dlnd_face_generation.ipynb

{
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   "cell_type": "markdown",
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   "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": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 231
    },
    "colab_type": "code",
    "id": "4QYNcQksdi71",
    "outputId": "37323a09-ccea-4417-b3ac-87fd124d3d74"
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "--2019-05-08 13:03:39--  https://s3.amazonaws.com/video.udacity-data.com/topher/2018/November/5be7eb6f_processed-celeba-small/processed-celeba-small.zip\n",
      "Resolving s3.amazonaws.com (s3.amazonaws.com)... 54.231.33.218\n",
      "Connecting to s3.amazonaws.com (s3.amazonaws.com)|54.231.33.218|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 247694507 (236M) [application/zip]\n",
      "Saving to: ‘processed-celeba-small.zip’\n",
      "\n",
      "processed-celeba-sm 100%[===================>] 236.22M  50.5MB/s    in 4.6s    \n",
      "\n",
      "2019-05-08 13:03:44 (51.1 MB/s) - ‘processed-celeba-small.zip’ saved [247694507/247694507]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# can comment out after executing\n",
    "!wget 'https://s3.amazonaws.com/video.udacity-data.com/topher/2018/November/5be7eb6f_processed-celeba-small/processed-celeba-small.zip'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MmBvPMUhfz7u"
   },
   "outputs": [],
   "source": [
    "!unzip processed-celeba-small.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
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   "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": {
    "colab_type": "text",
    "id": "GQe9tmwDdi8F"
   },
   "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": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "KnJjr_s2di8H"
   },
   "outputs": [],
   "source": [
    "# necessary imports\n",
    "import torch\n",
    "from torchvision import datasets\n",
    "from torchvision import transforms\n",
    "from torch.utils.data import DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
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   "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",
    "    data_transforms = transforms.Compose([\n",
    "        transforms.Resize(image_size),\n",
    "        transforms.CenterCrop(image_size),\n",
    "        transforms.ToTensor()\n",
    "    ])\n",
    "    \n",
    "    dataFolder = datasets.ImageFolder(data_dir, transform = data_transforms)\n",
    "    \n",
    "    dataloader = DataLoader(dataFolder, batch_size = batch_size, shuffle=True)\n",
    "    # TODO: Implement function and return a dataloader\n",
    "    \n",
    "    return dataloader\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-hZZ2D3Tdi8M"
   },
   "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": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZbNKiM0sdi8N"
   },
   "outputs": [],
   "source": [
    "# Define function hyperparameters\n",
    "batch_size = 128\n",
    "img_size = 32\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)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "IKYcOS3edi8P"
   },
   "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": 5,
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    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 271
    },
    "colab_type": "code",
    "id": "mUH9eL5Ydi8Q",
    "outputId": "e9b8d8c0-f804-4234-907d-2446bd4a3a33"
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   "outputs": [
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eQ7xkm6DNxujb9+/KPeyf/Isf5uV9uG0EJcUPjy4NkFmBhe/CEBJJjhE40qzA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\nRc4IbFOmBaZL3dAFkvWYeJqhfHKutub9R0Dld4CXX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+JUhbs034EYqlmAiDux7MNaFQMX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\nnJcxBzYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x288 with 20 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "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": {
    "colab_type": "text",
    "id": "oCV3BoQUdi8S"
   },
   "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": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "colab_type": "code",
    "id": "OoiP-UyGm8FN",
    "outputId": "3366b139-e04b-46dc-9b78-d33aaf789173"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min:  tensor(0.)\n",
      "Max:  tensor(1.)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "img = images[0]\n",
    "\n",
    "print('Min: ', img.min())\n",
    "print('Max: ', img.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pLdFewkMdi8T"
   },
   "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",
    "    min_i, max_i = feature_range\n",
    "    x = x * (max_i - min_i)+ min_i\n",
    "    \n",
    "    return x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "colab_type": "code",
    "id": "NoUbcBJndi8W",
