10] | d_loss: 1.1472 | g_loss: 0.9375\n",
      "Epoch [    1/   10] | d_loss: 1.1318 | g_loss: 1.1407\n",
      "Epoch [    1/   10] | d_loss: 1.1512 | g_loss: 1.1352\n",
      "Epoch [    1/   10] | d_loss: 0.9958 | g_loss: 1.0495\n",
      "Epoch [    1/   10] | d_loss: 1.0259 | g_loss: 1.0811\n",
      "Epoch [    1/   10] | d_loss: 0.9979 | g_loss: 1.4897\n",
      "Epoch [    1/   10] | d_loss: 1.0939 | g_loss: 1.9969\n",
      "Epoch [    1/   10] | d_loss: 1.2600 | g_loss: 1.4050\n",
      "Epoch [    1/   10] | d_loss: 1.0243 | g_loss: 1.2374\n",
      "Epoch [    1/   10] | d_loss: 1.0546 | g_loss: 1.8565\n",
      "Epoch [    1/   10] | d_loss: 1.1805 | g_loss: 1.4916\n",
      "Epoch [    1/   10] | d_loss: 1.0620 | g_loss: 1.1246\n",
      "Epoch [    1/   10] | d_loss: 1.1040 | g_loss: 1.8763\n",
      "Epoch [    1/   10] | d_loss: 0.9365 | g_loss: 1.6993\n",
      "Epoch [    1/   10] | d_loss: 1.1162 | g_loss: 1.5718\n",
      "Epoch [    1/   10] | d_loss: 1.2212 | g_loss: 1.3898\n",
      "Epoch [    1/   10] | d_loss: 1.1535 | g_loss: 1.1173\n",
      "Epoch [    1/   10] | d_loss: 0.8540 | g_loss: 0.9959\n",
      "Epoch [    1/   10] | d_loss: 1.3365 | g_loss: 0.8695\n",
      "Epoch [    1/   10] | d_loss: 1.1811 | g_loss: 1.6755\n",
      "Epoch [    1/   10] | d_loss: 1.1899 | g_loss: 1.6780\n",
      "Epoch [    1/   10] | d_loss: 1.2363 | g_loss: 1.0889\n",
      "Epoch [    1/   10] | d_loss: 1.2286 | g_loss: 1.1776\n",
      "Epoch [    1/   10] | d_loss: 1.5886 | g_loss: 2.1862\n",
      "Epoch [    1/   10] | d_loss: 0.8491 | g_loss: 1.0875\n",
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      "Epoch [    9/   10] | d_loss: 0.9904 | g_loss: 2.2753\n",
      "Epoch [    9/   10] | d_loss: 0.3982 | g_loss: 1.6896\n",
      "Epoch [    9/   10] | d_loss: 0.4775 | g_loss: 2.7203\n",
      "Epoch [    9/   10] | d_loss: 0.1314 | g_loss: 2.7250\n",
      "Epoch [    9/   10] | d_loss: 0.6741 | g_loss: 1.2399\n",
      "Epoch [    9/   10] | d_loss: 0.5442 | g_loss: 3.6056\n",
      "Epoch [    9/   10] | d_loss: 0.5706 | g_loss: 3.2123\n",
      "Epoch [    9/   10] | d_loss: 0.3421 | g_loss: 3.6098\n",
      "Epoch [    9/   10] | d_loss: 0.5056 | g_loss: 3.1263\n",
      "Epoch [    9/   10] | d_loss: 0.8287 | g_loss: 0.6977\n",
      "Epoch [    9/   10] | d_loss: 0.3183 | g_loss: 1.9630\n",
      "Epoch [    9/   10] | d_loss: 0.7512 | g_loss: 3.4064\n",
      "Epoch [    9/   10] | d_loss: 0.4421 | g_loss: 3.0119\n",
      "Epoch [    9/   10] | d_loss: 0.7957 | g_loss: 2.4249\n",
      "Epoch [    9/   10] | d_loss: 1.2198 | g_loss: 0.9126\n",
      "Epoch [    9/   10] | d_loss: 0.4611 | g_loss: 0.9119\n",
      "Epoch [    9/   10] | d_loss: 1.4744 | g_loss: 2.1069\n",
      "Epoch [    9/   10] | d_loss: 0.9959 | g_loss: 2.2386\n",
      "Epoch [    9/   10] | d_loss: 1.1467 | g_loss: 1.7625\n",
      "Epoch [    9/   10] | d_loss: 1.6093 | g_loss: 0.9334\n",
      "Epoch [    9/   10] | d_loss: 0.6258 | g_loss: 2.5195\n",
      "Epoch [    9/   10] | d_loss: 1.4208 | g_loss: 1.5466\n",
      "Epoch [    9/   10] | d_loss: 0.7716 | g_loss: 3.2146\n",
      "Epoch [    9/   10] | d_loss: 0.4331 | g_loss: 1.8430\n",
      "Epoch [    9/   10] | d_loss: 0.3041 | g_loss: 2.7116\n",
      "Epoch [    9/   10] | d_loss: 0.2569 | g_loss: 0.4608\n",
      "Epoch [    9/   10] | d_loss: 0.4678 | g_loss: 2.1444\n",
      "Epoch [    9/   10] | d_loss: 0.7170 | g_loss: 1.7864\n",
      "Epoch [    9/   10] | d_loss: 0.7963 | g_loss: 2.2328\n",
      "Epoch [    9/   10] | d_loss: 0.3785 | g_loss: 3.0305\n",
      "Epoch [    9/   10] | d_loss: 0.7962 | g_loss: 3.3352\n",
      "Epoch [    9/   10] | d_loss: 1.1688 | g_loss: 1.3366\n",
      "Epoch [    9/   10] | d_loss: 0.9203 | g_loss: 3.1253\n",
      "Epoch [    9/   10] | d_loss: 1.0434 | g_loss: 2.3699\n",
      "Epoch [    9/   10] | d_loss: 0.4771 | g_loss: 2.5763\n",
      "Epoch [    9/   10] | d_loss: 0.4386 | g_loss: 1.4157\n",
      "Epoch [    9/   10] | d_loss: 0.5367 | g_loss: 1.2537\n",
      "Epoch [    9/   10] | d_loss: 0.8701 | g_loss: 2.7485\n",
      "Epoch [    9/   10] | d_loss: 0.6687 | g_loss: 2.7519\n",
      "Epoch [    9/   10] | d_loss: 0.9915 | g_loss: 1.8884\n",
      "Epoch [    9/   10] | d_loss: 0.7576 | g_loss: 1.6637\n",
      "Epoch [    9/   10] | d_loss: 1.3191 | g_loss: 1.1656\n",
      "Epoch [    9/   10] | d_loss: 0.9380 | g_loss: 1.7193\n",
      "Epoch [    9/   10] | d_loss: 0.5174 | g_loss: 2.0139\n",
      "Epoch [    9/   10] | d_loss: 1.4001 | g_loss: 1.0187\n",
      "Epoch [    9/   10] | d_loss: 0.2784 | g_loss: 3.1884\n",
      "Epoch [    9/   10] | d_loss: 0.6158 | g_loss: 2.8103\n",
      "Epoch [    9/   10] | d_loss: 0.7719 | g_loss: 3.5508\n",
      "Epoch [   10/   10] | d_loss: 1.6178 | g_loss: 1.4234\n",
      "Epoch [   10/   10] | d_loss: 0.4638 | g_loss: 2.0380\n",
      "Epoch [   10/   10] | d_loss: 0.2055 | g_loss: 1.3971\n",
      "Epoch [   10/   10] | d_loss: 0.5339 | g_loss: 1.2935\n",
      "Epoch [   10/   10] | d_loss: 0.3860 | g_loss: 1.5060\n",
      "Epoch [   10/   10] | d_loss: 0.5179 | g_loss: 3.1770\n",
      "Epoch [   10/   10] | d_loss: 0.4069 | g_loss: 4.1367\n",
      "Epoch [   10/   10] | d_loss: 0.4442 | g_loss: 2.3403\n",
      "Epoch [   10/   10] | d_loss: 0.5984 | g_loss: 2.9084\n",
      "Epoch [   10/   10] | d_loss: 0.3874 | g_loss: 0.8845\n",
      "Epoch [   10/   10] | d_loss: 0.6062 | g_loss: 3.6476\n",
