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\n",
    "\n",
    "# my imports\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import TensorDataset, DataLoader\n",
    "from torch.autograd import Variable\n",
    "import numpy as np\n",
    "import torch.optim as optim"
   ]
  },
  {
   "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",
    "    # TODO: Implement function and return a dataloader\n",
    "    transform = transforms.Compose([\n",
    "        transforms.Resize(image_size),\n",
    "        transforms.ToTensor()\n",
    "    ])\n",
    "    dataset = datasets.ImageFolder(data_dir, transform)\n",
    "    loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)\n",
    "    return loader\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 = 36\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)\n"
   ]
  },
  {
   "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 0x7f5123c69c50>"
      ]
     },
     "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",
    "    lower, upper = feature_range\n",
    "    return lower + (upper - lower) * x \n",
    "    \n",
    "    return x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min:  tensor(-1.)\n",
      "Max:  tensor(0.9529)\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": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_conv(inputs, outputs, kernel_size=4, stride=2, padding=1, batch_norm=True):\n",
    "    nn_layers = []\n",
    "    layer = nn.Conv2d(inputs, outputs, kernel_size, stride, padding, bias=False)\n",
    "    nn_layers.append(layer)\n",
    "    \n",
    "    if batch_norm: nn_layers.append(nn.BatchNorm2d(outputs)) # batch normalization\n",
    "        \n",
    "    return nn.Sequential(*nn_layers)\n",
    "\n",
    "def make_deconv(inputs, outputs, kernel_size=4, stride=2, padding=1, batch_norm=True):\n",
    "    nn_layers = []\n",
    "    layer = nn.ConvTranspose2d(inputs, outputs, kernel_size, stride, padding, bias=False)\n",
    "    nn_layers.append(layer)\n",
    "    \n",
    "    if batch_norm: nn_layers.append(nn.BatchNorm2d(outputs)) # batch normalization\n",
    "            \n",
    "    return nn.Sequential(*nn_layers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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.dimensions = conv_dim\n",
    "        self.relu_tilt = 0.2\n",
    "        \n",
    "        self.conv1 = make_conv(3, self.dimensions, batch_norm=False)\n",
    "        self.conv2 = make_conv(self.dimensions, self.dimensions*2)\n",
    "        self.conv3 = make_conv(self.dimensions*2, self.dimensions*4)\n",
    "        self.conv4 = make_conv(self.dimensions*4, self.dimensions*8)\n",
    "        self.convolutions = [\n",
    "            self.conv1,\n",
    "            self.conv2,\n",
    "            self.conv3,\n",
    "            self.conv4\n",
    "        ]\n",
    "        self.fc = nn.Linear(self.dimensions * 32, 1)\n",
    "        self.dropout = nn.Dropout(0.5)\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",
    "        \n",
    "        for idx, conv_layer in enumerate(self.convolutions): \n",
    "            x = F.leaky_relu(conv_layer(x), self.relu_tilt)\n",
    "#             x = self.dropout(x)\n",
    "        \n",
    "        x = x.view(-1, self.dimensions*32)\n",
    "        x = self.fc(x)\n",
    "        x = self.dropout(x)\n",
    "        \n",
    "        return x\n",
    "    \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": 24,
   "metadata": {},
   "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 self.dimensions: The depth of the inputs to the *last* transpose convolutional layer\n",
    "        \"\"\"\n",
    "        super(Generator, self).__init__()\n",
    "\n",
    "        self.dimensions = conv_dim\n",
    "        \n",
    "        self.deconv1 = make_deconv(self.dimensions*8, self.dimensions*4)\n",
    "        self.deconv2 = make_deconv(self.dimensions*4, self.dimensions*2)\n",
    "        self.deconv3 = make_deconv(self.dimensions*2, self.dimensions)\n",
    "        self.deconv4 = make_deconv(self.dimensions, 3, batch_norm=False)\n",
    "        self.deconvolutions = [\n",
    "            self.deconv1,\n",
    "            self.deconv2,\n",
    "            self.deconv3,\n",
    "            self.deconv4\n",
    "        ]\n",
    "        self.fc = nn.Linear(z_size, self.dimensions * 32)\n",
    "        self.dropout = nn.Dropout(0.5)\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",
    "        \n",
    "        x = self.fc(x)\n",
    "        x = self.dropout(x) # should help\n",
    "        \n",
    "        \n",
    "        BATCH_SIZE = x.shape[0]\n",
    "        x = x.view(BATCH_SIZE, self.dimensions*8, 2, 2)\n",
    "\n",
    "        for idx, deconv_layer in enumerate(self.deconvolutions):\n",
    "            if idx != len(self.deconvolutions) - 1: x = F.relu(deconv_layer(x))\n",
    "            else: x = deconv_layer(x)\n",
    "        \n",
    "        x = torch.tanh(x)\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": 25,
   "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",
    "    \n",
    "    print(classname)\n",
    "    if 'Conv' in classname:\n",
    "        print('Found conv layer!')\n",
    "        torch.nn.init.normal_(m.weight.data, 0.0, 0.02)\n",
    "    elif 'Linear' in classname:\n",
    "        print('Found linear layer!')\n",
    "        torch.nn.init.normal_(m.weight.data, 0.0, 0.02)"
   ]
  },
  {
   "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": 26,
   "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\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise: Define model hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conv2d\n",
      "Found conv layer!\n",
      "Sequential\n",
      "Conv2d\n",
      "Found conv layer!\n",
      "BatchNorm2d\n",
      "Sequential\n",
      "Conv2d\n",
      "Found conv layer!\n",
      "BatchNorm2d\n",
      "Sequential\n",
      "Conv2d\n",
      "Found conv layer!\n",
      "BatchNorm2d\n",
      "Sequential\n",
      "Linear\n",
      "Found linear layer!\n",