    "outputId": "4d4bf95f-f163-42b8-ca36-b1eae6a74ffe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min:  tensor(-1.)\n",
      "Max:  tensor(1.)\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": {
    "colab_type": "text",
    "id": "auceRKVGdi8Z"
   },
   "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": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Nz5dBMMxdi8a"
   },
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7F_biKdjnhIY"
   },
   "outputs": [],
   "source": [
    "def conv(in_channels, out_channels, kernel_size, stride=2, padding=1, batch_norm=True):\n",
    "    layers = []\n",
    "    \n",
    "    conv_layer = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, bias=False)\n",
    "    \n",
    "    #add to layers\n",
    "    layers.append(conv_layer)\n",
    "    \n",
    "    if batch_norm:\n",
    "        #adding batchnorm 2d layer\n",
    "        \n",
    "        layers.append(nn.BatchNorm2d(out_channels))\n",
    "    \n",
    "    #using Sequential container\n",
    "    return nn.Sequential(*layers)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "KS4kCz8Udi8d",
    "outputId": "3e6cebd5-305d-4015-80b3-12ba44cc4adb"
   },
   "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",
    "        self.conv_dim = conv_dim\n",
    "        \n",
    "        self.conv1 = conv(3, conv_dim,4,batch_norm=False)#input: 32 X 32 X 3 ==> output: 16 X 16 X conv_dim\n",
    "        \n",
    "        self.conv2 = conv(conv_dim, conv_dim*2, 4) #input: 16 X 16 X conv_dim ==> output: 8 X 8 X conv_dim*2\n",
    "        \n",
    "        self.conv3 = conv(conv_dim*2, conv_dim*4, 4) #input: 8 X 8 X conv_dim*2 ==> output: 4 X 4 X conv_dim*4\n",
    "        \n",
    "        self.conv4 = conv(conv_dim*4, conv_dim*8, 4) #input: 4 X 4 X conv_dim*4 ==> output: 2 X 2 X conv_dim*8\n",
    "        #fully connected layer\n",
    "        self.fc = nn.Linear(2*2*(conv_dim*8), 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",
    "        # defining feedforward behavior\n",
    "        \n",
    "        out = F.leaky_relu(self.conv1(x), 0.2)\n",
    "        out = F.leaky_relu(self.conv2(out), 0.2)\n",
    "        out = F.leaky_relu(self.conv3(out), 0.2)\n",
    "        out = F.leaky_relu(self.conv4(out), 0.2)\n",
    "        #flattening the tensor\n",
    "        out = out.view(-1, 2*2*(self.conv_dim*8))\n",
    "        \n",
    "        #output of final fc layer\n",
    "        out = self.fc(out)\n",
    "        \n",
    "        return out\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": {
    "colab_type": "text",
    "id": "wGljhJ-gdi8g"
   },
   "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": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tcF8Polirw73"
   },
   "outputs": [],
   "source": [
    "#helper function deconv\n",
    "\n",
    "def deconv(in_channels, out_channels, kernel_size, stride=2, padding=1, batch_norm=True):\n",
    "    '''It creates a transposed convolution later with or without batch normalization.'''\n",
    "    \n",
    "    layers = []\n",
    "    \n",
    "    transpose_conv_layer = nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride, padding, bias = False)\n",
    "    \n",
    "    layers.append(transpose_conv_layer)\n",
    "    \n",
    "    if batch_norm:\n",
    "        # appending a 2d batch normalization layer\n",
    "        layers.append(nn.BatchNorm2d(out_channels))\n",
    "        \n",
    "    return nn.Sequential(*layers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "tB2UkgwHdi8h",
    "outputId": "bce18fad-7715-400a-ad71-bcece91050e8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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",
    "        self.z_size = z_size\n",
    "        self.conv_dim = conv_dim\n",
    "        \n",
    "        #first fully connected layer\n",
    "        \n",
    "        self.fc = nn.Linear(z_size, conv_dim*8*2*2)\n",
    "        \n",
    "        #transposed Convolution layers\n",
    "        self.t_conv0 = deconv(conv_dim*8, conv_dim*4, 4)\n",
    "        self.t_conv1 = deconv(conv_dim*4, conv_dim*2, 4)        #input: 4 X 4 X conv_dim*4 ==> output: 8 X 8 X conv_dim*2\n",
    "        self.t_conv2 = deconv(conv_dim*2, conv_dim, 4)          #input: 8 X 8 X conv_dim*2 ==> output: 16 X 16 X conv_dim\n",
    "        self.t_conv3 = deconv(conv_dim, 3, 4, batch_norm=False) #input: 16 X 16 X conv_dim ==> output: 32 X 32 X 3\n",
    "        \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",
    "        # defining feedforward behavior\n",
    "        out = self.fc(x)\n",
    "        \n",
    "        out = out.view(-1, self.conv_dim*8, 2, 2) # (Batch_Size, Depth, 2, 2)\n",
    "        out = F.relu(self.t_conv0(out))\n",
    "        out = F.relu(self.t_conv1(out))\n",
    "        out = F.relu(self.t_conv2(out))\n",
    "        \n",
    "        #last layer with tanh activation\n",