      "Epoch [   10/   10] | d_loss: 0.4488 | g_loss: 1.0984\n",
      "Epoch [   10/   10] | d_loss: 0.8802 | g_loss: 1.5527\n",
      "Epoch [   10/   10] | d_loss: 1.2185 | g_loss: 3.2043\n",
      "Epoch [   10/   10] | d_loss: 1.0571 | g_loss: 1.4240\n",
      "Epoch [   10/   10] | d_loss: 0.1583 | g_loss: 1.5947\n",
      "Epoch [   10/   10] | d_loss: 0.6772 | g_loss: 2.5129\n",
      "Epoch [   10/   10] | d_loss: 0.8002 | g_loss: 1.3149\n",
      "Epoch [   10/   10] | d_loss: 0.7527 | g_loss: 1.7312\n",
      "Epoch [   10/   10] | d_loss: 0.8978 | g_loss: 2.0233\n",
      "Epoch [   10/   10] | d_loss: 0.6966 | g_loss: 2.3795\n",
      "Epoch [   10/   10] | d_loss: 1.5026 | g_loss: 2.1463\n",
      "Epoch [   10/   10] | d_loss: 0.6059 | g_loss: 1.9938\n",
      "Epoch [   10/   10] | d_loss: 0.5325 | g_loss: 1.7338\n",
      "Epoch [   10/   10] | d_loss: 1.0086 | g_loss: 3.8216\n",
      "Epoch [   10/   10] | d_loss: 1.7010 | g_loss: 3.5162\n",
      "Epoch [   10/   10] | d_loss: 1.2995 | g_loss: 0.8865\n",
      "Epoch [   10/   10] | d_loss: 0.9065 | g_loss: 2.4206\n",
      "Epoch [   10/   10] | d_loss: 1.0253 | g_loss: 1.4951\n",
      "Epoch [   10/   10] | d_loss: 0.4526 | g_loss: 1.1957\n",
      "Epoch [   10/   10] | d_loss: 1.4831 | g_loss: 1.3093\n",
      "Epoch [   10/   10] | d_loss: 0.5138 | g_loss: 2.7939\n",
      "Epoch [   10/   10] | d_loss: 0.3483 | g_loss: 1.4328\n",
      "Epoch [   10/   10] | d_loss: 0.4390 | g_loss: 4.4212\n",
      "Epoch [   10/   10] | d_loss: 2.0585 | g_loss: 1.1346\n",
      "Epoch [   10/   10] | d_loss: 0.4625 | g_loss: 1.5938\n",
      "Epoch [   10/   10] | d_loss: 0.2332 | g_loss: 2.9505\n",
      "Epoch [   10/   10] | d_loss: 1.5212 | g_loss: 1.9112\n",
      "Epoch [   10/   10] | d_loss: 0.8092 | g_loss: 2.5611\n",
      "Epoch [   10/   10] | d_loss: 0.5783 | g_loss: 1.5204\n",
      "Epoch [   10/   10] | d_loss: 1.6171 | g_loss: 3.5798\n",
      "Epoch [   10/   10] | d_loss: 0.3380 | g_loss: 2.5675\n",
      "Epoch [   10/   10] | d_loss: 0.5035 | g_loss: 2.0964\n",
      "Epoch [   10/   10] | d_loss: 0.7992 | g_loss: 2.4534\n",
      "Epoch [   10/   10] | d_loss: 1.0256 | g_loss: 0.8928\n",
      "Epoch [   10/   10] | d_loss: 0.2545 | g_loss: 1.4782\n",
      "Epoch [   10/   10] | d_loss: 0.6350 | g_loss: 1.0221\n",
      "Epoch [   10/   10] | d_loss: 0.7109 | g_loss: 1.4245\n",
      "Epoch [   10/   10] | d_loss: 0.4936 | g_loss: 1.8577\n",
      "Epoch [   10/   10] | d_loss: 0.8118 | g_loss: 1.3249\n",
      "Epoch [   10/   10] | d_loss: 0.5332 | g_loss: 2.1765\n",
      "Epoch [   10/   10] | d_loss: 0.5345 | g_loss: 2.4255\n",
      "Epoch [   10/   10] | d_loss: 0.6343 | g_loss: 1.3753\n",
      "Epoch [   10/   10] | d_loss: 0.9496 | g_loss: 1.7303\n",
      "Epoch [   10/   10] | d_loss: 0.8075 | g_loss: 2.3603\n",
      "Epoch [   10/   10] | d_loss: 1.1994 | g_loss: 0.6629\n",
      "Epoch [   10/   10] | d_loss: 1.1350 | g_loss: 4.0611\n",
      "Epoch [   10/   10] | d_loss: 0.9711 | g_loss: 3.0008\n",
      "Epoch [   10/   10] | d_loss: 1.2437 | g_loss: 4.5242\n",
      "Epoch [   10/   10] | d_loss: 0.9529 | g_loss: 2.4239\n",
      "Epoch [   10/   10] | d_loss: 1.0875 | g_loss: 1.5459\n",
      "Epoch [   10/   10] | d_loss: 0.7907 | g_loss: 1.5542\n",
      "Epoch [   10/   10] | d_loss: 0.6726 | g_loss: 1.6241\n",
      "Epoch [   10/   10] | d_loss: 0.9004 | g_loss: 1.5078\n",
      "Epoch [   10/   10] | d_loss: 0.7843 | g_loss: 3.2338\n",
      "Epoch [   10/   10] | d_loss: 1.2135 | g_loss: 1.2646\n",
      "Epoch [   10/   10] | d_loss: 0.5601 | g_loss: 0.6589\n",
      "Epoch [   10/   10] | d_loss: 0.7888 | g_loss: 2.1645\n",
      "Epoch [   10/   10] | d_loss: 1.1410 | g_loss: 1.1449\n",
      "Epoch [   10/   10] | d_loss: 0.4367 | g_loss: 2.6037\n",
      "Epoch [   10/   10] | d_loss: 0.6904 | g_loss: 2.4969\n",
      "Epoch [   10/   10] | d_loss: 0.5946 | g_loss: 2.9485\n",
      "Epoch [   10/   10] | d_loss: 1.7531 | g_loss: 3.1584\n",
      "Epoch [   10/   10] | d_loss: 0.4559 | g_loss: 1.3770\n",
      "Epoch [   10/   10] | d_loss: 0.6233 | g_loss: 1.2214\n",
      "Epoch [   10/   10] | d_loss: 0.6960 | g_loss: 2.6218\n",
      "Epoch [   10/   10] | d_loss: 1.0987 | g_loss: 2.8439\n",
      "Epoch [   10/   10] | d_loss: 0.3432 | g_loss: 1.7479\n",
      "Epoch [   10/   10] | d_loss: 1.0010 | g_loss: 1.1198\n",
      "Epoch [   10/   10] | d_loss: 0.5604 | g_loss: 2.2923\n",
      "Epoch [   10/   10] | d_loss: 0.4007 | g_loss: 1.6913\n",
      "Epoch [   10/   10] | d_loss: 0.5948 | g_loss: 3.1641\n",
      "Epoch [   10/   10] | d_loss: 0.8013 | g_loss: 2.1843\n",
      "Epoch [   10/   10] | d_loss: 0.4804 | g_loss: 1.0788\n",
      "Epoch [   10/   10] | d_loss: 0.4544 | g_loss: 2.9433\n",
      "Epoch [   10/   10] | d_loss: 0.9967 | g_loss: 1.4027\n",
      "Epoch [   10/   10] | d_loss: 0.4629 | g_loss: 2.0807\n",
      "Epoch [   10/   10] | d_loss: 0.3881 | g_loss: 2.4889\n",
      "Epoch [   10/   10] | d_loss: 0.2310 | g_loss: 2.5796\n",
      "Epoch [   10/   10] | d_loss: 2.0423 | g_loss: 1.0634\n"
     ]
    }
   ],
   "source": [
    "# set number of epochs \n",
    "n_epochs = 10\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL\n",
    "\"\"\"\n",
    "# call training function\n",
    "losses = train(D, G, n_epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Training loss\n",
    "\n",