      "Dropout\n",
      "Discriminator\n",
      "ConvTranspose2d\n",
      "Found conv layer!\n",
      "BatchNorm2d\n",
      "Sequential\n",
      "ConvTranspose2d\n",
      "Found conv layer!\n",
      "BatchNorm2d\n",
      "Sequential\n",
      "ConvTranspose2d\n",
      "Found conv layer!\n",
      "BatchNorm2d\n",
      "Sequential\n",
      "ConvTranspose2d\n",
      "Found conv layer!\n",
      "Sequential\n",
      "Linear\n",
      "Found linear layer!\n",
      "Dropout\n",
      "Generator\n",
      "Discriminator(\n",
      "  (conv1): Sequential(\n",
      "    (0): Conv2d(3, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "  )\n",
      "  (conv2): Sequential(\n",
      "    (0): Conv2d(32, 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",
      "  (conv3): 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",
      "  (conv4): 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",
      "  (fc): Linear(in_features=1024, out_features=1, bias=True)\n",
      "  (dropout): Dropout(p=0.5)\n",
      ")\n",
      "\n",
      "Generator(\n",
      "  (deconv1): 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",
      "  (deconv2): 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",
      "  (deconv3): Sequential(\n",
      "    (0): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (deconv4): Sequential(\n",
      "    (0): ConvTranspose2d(32, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "  )\n",
      "  (fc): Linear(in_features=100, out_features=1024, bias=True)\n",
      "  (dropout): Dropout(p=0.5)\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": 28,
   "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": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def real_loss(D_out):\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",
    "    labels = torch.ones(BATCH_SIZE) * np.random.uniform(0.7, 1.2)\n",
    "    if train_on_gpu:\n",
    "        labels = labels.cuda()\n",
    "    criterion = nn.BCEWithLogitsLoss()\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) * np.random.uniform(0.0, 0.3) \n",
    "    if train_on_gpu:\n",
    "        labels = labels.cuda()\n",
    "    criterion = nn.BCEWithLogitsLoss()\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": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.optim as optim\n",
    "\n",
    "# Create optimizers for the discriminator D and generator G\n",
    "# d_optimizer = optim.Adam(D.parameters(), 0.0003)\n",
    "# g_optimizer = optim.Adam(G.parameters(), 0.0003)\n",
    "\n",
    "# new hyperparameter tuning attempt\n",
    "lr = 0.0005\n",
    "beta1= 0.1\n",
    "beta2= 0.99\n",
    "\n",
    "# Create optimizers for the discriminator D and generator G\n",
    "d_optimizer = optim.Adam(D.parameters(), lr, [beta1, beta2])\n",
    "g_optimizer = optim.Adam(G.parameters(), lr, [beta1, beta2])"
   ]
  },
  {
   "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": 33,
   "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",
    "            # clear gradients\n",
    "            d_optimizer.zero_grad()\n",
    "            \n",
    "            if train_on_gpu:\n",
    "                real_images = real_images.cuda()\n",
    "            \n",
    "            d_out = D(real_images)\n",
    "            d_real_loss = real_loss(d_out)\n",
    "            \n",
    "            # Generate 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_out = D(fake_images)\n",
    "            d_fake_loss = fake_loss(d_fake_out)\n",
    "\n",
    "            # add up loss and perform backprop\n",
    "            d_loss = d_real_loss + d_fake_loss\n",
    "            \n",
    "            d_loss.backward()\n",
    "            d_optimizer.step()\n",
    "\n",
    "            # 2. Train the generator with an adversarial loss            \n",
    "            g_optimizer.zero_grad()\n",
    "            \n",
    "            # Generate 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",
    "            # D.eval()\n",
    "            # Compute the discriminator losses on fake images \n",
    "            # using flipped labels!\n",
    "            g_fake_out = D(fake_images)\n",
    "            g_loss = real_loss(g_fake_out) # use real loss to flip labels\n",
    "            # g_loss = torch.mean(g_fake_out**2)\n",
    "            # D.train()\n",
    "            # perform backprop\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": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [    1/   10] | d_loss: 1.4168 | g_loss: 1.0083\n",
      "Epoch [    1/   10] | d_loss: 1.1719 | g_loss: 2.1707\n",
      "Epoch [    1/   10] | d_loss: 1.1487 | g_loss: 1.9458\n",
      "Epoch [    1/   10] | d_loss: 1.1207 | g_loss: 1.1634\n",
      "Epoch [    1/   10] | d_loss: 1.0578 | g_loss: 1.7901\n",
      "Epoch [    1/   10] | d_loss: 1.1338 | g_loss: 1.3823\n",
      "Epoch [    1/   10] | d_loss: 1.4902 | g_loss: 0.7836\n",
      "Epoch [    1/   10] | d_loss: 1.3783 | g_loss: 1.0377\n",
      "Epoch [    1/   10] | d_loss: 1.5394 | g_loss: 0.6669\n",
      "Epoch [    1/   10] | d_loss: 1.3965 | g_loss: 2.2816\n",
      "Epoch [    1/   10] | d_loss: 1.3434 | g_loss: 1.0633\n",
      "Epoch [    1/   10] | d_loss: 1.2486 | g_loss: 0.9858\n",
      "Epoch [    1/   10] | d_loss: 1.1811 | g_loss: 1.0152\n",
      "Epoch [    1/   10] | d_loss: 1.2161 | g_loss: 1.0257\n",
      "Epoch [    1/   10] | d_loss: 1.2079 | g_loss: 1.0308\n",
      "Epoch [    1/   10] | d_loss: 1.3714 | g_loss: 1.1625\n",
      "Epoch [    1/   10] | d_loss: 1.2695 | g_loss: 1.1027\n",
      "Epoch [    1/   10] | d_loss: 1.3873 | g_loss: 0.9140\n",
      "Epoch [    1/   10] | d_loss: 1.4645 | g_loss: 1.6829\n",
      "Epoch [    1/   10] | d_loss: 1.1979 | g_loss: 0.9101\n",
      "Epoch [    1/   10] | d_loss: 1.1110 | g_loss: 0.8457\n",
      "Epoch [    1/   10] | d_loss: 1.2581 | g_loss: 1.5874\n",
      "Epoch [    1/   10] | d_loss: 1.3476 | g_loss: 0.7194\n",
      "Epoch [    1/   10] | d_loss: 1.2352 | g_loss: 0.9569\n",
      "Epoch [    1/   10] | d_loss: 1.3439 | g_loss: 0.9331\n",