    "        out = self.t_conv3(out)\n",
    "        out = torch.tanh(out)  #as F.tanh is deprecated using torch.tanh\n",
    "        return out\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": {
    "colab_type": "text",
    "id": "3QFIuoU1di8k"
   },
   "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": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5tplWgkedi8l"
   },
   "outputs": [],
   "source": [
    "from torch.nn import init\n",
    "\n",
    "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",
    "    if hasattr(m, 'weight') and (classname.find('Conv')!=-1 or classname.find('Linear')!=-1):\n",
    "        init.normal_(m.weight.data, mean=0.0, std=0.02)\n",
    "        \n",
    "        if hasattr(m, 'bias') and m.bias is not None:\n",
    "            m.bias.data.zero_()\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "collapsed": true,
    "id": "3_C3U8vrdi8n"
   },
   "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": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "52373ybDdi8p"
   },
   "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\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "20UgMg2Xdi8s"
   },
   "source": [
    "#### Exercise: Define model hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 668
    },
    "colab_type": "code",
    "id": "142Ox22ydi8t",
    "outputId": "6754035a-690d-4fc0-a1a8-91c4b6663313"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Discriminator(\n",
      "  (conv1): Sequential(\n",
      "    (0): Conv2d(3, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "  )\n",
      "  (conv2): Sequential(\n",
      "    (0): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (conv3): Sequential(\n",
      "    (0): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (conv4): Sequential(\n",
      "    (0): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (fc): Linear(in_features=2048, out_features=1, bias=True)\n",
      ")\n",
      "\n",
      "Generator(\n",
      "  (fc): Linear(in_features=100, out_features=2048, bias=True)\n",
      "  (t_conv0): Sequential(\n",
      "    (0): ConvTranspose2d(512, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (t_conv1): Sequential(\n",
      "    (0): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (t_conv2): Sequential(\n",
      "    (0): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (t_conv3): Sequential(\n",
      "    (0): ConvTranspose2d(64, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# Define model hyperparams\n",
    "d_conv_dim = 64\n",
    "g_conv_dim = 64\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": {
    "colab_type": "text",
    "id": "E3Bky1stdi8v"
   },
   "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": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "Jm29Y5Jjdi8w",
    "outputId": "2f0b6879-dfa6-45cf-ab0a-0b89d457f78c"
   },
   "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": {
    "colab_type": "text",
    "id": "qG7ejRFXdi8y"
   },
   "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": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4dDPD7xbdi80"
   },
   "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",
    "    \n",
    "    batch_size  = D_out.size(0)\n",
    "    \n",
    "    labels = torch.ones(batch_size)\n",
    "    \n",
    "    if smooth:\n",
    "        labels = labels*0.9\n",
    "    if train_on_gpu:\n",
    "        labels = labels.cuda()\n",
    "        \n",
    "    # using binary cross entropy loss with logits loss\n",
    "    criterion = nn.BCEWithLogitsLoss()\n",
    "    loss = criterion(D_out.squeeze(), labels)\n",
    "    \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",
    "    \n",
    "    batch_size = D_out.size(0)\n",
    "    \n",
    "    labels = torch.zeros(batch_size)\n",
    "    \n",
    "    if train_on_gpu:\n",
    "        labels = labels.cuda()\n",
    "        \n",
    "    criterion = nn.BCEWithLogitsLoss()\n",
    "    loss = criterion(D_out.squeeze(), labels)\n",
    "    \n",
    "    return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_YvE7QDUdi84"
   },
   "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": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8bKSOy1idi87"
   },
   "outputs": [],
   "source": [
    "import torch.optim as optim\n",
    "\n",
    "# Create optimizers for the discriminator D and generator G\n",
    "\n",
    "lr = 0.0002\n",
    "beta1= 0.5\n",
    "beta2 = 0.999\n",
    "\n",
    "d_optimizer = optim.Adam(D.parameters(), lr, betas = (beta1, beta2))\n",
    "g_optimizer = optim.Adam(G.parameters(), lr, betas = (beta1, beta2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rPN_fTxvdi8-"
   },
   "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": {
    "colab_type": "text",
    "id": "cNAWC_Tfdi9C"
   },
   "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": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VQOLeOqwdi9D"
   },