    "Plot the training losses for the generator and discriminator, recorded after each epoch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd283cd4908>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XXnhBm5z81re+hWeeeQbr1q3Ds88+iyeeeCLr90AgEFwYeArr6hcR1nVqIZ6NwJHxAWD394HSmcBlIr57rsh6WFeBQCAQFA5CcAsEAoEdlAKNrwMTQ5NdEwN5Fdy5MMsIMkM8E4Er07mNjPUCrbuAY5MTGsOOvAnukpIS9Pf3C0FRQFBK0d/fj5KSEvtEJ38HHP9N/iolKCySceA9yxaz2Sc6DBz/LyBVYGEdVHlFU5NbDxN58+OeP38+2tra0Nvbm68iBR4oKSnRFhhx6Twkf178ofxUSFBYxEbd02SDM38E+s4A9auA+hX5KXMKkzfBXVRUZLtqUCAQFCgXeijX0S5guB2Yv9klYWHdh2m75F0gEBQQqkki3x3F3mfkT1vBXZimXeFVIhAIvHGha99TCCG4BQKBPXkT1qpmKzoHLwjBLRAIBG4U2GhDCG6BQDD5TJaNe4oiBLdAIHBgmgvSAl13IgS3QCAoAApTQBYqQnALBAJ7hOmiIBGCWyAQTD4Fb+MurHoJwS0QCAS2FKYJRwhugUBgj2FyLpdaZw78uEe7gMGW7OVXQAjBLRAIPFKY2qcte58BDv50smuRE4TgFggEDuRJWBe6jbvA6uVZcBNCgoSQA4SQl3NZIYFAUKgUlvDKC6qppcD8uf1o3H8F4ESuKiIQCAqQvAmsAo1V0rRjsmvAxZPgJoTMB/BBAD/IbXUEAoGgAJmippJvAvgSACmHdREIBBcqvaeAwebJrsUFg6vgJoTcCqCHUrrPJd2DhJC9hJC9YnsygUBg4OivgYM/s/+90CcnCwwvGvd2ALcTQpoBPAfgOkLIf5oTUUqfopRuoZRuqa+vz3I1BQKBQKDiKrgppf+LUjqfUroYwIcBvEYp/VjOayYQCCaffE9OFpj3hk5hjQSEH7dAIBBMMXxtFkwp3QFgR05qIhAIChBGA86l/VnTtCdJ46Z0StnXhcYtEAimF6kkkIhOdi0yQghugUBQOOTDxn3wJ8Db38h9OTlECG6BQGBP3ldO5oGRDk7xLuUXmBlFCG6BQFBAFKpXSWEhBLdAIHAgz9EBJ43JLt8fQnALBNOZWARIxia7Fjr5FODS1I3gIQS3QDCdeffbwN6n7X+/EG3cWpEpH4mFjVsgEBQSE0OTXYPcMnCOf1xK6n9PuqnGH0JwCwQCBy4AG/eh54Ch89bjrOCeYgjBLRB4JTZ64Wunk40qwOPjwOv/DOz5YXbyTYxbj0l+TCWFhRDcAoFX3v0OsOvJ/JWXjE1p4eIPk8bd8q78GenJflGqTzZ7b6UEcOJlebJ2CiAEt0BQKCSiRk+Ht74OHP3V5NUHmATbr1JeIAPRdH4PEB2x/50ElaIYwd1zAug6AjS9nn65eUQIboGgUHj7G8BJ017c/Y2TU5fJhqQpmiaGgLOvAEeft08TUAQ3z8Y9RSYpheAWCAoBVWB0H8t/mc6Jcl4NuRhTPO50BTdVRizJuH0aNW8/k5NiybtAIHAlH5pfQWqXquAOZpaNk6B1FMKFeE+sCMEtEBQCZiFK87Cqz0sZkyXcWY3bTx20tB40ZDbfAtOo3RCCWyAoCEzCKVNvkq6jsjud0yRdPjoHv/BMJb46Dw9pnfIryFGIFSG4BYJCwLzLua/l2By6Dsuf4/0OZfoU3HnZAUchwJhK3vgXYKzPXz5p11UIboFAkC6ZatyqUHaa5PMkuCfJHdAseHuO+zvfd1o3QV9YphQhuAWCgsBs41YEd7reFargDzhM8mWq1WcV0/Wbr5vXyaQS8jZkhnQZatzCVCIQ+KTxNWC4bbJrMTmYBYaUoeDWBJ2DACvEyUk7d0BeXd98HNjz7+YMlE+fk5NTDCG4BYVD625g/7OTXYvCQBVU6a4gVM93Es6FNDlprovZHdCurubYMcLGLRAIckLfGfmfAZOmmC2N28kcUogLcGCncXuthw+Nm702bVLYppwCcxcUgnu6QOmU3vFjytB93D2+yJHn5X8sFj9uVXCnuRAlnxp3fBw49mJmO+lYNG7i/LttPplq3A7ExwvmHRKCe7pw6DnZrapQmcL2RgPHfwP0ns48n2xp3E6CJls27tZ35SBNHQe91Y1fkLE8uwVJsQgw2OyeT7ZJxoB3npDnYQoAIbinC46NvcBp2iEvJrmgybJXiSdTSWFojwDc66L+vv/HwMGf2adT/b3Ngv/or4HxATUzfhonklH5s++U93NyiBDcAkdSEkVKmuS4GS07c1PeYHPhaPrmIb7mzhdKM0PVZutB4063c8gmZhu35XelrupKULvndur39mWMdqZVNUP5atlqJyCl5FWqeW5HBfDEBIXM3pYBvNc84J5wqtF3Wtbc2vdPdk34JCbkz1BxeudrEfA8aNzZCrqUiV3Z0sG4xG7J+mjBZXKSjYHSugvY/X0g0gu0vAOceAnoza8mLgS3wJF8KNsyHgrK5sSQqrlNDGYvz4wwXb86NB/rSW/EQbxo3H4frpJnIgrEx7KQn+Fk5zzM18F2SANN1vQ5cwekwKCy+XA8ou+Yk5xIs7z0EIJbUBh4eekLaqVfljFfv6pxp5Kyjd8vqsad6T3jPZed3wHe+VZm+VrKMWvULho3G0v7/J7s1oULUx+17EBo0twEheAWTB1ysv9ijoYUvrVPk/+xqnGnixeN23ztE4POey5GemTvilTCucx0oJY/TL+bV5ayS93TfIa8Z+RqKoF+/cEi9/NyhKvgJoSUEELeI4QcIoQcI4T8Yz4qJphuXOAad+tuf+5yKYcdXLyg2bh9mJd2fQ9499umg6bnkovNe9lyOg8rE38+NG6u0OR1Iplox0zHqpad6WYPGeBF444BuI5Suh7ABgA3E0Iuy221BNMOJ41FG/Zn0cbttlIuU8z5Nr7m7PFgMQ1kWq9c2LiRO9OAWs/hNtnlz+53FcPoy++qSs537bp8aNyg0DuDAtO4qYw6fipS/hWID5WgIOg4AIxk4GrlhhcPiYIjXVOJ3c8+88uWjTtfsNeXiLrbuNnrykbna7fwR4U1i/jZqzJHeLJxE0KChJCDAHoA/JFSupuT5kFCyF5CyN7e3t5s11NQqPSdBU79N7DvR97Pad8vL6iJjzMHnTRuL/baAiNjYZLhVmaeRhQe4noUmp+79p29H5w6ckcG5mX0zHl2HVxRqfwZLtePSQl+nfKIJ8FNKU1RSjcAmA/gUkLIJZw0T1FKt1BKt9TX12e7noJC5cgv/Z/Tqdh6Yw7barGoMaWzqnHn2hvA50utLcAxfdd+T7fTyvY2XZz75mvvAkazbdkJRIftEjp/Ny+IyZRT/80vR9W02TK0dkj1DuL0//BdJHOEL68SSukQgB0Abs5JbQTTAz+z+UCOh/2F5lVi93OagjuXo5TxAeC9f/fnw6ytgByS3RzNwbZsz8tWR8bJK1vn95zILF8fePEqqSeEzFD+LgVwA4CTua6Y4EKGN0T3ILgvRBu3lLLu4sI73++1u9lsATnwWCa07pRjg/T5CKrVccBYLzvvGUu9ne6HT0Ug4/PSSJtlvARCmAvgx4SQIGRB/wtK6cu5rZbggoaNSyGlnLfXAnS3q6ky0QZ4f6l3/T/Zd3rbp5UDNrbpXJhKnOg5KZsJzPVgbceU1wG7EI9Y8/GC0/3gjuCydL/cYqhMEq6Cm1J6GMDGPNRFkA6UAs1vA3PXASXVk10bf+x9Rn6Br/mKN1OJH62TUqDxVWDWxUBVAydPJl26DJ0HDvynLHTLaswV8JaH7YKXTAW3B43biWMvyJ91y93L8EPpTFMWdnm42bip/W+At04+U+16348mLUCXWDk51RnrlQW3+qJNNbxoNOn4cVMqL4Xe/x9pV82V7mPypxq7wlx+NvE62pAk2fasmUo83DMn5deyUw/v/HQmehn/Z0/hDnxq3Fnf8MDOv3tyPJ2E4J7qqI3Wq29pIbh3pVsHX+eloXFGh7PoGWBTbsJmIs9tAY7X6zi3Q45cF1X3Yszy82ZXTjrVKZWUFxwZXD5tzvF0zHw/XDoyywiIw3if93IL4b1hEIJ7ulFgDVDD0VSSp9VpO/9f9oIn2V3P8d/YneD83atmN9Qqfyaixmy6j6UXrMoMd/UnR+PuPiov8T/3Bj8f1s88nZ14DBp3Chg4J3cWFbOU/J1Em5IXdzRhp1mr7pqF4fMuBPe0oxAEdx40bte0ue4MbPI170puxk4wpO1Ro9Tj+H/lYEMKL8LMJc42Nw2A8X7n89hzIr2yh0zPcedyMqIQ3hsdIbgF+YcrVNN03ypUPE+62aQ3f490pafR5VILtOTtwdZtdlNMxYH+s9Z0bXudy+o+DgvJKPyZNjyYaPzMFeQRIbinOpm6VfkhGZf9cL34CPslnQkq58TOP+c6yNTep4Gzr/o4waUeJ38HdB3xX4+8CJx0Ri9K2mTMwXzkAG/zBMMS9mxPDheW0iAE97QjgwbY+Kq8NDjjjYfz/BJEenSbb75ITADn3/Oe3uwTzRMUFvOBenxAtl+nPZJJFwdTiW3HmK6ZzGMH5Mfv2tf9EoJbMJlkojmoHhepDIPs5MNUwpax54fAoZ96P5cHb2iuEhsF9vxA/swldhNuR563j/tRcKYSu3P9luWUOdIfaQivEkFhkoUGmAsvD/OL8fo/A6f/YCzP6eUZ63OewBvtzqx+6lJtHu375QkyL6aMdBecAPaCW3WN6zrCCa+bD4HjFIkvW+XnWNB7TZvpzkRZQgju6Ua+7dL8E70ls+zAbnNedEQOdnT2FR9l+BQsXuYSmkyubzxBaxcNT72XyRgw0sG/t7ahAZS6Nb9tn29OSCfvNDVY33MgWW6bat5OJreuwzncIciIENzTDirbRF//Z2C0a5KqkKapxO7lVbUg1Ye5UAik+Xrt4+wAA9hvleXks5zLyUkvvs1252R7owlz/tky4/mpb6RHnpTOA0JwFyJDrbJgHenwfo6fhqq6X3Ud9VcvtYwjzyuLOfKtvVN5KfPZV9xXOGZd03QSTnZl+fH4ycBU4ig4vdyHHMQmz7bXjmfTh4Og9dTBeHTXtD09P7ZwIbgLkf5G+TNj7w0OlCIr9seWnbk3lWgwQmDwnByDRLV/pwsrWN78V+Cwy4YQudprUcXLEnc7DX7SV/Ol0UH5rhcFBltckri4Ax57Ud7J3imNXb28jlyKSrylyxAhuKcdND0hVCi28YAS0DIx7pzUT+eQSvIXgXjG5n5mJOw9atyxUXli1E8+vmpB0TowjmiCM/Hr+Bxtgkilayqh1N4dUk/EmEpsBK2bOc12pafH+haVu6fJAlNDcA+2AGNuD03gCVbjzlgY50vjVk+j+lZSyViaZadLpuYIF7x4pPBs3Id/4XwO84zP9IzibM+o5bgFNeohgGhCQvvQBE51O7g65kURyNbkpEtdxwesgbEseTsQ9LLFQeYUluCWJL5L18GfAu89lf/6TDq5GJ5TJts0/aKdjqULm5fZREQ42pud4G5+xxjWlMVwjpLn2CRubM3OMZjdDXn152rcLvt2MhpkXySOlw6Z3QU5HP8vazaZLO7JhreHJ68SSc/fKb362xgnOiAgLzSLDpueiVfvo/yI1MIS3IefA974WvrnJ+POCyUEhaFxuwmBgz+zP08VRCmO4I5HgHNv2m9g/NbXgeF24zGvnjW5sHGfeMnhR4/ugFldBOU3j3Si+infWziui84ZeXgG2XAHhFzHQz+XVwlrURaF4LbHbfLBjTP/I8c9ML+cFzLpDDnTWUDTc8w9jZ86ZHqu0wgglbAvI5LhQhxf5HonecD1XnpZ8ENIeoLcyfXOINQ5v/f62KfSrgxeGjd3wMZX9ZW/dgSCQEzxt/frHTMtBXemqMNGuw1IBelr3CdeBkZNw+x0X3beIgav7l6sQIiO+K+DqrX61qDzIYQZvFxXbFQeZTpnZPg2f3if9/zBmkh41+80cWf3WyaddhY0bi8xa0hA30HHb3RAIbizzPgAP6LYlCeNFyHdJeuSWVOxOf/MK/ZLxKOceNSUutRF7Wigv0DJGLDzu8D53Zz01F4wBdKcPBo+r/8tSfJ+k1pxOXC5G+OswDOXw7q22dHfCHQe0r5qgtstby84CTNbt7ocTmh7tXG7QYLeBbXlXCG4/eO0kmvfM7LdqsCCxfDJcNLQa96KKkvvAAAgAElEQVSZ3gu789v2yPZBHjzPCM/14Ahk1rzmRYtWBbefa09MGCc2W3fKmwSrwtyu3Ezs4lxN2lRnu5WUZk7+zj2vdBQAJ7NE2x5rukygHmzcBoU7E8GdgVgUgjvLqC+C2yz8hU5GC3ByZC6gkjcNjZ2c1KpkVyc3jdvHtZv38xxukz/tvBLyhW3sEg94WfDjmof6LDgKk3pv7KLt+S/MW5q0l9QzZNLhCsGdRdgXzC7Iz5QlA1OJ35fV0qDTKJvrxudgKuk+akxniXHt8yXz+mKdf08OO5CMW8swe7TY3scsd3Tmcrxq3PzM9D+TcdO2Xz7zcI0PkqVR3viAS3WyVE4mHWKuV9gqTA/B3XlQ/3tKmEp8kFaUtSxp3Oncyz0/4NTJwZ44MWTsaMwr3+xeFKe6SSnnSb3+Rn3rLN5O4H6IRWSzSi7IppA4+Vv/55g17lySSgKtu5zTsDbujDRuYSrJD5Q6B7Gntl+mITQDjdvcXHyeb6cxDbc5mEpc3MpY4eXVM+XIL00hYE2wqxF5kfpCpngUTgL03W8bJzIzgtFsu4+7u7UpjEYTSKRMnaPPZ8+9RE9mq2x6lbiRJY17Cgju/KzPzDXn3gRa3gVKZyoHHIb0F5rGnVaA+VzvcG7D0V/xjx95Htj0cf5vVIKj+6LhRfHgDkYpMHDOpaJwDphvfjnz1abUcoZa5PUKMxZ4Ou1oxwjKwubhf5YmDAH+EnFeOvPf2cbgx51BONtMTFB5aguFrXG/8TXZf9iNASWaXsIm1Gc2ltwWKpkswOk56XOInMGE1kCTvX99IGjNWyvDReOGX43bI06xUDz79GbZ3tmnLFpJKZOlExzXShvG40woCVbA5YJ0BXUmtmXAQ1vxSEZa83QW3OrDllL+drbO9eTIVERKcYbUjEDpPOw9L9vIaWx5TBpVsER6ZFfMqI1HTyDk3avEDAlYPQmcBFMaz/9cXwQ7m/qRVK/NT5z0bNJ3Rv5Uw7uavV04UN4zOvor06Rvmpjbg+0kpUlxkmw6PjWAWPoVgqENpEuezB2ZUNCmkkRKQiBAEMzTTG3WiY/LWkSoOIeFMA1USgHvfBNYcTMwe42sNb71dVNyToNWfWQTE8A73wIqZgGX/ClQUuV+riUv5qWMdAOlM+R8nXB6YanDPpJAGlqt/xd6X/MA5gGYSKRQWRxwvx6NHLVbdSjvQXBz6TujdwKZYNeROz0zSu1/T3dxFJu3uS7pkO7ORZY65I7C7FqUBrG3ZRBH2/2476WpteWKd54Adn4nf+UlxmVvibOvyt+bdnAScVzvVFew0S753o922QRA8mAqoaYhuZRyF76BImveWh6S80pPEtCPe5mETuv5258TiSXRG8lziAVVI3TaHFklw+bueLqdqcZcL/M9t6t3IAsat6tropVY0sN99FOHPOAquAkhCwghrxNCThBCjhFC/irntWJuusE25xVHf+M8m0pcY0lkiKFTUjQg9cXm2ZR5ZoRYRDmPtRdzhrPm83h2YLOd8Y2vyWYSJwJBb14l3DRsgKTcPFttQS5Hgz7SPoxTXQ4eTbkkl/tJpoMmNNnOm6OV23XkmZoo2LI8joqGJxLY3zqEvgjTltNsRr2RGE505GeBn5c7lQTw15TS1QAuA/BZQsjFua1Wui+gh6FpOhrXcJu+nViuScZ9vpAOgts2MJDpHhRXeizKdF6XyT4+MWQcgnu9jqoGhzIlZ8HMvuyeRlbZ1bgdyZWJz0f7yLgrS+t2MfUzz68MtsjmOJWF2/S/M71fiagPM5bMeFw2N43G0jQ7MYxOJNAx7K/8dHEV3JTSTkrpfuXvUQAnAMzLaa3sGmYyLm+q4LYbTt8ZedWbukoyU9vX/mfddxpxIxbx1mm89W/ynooORBMpcAPbm2O18F4E7nnq/WbSc0N9mjV1k6a559+NXipeO8lQiTVvHtzJScKc60Hz9hLzwkT9gE1gpsng3JvOroqTCuNUoDLUCvSe0L+b45/XrdT/zlRwZ22P1vS7O1KINm5CyGIAGwFYQrIRQh4khOwlhOzt7c1wVxG7ix9slnvsptedz1cj00XUCGuT7FUSHZYXYrS84+88TkMeGIvjyR2NONLO6ZQ0we2icVviVKibE7gs6LCYWEyCO2XWWrzeayf3NJ5gZn+20bjTKotPzfBxrSaTTvM7QKNL+88ivu6UnQ91n8N+noY2XhB3OG0ZkU/J4llwE0IqAPwKwBcopRZDDqX0KUrpFkrplvr6+sxq1fI2sP8/OD+YNEq739Uen+sXKqehlOJ09yhSUh5ut2pDZjekPfQcf/m3CwNjst36XN+YrN2ynZjZVGKrcdsIbrudY8zpVNzCinp9AZzSsZo/d1KLMzKIj/PDx/qpE7cuPtOrzz3b+Ig3PwlqirdJUw3TKG+Kk69L8OR/Qwgpgiy0f0Ip/XVuqwRXU4EFOw2SF8JT+buxN4LfHu7EZUtrcfmy2jQrmgaqVutl9R4HQ8Mw+2BbAgXxBDcnEp+dacpNyLlt2OvZFsvpTLzmZzCVMNhOiPoXZZMi/JzwMweSsVeJnwxs3AH7p5jGndFNKxBTCSGEAPghgBOU0q+7pc8pmiCx2WrJPFTnhvCU/1a9VcayMClhqF/nIavGwcYGefsbsh07F7S8q5TnpHGnYNW4bbw1Msarxu30GxM4iKfJ+VVxKAVCYX/nuEDSfFklSrGzqd//hFYevUlSFfOwa+GD3hJrI6M036lC0bgzMJUUko17O4D7AFxHCDmo/Lslx/VyZvg88Nbj3rVW9ma27jIIAL9tZSyWxH8f7bIG7QGAnhNy0HqLLZspRErZN4xMHjp7rpONm7dqLVeCwPP1UDn2hlseXDcy4t8LZ+ZiH+kNJWWVpGKm6xjKneD2pzHzzvdJpNdbmAqNAtS4eRtRe8H/vHfauJpKKKVvo2DuqNKM1KA24wNAzRKH5BQ4/Qegm9noNtIDnN8NGkjPo/GtM3040TmCBTWlWNNQbfwxOWGsn139nerrFadFLbzWEwjqi2EsphK7vDIdZ/uwcTvdMycbd9se93Cf6dSpkCnYa6D+523YtlooS82bfToRTAIFcqc8Ym6wrj0jBdr3Ww8no5pIssi4kQ7g6K/t4ykoZ/IWY9jiFEaV3eLLx+48QacJKp6pRP1b4plK7GzHmeJRwLTL7nY7m/rR3G8KFMZOpvLq6WsijM3Tf/KCEZdeNe7lN2ZeVM47CQJs/Biw7s8Kx1SSNrSgTCWTgqchntvsut1L7dTwj70A9J6yFaJmV2kjNg0v4RD2kt1Ud9eT9unMJZk37o0zrnk8U4l6jKtx29yPSLfn+nBJw9bZOWzyUTasnMzG0uR0XqyCEdkyTu23crb8WVwJzN+M5PqPZViWn1Egk7Zho/fzZiwAapehYAb2GUDINBfcfEw3xW05eetO/nFJ0jQJi+asCT3nB8AX3Mw5cUZz1DwcsvdQCUwvr+FecCqnBSbiCD+uICA+bZUcfAhu+46aMZWoW9BlMqTOJKSp7WkFJNiDYeDKR4DLPiN/z5IW2zTvdg+p2HkWvdzeSAwHWgf5z5g3KixwKCgmEtb3iDL/55rCFdxert/NVGK3vyQrqCxtxXl3GM+P5eBPvabMPuoLwO60rfq0c71KbDS4TF8kRnCPx5No6R+zF9C2cpv5wW3PQU94dD00nVHwVCuLmSkFikq0CHc0Qy1WvfZEqMzfiUzn2tgTQTQp2bxSBTg56ULHUBQHzw9hLG5VTPJ1BQUruLnP2GLjTjOAE03Zv4xqg7MxbzgFHDLA2wHcSdPzqQU62tJ4vtpsRLneU8xxG6+ModbMdzFnBPeprlF0DEcRT/I7CXtllvNLJl4wGWjcdmfx3AGb+8fyFrcCGz8KzN+ao8wZF1zXpOxz8SjCJkPjDoURzSAi4GhUNlPGeG1Z2Lh5BznmgfgYs7Tda+YSI4DZ4xQYV+Kg7H9W9nQwPQhVY/Rl43aDu9mBiYEm03U6NBApZe14VFNJx0E5bIBKIMQXhNlwEWTMMmpt/bfrDEwbHI7u+AWa++1XNI5GE9YJObfiOfXrHI6ipd9hbqNytu3EHwXlanO2BMPQ256pvebTU4M1wynVGY8nvY9Y8lTXs8s/iQOtQxgcz27kzjTC4KRNwQpu7tM2T06l4sDeZ9LI20Yonfq98XvvSYudnCvwM2Xv0+6Lcg79HNjzQ7YmDompIQJbc/8YDp1RfN5jJvNRIAgpJWFnI7PDiwsTiZS3UAGMxq026HhKQteINUiSfTA/zg8Z7JQyGk3iZCd/4nkslsTRjhG0DhoFrtbpZNNo4tAZ9Y7GcLhtGOcHx7GzqR+jMbdNgYmtxMjYVKJVMz2N+xgT5tT97uVH6nWPyCbWrC6+U0h3MZZfClZwc18Ss8BNxZ13d7eDcYkjbIPvPGRMd/oPwEATxuJJpLKh9dnlYWuSsG/IjqaS8lmGr53DUT2uOceE0j0yjl1N/Wjuc9n0VeHg+SGc7PLgusgKbuVaznSP4lzfmGVyx3Fykv0tEJR398kAu5dLXVQ1HvM3jE5X3NhdsSpQuhQPm6FxF8FNCACCkWhCW9Sj/5bZK+6r1RvmjuS7YujgucoYf0Izl6jvfKZv9GRa5AtWcHMxa4RusTLsoJJRSI122ewWAyQkisNtw2jskYfXdv7fiZSEPS0DitbqJISyhZOpxEGTYF+uJVcBJAApJQsqycYsxP6tfo5EPWgr3XrsFF3jVvLx2hEOnDNOShZX2af1Sh69Shy1dOWn8XAdsPl+bas