      "Epoch [    1/   10] | d_loss: 1.3140 | g_loss: 0.6908\n",
      "Epoch [    1/   10] | d_loss: 1.1705 | g_loss: 1.3363\n",
      "Epoch [    1/   10] | d_loss: 1.3491 | g_loss: 0.9286\n",
      "Epoch [    1/   10] | d_loss: 1.3134 | g_loss: 1.4583\n",
      "Epoch [    1/   10] | d_loss: 1.2552 | g_loss: 2.0585\n",
      "Epoch [    1/   10] | d_loss: 1.3677 | g_loss: 1.0739\n",
      "Epoch [    1/   10] | d_loss: 1.1795 | g_loss: 1.3937\n",
      "Epoch [    1/   10] | d_loss: 1.4386 | g_loss: 1.3012\n",
      "Epoch [    1/   10] | d_loss: 1.2067 | g_loss: 1.2679\n",
      "Epoch [    1/   10] | d_loss: 1.0931 | g_loss: 0.9680\n",
      "Epoch [    1/   10] | d_loss: 1.3016 | g_loss: 1.1514\n",
      "Epoch [    1/   10] | d_loss: 1.2631 | g_loss: 1.2803\n",
      "Epoch [    1/   10] | d_loss: 1.2445 | g_loss: 1.3200\n",
      "Epoch [    1/   10] | d_loss: 1.2139 | g_loss: 1.0816\n",
      "Epoch [    1/   10] | d_loss: 1.0237 | g_loss: 1.1228\n",
      "Epoch [    1/   10] | d_loss: 1.0482 | g_loss: 1.0063\n",
      "Epoch [    1/   10] | d_loss: 1.0092 | g_loss: 1.4472\n",
      "Epoch [    1/   10] | d_loss: 1.2720 | g_loss: 0.7876\n",
      "Epoch [    1/   10] | d_loss: 1.2894 | g_loss: 0.6918\n",
      "Epoch [    1/   10] | d_loss: 1.2013 | g_loss: 0.8429\n",
      "Epoch [    1/   10] | d_loss: 1.3436 | g_loss: 0.8694\n",
      "Epoch [    1/   10] | d_loss: 1.3096 | g_loss: 1.2264\n",
      "Epoch [    1/   10] | d_loss: 1.1569 | g_loss: 1.4523\n",
      "Epoch [    1/   10] | d_loss: 1.2445 | g_loss: 0.8034\n",
      "Epoch [    1/   10] | d_loss: 1.1760 | g_loss: 1.4108\n",
      "Epoch [    2/   10] | d_loss: 1.1994 | g_loss: 0.8545\n",
      "Epoch [    2/   10] | d_loss: 1.3705 | g_loss: 1.0472\n",
      "Epoch [    2/   10] | d_loss: 1.2090 | g_loss: 1.1091\n",
      "Epoch [    2/   10] | d_loss: 1.2657 | g_loss: 1.7144\n",
      "Epoch [    2/   10] | d_loss: 1.1918 | g_loss: 0.7290\n",
      "Epoch [    2/   10] | d_loss: 1.4925 | g_loss: 1.1220\n",
      "Epoch [    2/   10] | d_loss: 1.3508 | g_loss: 0.8998\n",
      "Epoch [    2/   10] | d_loss: 1.3123 | g_loss: 1.9776\n",
      "Epoch [    2/   10] | d_loss: 1.2927 | g_loss: 1.0301\n",
      "Epoch [    2/   10] | d_loss: 1.2709 | g_loss: 1.3782\n",
      "Epoch [    2/   10] | d_loss: 1.2522 | g_loss: 1.0224\n",
      "Epoch [    2/   10] | d_loss: 1.1343 | g_loss: 1.2752\n",
      "Epoch [    2/   10] | d_loss: 1.0382 | g_loss: 1.1372\n",
      "Epoch [    2/   10] | d_loss: 1.1065 | g_loss: 1.1795\n",
      "Epoch [    2/   10] | d_loss: 1.4261 | g_loss: 0.8708\n",
      "Epoch [    2/   10] | d_loss: 1.2601 | g_loss: 1.4462\n",
      "Epoch [    2/   10] | d_loss: 1.2262 | g_loss: 0.6377\n",
      "Epoch [    2/   10] | d_loss: 1.3437 | g_loss: 1.0157\n",
      "Epoch [    2/   10] | d_loss: 1.0620 | g_loss: 0.8773\n",
      "Epoch [    2/   10] | d_loss: 1.0970 | g_loss: 0.9227\n",
      "Epoch [    2/   10] | d_loss: 1.1607 | g_loss: 1.1213\n",
      "Epoch [    2/   10] | d_loss: 1.1030 | g_loss: 1.2056\n",
      "Epoch [    2/   10] | d_loss: 1.1994 | g_loss: 1.1615\n",
      "Epoch [    2/   10] | d_loss: 1.1522 | g_loss: 0.7413\n",
      "Epoch [    2/   10] | d_loss: 1.3136 | g_loss: 0.9627\n",
      "Epoch [    2/   10] | d_loss: 1.1317 | g_loss: 1.2499\n",
      "Epoch [    2/   10] | d_loss: 1.3389 | g_loss: 1.0246\n",
      "Epoch [    2/   10] | d_loss: 1.3718 | g_loss: 1.1743\n",
      "Epoch [    2/   10] | d_loss: 1.2773 | g_loss: 1.4399\n",
      "Epoch [    2/   10] | d_loss: 1.2409 | g_loss: 1.3150\n",
      "Epoch [    2/   10] | d_loss: 1.1874 | g_loss: 1.7478\n",
      "Epoch [    2/   10] | d_loss: 1.2362 | g_loss: 1.1064\n",
      "Epoch [    2/   10] | d_loss: 1.2098 | g_loss: 0.8441\n",
      "Epoch [    2/   10] | d_loss: 1.7116 | g_loss: 0.7604\n",
      "Epoch [    2/   10] | d_loss: 1.1161 | g_loss: 0.8581\n",
      "Epoch [    2/   10] | d_loss: 1.3093 | g_loss: 1.4129\n",
      "Epoch [    2/   10] | d_loss: 1.7596 | g_loss: 0.6962\n",
      "Epoch [    2/   10] | d_loss: 1.5291 | g_loss: 1.6662\n",
      "Epoch [    2/   10] | d_loss: 1.1868 | g_loss: 1.0131\n",
      "Epoch [    2/   10] | d_loss: 1.1797 | g_loss: 1.4801\n",
      "Epoch [    2/   10] | d_loss: 1.2094 | g_loss: 1.2589\n",
      "Epoch [    2/   10] | d_loss: 1.1851 | g_loss: 1.2946\n",
      "Epoch [    2/   10] | d_loss: 1.5081 | g_loss: 1.2349\n",
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      "Epoch [    2/   10] | d_loss: 1.1857 | g_loss: 1.1182\n",
      "Epoch [    2/   10] | d_loss: 1.4551 | g_loss: 0.5576\n",
      "Epoch [    2/   10] | d_loss: 1.1605 | g_loss: 1.0193\n",
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      "Epoch [    2/   10] | d_loss: 1.1836 | g_loss: 1.3881\n",
      "Epoch [    3/   10] | d_loss: 1.8220 | g_loss: 1.2591\n",
      "Epoch [    3/   10] | d_loss: 1.4421 | g_loss: 1.1473\n",
      "Epoch [    3/   10] | d_loss: 1.1497 | g_loss: 1.2289\n",
      "Epoch [    3/   10] | d_loss: 1.1420 | g_loss: 1.1018\n",
      "Epoch [    3/   10] | d_loss: 1.3268 | g_loss: 1.4655\n",
      "Epoch [    3/   10] | d_loss: 1.2596 | g_loss: 1.3437\n",
      "Epoch [    3/   10] | d_loss: 1.1026 | g_loss: 1.3246\n",
      "Epoch [    3/   10] | d_loss: 1.0828 | g_loss: 1.1022\n",
      "Epoch [    3/   10] | d_loss: 0.9454 | g_loss: 1.1696\n",
      "Epoch [    3/   10] | d_loss: 1.1596 | g_loss: 1.0597\n",
      "Epoch [    3/   10] | d_loss: 1.1355 | g_loss: 1.2292\n",
      "Epoch [    3/   10] | d_loss: 1.2994 | g_loss: 1.1193\n",
      "Epoch [    3/   10] | d_loss: 1.0899 | g_loss: 0.9478\n",
      "Epoch [    3/   10] | d_loss: 1.2680 | g_loss: 0.9655\n",
      "Epoch [    3/   10] | d_loss: 1.2265 | g_loss: 1.0765\n",
      "Epoch [    3/   10] | d_loss: 1.0563 | g_loss: 0.8290\n",
      "Epoch [    3/   10] | d_loss: 1.1498 | g_loss: 1.5818\n",
      "Epoch [    3/   10] | d_loss: 1.3455 | g_loss: 0.9616\n",
      "Epoch [    3/   10] | d_loss: 1.1285 | g_loss: 0.9073\n",
      "Epoch [    3/   10] | d_loss: 1.0304 | g_loss: 0.9479\n",