   "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. Training the discriminator on real and fake images\n",
    "            d_optimizer.zero_grad()\n",
    "            \n",
    "            # Finding Discriminator loss on real images\n",
    "            \n",
    "            if train_on_gpu:\n",
    "                real_images = real_images.cuda()\n",
    "                \n",
    "            D_real = D(real_images)\n",
    "            D_real_loss = real_loss(D_real, smooth=True)\n",
    "                \n",
    "            # Generating Fake images\n",
    "            z = np.random.uniform(-1, 1, size=(batch_size, z_size))\n",
    "            z = torch.from_numpy(z).float()\n",
    "            \n",
    "            if train_on_gpu:\n",
    "                z= z.cuda()\n",
    "                \n",
    "            fake_images = G(z)\n",
    "            \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. Training the generator with an adversarial loss\n",
    "            \n",
    "            #Generating Fake images\n",
    "            g_optimizer.zero_grad()\n",
    "            z = np.random.uniform(-1, 1, size = (batch_size, z_size))\n",
    "            z = torch.from_numpy(z).float()\n",
    "            \n",
    "            if train_on_gpu:\n",
    "                z = z.cuda()\n",
    "                \n",
    "            fake_images = G(z)\n",
    "            \n",
    "            D_fake = D(fake_images)\n",
    "            \n",
    "            g_loss = real_loss(D_fake, smooth=True)\n",
    "            \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": {
    "colab_type": "text",
    "id": "DFi9S9rMdi9N"
   },
   "source": [
    "Set your number of training epochs and train your GAN!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 5262
    },
    "colab_type": "code",
    "id": "anZWdkbqdi9O",
    "outputId": "9ea92b22-eb79-467f-b6e5-d24704694afb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [    1/   20] | d_loss: 1.4475 | g_loss: 1.2733\n",
      "Epoch [    1/   20] | d_loss: 0.4071 | g_loss: 4.8757\n",
      "Epoch [    1/   20] | d_loss: 0.7227 | g_loss: 2.2408\n",
      "Epoch [    1/   20] | d_loss: 0.6750 | g_loss: 3.4634\n",
      "Epoch [    1/   20] | d_loss: 0.7239 | g_loss: 2.7190\n",
      "Epoch [    1/   20] | d_loss: 0.8839 | g_loss: 2.1199\n",
      "Epoch [    1/   20] | d_loss: 0.7505 | g_loss: 1.9007\n",
      "Epoch [    1/   20] | d_loss: 0.8090 | g_loss: 1.9196\n",
      "Epoch [    1/   20] | d_loss: 0.8978 | g_loss: 1.6400\n",
      "Epoch [    1/   20] | d_loss: 0.9493 | g_loss: 1.5388\n",
      "Epoch [    1/   20] | d_loss: 0.8508 | g_loss: 2.6101\n",
      "Epoch [    1/   20] | d_loss: 1.0730 | g_loss: 1.6939\n",
      "Epoch [    1/   20] | d_loss: 0.8459 | g_loss: 2.8730\n",
      "Epoch [    1/   20] | d_loss: 1.1948 | g_loss: 2.9742\n",
      "Epoch [    1/   20] | d_loss: 0.9015 | g_loss: 2.9377\n",
      "Epoch [    2/   20] | d_loss: 0.9321 | g_loss: 1.9410\n",
      "Epoch [    2/   20] | d_loss: 0.8779 | g_loss: 1.5531\n",
      "Epoch [    2/   20] | d_loss: 0.8858 | g_loss: 1.4692\n",
      "Epoch [    2/   20] | d_loss: 0.9993 | g_loss: 2.6282\n",
      "Epoch [    2/   20] | d_loss: 1.0963 | g_loss: 2.2195\n",
      "Epoch [    2/   20] | d_loss: 1.5620 | g_loss: 3.3900\n",
      "Epoch [    2/   20] | d_loss: 1.0163 | g_loss: 1.7763\n",
      "Epoch [    2/   20] | d_loss: 1.0161 | g_loss: 2.4519\n",
      "Epoch [    2/   20] | d_loss: 0.8356 | g_loss: 1.5807\n",
      "Epoch [    2/   20] | d_loss: 0.9428 | g_loss: 2.4646\n",
      "Epoch [    2/   20] | d_loss: 1.0357 | g_loss: 1.5407\n",
      "Epoch [    2/   20] | d_loss: 1.0978 | g_loss: 2.4524\n",
      "Epoch [    2/   20] | d_loss: 0.9801 | g_loss: 1.7794\n",
      "Epoch [    2/   20] | d_loss: 1.0399 | g_loss: 1.2124\n",
      "Epoch [    2/   20] | d_loss: 0.9419 | g_loss: 1.8072\n",
      "Epoch [    3/   20] | d_loss: 0.9637 | g_loss: 1.8844\n",
      "Epoch [    3/   20] | d_loss: 1.0102 | g_loss: 1.7423\n",
      "Epoch [    3/   20] | d_loss: 1.0043 | g_loss: 2.3877\n",
      "Epoch [    3/   20] | d_loss: 1.1882 | g_loss: 0.9445\n",
      "Epoch [    3/   20] | d_loss: 0.8586 | g_loss: 1.5016\n",
      "Epoch [    3/   20] | d_loss: 0.8982 | g_loss: 1.8915\n",
      "Epoch [    3/   20] | d_loss: 0.8803 | g_loss: 1.1680\n",
      "Epoch [    3/   20] | d_loss: 1.0126 | g_loss: 1.5670\n",
      "Epoch [    3/   20] | d_loss: 0.8772 | g_loss: 1.4406\n",
      "Epoch [    3/   20] | d_loss: 0.7975 | g_loss: 1.9430\n",
      "Epoch [    3/   20] | d_loss: 1.0409 | g_loss: 2.3984\n",
      "Epoch [    3/   20] | d_loss: 0.9247 | g_loss: 1.6258\n",
      "Epoch [    3/   20] | d_loss: 1.2712 | g_loss: 1.0912\n",
      "Epoch [    3/   20] | d_loss: 1.1273 | g_loss: 1.2723\n",
      "Epoch [    3/   20] | d_loss: 1.2103 | g_loss: 0.7278\n",
      "Epoch [    4/   20] | d_loss: 1.1534 | g_loss: 2.1813\n",