4uzZmb5cliMSTONYxguOmQP6ZatyGMBNuGDyW5PSGvTkybP9j8SR3tGbmZNcIjtuMquSaWa8lnpSws6kfIxlo4RRicpIPT+MGMBpL4GjHsEXw2N5GKqF1YFxPsfcZoIXvOqiubFcnJFSBYy7qaPswznRF0D5oPyF1vGMYZ3sy2y3FU8Nw2/NPZf4WRSPLfdAit3p7lqUuO4H3j8Us4Qh2NfWjnVlWXpqwiRyoOhTZ1dFjFQ3neDiJkoBxMwnOBPjwRAIHWofQO8pXVtQBVcRkVvFT51gyhf4xY/6+rplVGBRN3729ete4D7cNy1ExXRgcT2B4wn6UQgLWB62mV80oaTPtJye5wyq+4D7XO4bRaBITXrc5YzJ3G53p2g/R93IEYB6Rqp2G02PrHon625+w8TV5VMG5GYRKiKck9IxyNBDPqwmJPGlpZ9um6gdh/k4PdXgaD5bh6GwvPsF2Gdk32XhKwunuCE53GztHCqB1YFzT+IqkPHl7wHq/kpKEAWVSzHIvTW2I3WpT3aklwtMIib2N24+p5FjHCE53R/ijIS9mDDa0BCFAUalP80eeTCXKZ05E7HSfnOR7lfA3IfUzf2LOvbL/iGNKKcaYVM6/x+Tg8Ng7DnqtCEajCedJwfb9xg6L2cbrVNcoGnvHEE/JC4qiqt3YzzLwQBBFIy0ojVt9pNWSokXVlt/ShZIA4sEKy93z/BI57E+pChzWTcvwnFwKMQyhiyuAxVcq+XqtnH2dVM72RHCqaxTjgVLXc70LGPuG78dUorpqsuUlqhd7rAOnTpsfMJTueh/nbfJdSjoQTmei2eIzsLNTmrFhyjMFK7hZOoYm5MlBFxc19aZFEymc6BqxF4hMC6ppf9258DHZBU/bDUyxq6tZU0rlhSUevE3Mk2ISpTjYFXfebDYQAkbamaprBRt8os8PTuDA+SHZDup1xSIhQLAIgcQ41nc979hmqenTL3reBBTEuureq3TMYLLNV91La4CyGsOZ2RgFRxMSuirWYGLpLfrqXa1dp6mIEPtVBdTHK265vBkLMNFwuefzzXVC6QzQgEscO/amzlwMzFqVXnkeiSZSWlgFfpvj3Ekf0SRFrBLmBrQMjKNtcNxWk9ReAEU6tA6MY2g8oe0WwzlD/9NFEEhKTxxNSOgcnkAwGQGhKRT1HQcoxfHOEfx6f7vu9uT4slkFNwVxdksKhoADP9G+/vFYh5IVNWiTqo0umaLONm4DBAi4u9YRg8bvMWtrScrp8rD+dPeoZdgvES/7evjTaTh7aHg4hwILL/NVlt0IzOLEQ4De8uWQQrrGbfFyMdm4DXnbVUkLZKYf6ovE8Kt9bW5VN3B81q2Qll0vfwmXaxo7T0t1hjD/W6pmj0175IZRToMD54fQ1CvbyUejSU2IR0PVODj3z0B4704gCGz5hKf8p2esEofGIQskf6vu7J0AmKG0g+A+2jGMo8wsfXO/3Hk0jBxEdfPvgd6TGFW8K4bGE1gyaB8OcnA8juKkceEHpcpL6/RSBPWg/ylKUTbaDEJTmDl4mJucgvrWuN2aGitYVCFyvnqztzK0otRrlD9jSclgi6aQXx4PGfkql8WrV0PnrKuA2mXoGo3hSPswUl5Oo7L9ety0cIZ7KgnA2O2qEwjGcQ1r43ZEcQc0J363sR/jCb2tn5upmH5AgTlruVlFQ1WgReWW+vNic7jXycPjChUDkM00h84PgS67FphnbVtH2/X1Bxn70yuVGo0lNbNaMlCMaNEM+3Mq58hKVIFQYIJbrw7fxu2s1eiat/w9OMHYbev13aQ72AUWJACJUsQ4rlajHJc3QiWEU+NymfFxFEf7UDt2VpmcpCAgaB+awM4mfSf68XgSJznmEPfpTABRveOIJVJY3vcqVvf8FlWjZy3XraGMTIYnEu6aCjOU7YvEEU+x9mHrXyqJYLnlmAbnbdXECvObodoUkIizx4iWU9h73Iy0NG6lirubhxCJJT3vS3qkbRiH2mzi4ygQAoupiJgUEm1y0vTdIVeAo4oTGE0lE4pg2tU0ANQs5eYkkSB+c6hTFo6UavWMRFOGtsEyHk9i97l+fY6FqT1rxLG0062f0lwg3zjdi9dO9qB1FMAKazjaKGMWzMU+sdSzF4xbRhlXxRMFJbjbh+MYnkhgZ1O/xe2HAq4mAEuDZ28is9ruaAfzcpEAGnsj2N865MnOGkqMoiQ5ognd2ad+guX9rxnSqK6GqveAnQuTJFGEJBf3I8a/XK1eVazLkETW3NUvAKQUKGQzjpM/KyBr3Gxja7apq6YLKn84ClnlJUikJLzXPIDRaMIoy7VJZWPXIHmxXxMClDhoRpZ6O3U/toUAAEIB42vspOlRGIWLdtzUpggIqKIscNxKjGnZTs5QM3N1CUjAeu8Icbhmu5WWJIixeEqZwzGOC2LJFHY29Vt8qXtGY5Co3t7l/ANOxUCiFM8eHUerEhZAHalYFhBxOHjexp0Tpjr4IBvTihTT1FTSMjCBIeXGW1aLUdibSiwzZxzbIGNyYCcQKCGaLdxLRz6/4w+ojrbLL2R8zKaXkDnVNQpKqWWxiuYLbqitO+YS7CYMqZTQJi7HnVwkidXGbRSw8kdP+WrQBZcZ0nkR3KPRBFISRQcTY1sek9iMnLzauOtXAuV1HtKa8vc8cSTfhKB5aOxVY2c7C7ONG7rGrT1/ZsIZ0IWIP1GitHnW1RXENIollvRm5M5TtZfrGjcF0Sbkzw8YV9jadSbMh5KHTiwpoW80hldP+o/5nrCxXUWTKdljp8j/5t+OMV18TDhOz8lJEnDWaewmJ21m41WhNRJNoHkwbjgDAKqi7SBU0hre0EQckVgSiZSEEzaaqvpiNPePg7bu1M4N0KRWNNuQeV2N2kFYFww5w76UFPrEae9oTH/XAHQOjmF/65BWH0dMwilg0PLk/NurNoCGK7T8AW+Cm+2YVO2R2Ag1SqlnjZuGSrCz9k6LGYh3N9N5j1TzQijocPccvB+cylRNJexw3xxb3dyOudkVV7C56oKSSa2WZTigfreNbRKUOxZzqV7mFgyncDqfHMs09X3wZnIz4knjdqn/SOVFvstNl8KxtgOy4LYxh8imEmMDT0kU7zUPMGkUG7fyvXM4ipryMI51jKA9MoLFipJGQDFjogWrev+ADia/0932UeNUWO15T/MA5leXANAFt5nDnGFdUpIQiUm+Z8rZdsOOSNpNm80ORvTvhOiCZCyWxHgiBQKgfyyOmkgc5UnjSxogRLNlapYCQgwjFsDFA0QV3FQ/XS2iNDGEZKDYcj0U8jCdx+nuUVSWhDC3uhQAQUu/HFulbmAMy2dVGjNxwLONW/kMmlZpGk6vmg/0nHQtmnucEMWjSGmvpiGjRePmZcJ2cjahUc2i02y04WIS0OkqkLGUhJf2tWGBTRjfxOJrgBZ50jNbwZ58T6AyZCuK4rQ0lSAQsNVC5bk/44OxTCiqgoJNo8yqq4ImKUkgNIWSpPf9He1IpqhmmwvShFY42/Z5ds+m3jEc7xzBGSX+iVdHCc+uzjZeJYfbh3G2J4IzPREMjMXxn7ta8D8n+w1pAgFgX8sg9rUMGo6/cmYYwxN6yFMvGrfhEHONUqAIbdWbLRdk53PcPxaXPXqUvFU7qNsk1fBEwmaylQ819DRAMOjwepiv0TB64OSpnqZovSlJt5XEQ/yJXt3Gzam5oXxmstOwKpgYJoMN+jjnGZ2tvcZ4DUznwtXCFSIzrRtvnx+I4vzAuPaszs280nC2VL0IgPx+PvVmEzdfVzZ8BLjkTjmfZIpRvOyf9HhRDfc4t+0t2Kr9akzLh9JpayohzuYDk8YdMGsHWj7sMeUlV7wn9jQPIkAlBLIRbxrQXMWCkvdJEe6yZcimk57RqOEFGRiLa7GZnWy07VXyHn/j8ZThPrnZ7WMpowBk7ynrB982RnCqe1S3q7to3PGUPqIgnAU3bVWbcGDeR0Eple8HdR6uah0Fq1HaPX/I9+p454jB5GUwNXHupXokpQxEQybBbbQE2NfVaUJUPUvWuGXOzv6A6XyjOYOvtZs0bp4nj8lUQs3ad+0yU7nKSInpMMyeP0y1NPrn38CpnunZmE/KRiTAcKXmLWae1EwFwtb0W/8cx2bfxs/LXJ/aZcDMJb6qQ+FrC/GMKCjBTUAQTZjtfUzjpRRHFK0RsDYGSuXZ6TFTWM6e8pXoKV+FY4o3CUEKxMa04a++FBHFdBJUNu8dmUi4CkuzpqheRfvgBBp7xzDICMxT3aM4rLiYOWU7VLoAgOzB4jlmC+T728wE/GcFdxsTMCsRKJVtiMxkFSALwN7RGNTg/z2jUYAEsK9lkNGSOQUTgmiwAo29YzjSPiyv+HRo9cahrLtWo7YidiKLPatHDdakbq7LEAvL/uQBp+GzxYzCr9MZRQvsH4thcCLB2Lj1NMmgshjH1JlIpEivuHk0adK4hybsttEyTUgqz1cCAS75U8TqeCFyifa+scdO2K3w5QhhvZPQP6kpv4yxEf4EFPsaPgq871HjDxWzkFLMdGa4GreS//BEAsksLQDKFgUmuGXtK0V0T4fe8hUAFA1JSiESS6I3Ir90vePWl+VQ27BBox2ZSKKp9mpIgSLNPl2cjKAi3puVOqtDcdWtzy7cKcdZw/KraqOzE/yOgxGmBD+2c7Ntj+O1B0AWLoahrqIBd41EcbY3ogX/b+wdA0gAp+t0LYwxcZvqTLVn6Wb2oNA1brv74KZRH2Z8rJMSlVdHMos91DNi4ZnyH2YXO4PcsX912KInErJr5unuCF5Ibkf3nKsRD1UgJVFX05fEKi1m2P1UCcEfj1t3gSJmTZwQrZ1IFOgYieO3JwYsEQHla6CWa7GDKz+10YLxU8WuhWqpwuwiIJdKrLgJiYYtxvwDRdriHtmzyzmmud3EeDSZws7GPrx+qhcpSh0tApT5P9cUmOAG4sFyHJ57l3YspbirUQCQEhgvkl+qiUQKTUPmVWrWm6YKBjMzJ1oN39VVZeni5o/t6KEAeeisDvf0qJNm25q3RuGv6Rjr1cK4erGNNBUI40zd9bpwC8leDapG29ir+39TEjDYwMfjKUxwtuJi3wGJwrIQxZCWmDU4Z9wEDiEAll0LFBsnN/c33KtNklqXeTOZkqBtLBzLy63ZsiswXLNOS3PGEuLXKCwdPR1SCVdzg8laYchPgm4KG1QmutWoiXo6/miFANxdiAwdu+rhSHTzy8mii7GzqR8SpZCoy3O89EHgcnmn+r5RFzPkvE2Q6q12dpWO4SiOdYxY3BiNdbeKQkp118eukSj2tgxh9znZGWKgdDEnj2lrKqEYKpmPWEh/mdThIqUAUnEkAyWQSAgt/WNIEeOwJ5N5AT+zyjwZrE1O2lDkMNFFCLD73IAWbyRldOvV6+hR4/aDo13ZVGB/2TJdE1Ns3DzZIYEYXoRILImJhPMoQKIUAS8xVog+wWbWzjjmWFs0k1DNUmD9h4FN92Fi9ibEg+WMN4zxmaWKmSX5JGC7EMRctjYvAKIJ05REbf