      "Epoch [    3/   10] | d_loss: 1.2082 | g_loss: 1.6468\n",
      "Epoch [    3/   10] | d_loss: 1.1034 | g_loss: 1.3869\n",
      "Epoch [    3/   10] | d_loss: 1.1500 | g_loss: 1.4074\n",
      "Epoch [    3/   10] | d_loss: 1.0319 | g_loss: 1.0059\n",
      "Epoch [    3/   10] | d_loss: 1.3981 | g_loss: 1.1903\n",
      "Epoch [    3/   10] | d_loss: 1.0359 | g_loss: 1.0565\n",
      "Epoch [    3/   10] | d_loss: 1.2719 | g_loss: 1.7639\n",
      "Epoch [    3/   10] | d_loss: 1.3704 | g_loss: 1.4931\n",
      "Epoch [    3/   10] | d_loss: 1.1544 | g_loss: 1.3837\n",
      "Epoch [    3/   10] | d_loss: 1.1943 | g_loss: 1.0845\n",
      "Epoch [    3/   10] | d_loss: 1.1003 | g_loss: 1.0189\n",
      "Epoch [    3/   10] | d_loss: 1.0359 | g_loss: 1.4722\n",
      "Epoch [    3/   10] | d_loss: 1.2004 | g_loss: 1.0687\n",
      "Epoch [    3/   10] | d_loss: 1.0815 | g_loss: 1.3848\n",
      "Epoch [    3/   10] | d_loss: 1.5309 | g_loss: 2.4674\n",
      "Epoch [    3/   10] | d_loss: 0.9045 | g_loss: 0.9955\n",
      "Epoch [    3/   10] | d_loss: 1.5189 | g_loss: 1.4469\n",
      "Epoch [    3/   10] | d_loss: 1.2060 | g_loss: 1.1232\n",
      "Epoch [    3/   10] | d_loss: 1.3040 | g_loss: 1.1033\n",
      "Epoch [    3/   10] | d_loss: 1.2230 | g_loss: 1.4066\n",
      "Epoch [    3/   10] | d_loss: 1.1709 | g_loss: 1.1061\n",
      "Epoch [    3/   10] | d_loss: 1.1790 | g_loss: 1.3983\n",
      "Epoch [    3/   10] | d_loss: 1.1531 | g_loss: 0.9639\n",
      "Epoch [    3/   10] | d_loss: 1.2529 | g_loss: 1.5768\n",
      "Epoch [    3/   10] | d_loss: 1.0313 | g_loss: 1.1269\n",
      "Epoch [    3/   10] | d_loss: 1.2017 | g_loss: 1.3340\n",
      "Epoch [    3/   10] | d_loss: 1.1498 | g_loss: 0.5756\n",
      "Epoch [    3/   10] | d_loss: 1.2009 | g_loss: 1.3427\n",
      "Epoch [    3/   10] | d_loss: 1.3151 | g_loss: 1.1965\n",
      "Epoch [    3/   10] | d_loss: 0.9786 | g_loss: 1.2203\n",
      "Epoch [    4/   10] | d_loss: 1.4442 | g_loss: 2.3486\n",
      "Epoch [    4/   10] | d_loss: 1.0877 | g_loss: 2.0121\n",
      "Epoch [    4/   10] | d_loss: 1.2008 | g_loss: 1.0017\n",
      "Epoch [    4/   10] | d_loss: 1.1704 | g_loss: 1.0674\n",
      "Epoch [    4/   10] | d_loss: 1.6461 | g_loss: 1.7960\n",
      "Epoch [    4/   10] | d_loss: 1.0178 | g_loss: 1.2178\n",
      "Epoch [    4/   10] | d_loss: 1.1463 | g_loss: 1.5301\n",
      "Epoch [    4/   10] | d_loss: 1.0400 | g_loss: 0.9830\n",
      "Epoch [    4/   10] | d_loss: 1.1702 | g_loss: 1.1058\n",
      "Epoch [    4/   10] | d_loss: 1.3272 | g_loss: 1.2887\n",
      "Epoch [    4/   10] | d_loss: 0.9077 | g_loss: 1.0990\n",
      "Epoch [    4/   10] | d_loss: 1.1495 | g_loss: 0.8384\n",
      "Epoch [    4/   10] | d_loss: 1.0997 | g_loss: 1.1158\n",
      "Epoch [    4/   10] | d_loss: 1.3108 | g_loss: 1.9290\n",
      "Epoch [    4/   10] | d_loss: 1.1632 | g_loss: 0.8941\n",
      "Epoch [    4/   10] | d_loss: 1.2766 | g_loss: 1.3441\n",
      "Epoch [    4/   10] | d_loss: 1.1540 | g_loss: 1.3844\n",
      "Epoch [    4/   10] | d_loss: 1.2811 | g_loss: 0.6633\n",
      "Epoch [    4/   10] | d_loss: 1.3190 | g_loss: 1.3113\n",
      "Epoch [    4/   10] | d_loss: 1.1853 | g_loss: 1.8073\n",
      "Epoch [    4/   10] | d_loss: 1.2976 | g_loss: 1.1658\n",
      "Epoch [    4/   10] | d_loss: 1.3799 | g_loss: 1.1327\n",
      "Epoch [    4/   10] | d_loss: 1.3874 | g_loss: 1.2552\n",
      "Epoch [    4/   10] | d_loss: 1.4562 | g_loss: 1.4446\n",
      "Epoch [    4/   10] | d_loss: 1.1549 | g_loss: 1.1071\n",
      "Epoch [    4/   10] | d_loss: 0.9466 | g_loss: 1.4993\n",
      "Epoch [    4/   10] | d_loss: 1.1214 | g_loss: 2.2280\n",
      "Epoch [    4/   10] | d_loss: 0.8773 | g_loss: 1.0037\n",
      "Epoch [    4/   10] | d_loss: 1.1492 | g_loss: 0.7467\n",
      "Epoch [    4/   10] | d_loss: 1.1151 | g_loss: 1.1217\n",
      "Epoch [    4/   10] | d_loss: 1.5559 | g_loss: 1.3465\n",
      "Epoch [    4/   10] | d_loss: 1.4076 | g_loss: 0.6406\n",
      "Epoch [    4/   10] | d_loss: 1.0450 | g_loss: 0.7404\n",
      "Epoch [    4/   10] | d_loss: 1.0450 | g_loss: 1.6529\n",
      "Epoch [    4/   10] | d_loss: 1.2675 | g_loss: 1.5474\n",
      "Epoch [    4/   10] | d_loss: 1.1613 | g_loss: 1.3701\n",
      "Epoch [    4/   10] | d_loss: 1.3914 | g_loss: 0.7000\n",
      "Epoch [    4/   10] | d_loss: 1.2827 | g_loss: 1.7348\n",
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      "Epoch [    9/   10] | d_loss: 0.9924 | g_loss: 0.6643\n",
      "Epoch [    9/   10] | d_loss: 1.1799 | g_loss: 1.6947\n",
      "Epoch [    9/   10] | d_loss: 1.0815 | g_loss: 1.6624\n",
      "Epoch [    9/   10] | d_loss: 1.1610 | g_loss: 1.1175\n",
      "Epoch [    9/   10] | d_loss: 0.9306 | g_loss: 1.3816\n",
      "Epoch [    9/   10] | d_loss: 1.2110 | g_loss: 1.6496\n",
      "Epoch [    9/   10] | d_loss: 0.7173 | g_loss: 1.0388\n",
      "Epoch [    9/   10] | d_loss: 1.3959 | g_loss: 1.5163\n",
      "Epoch [    9/   10] | d_loss: 1.3406 | g_loss: 2.5491\n",
      "Epoch [    9/   10] | d_loss: 1.1302 | g_loss: 1.8482\n",
      "Epoch [    9/   10] | d_loss: 0.8798 | g_loss: 1.8975\n",
      "Epoch [    9/   10] | d_loss: 1.1506 | g_loss: 1.2491\n",
      "Epoch [    9/   10] | d_loss: 0.7307 | g_loss: 0.7115\n",
      "Epoch [    9/   10] | d_loss: 0.7423 | g_loss: 1.4389\n",
      "Epoch [    9/   10] | d_loss: 0.9669 | g_loss: 1.0870\n",
      "Epoch [    9/   10] | d_loss: 1.5016 | g_loss: 2.0293\n",
      "Epoch [    9/   10] | d_loss: 1.0387 | g_loss: 1.8072\n",
      "Epoch [    9/   10] | d_loss: 1.1881 | g_loss: 1.7717\n",
      "Epoch [    9/   10] | d_loss: 1.2384 | g_loss: 1.6042\n",
      "Epoch [    9/   10] | d_loss: 1.1115 | g_loss: 2.2941\n",
      "Epoch [    9/   10] | d_loss: 1.2225 | g_loss: 1.5713\n",
      "Epoch [    9/   10] | d_loss: 0.6019 | g_loss: 1.2170\n",
      "Epoch [    9/   10] | d_loss: 1.0332 | g_loss: 0.8928\n",
      "Epoch [   10/   10] | d_loss: 1.6124 | g_loss: 2.6380\n",
      "Epoch [   10/   10] | d_loss: 0.8200 | g_loss: 1.9964\n",