      "Epoch [    4/   20] | d_loss: 1.0874 | g_loss: 2.4238\n",
      "Epoch [    4/   20] | d_loss: 0.9854 | g_loss: 1.6290\n",
      "Epoch [    4/   20] | d_loss: 0.7340 | g_loss: 2.2057\n",
      "Epoch [    4/   20] | d_loss: 1.1354 | g_loss: 2.3536\n",
      "Epoch [    4/   20] | d_loss: 1.0723 | g_loss: 2.2909\n",
      "Epoch [    4/   20] | d_loss: 0.9208 | g_loss: 1.5110\n",
      "Epoch [    4/   20] | d_loss: 1.1343 | g_loss: 1.2530\n",
      "Epoch [    4/   20] | d_loss: 0.9773 | g_loss: 1.6676\n",
      "Epoch [    4/   20] | d_loss: 0.9732 | g_loss: 1.3030\n",
      "Epoch [    4/   20] | d_loss: 0.8562 | g_loss: 1.6684\n",
      "Epoch [    4/   20] | d_loss: 0.9151 | g_loss: 1.4762\n",
      "Epoch [    4/   20] | d_loss: 0.8896 | g_loss: 1.1060\n",
      "Epoch [    4/   20] | d_loss: 0.8248 | g_loss: 1.7801\n",
      "Epoch [    4/   20] | d_loss: 0.9157 | g_loss: 1.2728\n",
      "Epoch [    5/   20] | d_loss: 1.0309 | g_loss: 2.3353\n",
      "Epoch [    5/   20] | d_loss: 1.0185 | g_loss: 2.3918\n",
      "Epoch [    5/   20] | d_loss: 0.7715 | g_loss: 1.4377\n",
      "Epoch [    5/   20] | d_loss: 0.8490 | g_loss: 1.8805\n",
      "Epoch [    5/   20] | d_loss: 1.0578 | g_loss: 2.2103\n",
      "Epoch [    5/   20] | d_loss: 0.9080 | g_loss: 2.2679\n",
      "Epoch [    5/   20] | d_loss: 0.9384 | g_loss: 1.4561\n",
      "Epoch [    5/   20] | d_loss: 1.0513 | g_loss: 1.2877\n",
      "Epoch [    5/   20] | d_loss: 0.9417 | g_loss: 1.6757\n",
      "Epoch [    5/   20] | d_loss: 1.0248 | g_loss: 2.2741\n",
      "Epoch [    5/   20] | d_loss: 1.3031 | g_loss: 2.2973\n",
      "Epoch [    5/   20] | d_loss: 0.9543 | g_loss: 1.8207\n",
      "Epoch [    5/   20] | d_loss: 0.8422 | g_loss: 2.0898\n",
      "Epoch [    5/   20] | d_loss: 0.9732 | g_loss: 1.8176\n",
      "Epoch [    5/   20] | d_loss: 0.9178 | g_loss: 2.0467\n",
      "Epoch [    6/   20] | d_loss: 1.1416 | g_loss: 3.7823\n",
      "Epoch [    6/   20] | d_loss: 0.9135 | g_loss: 1.8914\n",
      "Epoch [    6/   20] | d_loss: 0.8900 | g_loss: 1.5362\n",
      "Epoch [    6/   20] | d_loss: 0.7047 | g_loss: 2.3426\n",
      "Epoch [    6/   20] | d_loss: 0.9065 | g_loss: 1.9855\n",
      "Epoch [    6/   20] | d_loss: 0.9146 | g_loss: 1.5380\n",
      "Epoch [    6/   20] | d_loss: 0.8238 | g_loss: 1.2076\n",
      "Epoch [    6/   20] | d_loss: 0.8013 | g_loss: 1.3962\n",
      "Epoch [    6/   20] | d_loss: 0.8678 | g_loss: 1.8404\n",
      "Epoch [    6/   20] | d_loss: 0.8765 | g_loss: 1.4180\n",
      "Epoch [    6/   20] | d_loss: 0.9620 | g_loss: 1.6263\n",
      "Epoch [    6/   20] | d_loss: 0.8741 | g_loss: 1.6504\n",
      "Epoch [    6/   20] | d_loss: 0.9117 | g_loss: 2.2006\n",
      "Epoch [    6/   20] | d_loss: 0.8121 | g_loss: 1.5114\n",
      "Epoch [    6/   20] | d_loss: 0.8524 | g_loss: 1.6972\n",
      "Epoch [    7/   20] | d_loss: 0.9111 | g_loss: 1.3097\n",
      "Epoch [    7/   20] | d_loss: 0.9388 | g_loss: 2.1506\n",
      "Epoch [    7/   20] | d_loss: 1.3067 | g_loss: 2.3430\n",
      "Epoch [    7/   20] | d_loss: 1.0614 | g_loss: 1.0540\n",
      "Epoch [    7/   20] | d_loss: 0.9016 | g_loss: 2.1296\n",
      "Epoch [    7/   20] | d_loss: 1.0186 | g_loss: 1.2926\n",
      "Epoch [    7/   20] | d_loss: 0.7966 | g_loss: 2.1985\n",
      "Epoch [    7/   20] | d_loss: 0.7464 | g_loss: 2.0204\n",
      "Epoch [    7/   20] | d_loss: 0.9932 | g_loss: 1.6473\n",
      "Epoch [    7/   20] | d_loss: 0.9696 | g_loss: 2.4491\n",
      "Epoch [    7/   20] | d_loss: 0.8552 | g_loss: 1.4740\n",
      "Epoch [    7/   20] | d_loss: 0.7564 | g_loss: 1.9565\n",
      "Epoch [    7/   20] | d_loss: 0.9893 | g_loss: 1.4124\n",
      "Epoch [    7/   20] | d_loss: 0.9379 | g_loss: 2.1823\n",
      "Epoch [    7/   20] | d_loss: 0.8545 | g_loss: 1.4560\n",
      "Epoch [    8/   20] | d_loss: 0.6526 | g_loss: 2.1328\n",
      "Epoch [    8/   20] | d_loss: 0.9354 | g_loss: 1.6611\n",
      "Epoch [    8/   20] | d_loss: 0.7687 | g_loss: 1.5679\n",
      "Epoch [    8/   20] | d_loss: 0.8173 | g_loss: 1.2923\n",
      "Epoch [    8/   20] | d_loss: 0.8320 | g_loss: 2.1104\n",
      "Epoch [    8/   20] | d_loss: 0.9169 | g_loss: 1.9356\n",
      "Epoch [    8/   20] | d_loss: 0.8248 | g_loss: 1.3850\n",
      "Epoch [    8/   20] | d_loss: 1.1298 | g_loss: 2.6135\n",
      "Epoch [    8/   20] | d_loss: 0.9930 | g_loss: 2.6267\n",
      "Epoch [    8/   20] | d_loss: 0.8459 | g_loss: 1.9359\n",
      "Epoch [    8/   20] | d_loss: 1.1112 | g_loss: 1.0763\n",
      "Epoch [    8/   20] | d_loss: 0.8939 | g_loss: 1.6710\n",
      "Epoch [    8/   20] | d_loss: 0.8903 | g_loss: 1.3612\n",
      "Epoch [    8/   20] | d_loss: 0.8872 | g_loss: 1.9405\n",
      "Epoch [    8/   20] | d_loss: 0.9284 | g_loss: 1.5654\n",
      "Epoch [    9/   20] | d_loss: 0.8475 | g_loss: 1.4378\n",
      "Epoch [    9/   20] | d_loss: 0.7698 | g_loss: 1.4600\n",