2R9XlGjuA03G/nzsz8bIwTygTFIdlnWx3thFPj2m/myUkLy67jH1d4r1me3NYENwJ4I7UW8WC5PNowdypaZ6VQVAKUyPebt6oZkCf+25RV0JTJ0DxBqI5keTG6zfMJOgQjEwn0MWGYE8yo8HT9jYjXrDScYbHj55CCEty84a2qcQ9PJHC6uRWUBEAJweB4AkllAqIs7N9v01K2D8EX5KxSc9W4zeoPg3m3aHWbNMuONE6CO213JifBbT02seUvcWju3Y45Sgj4nnySZ+RNLyh3kY1Xjdta+WhI3z3HkEvNEqB6PsYWXqPVO5pI4eB50/J1Ns/KOUb7OeUnA4yCW7V5v3aSs8G11ikGtPTs+fJvTFtXn7nd5KTZPEFCUK88JQHFIdlnW7XfdlauVU/Uaqxdi8k7BdXzrfUHgNKZSj3VuvPbJbXRuJ0mjc38/mgXfrm3DcmUBKmozPYM9dXjrcpsnnmFoZ6GqwyoswRGVUELf2u+59Tgt5NTCktwg1r9SJkq9g9HlMas2OkUra+uQta8j/RmsO2Qg+Azr8TimT2CLoI76CC4zagakLmdmXc3YUkGSjznz+JXS0iFKzChmKtiyRRXjPKP8mG9LAImy2f/invw7LBsWhgpbpDtlIyNu3P+LcD6e5CiwP7WQc23/WTDnULGpZMAACAASURBVNxOrrtCH07zLlvX+qisnVlc/pT6rb0bKDUuu2fjdJiFj9nNUGWwdCFGi9nJUdVKqk4gGg4DACYqGU8HYvnD2GtYNG7dE0jLmyga97JrkQiWaeXrnY0xMwqCA8seMpqYFNoGx5l7xus0mO+Ws5Xj/MkQE3LefWNKREyJQgpX4MSsW7h5qqOrFMe0lVAmhqMh6/UYF0npz0Vd2GftfOxmc7JPYQluiRo030W1ZRaBGqQJnKy/Gf1lSzU7q24Tdr6cqpIQasplLX3eDGMgeyeNe1yZrIorgZV4L73qVWKH2bXMCW0FGNNwRqIJdHG2VeqoWo+mmqsQD1Xg3MwrPYZGZfEnuGOMyeNoO98XXqLEt92KmmzckVgS/3WsH33ly7Fn3scxWLpAsckSdCuxMsaqlwE1SxFNpBBLytvRna29BrGiKu31GSjTBR1rKuAJCKel6vIxe3MPG0XSdvLU1D5P1d+MY7M/ZJuOZ2uGIb616tljFwrC7KUShLpKUyIhbUQnx03hdDacyTi9fVnbjXFfYEVbpRKCAWJxz7PTuHmTf24tKSnJ9Uxom1OYNW65rCTHNDVQugQn629CZ+U6y2/sBuGyw4Ocj7qD0574Qs1VFwAoyYqvjCcKS3ADUC/9yOw7MGPDbYiEjcPlotQERovn4EzdDVpjUB+M6hJnR/+6B3HRlvdjwcxSq+B20LhVgaJqtQHO4zGbSswatpOpxEyKyq517P6CgwusvrIA0F2xGj0V8vLr7sqLkfCpefu1jbOxT+IpSXPfZIViigLmZdx2HGm4C+1VGyFRajCVHGkf1jToVLAEhFIQIofiZBcHRRMpDIzrL89QyUKkSJEmtLoYLZs1M3CH5FT/fPlwh0VgSGFFKwvxXcoAoKNyndXG7bUPM01KtgYXsoflvwMh9I7G5BGBEuB/JGozSWo6LJGg1pZpoMikVeulTISU0cTsNcx9kj/Hw8ro07BCk9OGtCX4EmrLwwbBTWHVuNVO02kzKDY1wGjSKQrZU5XflgMEOFl/s0EQs/UcKl3k7NJIKSbiKcsEeWO8Bs8UfVhP7zbhmkVcBTch5GlCSA8h5GiuK0OZp7Z13RqULd6K923ZYPD4iDPhRFW3wejsjTjQ8BGcm7nd2f5aXIlg/XLMn1lmEaxO2rra2BNBWSiGQ9a05QnjCsTq0iLMqdaFaFCzl7mTUnaWb2OWspNi/uo6c70D8ObDvb/hXvl8n6YSdtedU3U3Yn9wLfY33IvhknnacQmEu4JspHgOBksXGo7FimsxXNLgaXKSgOBMzxjzHfivgx1o6tVDFUgkCEqCSFYtVNJQrcNlO5f+VCn2Nhu3a2P3DeWF9D0c3gR68e3ADFlBOD7rVkua1pmXWecm1E+P8xDqMx0L12PXwgfRuOohw29neyM43jkCuvp2SJs/iXeadVv8RDyF3U39cuwX8zNgyj/RE8WLB9oNwkklESpH5PK/Aeau1ybKU4FinKj/AE7W3ait/nS+Bl3jDhCClGkkaD8q4XWo/MSq10/H8IRhdGq2MxNCMFS6kKtx20KIIU+5bsTwCSh2880PAGv+hFt2rvDSkn4E4OYc1wOA/NAoCN5/8WxsXCibJ1bNqcIdl63AfEVDPlt7rZZenbgk4XLEQpWgJIiJ0AzN99tMKKBMqtRag8E4CTBVe1QFQGmRdTK0ODlqOgeYW6ULblbjVl0ax8L8CHe8nXu8LqX15JkB2TUNAAZKl6K3fLnBk8eJk126SWCwbDHaqzchHqowmDn2tgxyG/Dx2bfjVL2xKRFCIJEgkimq3WfzZgSA4nE0Hsf5wShzrhynxeDmpggInimEFdyvlX8Qb53pwzf+eBr7WgZAKc+vWs83FqrEibEK/PhsmWb7HClpMJ8AgDM5qXUI3jrJlElAsCOIWNUiLd3O5hE8sXsY1aXsZhgU7zb2o3sk5rgQ7L3zagdIbGwR8oQnK/yHSxcgpSgv7pPP8u8BpECIcSUjt1PRfuMcsylB1bj/+2iXfJ5d4CwlUy86SvdoTDPFWZdDWL14CAhQNReYtQo9NZtwfoG1M88FroKbUvomAOtOsrmAUnnCutToI1oWCqCqtAgDZUsQD1Xgknmym1CKKKYS1n5MCBprr9GE91yO1osiozlh6+KZKCmytw33lcmCfqR4DkqLgqirtB8qqwQIUMIIeFbD76jagF0LH0RP+UqEOROdvNlv3sQKYBUG2lDYo5CQAiE01l5rGMkwueFAgzwULFU8d9jVlMaUOvI8nX4N7VUbcbru/fZ1ICH0j8W1Toe3GUEkXAcKYMiksVsK5y4TV4bhrDknqLeBN0/3adEU5eTGs8eLZuJAw0cgBYowOJ5Ax9CEwYxlxk5DdH0mC+XQuUlisgezZoxqfZm6ep94C65SlDqGSGAXuUWTEiYSpm3kqLyxsdnjCVBvt1eNmyoat9FUYlc1iVK0DY5zO29LPRhJ3Dk8gViwEpQEcL56qyGdXx24qW8MWHotY86x73glKm/Svb91EOfHw4iXzPJZWnpkzcZNCHmQELKXELK3tzf9VYkUxOqBQSWUFwcRCASwoKYMN6yWb46qcQc47nmNSsCc2nK9wWgC1xSDOhQIYKx0HvrL+LuC9JdfhNZ1n8dE0UwsqCn15CFSo3i6qEnLi0MgABbUlKGvfLmWbvMiPeiNk1tjysaX1axGqHZi/5OUOupIIBqqQkxxoSvhmIdYGqpZYUM0zxMA6KlYZZgkZIknJa2uTgtwxsN12DHrftt8zGgaHeOiZRd9EAD6IzHPtuhAgGhx41kiYbldDmzVl1oPl8x3zVf7ecn7gCs+hwnGbREwjh6McWXkT54JQJKoc+gF08rG4+3GzrIvEsNLhzqw3xRsjDmV+31kIm5Yidk6YysIMfmQU/uQxhIFfrm3Db/Yc15PbncdjOnn3cZ+SIEQdi/4lKWNsHMXnimvtZhKdIXbeAOaesfwxqnsrMT2StYEN6X0KUrpFkrplvr6+nRzAcCZyKMSQoEAPrB2Hu7aPF/raVWtgedXrcIK2WX1SgxjZWae3eMwRYI4U8efAARkwatPhvLTqIJ3cW0ZasqMWlMoQHDZ0lrUVhgFHMv6+fY7u9hFkLNo3B6ElBvnZm5X8tDzLnXxlZ9RadynkBXcbpqmtlLRJfCXZNoxXFc0eb59nPNhfw0BxiygT8fx6z0eS2LItJjj4Nw/w7HZtyrl6PSVMYGcbMbqE/EUBsbiONA6CBqusAhcVnPmade8LcV+vb8dzf2yOYQ16UyY91VU3yWTVDvS7rwFm9217GsZxOnuiGbP76jagM6Nj1jdfG2EqNoxDTJhi+0WAVkEqw3m5+qVvS1mQwNvNGesX77W4BRWPG4KgKdxVyi+rvXGlUq6xm3/QmqTgmyWynJdTTAFQrYPdfPWy7Fh1TK8cqJbXvCTMg7RWNbPn4FYMoXikF4fNaVqjwsS3aboNjGYDJQgGQijJDmCibK5ODTnLqzvet6UysatykZwj1avQFfIZvGEhvVusNfEIxGeyXzz13rViasA/PnhjzkMp/XtMSi0oa7TPIadqRfWCaeXD3da0iSCZdpqUlaOSiRo8YXm8dKhDgyMxVFTHtYEv1ouq2UnUhLO1F6HRLBME7byhLHRdVCiFPGk/Dc7iXp09h0ISKxQVD5NpriuYeP2ZCyqqeR45whmKaPYU3U3ojLejZrxZiVf/VrNnrA8MaoKcm4wJ5sHE/couHWN25/oHhhLYi6AhhmlOA77jjyTFdvpUlDugESZnLT4PFfUA1d90SC4P3fdRSguVtzzHF5IVXAbbm5I3fwgJXtXXP5ZrVd+b/4nsXvBJ3Gg4SPYteBTCK/7U03b1GzqTHGxYAViQX03EouAI8aOoygYwNp5qqavZ0RBgKu/pH0/MudOHJp7F47OvgNH5tyJsVgSE2FrLOEbLp6N+y5fZDnOTtA21lyt/d2/8Gb0lzvv1KGaLigIVs6RJy3jNgHxVSJzLtXjylgWUWVH4zajmggiYesIT+K+7XI9eCapANGj16XzIrLePWwMk1iowlMMc9XNkoAwu8qrQliXXSc6R9FffhFGSho0jdPt2bCkAmEkOKM3P9ecSMlxdYYnEjjTEwGl8kR164xt0GuqP3NL+GVqb8bhBRtTj/SUr8SBho9ox2dXyu+x28pp9f7bBYBzo7ZcNa1aJyfl/NPKNiO8uAP+DMBOACsJIW2EkD/PVWWosnSX6/Ns2mKrKBgAgnJvL9kIho23P4zAxdYFDqofblBKyN4V4TJsWCAPITcsqcdnr18le1mYY1UoQ3Wi6RzAxu03YfXcCmxayDdz8DxQNi9SlgWb682MHMbCdUgEy5AMlmAsXIe9SuyHRbXGHc5Xz63WVo6y9DJ29D5GUPPqA6U2AHCmlo1BQVCvaFTVZdagQiqVJSFIwWI01lyjnWfAZWShCjS/MdJVAamXq6MtVoQu9dT7XVTCMTs5aNxeYF14Vbk9p7oEkeI56F52F3Yt+JSj+UrVNItCuuBWPYns7MGqcmq3+7o35Iq3DfI3ieYRiSXxByYaIVs/dYKZNWuxI+jTddcjWXMRkkV8098oJ1Y9BbBn3sfRVHOVwftJbVaO0TDD5doIprHHuMMVL1BUb/kKnK/ebDgWChjjlJvfW9bMZDcazzauphJK6Ufc0mSLPfM/AcD78vCa2lr09qzARTMXAuetkb9K6hVN9OhprJjNuLsVya6FQapPMF2+tBaXL621vfEUACUh7ZEtqy9HRXEIZaVFqK4sAWyWo6+aU4nRWEp7+IBVA9m4cAZSHp33G6pL0VBdip1N/bZpCHSt+aL6Cow0VAPKXI9dlEJV25WYawQIqkuL8OFLF6C2vBjHO/grJbcsrjFoSuaG7UXj7i9bhp6KlVjd8zvHtCyqlmq2fQNGLUj1backiKOzP4TF8+YAfUZheKJzRFsVqoYF1mOGuM8XmDXu8aIaBIjcJl88F3S1HiUZ7TkpWTVUrs2e+te4rbi3O0KsWuXp7lGo20ezP6lb+LF+2+z7HCmeg9iqbZBsvHLMbUy9dtYLSEWxBNkG6wIAXPaXaDzSAfQay9u14FPgXXsjswuQ2jOoiqRdO/a7d2w2KCwbt4LXVYY3rmlA76L7UD+jFNevHgIBwSsnrLtGf/bai4x5Kho3OzT32lOOz7sCNUP7MGvFpUB0BJi9BmjdaUxUtxzoOwNAHhnUzFsCDLboZamxgRQNuyQU1BrJxXOr0Dk8ge0X1eFA66DtLu015WF5IRAnvGaAyDEYRotno74yiRmlut00ZDORu3lhNU6d7QUlAS14V6S4HgECZa9H4OqV9dzZc3koqtuSLb97EA5n6q53TWOG3TDiyOw/Mfymd4SUWUBVikSwDKS4CoBx8o1dyq8KwmioGu1VG9FTYZxb4aNfY0qiODbrVlQ3BACfm5j/en87lrKjBeimEjXEgxlZ405P01O1SKeFI6EAsRWOY+E6BA0atyq49XZpXexmjTpoWSuknLJLCaMKzo5jbiZuSilIsAiJQBEAU0fhshjqLKOd65Zb/r0yLv7JDwUpuL1q3OFQQFu6vm7+DCRTEldwW1Y6Bt39sD95Jd/tTHtoxZXAqg/Kf6utbtUt8iayJVVAxwHg7Kvy8dW3A4lxYM8PAegatxSwCt3q0iLZj31JDbYsmome0Rh+9l6rJd3K2ZXAtf+LX0dCQEkQwU33Aatng57TZ8fDIf69LQurw8EAYqEqHJn9JxgPz8QlTIe2ak6lrdsTZdzuHP3FcsRYsdHOzfNEUGNZBF1io2sQgvMztuKD6+bit5wJSXNalZREkQqWIFVRC3Tbj4zc89JNJW2rP4nD5/nmjHhSguqrlIuVe0XBABIpqwJxZM6diIYqsZYd3WimEkZwczxKzILarLW6RbYeHI+jMx4FbHa0UcshxF3A83jpUAfUblJX+ozPReWtM33+C8iQgpqcVEnXThQKBrBsFl8rMcAZWpvRBKiat2XJOltH5UEWlQPltbIWXMLEbg4WARWzZBNNuFyPycyGolxp3HcQkP2FK0q89a3r5ldjab0eBOuz116Ea1fNhnnHGDuNO8CYSgBZEFISMph12A71sqXGiIkU0N5Gq4bt/3k6bkbsQiSW5O+oo1xLkYP7KA82ro0XpeLdRllYewksVlHMMfMwi1cAYCyWxO72BH8fRcgdhWr7bTPZZ91RrsdhuF9s48M/Fq5DKlDMjUvOatzm17l1YNyyI41ZcLvd5pNdo3BfBKTknY7kVqivLNYcDrTRicO9mp7ugFnAk8tP0P9lX7tyFmaUFmEO6QCGYHxCapm2bolK2is+L39TjHOpICO4567nnum28EXl+tWzkUhJ+PeOe7BtUZVhlEGpLkptbdyg2LJoJk4ljffGYGFiBJ45SNfSunKcYHJjSWeTB7eY324c7xzFTMgv2ZE5d6IopWurbrsRmWEfdTBAXLdZY9O6UVwUgHWTJqNmZ95DlYcUKMKuhQ96qheLlysJ+AiQptXHYQHY2xwNlbda2E3gus6dyOvguc/L63MsDgYs7oxOdy1fe+AUpMadczxo3GZKw0FccVGdLshYG1lK0R6KGGEWYiZTVIEeCACBgL4goMgo/AAAVcb4F37CwRYFA/jMLVuxeY1pZw7m7wUzjV4peiIJRcEA5tcaRyysxm0Q4ibhV1tRjI9tW6CUZ9W4K0tCnkIFqMRMqwczYSxcJ0eAU/ATqREwvox+NCov5fBcWdXwA0kHM0C2cTKx+BkB83eUcT9fkijqmMVpKUr50fzSgJePV3MsIWw36j4fkC8KSnB/7LJFuH0DP3BPVmEEd8MMnxsQUH15hIakuDCxgnjmIuDi24Ftn7Zo4mXhIDYvmok7Ni+25r/xY8D7/tq+/LAieDc/4L3KTDtbUFOGv7xmmSWN2hivXjnHcJzVttgXmLcVm8UUqJZPgJvWzMmqLqJWhY1Fw6Ku3ExwvBH8bGrBlgVY7bVOOK0vUOEJ9/aq9ThTex36y6zPKfvw7bYsAQJ85FJOjBgOh+be7RiXxo6+SBxVpUVYPltWHChlfbrt7mP6phIvzwYgpnTu92pamkrqK4s1v+F0CSsCxVG7UyZOLltSg22bnWN4W7DZzQQAUGTSZmev4WZBCMFVK2zCAgSCFkH/hRuW45uvyF4q2HQ/MNQiRyTziERlhzZ1t48Sji+36nlRVFSEypKQFtbUTsYZTDHaH/Y27gU1ZVnVU4KEIEmp7V6e56u3YLhkHiLFcyy/+RXcLN5eeBkvSblmCBJwXSTlxnWrZvG3RzPhxaskQAhmOPjxs0SLZiBqWlbv9Zb1jsY0DyYAeHJHI6rrP4BoUTU3vZsHrfqqshp3bUUY/ZE4x/zBR6670q61e+WQ3lu2GVNQGnc2uHbVLFy5vA4f2+agIQT0Xbz92+8ctACOa142MAxVS2fY2sPtoACOzb4dvSvvtS9D8wgJYgYTZ8VOUBVx7cTGexNWJ3U8TID5RfUMsbNXUxLEcAl/aT97TavnegtnC8gC30978XK5fs02XuF1znzcy/czysgEQohFURguXZC22UzfnEF/EKvmyHl56YDHi2ohldUBy67HR7ctxEjtBgDARIjfkeSTC05wlxQFsXVxjbNdLpOGqMkmJo+NH3Xd9Tob+NH2WCilGC2eg8UNuvZ5+4YGrGlgNs9VTUCBIG5dp2vzdkUGAwTXrJRHDUtqy9WC5A9FGATX/SlGiudCFQ7qrUv3Ogzlqx4ijOpU6eCBYxdi105j5xEKWgVL3YLMNONcycTyYp9eOU6dDPF3n8zMrvJmjgwFCGbbmL746DdvVlWx5flTKq9IZXdqV5+9l1GXFAhhZO0DwIwFmFVVgqK5F2PXwgeRDHLmptQa5UnlvuAEd+7haNwzFgILt+W01PuvWIxPvW9JWufqAlM/tqy+Ajeu0QW5ttUYCaKkKIgqxRXSVuMOBLBx4Uw88v4VmKmFzjW+/YOli3F89m1aa3ayMrlxkcnNU12YxAoUu7rWVYQNdn32pfXSiahlXLW83vLC9y65E7sXfNI1Dzv6Rq3hYb3gVm2emyEPdsJtcV0ZvnDDcly6xLjaJRSQA7/91fXLeVk4Eg4FUFEc4u4aZakLpdi4YAaKi+zTblrEjzrJGxVIlOLbr5011kd5ll4Hf2wcFK/eRPlACG6/ZCJ9MqCmPIxyjy+jmUsX12BpfTlWz7UOObctrZGFEaNxA3q3ZCfYuCYD5t7MrS7BpoUzTT/Lv3sVKixmwa0SMghh6+/3bF2Au7cY5zHYdHZCwrzt3CPvX4FL5lVbRnKUBLSogGa8XKfXZmQ2fbhFaywLexTcJIj6ymJIJIDZlSUghFjqpHo2peMW+NlrvY9IKGRzyaxKe63bbjI6GDBPJBpXw6pUlcr3xctGDYAxnLE3wS3cAXPLrNXAcv+z34427nRYdAUwZ2128rKhvDiED22Yx7V7XrGsDp+/fjmw4maguMLiKunvXaUoDgWwuK4CH750IarLilAWDmL+zFLlV5kPrJ2D7Rfxt23j8cF1c+23umK0fJ5gaZhRarlu9gW/dDFnLTWA61fxdzLhrQK0Q71uM+zEuVdXu5hpI40SB60UkDXd+y5fxDUfsUX2li9HrOFStFVv0epivkYvdvjFdWWa6cwPq+bocwyqXHQqju/lIQtu86083W3cThAAqkrkkaRjfBMGtgO8wkObFaaSXLPmDmD+Fv/nZVvjXno1sDo/+9Q5MmctcMXD2nWpl+drPpECmxbOxPoFuqb96auXaRrvSiXQ18yyMC6Z523Cae28aqyYXWlbj/+/vXONkeOqEvB3+jHdPe/ume7xuD2eh8fvtz3jeJzY8SNZQrJ5sArGBEICWdBqg3hopRURv/i50mrZXQkhELv8WK14I4iyiIC8UX7sj0B4CDAh4CTgmAQyIQ4hdvDG48uPekxVdVV1zXh6umf6fNJoum5Xd906dfvUqXPPPcdb/aWeEgz6N/s7s5Fx8lE+0JoaH3VCw8KeWI5GRRTFEDx/xyoNnyS2GOzO1Z2kNJLmteEbmEt1uOeSC3wmieLO2K6zheJdeOM8kSVJ0xwkm07VPJE6K1i9OK6bwxvrK+G+QpZOjyyq/QU3zXGzaalwwJVBc1wlK4P4p5GZDQPsGy2Sz6ZDU3Ee21Lhe5fvIjfnTfDj/65qf4Etwz1kUikePfO70FJeUbzvhnEuXb7i1lGMq/odqbiDCZPsr3DCzLxE3UhGSvNho/VG0U1bh0Lz7wx0d8DvnWMEl4vPf2uY0o1K6OR8zc5qH1eN4flXLvHs7MVEK02Dp9pbyPLaG+EZMx3KPTmObi4zd9Xw3MvzK1vjflq+8/HsmM+mObG1wmcefzb2mCkR3n/EKlFYL8fIvTOjNde7nrtEwwFbFbPErpIWpct+RAxaP7Gz8e7TSPiwEhHXAsymU9x3aMz3fl8hy8VchVc8tT+d4zn5MqrFArvW9bvKZM4Y9qzvZ2qsyLYQH76X7lyGSm/ezS2+fiBiFal93P2jRaYDrpSaogD2/+1re3nb3qrb7lXOcdRLCTpSinC32KsMwxSz9yEiycpb55ycnqRTwr71RXcMROW38YoiKJdT0yOcnI5fI3FwYoCefJYb7ScQRxbPzkbnBo/ys6dTyfz6Se2t68ZLoZE0zUjhGoZa3AvFmcRb5Rb3bbuGeWb29ZoCCu8/PMGbERXnWbPTWhy0/mD4+wGCj/kpgb/aPkRnR4YfnbvAb/5wyVXck5Vubt425PpEHUUxd9VwbLPljzbG8MNzr9a19HrzWe4/NBZbRzMVsUgq+Ft2FGipK8fY4HxxAK8Sj6OeHvBGY5ycHqEjnaIjk6Inl+HIpkHy2TTfOeO3yL01WONcKTuqfZS6sq5vOdgX52viXCUntlY4/dRL7nU6OT3CC6++QVcu43Nd3LR1iG/91J9h0fmMo4zryeKuvVXSIpyp3M7AJb9lHff0FHbMekwOJUhWF8JyFVJQi3uhdNpZ8XJLl0ujFenKZdgVUry40JF2J3hqyOZh593WJGcCwiya7Wv7GB/scpNYOfuICDuqfa4F6VjgHZ7vEBF6EkasFLs6XOv/3plRHgiEWkZG0wTatw33cu/MKOODXYH9rP/13Az1LDivjKr9Bco9OfoKWVIpYf9oybWKvXjFWuyszSjoRNKsKxbYP1ryLOT298XpWpzV7ih95wZR7S/UPKUAbF7Tw1u2+1exOjeE+UIF0bz3+jHGB7vIpIU/5Yf5dckqaD1R7uK68VKiiUPwX7+/PxadUiBq0dHxLRX2jPTXhEbeaE/M/vnN+gnBlgJV3Atl5DrYcw+UFhdTrcyTz6a5ffewG2Xh1WF71xeZGiuyN6Ik3LpigSObyhwLRH/csnMNe9f3M1Hu8iUtimOwO1dzM4qyVK+fHKTS648KCSsd51he75geic2Hc5dtmUctUqk3MRh2g/G2hUXvOKGEbqriiIloZ9trpR7ZVGZscN4NNGc/fUW5U7ysCYTyBSeL425izmre4GSriHBocjBxiKlXnB0xN6Qoy7wnn+XYlkqNy8YxGF4PKb3WCFRxL5RUykogpSwJk5UeukJcFh2ZFIc3liNX7IlYPujgD7k3n+Xo5gp37qly78zYovr0weOTkVbmYHeOexImXHL2d1w5YQz15nnPzCi37/bnnunIpDgYU0rPIaxmxYmtQ+52mAJynlbmlU+4xevk+PDexPaPFjm+ef77nbC6JJEnwQruNZVx7A68fSo8VYG37w6718UvP/+bfVVu860EltDXQRYSs/6hExtdt9tyWdzq41aajmvxNbcbLvWWd4sIBycG3MIVXk4dGOG3F97wtVV685ycHuHL338+9PsGunM1P3jvwpViZ9ZfM9VD0Nq//9CYL9dMkDV9eYqdHcz+6bJ7TEf+QYvXifwJLvZx8sQYM79PkknQ4PV1jpvLpFhXLDBlu1jiQhi9dQyzUAAAB6FJREFU1+Z9N4z7ip0E6StkGbXTMfwPdSoYBVhIfpZ0Sty5iNjCxUuIKm6l6Tj5rhMVwWgRZjYMhLYP9xV8Ge4cqv0FNlS6IwtjxOmJ+6+PdssVOtJ89OZNfP7/nuPVS/GTsgB371/HlTmDMYYNZWsuwj10QPyOEgouV/cqNed1vQVBAJWeHIc3DvLDcxe4eHnOdemIiG91a1KlGTfxCskMgQePTfKpx6xl8XfsWcvDP37B6kMCi/tvD4+7Txy5tHWzSbqw51pRxa00nVUeoONyx+7oXPNLkXgL6kdmZFJCNp3irTtr3QfByck3r9S6SsCv1KbHS2TSKbavrZ8xT0SYGivx8xdf4+LluciAWq+b4u9u3BC5PL2eXz2JIeC9KW0od5NJCVeumkTXo8czL+J8j1NoutGoj1tpGVaOvb30eNXEkU3J0wEkYUd1XqmG+XXdqJLABbjsWNwBN0gwu+KB8dKCcpw7fuqoz3jbCx1pBkImf61j135+Z7WPakSqgSSEJWRLQpIkWkuJKm6lZWi2p+TOPWt5R51FI43Ca+HtHw3PnxJHYPGjj5u3DYW0ej4bEVVSthVmZy48f02Uu6get+1ay42by5G++HqukpPTI+wfLYbehG7aNsQtO2qLZyTl9t1rGR3oXHCxjWspzrEY1FWiNJ35H2BzNfdEeXGLLhbDkU1l3/JpRwTFhJVmFsq7D45GhqoFV046nNhqxSwHQ+1ErGyJi6U7l6nJHOmlnhKs9hdqilV7cc8n4XC657r1nL9wCYDxwa6amPykHBgvRdd0XWJUcStNp01c3D72j/oVl4jwtr3VRZfu2zXSz+NPz/ryR3tJUhYw6BPOplM1sdfLwbVar10daXaP9PlcRHEM9eYTF3uIYyEZL68VVdxK0zm2pUI+m2ZsYHGWzmphbJGWHlhZGeOs2DicBUXXcvyl5Fq9DiLC8S3x7qGVjipupel05zJ1/bBK46j05Hnw2OSyT7BF0Yh8H6cOjPD6n5dnVeNyoIpbUZSWUdoORzeXryk6JMhwXwGaX+N3yVDFrShKy7GYogztRKLbrIjcIiJPi8hZEflYozulKIqiRFNXcYtIGvgU8FZgG/BOEdnW6I4piqIo4SSxuA8AZ40xzxpj/h/4InBnY7ulKIqiRJFEcVcBb1qz83abDxH5gIg8KSJPzs7OLlX/FEVRlABJFHdYbE7NmiRjzGeNMVPGmKlyeeFVrBVFUZRkJFHc5wFvAod1wAuN6Y6iKIpSjySK+/vARhEZF5EO4BTwcGO7pSiKokRRN47bGHNFRD4IPAqkgf80xpxpeM8URVGUUKQRVUdEZBb4zSI/Pgi8vITdWemoPOZRWfhRefhZ6fIYNcYkmiBsiOK+FkTkSWPMVLP70SqoPOZRWfhRefhpJ3m0VoICRVEUpS6quBVFUVYYrai4P9vsDrQYKo95VBZ+VB5+2kYeLefjVhRFUeJpRYtbURRFiUEVt6IoygqjZRR3O+b8FpEREXlMRJ4SkTMi8mG7vSQi3xWRX9n/i3a7iMi/2zL6iYjsa+4ZLD0ikhaRH4nII/b2uIg8YcviS/bqXUQkZ2+ftd8fa2a/G4GI9IvIV0XkF/YYmWnzsfFR+3fyMxH5gojk23V8tITibuOc31eAfzDGbAUOAg/a5/0x4LQxZiNw2t4GSz4b7b8PAJ9e/i43nA8DT3m2/wn4pC2LC8ADdvsDwAVjzCTwSXu/1ca/Ad82xmwBdmPJpS3HhohUgQ8BU8aYHViruE/RruPDGNP0P2AGeNSz/RDwULP71QQ5fBO4GXgaGLbbhoGn7defAd7p2d/dbzX8YSUwOw0cBx7Bykz5MpAJjhOsFAwz9uuMvZ80+xyWUBa9wHPBc2rjseGkly7Z1/sR4C3tOj5awuImYc7v1Yz9KLcXeAIYMsa8CGD/r9i7rXY5/Svwj8BVe3sAeNUY45Tn9p6vKwv7/T/a+68WJoBZ4PO26+hzItJFm44NY8xvgX8GzgEvYl3vH9Cm46NVFHeinN+rFRHpBr4GfMQY81rcriFtq0JOIvLXwEvGmB94m0N2NQneWw1kgH3Ap40xe4GLzLtFwljV8rB9+XcC48BaoAvLPRSkLcZHqyjuts35LSJZLKX938aYr9vNvxeRYfv9YeAlu301y+l64A4R+TVWebzjWBZ4v4g4WSy95+vKwn6/D3hlOTvcYM4D540xT9jbX8VS5O04NgBuAp4zxswaY94Evg4cok3HR6so7rbM+S0iAvwH8JQx5l88bz0M3Ge/vg/L9+20v8eOIDgI/NF5bF7pGGMeMsasM8aMYV3//zXGvAt4DLjb3i0oC0dGd9v7rxqLyhjzO+B5EdlsN50Afk4bjg2bc8BBEem0fzeOPNpyfDTdye6ZfLgV+CXwDPDxZvdnmc75BqzHt58AP7b/bsXyxZ0GfmX/L9n7C1b0zTPAT7Fm2Jt+Hg2Qy1HgEfv1BPA94CzwFSBnt+ft7bP2+xPN7ncD5LAHeNIeH98Aiu08NoBPAL8Afgb8F5Br1/GhS94VRVFWGK3iKlEURVESoopbURRlhaGKW1EUZYWhiltRFGWFoYpbURRlhaGKW1EUZYWhiltRFGWF8ReIEKC1z7pz5QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd283d8ea90>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "losses = np.array(losses)\n",
    "plt.plot(losses.T[0], label='Discriminator', alpha=0.5)\n",
    "plt.plot(losses.T[1], label='Generator', alpha=0.5)\n",
    "plt.title(\"Training Losses\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Generator samples from training\n",
    "\n",
    "View samples of images from the generator, and answer a question about the strengths and weaknesses of your trained models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# helper function for viewing a list of passed in sample images\n",
    "def view_samples(epoch, samples):\n",
    "    fig, axes = plt.subplots(figsize=(16,4), nrows=2, ncols=8, sharey=True, sharex=True)\n",
    "    for ax, img in zip(axes.flatten(), samples[epoch]):\n",
    "        img = img.detach().cpu().numpy()\n",
    "        img = np.transpose(img, (1, 2, 0))\n",
    "        img = ((img + 1)*255 / (2)).astype(np.uint8)\n",
    "        ax.xaxis.set_visible(False)\n",
    "        ax.yaxis.set_visible(False)\n",
    "        im = ax.imshow(img.reshape((32,32,3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load samples from generator, taken while training\n",
    "with open('train_samples.pkl', 'rb') as f:\n",
    "    samples = pkl.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd283cf2390>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = view_samples(-1, samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question: What do you notice about your generated samples and how might you improve this model?\n",
    "When you answer this question, consider the following factors:\n",
    "* The dataset is biased; it is made of \"celebrity\" faces that are mostly white\n",
    "* Model size; larger models have the opportunity to learn more features in a data feature space\n",
    "* Optimization strategy; optimizers and number of epochs affect your final result\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:** (Write your answer in this cell)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "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 \"problem_unittests.py\" files in your submission."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}