      "Epoch [    9/   20] | d_loss: 0.9720 | g_loss: 1.1690\n",
      "Epoch [    9/   20] | d_loss: 1.3336 | g_loss: 2.9716\n",
      "Epoch [    9/   20] | d_loss: 0.9249 | g_loss: 1.3842\n",
      "Epoch [    9/   20] | d_loss: 0.9506 | g_loss: 2.8605\n",
      "Epoch [    9/   20] | d_loss: 1.0687 | g_loss: 1.1108\n",
      "Epoch [    9/   20] | d_loss: 0.7765 | g_loss: 2.4904\n",
      "Epoch [    9/   20] | d_loss: 0.8903 | g_loss: 2.6731\n",
      "Epoch [    9/   20] | d_loss: 0.9442 | g_loss: 0.9467\n",
      "Epoch [    9/   20] | d_loss: 0.8010 | g_loss: 1.2322\n",
      "Epoch [    9/   20] | d_loss: 0.9720 | g_loss: 2.0546\n",
      "Epoch [    9/   20] | d_loss: 0.6609 | g_loss: 2.0595\n",
      "Epoch [    9/   20] | d_loss: 0.8861 | g_loss: 3.3288\n",
      "Epoch [   10/   20] | d_loss: 0.7043 | g_loss: 2.9432\n",
      "Epoch [   10/   20] | d_loss: 0.6976 | g_loss: 2.4311\n",
      "Epoch [   10/   20] | d_loss: 0.9746 | g_loss: 1.9462\n",
      "Epoch [   10/   20] | d_loss: 1.1505 | g_loss: 1.3970\n",
      "Epoch [   10/   20] | d_loss: 0.7544 | g_loss: 1.4912\n",
      "Epoch [   10/   20] | d_loss: 0.9542 | g_loss: 2.7800\n",
      "Epoch [   10/   20] | d_loss: 0.9247 | g_loss: 1.7317\n",
      "Epoch [   10/   20] | d_loss: 0.7726 | g_loss: 2.3507\n",
      "Epoch [   10/   20] | d_loss: 0.8040 | g_loss: 1.5301\n",
      "Epoch [   10/   20] | d_loss: 0.7314 | g_loss: 1.6458\n",
      "Epoch [   10/   20] | d_loss: 0.9563 | g_loss: 3.0595\n",
      "Epoch [   10/   20] | d_loss: 1.1017 | g_loss: 2.5111\n",
      "Epoch [   10/   20] | d_loss: 0.7530 | g_loss: 2.1795\n",
      "Epoch [   10/   20] | d_loss: 1.4428 | g_loss: 0.8186\n",
      "Epoch [   10/   20] | d_loss: 0.8621 | g_loss: 1.4963\n",
      "Epoch [   11/   20] | d_loss: 0.7327 | g_loss: 1.2952\n",
      "Epoch [   11/   20] | d_loss: 0.7765 | g_loss: 1.8444\n",
      "Epoch [   11/   20] | d_loss: 0.8521 | g_loss: 2.1625\n",
      "Epoch [   11/   20] | d_loss: 0.8786 | g_loss: 2.3763\n",
      "Epoch [   11/   20] | d_loss: 0.7405 | g_loss: 1.9744\n",
      "Epoch [   11/   20] | d_loss: 0.9123 | g_loss: 2.2616\n",
      "Epoch [   11/   20] | d_loss: 0.9337 | g_loss: 1.7543\n",
      "Epoch [   11/   20] | d_loss: 0.7996 | g_loss: 2.3087\n",
      "Epoch [   11/   20] | d_loss: 0.6133 | g_loss: 1.2798\n",
      "Epoch [   11/   20] | d_loss: 0.8341 | g_loss: 1.5971\n",
      "Epoch [   11/   20] | d_loss: 0.7527 | g_loss: 1.9658\n",
      "Epoch [   11/   20] | d_loss: 1.1169 | g_loss: 2.7270\n",
      "Epoch [   11/   20] | d_loss: 0.6373 | g_loss: 2.3303\n",
      "Epoch [   11/   20] | d_loss: 0.7528 | g_loss: 1.9661\n",
      "Epoch [   11/   20] | d_loss: 0.7687 | g_loss: 1.9309\n",
      "Epoch [   12/   20] | d_loss: 0.7870 | g_loss: 1.4204\n",
      "Epoch [   12/   20] | d_loss: 1.2618 | g_loss: 0.6533\n",
      "Epoch [   12/   20] | d_loss: 0.7149 | g_loss: 1.5869\n",
      "Epoch [   12/   20] | d_loss: 0.7609 | g_loss: 2.9298\n",
      "Epoch [   12/   20] | d_loss: 0.8349 | g_loss: 1.8739\n",
      "Epoch [   12/   20] | d_loss: 0.7314 | g_loss: 1.8030\n",
      "Epoch [   12/   20] | d_loss: 0.8994 | g_loss: 1.5680\n",
      "Epoch [   12/   20] | d_loss: 0.9399 | g_loss: 2.3619\n",
      "Epoch [   12/   20] | d_loss: 0.6849 | g_loss: 2.6918\n",
      "Epoch [   12/   20] | d_loss: 0.8039 | g_loss: 1.7284\n",
      "Epoch [   12/   20] | d_loss: 0.7411 | g_loss: 1.4800\n",
      "Epoch [   12/   20] | d_loss: 0.9695 | g_loss: 3.0172\n",
      "Epoch [   12/   20] | d_loss: 0.7215 | g_loss: 1.6828\n",
      "Epoch [   12/   20] | d_loss: 0.9342 | g_loss: 1.3801\n",
      "Epoch [   12/   20] | d_loss: 0.9334 | g_loss: 1.4545\n",
      "Epoch [   13/   20] | d_loss: 0.7412 | g_loss: 1.7730\n",
      "Epoch [   13/   20] | d_loss: 1.2025 | g_loss: 3.6040\n",
      "Epoch [   13/   20] | d_loss: 0.7160 | g_loss: 1.3707\n",
      "Epoch [   13/   20] | d_loss: 0.8056 | g_loss: 2.3708\n",
      "Epoch [   13/   20] | d_loss: 0.8168 | g_loss: 1.4711\n",
      "Epoch [   13/   20] | d_loss: 0.6808 | g_loss: 2.5040\n",
      "Epoch [   13/   20] | d_loss: 0.7037 | g_loss: 2.7797\n",
      "Epoch [   13/   20] | d_loss: 0.7604 | g_loss: 1.9892\n",
      "Epoch [   13/   20] | d_loss: 0.9961 | g_loss: 3.1543\n",
      "Epoch [   13/   20] | d_loss: 1.2510 | g_loss: 1.1247\n",
      "Epoch [   13/   20] | d_loss: 0.6728 | g_loss: 2.0130\n",
      "Epoch [   13/   20] | d_loss: 0.7578 | g_loss: 2.2351\n",
      "Epoch [   13/   20] | d_loss: 0.8278 | g_loss: 2.4313\n",
      "Epoch [   13/   20] | d_loss: 0.8559 | g_loss: 1.0849\n",
      "Epoch [   13/   20] | d_loss: 0.6399 | g_loss: 2.4286\n",
      "Epoch [   14/   20] | d_loss: 0.8581 | g_loss: 1.9333\n",
      "Epoch [   14/   20] | d_loss: 0.7088 | g_loss: 1.4787\n",
      "Epoch [   14/   20] | d_loss: 1.3655 | g_loss: 3.1688\n",
      "Epoch [   14/   20] | d_loss: 0.7295 | g_loss: 1.8174\n",
      "Epoch [   14/   20] | d_loss: 1.0852 | g_loss: 1.2763\n",
      "Epoch [   14/   20] | d_loss: 0.7051 | g_loss: 1.8976\n",
      "Epoch [   14/   20] | d_loss: 0.6282 | g_loss: 2.6461\n",
      "Epoch [   14/   20] | d_loss: 0.6766 | g_loss: 1.2670\n",
      "Epoch [   14/   20] | d_loss: 0.7509 | g_loss: 1.2196\n",
      "Epoch [   14/   20] | d_loss: 0.6910 | g_loss: 2.6333\n",
      "Epoch [   14/   20] | d_loss: 0.7452 | g_loss: 2.6079\n",
      "Epoch [   14/   20] | d_loss: 1.0277 | g_loss: 3.5180\n",
      "Epoch [   14/   20] | d_loss: 0.8231 | g_loss: 1.9951\n",
      "Epoch [   14/   20] | d_loss: 0.9382 | g_loss: 2.0388\n",
      "Epoch [   14/   20] | d_loss: 0.8537 | g_loss: 2.1359\n",
      "Epoch [   15/   20] | d_loss: 0.6272 | g_loss: 2.6608\n",
      "Epoch [   15/   20] | d_loss: 1.4784 | g_loss: 0.7614\n",
      "Epoch [   15/   20] | d_loss: 0.7130 | g_loss: 2.5867\n",
      "Epoch [   15/   20] | d_loss: 0.6453 | g_loss: 2.0358\n",
      "Epoch [   15/   20] | d_loss: 0.8332 | g_loss: 2.0068\n",
      "Epoch [   15/   20] | d_loss: 0.8105 | g_loss: 2.1539\n",
      "Epoch [   15/   20] | d_loss: 0.6677 | g_loss: 2.0297\n",
      "Epoch [   15/   20] | d_loss: 0.8944 | g_loss: 2.2363\n",
      "Epoch [   15/   20] | d_loss: 0.7296 | g_loss: 1.8712\n",
      "Epoch [   15/   20] | d_loss: 0.5943 | g_loss: 2.6930\n",
      "Epoch [   15/   20] | d_loss: 0.8454 | g_loss: 1.3797\n",
      "Epoch [   15/   20] | d_loss: 1.1624 | g_loss: 1.5146\n",
      "Epoch [   15/   20] | d_loss: 0.7320 | g_loss: 2.5238\n",
      "Epoch [   15/   20] | d_loss: 0.6558 | g_loss: 2.4803\n",
      "Epoch [   15/   20] | d_loss: 0.8924 | g_loss: 3.3545\n",
      "Epoch [   16/   20] | d_loss: 0.5099 | g_loss: 3.3419\n",
      "Epoch [   16/   20] | d_loss: 0.7437 | g_loss: 2.3327\n",
      "Epoch [   16/   20] | d_loss: 0.6241 | g_loss: 1.9324\n",
      "Epoch [   16/   20] | d_loss: 0.5531 | g_loss: 2.1387\n",
      "Epoch [   16/   20] | d_loss: 0.8442 | g_loss: 3.1618\n",
      "Epoch [   16/   20] | d_loss: 0.7613 | g_loss: 1.5288\n",
      "Epoch [   16/   20] | d_loss: 0.6011 | g_loss: 2.1460\n",
      "Epoch [   16/   20] | d_loss: 0.6314 | g_loss: 2.5704\n",
      "Epoch [   16/   20] | d_loss: 0.6014 | g_loss: 2.2177\n",
      "Epoch [   16/   20] | d_loss: 1.2479 | g_loss: 3.5567\n",
      "Epoch [   16/   20] | d_loss: 0.6455 | g_loss: 1.9533\n",
      "Epoch [   16/   20] | d_loss: 0.5424 | g_loss: 3.4280\n",
      "Epoch [   16/   20] | d_loss: 1.0240 | g_loss: 3.7916\n",
      "Epoch [   16/   20] | d_loss: 0.8106 | g_loss: 1.8283\n",
      "Epoch [   16/   20] | d_loss: 0.9300 | g_loss: 1.7576\n",
      "Epoch [   17/   20] | d_loss: 0.7013 | g_loss: 2.9784\n",
      "Epoch [   17/   20] | d_loss: 0.6378 | g_loss: 2.0969\n",
      "Epoch [   17/   20] | d_loss: 0.6259 | g_loss: 3.2358\n",
      "Epoch [   17/   20] | d_loss: 0.6773 | g_loss: 2.6312\n",
      "Epoch [   17/   20] | d_loss: 0.5568 | g_loss: 2.7512\n",
      "Epoch [   17/   20] | d_loss: 0.6440 | g_loss: 2.4582\n",
      "Epoch [   17/   20] | d_loss: 0.6161 | g_loss: 2.7986\n",
      "Epoch [   17/   20] | d_loss: 1.0842 | g_loss: 1.1568\n",
      "Epoch [   17/   20] | d_loss: 0.6672 | g_loss: 2.9212\n",
      "Epoch [   17/   20] | d_loss: 1.0623 | g_loss: 4.2052\n",
      "Epoch [   17/   20] | d_loss: 0.5827 | g_loss: 2.7056\n",
      "Epoch [   17/   20] | d_loss: 0.5702 | g_loss: 1.5433\n",
      "Epoch [   17/   20] | d_loss: 0.6887 | g_loss: 2.3437\n",
      "Epoch [   17/   20] | d_loss: 0.6920 | g_loss: 2.1600\n",
      "Epoch [   18/   20] | d_loss: 0.6581 | g_loss: 2.7932\n",
      "Epoch [   18/   20] | d_loss: 0.5593 | g_loss: 2.6878\n",
      "Epoch [   18/   20] | d_loss: 0.6039 | g_loss: 2.0260\n",
      "Epoch [   18/   20] | d_loss: 0.6392 | g_loss: 2.8349\n",
      "Epoch [   18/   20] | d_loss: 0.9489 | g_loss: 1.0738\n",
      "Epoch [   18/   20] | d_loss: 0.5532 | g_loss: 3.0567\n",
      "Epoch [   18/   20] | d_loss: 0.6442 | g_loss: 2.2197\n",
      "Epoch [   18/   20] | d_loss: 0.7334 | g_loss: 2.1865\n",
      "Epoch [   18/   20] | d_loss: 0.6885 | g_loss: 1.5656\n",
      "Epoch [   18/   20] | d_loss: 0.6197 | g_loss: 2.1989\n",
      "Epoch [   18/   20] | d_loss: 0.5794 | g_loss: 1.3947\n",
      "Epoch [   18/   20] | d_loss: 0.8011 | g_loss: 1.5418\n",
      "Epoch [   18/   20] | d_loss: 0.7166 | g_loss: 3.0086\n",
      "Epoch [   18/   20] | d_loss: 0.6284 | g_loss: 2.4046\n",
      "Epoch [   18/   20] | d_loss: 0.5350 | g_loss: 2.2870\n",
      "Epoch [   19/   20] | d_loss: 0.7711 | g_loss: 1.2394\n",
      "Epoch [   19/   20] | d_loss: 0.6714 | g_loss: 2.1607\n",
      "Epoch [   19/   20] | d_loss: 0.4883 | g_loss: 2.8761\n",
      "Epoch [   19/   20] | d_loss: 0.5623 | g_loss: 2.3326\n",
      "Epoch [   19/   20] | d_loss: 1.0380 | g_loss: 3.7313\n",
      "Epoch [   19/   20] | d_loss: 0.5569 | g_loss: 2.9212\n",
      "Epoch [   19/   20] | d_loss: 0.6137 | g_loss: 1.8223\n",
      "Epoch [   19/   20] | d_loss: 0.5553 | g_loss: 2.6896\n",
      "Epoch [   19/   20] | d_loss: 1.2476 | g_loss: 1.1812\n",
      "Epoch [   19/   20] | d_loss: 0.6272 | g_loss: 2.2309\n",
      "Epoch [   19/   20] | d_loss: 0.7761 | g_loss: 2.5703\n",
      "Epoch [   19/   20] | d_loss: 0.6829 | g_loss: 1.8990\n",
      "Epoch [   19/   20] | d_loss: 0.6335 | g_loss: 1.6893\n",
      "Epoch [   19/   20] | d_loss: 0.5143 | g_loss: 3.4242\n",
      "Epoch [   19/   20] | d_loss: 0.7358 | g_loss: 2.4253\n",
      "Epoch [   20/   20] | d_loss: 0.5791 | g_loss: 3.0081\n",
      "Epoch [   20/   20] | d_loss: 0.6482 | g_loss: 2.0424\n",
      "Epoch [   20/   20] | d_loss: 1.1252 | g_loss: 1.1166\n",
      "Epoch [   20/   20] | d_loss: 0.5584 | g_loss: 1.8959\n",
      "Epoch [   20/   20] | d_loss: 0.7708 | g_loss: 1.4590\n",
      "Epoch [   20/   20] | d_loss: 0.7094 | g_loss: 2.4599\n",
      "Epoch [   20/   20] | d_loss: 0.7163 | g_loss: 2.8847\n",
      "Epoch [   20/   20] | d_loss: 0.6934 | g_loss: 2.0816\n",
      "Epoch [   20/   20] | d_loss: 0.5605 | g_loss: 1.7977\n",
      "Epoch [   20/   20] | d_loss: 0.6395 | g_loss: 2.0259\n",
      "Epoch [   20/   20] | d_loss: 0.6228 | g_loss: 3.5999\n",
      "Epoch [   20/   20] | d_loss: 0.5631 | g_loss: 3.3592\n",
      "Epoch [   20/   20] | d_loss: 0.5644 | g_loss: 2.6031\n",
      "Epoch [   20/   20] | d_loss: 0.5788 | g_loss: 2.2680\n",
      "Epoch [   20/   20] | d_loss: 0.5140 | g_loss: 2.7085\n"
     ]
    }
   ],
   "source": [
    "# set number of epochs \n",
    "n_epochs = 20\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": {
    "colab_type": "text",
    "collapsed": true,
    "id": "xNODRuSFdi9S"
   },
   "source": [
    "## Training loss\n",
    "\n",
    "Plot the training losses for the generator and discriminator, recorded after each epoch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 299
    },
    "colab_type": "code",
    "id": "4Nt_VPA2di9T",
    "outputId": "d641eee8-e26a-4df4-a9db-b751a4dcef6a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ffa22398a58>"
      ]
     },
     "execution_count": 22,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZFVJx500YHCmwHIJvBZGCzLFKmiHS2fC4Z3MOR1l3hFpOujZmedzqNGryWkwc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\nBmm4JSQkJLoM/x/8UvTWq0aizQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "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": {
    "colab_type": "text",
    "collapsed": true,
    "id": "MMMbEFGidi9V"
   },
   "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": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "RPkJrhFVdi9W"
   },
   "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": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2nkU6G_Mdi9Z"
   },
   "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": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 244
    },
    "colab_type": "code",
    "id": "OXUKH4sPdi9b",
    "outputId": "72629799-7124-4e5c-8027-892baab57626"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kEj9mH2MSed+idw7ob9OwrFBfERBiS7ddwUaOjgQVtkD/2GftV/L9NzeCLILW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krOv+xwtCBfVZRfqI3QvfbKsQsQ57Mz1UsBh+Htto4G7N3R8beLS5XH5FRzXPsBnHJbarMJ1nRp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\nZDKZTHf0/wBjkdpQan/ZogAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = view_samples(-1, samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "evkC7gTydi9f"
   },
   "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": {
    "colab_type": "text",
    "id": "Kl5giTE3di9g"
   },
   "source": [
    "**Answer:** \n",
    "\n",
    "* Yes, as you mentioned we may say that the dataset is lightly biased biased. But, not to a large extent. we can understand it  from the sample images printed at the top of the notebook. That might be the reason why most of the generated images are mostly white.\n",
    "* I even observed that adding an extra layer and increasing the depths of convolution layers made the losses to converge to a better loss. I think larger models have the oppertunity to learn more features in the data feature space.\n",
    "* I think using Adam as optimiser yielded good results. The number of epochs also effected the final result.\n",
    "* I think training for larger number of epochs, making the size of model larger and using a more diverse dataset might improve this model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "OiEc0zy8di9h"
   },
   "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": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Q1dwb-eLHCf2"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "name": "Copy of dlnd_face_generation.ipynb",
   "provenance": [],
   "toc_visible": true,
   "version": "0.3.2"
  },
  "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",
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