      "Epoch [   10/   10] | d_loss: 1.0182 | g_loss: 0.9722\n",
      "Epoch [   10/   10] | d_loss: 0.8062 | g_loss: 1.2063\n",
      "Epoch [   10/   10] | d_loss: 1.0630 | g_loss: 2.2805\n",
      "Epoch [   10/   10] | d_loss: 1.0683 | g_loss: 2.0652\n",
      "Epoch [   10/   10] | d_loss: 1.2920 | g_loss: 1.7676\n",
      "Epoch [   10/   10] | d_loss: 0.9519 | g_loss: 2.1328\n",
      "Epoch [   10/   10] | d_loss: 1.0875 | g_loss: 1.5463\n",
      "Epoch [   10/   10] | d_loss: 1.1991 | g_loss: 1.7630\n",
      "Epoch [   10/   10] | d_loss: 0.8662 | g_loss: 1.0949\n",
      "Epoch [   10/   10] | d_loss: 1.8834 | g_loss: 1.5150\n",
      "Epoch [   10/   10] | d_loss: 1.1358 | g_loss: 2.4072\n",
      "Epoch [   10/   10] | d_loss: 0.9752 | g_loss: 1.9564\n",
      "Epoch [   10/   10] | d_loss: 1.1571 | g_loss: 1.2649\n",
      "Epoch [   10/   10] | d_loss: 0.6889 | g_loss: 1.6081\n",
      "Epoch [   10/   10] | d_loss: 1.1536 | g_loss: 2.1304\n",
      "Epoch [   10/   10] | d_loss: 0.9664 | g_loss: 1.3201\n",
      "Epoch [   10/   10] | d_loss: 1.9077 | g_loss: 2.3156\n",
      "Epoch [   10/   10] | d_loss: 0.5893 | g_loss: 0.8581\n",
      "Epoch [   10/   10] | d_loss: 1.0488 | g_loss: 1.9549\n",
      "Epoch [   10/   10] | d_loss: 1.0748 | g_loss: 1.3410\n",
      "Epoch [   10/   10] | d_loss: 1.0878 | g_loss: 1.9132\n",
      "Epoch [   10/   10] | d_loss: 0.5531 | g_loss: 0.9327\n",
      "Epoch [   10/   10] | d_loss: 1.1511 | g_loss: 1.2275\n",
      "Epoch [   10/   10] | d_loss: 0.9842 | g_loss: 1.5226\n",
      "Epoch [   10/   10] | d_loss: 0.7731 | g_loss: 0.6723\n",
      "Epoch [   10/   10] | d_loss: 1.4452 | g_loss: 2.5179\n",
      "Epoch [   10/   10] | d_loss: 1.2828 | g_loss: 2.5982\n",
      "Epoch [   10/   10] | d_loss: 1.1678 | g_loss: 1.5798\n",
      "Epoch [   10/   10] | d_loss: 1.1179 | g_loss: 1.4157\n",
      "Epoch [   10/   10] | d_loss: 1.0261 | g_loss: 1.1938\n",
      "Epoch [   10/   10] | d_loss: 0.7970 | g_loss: 1.1091\n",
      "Epoch [   10/   10] | d_loss: 1.0811 | g_loss: 1.9316\n",
      "Epoch [   10/   10] | d_loss: 1.3068 | g_loss: 1.9336\n",
      "Epoch [   10/   10] | d_loss: 0.9594 | g_loss: 1.7763\n",
      "Epoch [   10/   10] | d_loss: 1.1226 | g_loss: 1.7951\n",
      "Epoch [   10/   10] | d_loss: 1.0531 | g_loss: 0.8632\n",
      "Epoch [   10/   10] | d_loss: 0.9927 | g_loss: 0.8114\n",
      "Epoch [   10/   10] | d_loss: 0.8205 | g_loss: 1.2189\n",
      "Epoch [   10/   10] | d_loss: 0.9480 | g_loss: 1.1711\n",
      "Epoch [   10/   10] | d_loss: 1.3259 | g_loss: 2.5344\n",
      "Epoch [   10/   10] | d_loss: 1.0527 | g_loss: 2.2667\n",
      "Epoch [   10/   10] | d_loss: 1.0500 | g_loss: 1.8178\n",
      "Epoch [   10/   10] | d_loss: 0.9690 | g_loss: 1.9401\n",
      "Epoch [   10/   10] | d_loss: 1.2789 | g_loss: 1.8277\n",
      "Epoch [   10/   10] | d_loss: 1.4631 | g_loss: 1.6113\n",
      "Epoch [   10/   10] | d_loss: 1.2797 | g_loss: 1.4994\n",
      "Epoch [   10/   10] | d_loss: 1.0605 | g_loss: 2.7598\n",
      "Epoch [   10/   10] | d_loss: 1.5957 | g_loss: 2.4385\n"
     ]
    }
   ],
   "source": [
    "# set number of epochs \n",
    "n_epochs = 10\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": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f5122237710>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SSy/Fxo0bE+vS0+t+9KMfxS233IJ3vOMd2LBhQ2xBjzToE4FNul4DAwMJo7lH8IY3vAHFYhF333232iZT+L7lLW/B3XffjUqlAkDIIGNjY6llveIVr0BPT48y7pVKJXEBDUCMAs455xwAwNe//nW1XU/XC4iFsr/73e8CAL71rW/hta99bSOnaWBgcIrDGPcIiAgPPPAAHnvsMZx//vm45pprcNNNN+Ff/uVf8Md//Me45JJLcNVVV+Gyyy7Dn/7pnyYupiFRKBRw33334dZbb8UVV1yBdevWpYY13nbbbXj3u9+N173udVi+fLna/va3vx3333+/cqjeeeed+OpXv4rLL78c3/zmN3HHHXc0/RoY+DDx3QZzGDVT/k4VTMrfuYfT7v5svhcY6QLWfwhYcOZMt2b68fx/AYNHgXV/ACxZM9OtMfDRzJS/BganN073yTyn+/nPURjjbmBgYHAKYtYZd5OSZnbC3JfTEOqem3s/FzGrjHtrayv6+vqMIZllYGb09fWhtbV1pptiYDC30bUT2PXgtFQ1q+LcV61ahWPHjsEs5DH70NraqiZpGZxmMGSredj5Y/H5yrdNeVWzyrjn8/nUmZkGBgYGBtkxq2QZAwOD2QTD2KcM0zAaMsbdwCAVchLTaW7kjCzTfLA35VUY425gUAunvXE73c9/CuC5U16FMe4GBjVhjJtBk8HGuBsYzDxOd+Z+up//VMBLz0nVLBjjbmBgcHrCKQPu1BvZRBhZxsBgNuB0Z66n6Pk/8W/Alq9Ob50y06gx7gYGswCnqywhz/tUPv+x3umtj3yTazR3A4MZBJlQSIMamBgAikPZ95fG3TB3A4NZgFOZuWbC6X7+VfDMPcDG/5d9f2XcjUPVwMBgpnHad25NhOUvYG9kGQOD2QBj3AwS0Ii0Qnbjx9YJY9wNDGrhtGeuc+z8mYHNXwW6d01tPeXR+o+ZTZo7Ea0mokeJaBcR7SCiv0zY5wYiGiKibf6/T09Ncw0MDKYPczRahhkY6QzS604VSiO199n7P8Axba1oKctMg+aeJeWvA+CvmHkrES0AsIWIfsXMOyP7PcHMU5+k2MDAwKAqpqlTksY9V0jf5/gW8bnKX896NoVCMvNJZt7q/z0CYBeAc6a6YQYGMw8/FHKuMdemY46d/3TdL6ckPu2W7MfMJllGBxGtAXAlgGcTfn4NEb1ARL8goktTjv8wEW0mos1mtSWDuYNZbtwGOoCJwZluhcDhjcBTd4q/x/uBky/MQCOm6341UM9sNO5ENB/ADwF8nJmHIz9vBXAeM18B4AsAHkgqg5m/zMzrmXn9ihUrGm2zgcH0YrYz923fAZ77j6krv57zP7gBKI+Jv7d+Hdj98ylpUlVM1/1qpJ7ZFgpJRHkIw/4tZv5R9HdmHmbmUf/vnwPIE9HyprbUoLnY/iPg0X+a6VbMEcxy4w5MjYNOGa8Gz79SjJQzXZjF9dH0OVSzRMsQgK8A2MXMn0vZ50x/PxDRNX65fc1sqEGT0bNnpltgcLpguo37tNU3u2WZLNEy1wF4P4CXiGibv+1vAZwLAMx8D4AbAfwZETkAJgD8PvNsH8saGGTE6f4oN3L+oWNmMZNuSnUN1DcbQiGZ+UkEi0mm7XMXgLua1SgDg6aBGdj9M+Ccq4CFZzdaSFObNPfQiHGf+jVC0+ueLs19EgctvaCZLUmEmaFqcGqjPAZ0vgS89IP6jyUTCtkwdOM+FzX33v3Arp9mrKeO+piBxauBJec12rLMMMbdwMAgBZOYDBRi7k0y7qXRbCsnNaMzeekHQOf2yZcTA6OGENI0GONuYFALbinbVHODAM1m7szA018Adtdi00BTZbRqba9nMRN9XzLG3cBgdmDPL4GnT2eX0ixg7m5ZfPbuz1D3NBn3usqR18Mwd4PZgs7tYgbknMUU6b3du4DjW6em7NmGRi5hs5l7ZUJ85jJM9W+mM7dqWXVo7jI6xjB3g1mDXT8VMyDnKqbKmbfjAWDvf09N2acCms3clXFvzVL55OtTRVWJR6/n2QqFPhrjbmAwecxkSN50IMnANL1Dm6Qs04z2OPUw92Yad/88JgaCDqaR+tyK3NkwdwOD5mAyL/r0vIRNxUAHsOGfgeETky+rHodh2rHiy+TbIlMZ5NsmX1Y9kDNJn7kHePaeyI/1MHe/nGkMCzXG3eDUxqkeox49v74D4nPwyPS3BdDmBsxS5l4aAV78QZCut2ZZ2nnIDia+U+1ylCxjHKr146X7gEOPz3QrDGYbTnXjHjUsUzLxqp6yEox7MyAN62Q1944ngb79QNeOYNvwCeFX0vO9qOvYZM3dOFQbQO8+oOOpmW6FwWzD6ai5z2QdaqWhJjtUJXOnDCar3muy5+dCzhrr1TZm6SQbcagaWcbAoEk41Zl7FJIVztB5q+qbLMvIHPGZyqqzvqQ0vJJdZ8nemGkSk6a5G+ZucEqAeWalkabGPM/GjmIq29RIPvckWaYJbSwOZS+r3vuUuGh1Bnmpnnz3nl6OMe4GpwI2/DOw739mrv7ZOFuxmYi2aaaTnSUx3ma0pThcR1nNYO6yqCaRA70cw9wNpgXTYQRmcibnZF7O2EtY5VpVikDP3sbrahizTHOfCubuuUB5NPv+9T7TiQtoZHCo1pUNUg+FNMbdYDowlcZ9VjDdaWLuO38MbP+hJh9ME2JtmgrDUY9DdQpCIUvD9Ukg9e6jjLsT35ZFlslUnZ5bZnpgjLvB1GE2RKo0tQ1VXsyJAfE5DcunZcNMyTLSKDaxfn1maD0ZGLNCae6VYFs9DtUs7ZDPoXGo1sDz/zW1q72fVjjFmft0ae5TpXV7Xo0JNymaezMxWVlmstek7tmuWToA7W9lyOt0qNZyOOvtVp2EkWWqY/BoJCbVoGFkffEmvWDDDGG6mPtU4cAjwBOfS1+kIu2+NGpQK0WR8TJUxiRlmcmi3o4iSw72kCwjmXvSJKYM55Fa38wy9ywLZBuc0sj44nouYNf7uMwC5t5IG7Z+E1hwZkJR1cqaohe2259J6ZZSrn+Tr/Gun4oZnPPPaLCAqQiFrJO519uxSCmpNAKM9QHzliF0HjUThtXTJsPcDWYbqkYNpB0zG5h7A4Zl6BhwbHNSYZNuTt2wfIMuJYMDjwBHn9OalOZQbbCtxcFwfYl1VMFUOFRD2vVUhEL6bT76HPDcl8PbisPAk/9evZ60uRxJmrte9hTDGPfTHVlfvEYcS3Nec4+8hJk6K78+tyLyHUlHa6OQxl3KMkeeBfY/PLkyqyHR8ExhKOTQMeCpO6ok5Yoe3ySHaprhlZBsflLRTwmauwmFNJg+ZDXuGRYmjhXtvzTTxFSqtqEpZWVwqEr0HxT5jiZriGUkh1xmLt6o5HY01YlZB5Kck9XKOvQ4UB4HRk5WaUu997BOdp9IXOpMHJaZuZtomWyYDcxwriOzQ7WRkLApuD9upc5RRDPbkFLW4JHAwd/sZ5ISwvRCTZqGaKdGomVC96jK8bLTsgvis3t3fNJbvbJMvdckkbnXGQqZ2AHp7Tb53OtDI2zSoDF4DTBg9cA3kak8fjuw+d4G2tAEpL2Yz38r/ZjJsjQly6QY95jhbGbisAbKkOe7/yGtmGrG3T8vOy8+d9xfY/nCKTCOSQZcxetnnaFao13GoVonUh/4DDj4GPDCd5vXljmLKWTuU8VS6gmDnam85s2qt6ZxbzaapLlnPT5VbtIPnwLmHnL4JhGApNj3OtuVFOduJjFlRNpQNQsm+oGJwea1Za4iU0wwZr/mPt4v/qW1oRmYikiNWlDGPcUIpiUOawYakWWS6q/K3Mu196k7rLJe4x4hLroBziwBJtWZZPxnEXMnotVE9CgR7SKiHUT0lwn7EBHdSUT7iehFIrpqapobQdrEjixgb3aE6s04shr3yUyrn4aHed//APt+lfBDI7pxGmqws6lA0tT4cANSNjcjtrxZ55ZBlqlaVxOY+7bvAFu+ru2jvfvRZ7tWJE3Wds1w+oEss1IcAH/FzFuJaAGALUT0K2beqe3zWwAu8v9dC+Bu/3NqMRnNnZv58J6qSHAI1XX4NHaeabLFpIxc5NhaL/Ck60tALVlmKhOHNTRDtU4xIMvC0fK3zGUnlDXQIT7nrfB3qSLLsDcFzD0h4+QUo+bVYuaTzLzV/3sEwC4A50R2eyeAb7DAMwAWE9FZTW9tFJORZdgz0TZAdlmmEUOtXsr6D22gsuQ2TnkHEzXu0fom61CVoZD1PutNHKlMS1hlBuae1bhnqS+Ubz7K3KtINgnNylRnKBSydvOagbq6WSJaA+BKAM9GfjoHwFHt+zHEOwAQ0YeJaDMRbe7p6amvpUmYjJOpGcy9MiGmK89pNHE4HDt8Gpl76v3UtvXuq28R9RgrT+o8onU2wHazIIvjEZj8kH9KJIMGpJTjW8VcASDsu2mW3yMkyyQwd1lPZnWgDofqbGHuEkQ0H8APAXycmYejPyccEjtbZv4yM69n5vUrVqyor6VJmJQs0wTmvuXrwXTluYqszL2xwv3P6XiYMzD3l+6rcxH1LLJM1DA02ajL8tOe9WYnDtOPb0SWadgvEdln738DL3wvfDxZ2doy2Rmq+veqBLIW+UmqY/rUgkzGnYjyEIb9W8z8o4RdjgFYrX1fBeDE5JuX2iDxOSnm7mHSF3qyU8tnPZrE3KfDgcRpxr2plWTY1mzj7peXdYZqUzvSRu55hmt0dFN8xFv1+dKMe70dRWrnV0NzT2Pux7aklF3LoeoGu82WUEgiIgBfAbCLmT+XsttPAHzAj5p5NYAhZq4yn3iSqDVrLwuM5u5jGjT3aQEn1zcZaSiLs3SqHaqNMvdmdDIqFLJJ5QAiwm3/Q8C2/4ruVPv4Rph77P5zfHsic08x7mnrATMLeWesT5N5tHb07AVGe/xts8S4A7gOwPsBvIGItvn/3kpENxPRzf4+PwdwEMB+AP8B4M+nprk+mrF4bTOY+6mALIwp9reGF76bkkER2v2ZQeY+qXuchZXX2KcWSxvrFUv0pUVlKOOe5tibqmdY92FkqKM0GrQx15JQlvzTP5/oSCRLnHszmLs01pmZex0zVHc+IGTa3pS1dF/4NmZVKCQzP4kabyczM4C/aFajaqMJyZFMnLuPrMw9Zb/+Q+LfqvX1ld10ZNDcJ11FHcw967O56yfASBew6hpgYVKAmZRlMo5SVeKwbLsnFOAfnzISSgIz8PQXgBWv8I2XFf89+BKuJ7Y9sQL/kKzMvYrhltex6gxV7VnyO4PxsoOcbaFgW8CuB4GLfztep0yXXBrxt0Xa6pQw25j77MWkjbth7tWRgblXPXw2aO6TeUaawNwni1pRG1P2DCd07OUxkd88Cslue/aI46qFLKZFvjQzzl0vq29/+De5ZKEe4pjoFA8z9xeODWHrYd/H1vmSSAUcbb/l58dx5bKIKR3/bNHcZyXUxZmkcTeyTLaXqtZ+NY+fSeM+GeaeRXOvEmnRCCoTwKP/BPQdCNeZGhmW1sZJPtuh0FL/86k7gY1fjO8r2yYNdjXmnirVZWDull2/LLPzx+GfVLqDdObuuS4O9Yyi4oZH96GaieJtlnZJdSAJHf9sDIWclZiss8zY9hpoEnOfFqTJCM28ydPgUB3pFJ9HnvHLa9ShOlnUIcuotvkGrxozDTH3jKs1hRhvnQ7VKBKNe3j/YwNjONI/hoO9Y1UqiS7kopGLWnmADHOvhojm3nCYXpNejLks70wlc5eYUVlmEvHFWSYxNZshxK5Vncw9bVZp104RsZG1fp2517r3tZi73kbloMw6IUmrvxGHahRJicoi95U9F8QMrlZXbEKV1hlK5j7DmKPGXWKSxr1ZRnkuG/eqmGvMvYrm3oyOqq5QyCZJUrWYe632SOz8MbD9h/VUnF3iiTH3DLJMlIVnjXPPxNy1v6ORO0oySZdlCAwC1/fE651hzUyXhrmnI2kR3nrRVM19ao07M6N3dKrYwBzS3GuNMqox90YWbB7tjhaWVHHyd1nHWA/w7JeFll4NirFH/En1OFRDOvkkUc/9roe5689E1lS+jTJ3IqB9WfinpGiZ2OEZbEPsWmvPn5PiUJUwskwGTMZ5JJl7M1j3FDPULYcH8M2Nh9E5VG0R4QYx2Tj3KPSXeqx5AAAgAElEQVSc6s2OlqnV1qqa+yRHIWn112LuY73AeF+QJ6VW2dE1UJWOm0GWSQw5rAOx47UOptpKXLpxrzlTs5FoGS3OvR7NfcFZ6e9mlXeWWDD36lVxvP3yu5vmUFU1VCu4aZijxj3C3BvW3JuEKZZlOoeFUR8uTsVqPBmZu1MWibfSfgdEKNyzX9K03WZfl8kw93SNNVTG4aer17/3f0Q0S602ZtLrM0CXZWp2LpMgK+P9wIZ/BoZPBuXqRblVRo66LFOTuevRMlk7owaZu2Vrx0YdoFWMe5brmDjz1T9mtMePdfe/X3A9cPY6rQJj3Guj40lg108bOzZpuN4wpta4k9+Zzai0v/8hkXhLzwkSbdBol/gc8+WMZs9Qrcncq+SWycISJwbE8ovV6j++JbItJXFYvQnFYlJjwoijVm7xepygUSQuXai1wakyapTtksw6q+aemblrxj0L5P5WLqivMD+5zQkgeMLA16wjhbkDwschv7cvA5ZeGKphOjBHjbt2ETu3h79nLmISrD9W1tRaXUtNOmywHs8DKikvZ71t18O8ahmwpl+XDAYgjTFXnZHoo5HVpmLnmCIVZiUR1eLmE3MpRQxno9c8ySDr19SpknJY6thKc68Swx5KJZBVc5fH2NX3i5YVYvp13A+vgvrftqhMo5dPgK0lAzDMvQqaYUQmo9fHC5vU0T9/6SS2HknPMKkWhWl0kLH/V8CTn08xXhllmcT9a2iK062563VGt9fclnRslvrTOpM6jbtynEaO18tJcqpGZJmRooPNh/sxXq5TwouVHWGmVZm7Hi2DOiYxZWTuurSS5V0POWAz3o9cQf05b+8DsGqtPBZl6pLJL1kjvrctRej85OxV0bAaJ9AczE3j3gyDPJPMff/DId12T+cIHttTbfESX5Zp9Ly7totPlTSpzpcq9ec01hr9nvIw191bpbSncztQHvd3yTBjNFVzr8Xca2neECOb8lhmAnJicAIbD/ahWElIaBX9nphfJnwvD/aOoOIyjg+MY6LsouRkHI0kLRKtE6Bqi4Uoh2qKLJPI3BPixFPbpo8G6tTc0+TX6Pf25cD6D4mmZTK+kc5PyoKFeUDrIoSYPBFg5xPKmFrMTeNe06hkKmQSx0aLqtNIHX2urt2jARR1Q6VIzrBepY56nUqxiI8q16X/EPDYvwDDdaT9T2uP7nfJonWnlVNT084wiWnPL8U0/dSOLoxjAyJEcngiGqKXsH8G5k7yFoBxz2MHcO+THYn11iw7lHullnGXmnuaQzWp3HqZOzXA3O3w9bRy8X0kiLJr+knHS0mM9HZq5EZn7kaWqYZJyjJRPbFeHHkm4oBqzOr2jpbQNVw7vHHSj4J8aL2klebrZO71hNtFjb0OGRo4eKR6GaHyMnSiU8nc6+koMsoyAR+VDDPSAbMX6LW1ltrT47P9j2IlI3OPGfdIe3VdPfXYLJOYdImlDs2dLGR/E5KYO4ekl/j9iBt3OUM1ecScIMuE2hll7noC3lmS8ndWYrJkO+tDlQTPAw48ChzWlmtrkFJ/8+mOTL040SSjZawqzD1LlELdv2cYajQ0HMnC2iahudd0qNZRVsZoGXX7vTT5gAG7IOLcM8gyATJ0hE5JRJydf3383KPviDTg8lnSocsy7LPs1DamTCar9RwS0pk7swjlnLcsXEfUoZprTZfv6mbuaaGQlByyGWLu2auZDE4N5p72grkO0Ls/vr2R2YrRY0OTShq1utmOm3S0jGLuss2TYO5pL2ri/tqwNN6o4M+dPwYOPVGlHbK4DMx5Usy9gc4sNfKmkmk/xdxjIZQ6c/cZZwZZJjguw7PS8YSQCDtfjBv36Hc5arAS+GBoRFiLuac52auNgGSHkWIV+w+KRTK6dgbHEIVlGeZwKoLYKI3SiVbia8DhH6QaoGSZyCxX2zhUsyFrtMzBR4GXfgAMHYvsnyEsLrXuhIezQUpNGrtqcRLyZMv9ZLRMo32I0twTHKrVUHO/lN+jmnvSS6M7yLp2CgaZhPKYJt00YtzrkVIyMPfYZJg0YhFdaSilbHlvXVcw6SQGLQ1DTVkmkAJqxmkD4dFCNVmGWZNlqjB3Tzhw+8ajI4wkQhCNc68yk5QhOoy0GbDyupzcptWnG1l/W641vT6NuUdJVPKVjMgy0qGqOraILGOZUMiMyGic5ALW0RjvycgyiWy0QePu3/xl4wdw5YnvBjm8o/upSUyNMndpQRKYe73RMtVGPVk63fKYP3svoyzzwneB578VMlxV21hvCKJE9y5g+Hj1fWpJDjqiceE1JJ95e34EPPG5oP26nCCZe5IsE3Wo1hMooEtjUeO+979D5Sb7a3wouc/Fi0cH8cyhSFhvGnPPMoL2HKA05Lc1RZaRksfg0aAsihj3GHNP0NzVMxlpflK7Ep91vVPRmX10VGCMezomGy3TDFmmanuyQb6I88q+czaWqErtKKrJWrBTEsmqZCSKVY2519A6fYyWHOw8OQzHrWPUkzRD9ak7gafvyh7aNu7PiFVLlKW3MalN+7qGsbdrpOo+AIAdDwBHnq3elskw95TEXyrsrlrIXjVZJrpvXYEC+uizxqjFrTLqk8bdc1FxXHAmzT1j+oH9D4t0FoqgVeng2QNe/IGfCz+ifTMDdhXjHmLu6c0Pb0ww8KR3EhpznwHMPeNezzA7tYxJMHfFPCZRf2I70tsS+Nwy1jN8QhjFgxv8AqTmLl/g+pn7wd5RDE1UMDhWTPw9/D2L4c7I3KUk4RQbYu77uobRN1bHYszVUA9zjxn36sZTSQEcuUfMwZC+HodqtXPsOxDJE8TV28c1mLvqmFzfZEc194R9Y+kH0tqq+cyqOVQlxn2iREB4FmyEucfmWKQ7VBN9XUnMnb2gkwj9HnlmZssC2bMODYeM6GU0g7k3QZbxj+Mahs7yHwY3q3GXD2lo2jYmp7lLEpLVgITqr6G5V4NdEKzNKYVD2dLaEOkwqR6Hak1wgm1Pk2UiibbSmHv01ldl7hHjXpkQ+X6ibUQNzf3F74vP1VcHlVcdFbCWKreKcfeEE5GzpB9IYu41O+8UzV3fpodlSuMuJT3dqckeRksOcjahNWdXZ+6JiLY3IsskMXsr57fPyDIpyMDeahbRBOYe2jY54y7h1iin1u8K0dDHpkbLVNPc/c8sYY5ZOxpp2DIz94hxb8bzEio7KsukdBTRLIo1JBXVoli4Kqdr7lJjVoWEjaiVlIsmkVHWYO6huqsx98Bwp0Z2eTpzjzxLtZ6FLMxdTz+sSI5kJpq5Yw8vHR/C80cGVZuD/as3I1anaj8034Amkcn3wY917xwuYXC8hnO8CZh7xr0RWWasGzi6KXn/upl7ko7WoHGPjAJCenZC6TVn6ztl4ayUTF0O8atp7tXOX/tN/ZWlY4yF9SVALV6R1biXqrPGlO9NZe5KU82AqEM1RdNODYXUv9tpskyCLOYf1zrcgWuOfRW2F2lHcSi5vdU6n5Ask7hD5BtF+AMLzX60O8zcE5lvNTTA3AGEZsVe/Fag0J5QtC7LsPb/NE4RHZVKWcZKdqgCQK4NAPDwnh5sP54eHdcszD3jXgtJd+LgYyJlrdqn0WE5wg9ntTozIMoqvZRy5Pa03xWe/6ZwVqp2aZM5gGTNXf/bKQuH50BHwn4JbU7zGcRitRPaXRlP/02HMu4Tie2pKcuoF3USHXpwIOLMPV7WwHgZxVJk5aUUZhwb5KgOWGPwZPtD+mjsfPVzBxA37uNayuZQtExG5p6hg2WyYuYeO34EbPpKkICMLPSPlYIVxtKYu76NCCiNinUD0vYJjVYpso2As64AFp6TfI4pskzy0+K3NySByueDgt9lvQCQF6GYHgdzV6YSc9C41+i5s7y4k9LcM7DHzGWJ46Tm7qYxd7/OmrKMirbx9/Oixr2GFDLWLUIVZU7zWuea2p64kR8rOTg+qBm8qtEPGpRDNYW513gVJXMPk8Rss0cVLn1XUHSGyTe7O0ew/UgkEVwaM47OPtYiT9QPRGL0VYu5j3YHna+KsIq0V3aqlh3sBK4diZMo6clNUVNuRa43B2G+sv7BI3jkqY3Y1z2qlZuFuQPY/qNoA+JtoRTmLn+LFR2fxBRzDId+9OvR5yDIexWTjyRzbwWD/d2m3rrPPeOe8CK6nqdlv8sgMzR7EtMkHaoSaQ5T2ezM0TJa9IKoqIrmnqbBRvdTResML8oc06QFxvc3H8X3Nx0F8mJoql7yNK1prA+YGAza7BQT21ObuYvv4VFPSruTsHg1sPg87bjqzF12xJVy1lBIWTKH91PM3R/qW7m4cY8+u717EXSsidWF20FSDnQxOlFlpSVmjbkn/R5l7hHN3dE69fKo+vOskRfDdUwqiikKzbjrzB1A4kQsLRZdPbb+funzSziYGCUd6GQhthwgacadxXs/K5g7Ed1LRN1EtD3l9xuIaIiItvn/Pt38ZuqIX+gn9vZg65FBOJ6X7QFpikO1ibKMioZJNnTSpmeOlol1YlHDwRgpVdA9EpncFVu/M15f/vizySvI6/VEjTuAQX/WomtJ56BcIT5BDhjpFNPJn7lbZI8Ekpn7rgeDcE/VhKjmzvFTyZKiQP9NXpdDj6HWSCH1DtWYXRqTZTxX20iCIVaTZdqWAMVBtS04ksVoTCJBftncMYCN+7vgeB4GxsuoJI0gs4RCKkQslx45JHO7AJGc6ZxyHxKMZLV99H1jpMY/Pik/jix7+UUBtanGrlUEjubwV3VEHKoS+TYwgJxXgjUN1j0Lc/8agN+ssc8TzLzO//eZyTerChIMzuE+wQaE8avG3BMmitQty9R4AOuAkgz87+nGnUOfNaFC0yKRFxpz3358GAd6xvDQrk7NkZv+wKnR7nhvumwTu77xEYArWZNkgkmMtiuBR5RHgR33h7d1viT+JbVBwYNHdvjaZdCqQ/tKI+GUE2Y7R5l7Sjkpjkw5iUm1z9NS/+o+kyhz58izrmdARIRtPnVn8LenSxSi7v6xEix2UXI87O4cwc6TUWcfV5f0oteAhCxTdj30j5XDHVtlHImInk8itOdT76RSmXtkgpi8j2nMHQAueH3AhSRzT2rK4adEThvL94dIB3payl/AZ+4sjPtsYO7M/DiA/lr7TR/il9ryb15NZpsYxVGvYQ6z7VC5dSN8nOcBfaMlfP5Xe0P6tNLcMytIKQw6YZbh0f5xnByKph1OZ+7M0F7WNOMerz/nP80VTzq5pHFPeEmTnHtDx4N0EtUQaTOBwWRF8vLUYdyTpJgqx6aGAFaKWoSQ1j6ljfvQNXddTrDshPwvEePuSaeeZlaiI6MEWSZvEwgeHFccFUsTzKzJZ1qdQ8dFeojIiEImQdh+fAh7ukbAOnNPM+59+1MmaWnQ37lQOTWYO3sYLlbQNeK3Iyn5mZJsAklJae5Jt1ROlgKJ+RdSekpL+QsoSdL2SnNKc38NEb1ARL8gokvTdiKiDxPRZiLa3NNTbeWhKoi8vH1jJYyXhLFxvFq6XYJxb5C57+ocwcEe3RlUP4IJQb4swx6O+os37O0Mpswr25y1rVquj1D7ElgyMWuj/9iMGm1PTd5IW4wjytw1Bp+zxaOmRiduRFsOtTvB2KYZhSgSQiE95CbB3D2kywFATJapdosmBmObYrKRYsgeMOwnvMvC3P0MiIRwebHl4vRnwN8pb4n9EuUYtW9Cuug9PxeyWWRxbeFQZZQcf2QaMu4p6xcMnxBJ/mL16hdUuw8vfk/bJ6HdSvsG4DnYcWIYD+/2bY6VZPbizlaPcv75JEDPCmsXNOmpikN1yflgBgZbz1UTE6cSzTDuWwGcx8xXAPgCgAfSdmTmLzPzemZev2LFigarC1/qvV2j6gFuSJZpUHPvHikHTKDBaBmK5NxuPf4MFnRvBRAehcg/63aoRnOEOz577Nkd3j2p0wNCD2io5ujkENXQhIUm/O95OyUiKMTcqxj3rItXJzhUpUwQ7KONZDyv+v3jWsw9MvpKsu5yZm0xbtyDyclaB1xoFwZC+htAIjlWSLfn8DWxbHH9OFyeFc3rrib5QJ13iy3em1TjLvXjaOcfdSbK3aOGq9ri2tCev8TcSnoHpm0e1chh0jUnO2hv9NlJkmUUcw9MojTuidADK+yCFuIpRwyaLZJlz1+B0utuxVDbqtkhy9QCMw8z86j/988B5Ilo+aRbll5hbJPUrt0Ic+8bLeG5jn7haNWPrcbiatafNCmmfubO0DP4Sd0VmNclluBzPMaAnxNFGgzXY6GfhjL2JSDKgGX7KhMid7rUzCFkC9VnxBh7giwTkinSGDBjcLyMiVIg30hZxnGr5A2vNz1CEqKyDCdp7n47H/9XYOf9k2PukXTSDICjOUoK88VngiwTa7bnAPl2cYyUoSRz9yLMXW+3lQt9V16UKHPXl3j0988Tg5hRcSOjCFVYykIdegrd0DlT+BWLztaNoupgO/jxF9u70Jm4clltWUa2CwBg2QnyWWDclZmwakXL+MflWkLx+6rdkeOe3NeLTR1C4Z4TzJ2IziRfQCKia/wy+6ofNRnEL7Rk7k6Eue/rHoHrMcZKCcmY5Bb24jevPCYMqDQ8274NPH67f0CS81E7vv+QSB+bBp/FiFF1fEgvmdauk8P42tMdGBqvBMadWbTt+Nb08oE4A5aflfGYbn3e4DPw0uQQZgwXKxgvO+FLF3tpEP7OHn748NN4ZttL6iApy8SMe1ZZJisSomVixl2/Xz17M9RX5UU88Xyk/sARpyCNYqIzMtIizxXXN9cSDq+z7LAUMNAR7hjJ9r9LAy0+E2foqrrFPjaEVl9OZe5ypnMufB56Ii5tGbnoJCaWzD0pNxBQ1bar8wFjvOLhUK8W+cPRi6dBl2V8OUuNKKxc/FYoYxvca050vAaouB6ODIyDE2WZwP8hy9zU0Y9tfrqD6TDuNROHEdF3ANwAYDkRHQPwfwDkAYCZ7wFwI4A/IyIHwASA3+eGE49nQBJzhybLaL+XHQ95ALYVGU5qD/zXnjqEwnIP77v2vKDA/Q8DXTuARauBMy4BBg7H6g9NDtHr3PptFGwLWPnK5PY/9e/+8YEsIx86BseY1lg5eKEzyzJpCy9UxtUUaIkWZwT2yAlg5cI40wdjxwkROdGa02bvpcky8tp4Li7pftDfuAxAIMs4ruc/PQltrSbLZEWiLGOD2Undp3a0TPYX0WOGRznkoLFsmW88oR5PO0784fiONzvwM9g5Eeqo46UfAOf/ulaHFTIoSnNHmpxFqj2e54GYUXZSroOScqSxSzDuVg6AT1zSQiFbFydKL1WVr4TBdujAtAU8LDt4Tn0tXzlIyU44QmPu/havRiKxg71j6HBasGDhEixRzF0rK+pQ1Zs3DTOMahp3Zv6DGr/fBeCuavs0Ez0jRYz2jeGcxW3CiCIaLaMZWpeRh/biJGjunuehezgybEzLbe06cbatPcq7O4cxcHgAl569EAvTTsBnW8xiKEzsoMURzlOPASsSDkmEMHPPgpg+7R/nlMI5reWv1dbvDJcge6Vk+Net7MSjOmwr4lBNauuJ50VO9cXnplSQ3Lbw9rhxrxkKmZZvRZZXh3EXT2DkzVWMNz2MVjXJrQCFeeIYOcqyC8B51/l5yjX4RndP1wjmFcpYxS5YGuyazF2TdTwXBDGLOGiRXo+nzqPouNh/fBCXrV4W0fzDy8iF+J1bFtasbUmyca/+UIn/Jxp3D0DCeqVAmLlHYdnx0XqC5l41WgYB2Sojr4VC6rllVOHxJswFWWa6MTxRRudQMeT8ISXLhCcOVKS3PmSZEHnJkobKKT3u4/+qaWviQx8tnBwUv8mXpBrEIYyLeh/BGaNCxvGYA0OrQQWY1OtQBVA+/iIqjqZlO3HNMt+/VyxWEQs1TNHca8gy47HzZ/UyxZKj6cz9+BbxOR6OvkicdJJmtBJkGUYkFDI6waSKFl5LNEjaPeZQVMY9ThoCZUFj7mQHqY4BlGHj4b2a70jCKYLB6B8rY+vREVFYhOkSe8nhmQx1rdhzQfBQSbqkRJrmnsPOE8N4eGeXuI/6vdPS6cZkGSnltaRSnnSouSAajYo9f0nPKcWMOycY8ABaKGRkdJ72BIiJSAQ35HiV8wc0opnE3OeCQ3W6YUsHuPZyKs3dDTN3CcV4E2SZS7p/BisWIhjWykJQhsCXGbyAAclZZ1kItpRllkwEkg+zOK95pR4sGxOLFHgMzBs9hBZnGI6TNsSOQDufTQ99H4/t0dhSNM84gJbubcJPIB21SY5n6JuiIZMQHYNf70RZSBIUOw6KWR4fnMDAeDlsjJU0ETnPxEWZUzrQFOYeZmoc3q9WRsQ6IExp5LWy05m7EsB05m7lQsZye2cRLx4fRmd0PkJ5VMWmM1mA5ypDLt8PC26yddImSTELrT7mCAZEOyQhsGw/vFHriCS0DjiWFVKWk5SNETUusTS0+nOn9P3k53R/9yj+e1dvglHNoLmnsf0ECNtOqOgCiL5Yh1ZJlJjNpTj3aYPUblVOLATGvegEU7ZPDsUnASUzd6CtkjI5JuEGDI0XQ0zI9RjY/kPAKauIkLSZpK4nHJSyTRTZj1mUvLbrflzU94h/nozVx36GdSe+B093qFULDdR+6y+sAjGjxFac+Wn1RrZEPin8SyQ1KgDgmXuAoeNgZuw+IZxGhVygzcsOVo5MjvSPY3fnSCQUUspDkzHukZeIGUx2mLnvehDY+o3aZeltSkL7ssTqmazIkpnSoZpg3P12FSu+YWZPGEo7cD66aeppeTwYwaqZv1L28zezl8I8gw6OPde/TgnmINcaEALfgKsIqxRZJsbcAXE+diMOVQGPOQgdjslc4RJ6RktgEKIuBMX8rWyaOxRzT26hlFYcaCNLJcvoDlXEwkyNLJMAK8LchQFhLJ1XQLEsXpBjA+P47nPBQgbB5LpAW/aYsfGgCOrJeVHNPZm5D46X8cvtJ9EzUlIPiuqRBzrUDUt7WB/d3Y0dJ4YxUXEVc4+y26jT1PGkIskIJe1KkFeCE9YXLBAtevbwKF46nqwtx9SeCHMPyQwMLd45bqzKrodSUXSsepegUihEK9MNuWLukXKTZJlazP3oc8Cj/yTqJjte70inVla1EZF/3EVviv90/uuABWeE9066+ZYdSw8QLX+46KBv1NdtI8zd01cQ0lEeU+GLdj7stNU191RnpH/t2XVhUcKIAwgbZCtgzAxOlWWimjszi3JS7lmW+IvQLlHjHu3QITqYoWLKfY2N5KARlmDUoZ77lObJkXpFN+4pKX/jxj25zGZizhn3gAyy+rxoeRuWtOfhMjA84WihjwIeM7qGi9jXNYK+0RJ2nBhElxYvm+P0ONze0ZIKERuvuCD2MFIMlsqSw2IgiMpxXI4PoSEcroDfizNiWSE9jnME1/M0m6edV8pMv4HxMsZKgZEg9oQ0YdkoJoqqKS9XeRwoifbqKVxZf2gTjiu7HnJeGQVbOztm5IoDWDa2P15X0gsfNYJJIWkb/1/iuaD/gDDq+x8Wh0K86E41f0W1hShke5esSfhRvsja7kmyjGRz8v4NnxDzDZhDl1DO6BTGPTCqHgnDGRvKV8aU0cj5UoWMCpLFWuykMk99JrNNVZi7hBYKyYyILKOFQkaebAaEQ/WMS4H5K2IdYhYwNOdwbE3Z8Pm15m0w4sZdnZ9lJ8gy8jNoey3N3fbvR4V15i5DIXWJmFQnLGGYewJyMupCGjwGbGK05sUFHhovBaGPkPswDvaO4aGdnfjxI09i+IUH0dEXTGfPuSnJoIjwzY2HsfVwINtY7Aoj7N+ccS1UUd6vzuEivvPckVhvLW9wxRUjB/0BIIiwuGhUh+6A9BwHjucfO6oxT7k7GLs7R/D4bv83Oy+Mux+el4aY3ZsYAJ66Azj0hChXexBFsz0/9j3eWVQcDzaX0ZLXI1QYrzjyXVzU9wjYHzVpJ5XaLgWVipVTpSUFNatT8Ut4sFNXuaq4HoqlauluPXQPF7GrczT+W1IOcEbcSMrIDXne238EdO0ExvthO0HctpT1osbdTZKlAMAJMjjmcn4H4BtsxdyjM1R1yH09DxYhmbknGHcCwOXx8MIfunEnfRKTz4TJAloWAFf/cSysM5OPSjeWciT3zN0i9QFHjbuVwtyzyDJxo5vWPovEj05Ic9dyyyCQWqO2YBps+xw07mo+iH/xAFjkqbDIsXIlZgB0dv3yvocQRcH1DX3ndn8CkibL+DeHEbwsujEc1SJDok4T/fug5jysuFJqCdppWWIoqz9IBNdn7vJBcbGpYwC7Tg4DJ7bFzkMeW/IdmrByfh0Ml1KG9kgfFgehcUF+agZwoHsYX3rsIIYnwtPKe0ZL6Bsrw/bKaM1pESoMpbUzc8i4c61kUYAaMnf0j2NTx0D2kFBJACiXytw3Hx7Az14IzzL1mKH7ab717BE8urc3fnBSRAZr2q7efrlYMxC82Zv+A7aW61xdF8sOyzKSuSe0QD7bth1eN1cVFZFlSo4bjqn3P20kdEpAeOKRJo/Zm74UjjKKyDISLJlwlQlBqXdTd9IyAh+VXtbIyVgJRASGhVj+MykXVUs/AN22JFzxRasi7WZUODw7V3eoPnOoHw88fyI2h8Aw9wTIaBlpOJkZNhh537gXy27sQsqFPMSwLn5R8+44jg9OwNnxYxES6N9clxkWywgQUYbFjrr5NgmtVBqbJL0cEJEhX32qQzl+K44vy2hvne2P5KJRQK7rBl2NbxyGi45a9KDieqpt8pqQNqPQkrJMFeZ+YqgY6xDLrocXfY2eEXaQnRwUneFYycGzh/pwsGcUzIz93aPoHS0jzxUUcnbIQKq/ONw5Zg7vBNDvL8mW9RhVZw1ZRt5jXPk+AMCzh/qx3Z+8pfwOsMBgFCtusDScXAw5VCfHtknj7nouXjo2FEv3sKBV3Bu1PeJQVUY3wR7IZ8+NLEwRzFB1Q/du65FBbDk8IEZBbgUnBhAXwRUAACAASURBVCfAnivIRS3m7hvF+eXueNRVTJaJGMjQ4tQZ77lWZmnl5YHYo/tgIlEpYpMYWSepkMxIjnNP7DrFtvKyi4NN8+I5scos7x/j6YN9GC07AIKRxolI6DZgjHsictpqKex77S14sC2CTUImiV7Iom/sxSMXv6itzjDu37gb2zWHI4Ox8/hgYJBdhuexL8uIh3bFwla4HqNvtAQQxRil67OqPt8YyLJ6F12qnKQSFsWZuzLu/jbbN0KuVVB647efPYJ7HhNLmIXC3wBfcxehcNWMu+sxjvSFsy7q15CjrMYtY9n4AdiWuP5dIyWMlwOa1Gq5/uSrQKNXbMjz4HFQnuPWftE3HxnC8cEJpTlnnamrCCqlyzKAljnRyikGp0ZkKs6a4HqM548OYl/3aNBdJTF3ssJPmWUDZKN790bsePZXODFcDu0vX/SAuYdlmWoOx2AJxvCi6PISkZZeQ7bZ8RjjFReHe0ZwuH8cxVJJPH9JBifBoXpxzy/jbYpIR2penJ8hslqIYer5SSM+bznGz39TPFoGwHipjOODSRlD48adicQ1Tpih6jLwyO4uTJSDzlDaCmfhahxddLVfbFiiJABln7mPFB3s7RrD1iND0OdScKLmnnzKzcTcM+4yXYRmCG0Sf+RtCxMlJ3BMQTDikrrLCawKgoksLJ3EhLZfz0gJWw/3KVbneoJxEruK5S5uy6O9YOPkUBF88gW4ESeno6QI8V2WNYY2PxQyLMuI+VBR5h6EVEnfgGMV4PnGvX8sMBSS0erGisC+QzVX9YGK5hXR2bHO6BjAosGduKj3YeRHjqvtR/qDFyxvW0HkEIujVGnsBSwTqCKX9OPYgChzcMLBkf5x9V5llWUUeyS7KnNXI51EAyTLsHC0X5dQAMg8IvreSbKMlVdlnzf4TGiSG0MUYVPEuM9fGWlBMreUp+Wo2ZSB/AUAq4a3qudPv6eOy0qWsdjxNfcEuSLJoZqESDZFdY+ULKNf2wjTTi3TDspgaC9S0I5HdnVi4/6ecHQOxD1wYgX7BCwhzv1I/wReODqEjQcD+S1s5ON3QQq2JU97P4hgKf9KcA3imrth7jFYJP7pEobt9+j5nIWJshOSZSyLlOESsxXjF9ViF0snOgAArpUHWMz6IwSyjOMxXM+Dxa4fjUOwiHDWolaMl12MHNuJZcceCZWrpCNVjyhrsCQzMQZPGMHPSxJi7g48L2DuBVc431xqQbkcHhZ//ld71XnrUQXEYmq5S/mqQ8Go7QsZwwhbkazM05ZMG5wItPOcTUG2VX/YrIyQ4+D5o8FKP2kSS8Vlldte3jO5alFmWUZd+Fw25h6Jzx4rO3jUu1K1oW9MXHMmS7RBzzwo64TcThia8JO+2cK4y+tf1J5P+UQSURABaolcMixHPAmny2CUHDdg7vJV9g22fonkn2E5zIMNKTUKWcarQ3OP202tw7Za1OgqyOdSxdSk3U5Vnxg1BctSirIGxssYnqiIuHu9OH+3sho9BG1RskxENpLEWrfJnt/2UMBySF4SHxOuzEHjk0cKn5Rg7iYUsjaYQb4EojpyBMy9WK6g7AiZ5tcuWhYxaJxMfyCkGQDoGROz9WRaASmFDBcdVFzWFj8QBmzZ/BbkLELXcBFWKRxHLg0kM2Nh8QTOGX4ei9vyGCqLDkeXZchnODHN3ROTUCwC8p4wdo5VEAswR+jHgG9gY5o7i1jvaiZRRqJs6ujH0EQlZAyjq9nLNlaiGR595C3SpAZVAQA5WUcfCcRbFY2Rlpqz6jDq1NwLBREmm8b4leYeWbHpBe8CbKucq36ruAzbInhkax1MEnMXxn/nSX9WqW/c5TFFPUBIjj4tCjtUAXwv/3Z8ufTGxGt0YrCIrUcGVXir40sD8t4nnWmIuXuMHAUjPSHL1IqW0bXuyH7asa5VCCatwWfdOrP3ODZ6iaHQHmLuLgeTmI4OidHq7s4RtS1J2lEOVe1Web5EpHaPzE2xtFDIY0tfDZzzKpSWvkI7z/AlIGZMuLpUbIEk+0x4ZySM5p4IFs5HL3jkZZrcgk0YLQsjvKgtj0WthVAPqWvuF66YFyo170fMdA6NK6ZJ8NSL3zMiIkHUy0PCgNlEWNCaE1pdZDKMfJkcj3FJ94NYMbYPi9rzcCmP/d2jYVnGfx6G5l8QtJcduL4BtS3C/LJYoMCxWkTZbgUtTsCC5QujOiA7B4LIG8JkwWFKNBSAMHxjJReOJyaB6cw9qrnLBzUtHYJtW+q6y5dON5o6Q0yy0/q2owMT6p7JFyKzLOPvl/cn+BzoHk1ckEJn7roBbC/oTkLR5va8DQ+5Ksw9LMs4HvuyDKmydfmVITp2KyrLADhZbMF4bok2xyBAj7+4uRyVutKPocIbwx0kAFTaA6nHcRk5yEADV7xTiZOYgkRzegrc6B34/pbjONwnRpaO1RK02b8+ZY9Vm3adHFLOelFWwv0851VQqRPJCo1gth0Lnnnyte3QSIXFvXEUc5efhB9tPY4dJ8e0a+qTEDk61OxFxWoFXv5mcCx3jKxHfI4p5g5fqbMhnwR5TNS3ZIx7Enzm7mlGRjL3lryNcsXDwZ5RMXPV3xcQxvPcpW2QN6ctb6vwSQAouBOY35LD+EQRZT9cjJhjeWf0Zcvk/cnZIhqD3fC+8mXWZaL5LTnl3Iwyd48Z3YuvVBkULXbhucIhZhFh6fghUa5kRr17cOWJ72Lp+EEA8CdXaSGWZPsROaJTEzlWkllO2fGU0bSt8MMoEm9JJhYYX7eSHB9uUaApSqeqXqPOEJkZHIlA0FlO13BRGVCluVdh7gxWaZK3HfVzZ/vGsm+sjP09o+gdLYk2yedHGXcKGcWWvGbM/MrbCjY8SzL3JM09PBmICD5zJziqowsfQxAvezAYCGvfyhBpp112ZVl+R+vr5ZZbgkv58PX2P0ttZ6ptjscqJt5iV4wck5h7PkgR7aaEM4rrSDjhT9xzrUIwI9l3Zv9qVw+eOiD07K7Ighuxx/Hy3xNZMEmXZYL3xdN8A8KnxImjuTLrfh+xd89ICY/s7dPSk4T3IS1xmNbC4M+Y74Ax4VhKQlMdOzPYX7ydESckdaSwaRhzz7jDHxpzcEOk5t7ipyJwPMZoUYQjWdpL+ZZLVqqO17Io8l4ylrTnQexiYLwkHJ5gUCQftm7cZdl5i1SWvHktNlYvES+E4zH4+PMojQTri+csErq+X2dQljDOR0Yt7F/1O6ou1xPee31ilmMVhHEZPgkAWFDqirVxf+8Yxiqi/TIzIkNMLEpyLlbcIJ+343JokWTxsvot5oAVRh3IEgQKpYmIviu6425v1yi2tb8m9Ltu3EX6Bam5C5QcL7aIs8dCSuseLuHFY0MYnCgrI2rnAuY1OF7Bvu5RHOufUC+cLsvoL6Hebslq2wp2zGGoo/vl701m7n77AET0YRbrXxOhWHFxoGc0cI6quoN9JdSo0DfyjjJk8Zmm8jAnFKXkQc9xnxoK2bIgOIb1uHMWk5HOuUrMpNCug0v5EHMX8zosbO4YQP9YubaslmuBWhQcUKMeSVr0yC+SSc/87ztODGG46EDPLRPV1z2yMTBeCW1jjQTK+6MIDYs9ZVuCayA6F8932HueL8vIJG7++ynbr7/DhrknwX8Z9IkmFknjbqvX6tdetgxgTxmZnOUzeX8PO+HithXEw+RUyn5kjKdyxUu05kSdeohZzraEpus6gmHPK4ihYnkcnZvuh/Xit9XxtkVqQtGiVpmIKWC6TBZGygGj9FwnFC4HAK7VIoweB8xLB7GL7pEynj82ghZnVBgvEsz9aP84Nh8OJ0pry8tQLvHAj5Qc9PlROBW7TRmL7vkXo9y6PHDQpTB3yUSBmPQojosYn61d2oIkzDjQMxY5IizLHBuYwPNHw+uR7u8exXMd/SoGfaToYDy/VByXEOXRP17WwgU1WUZrbGgk7dfdXrDBkMydAc2Qo9COUtvKkAFwXE8wd824R2UZ0UbCeNlF90gJ+3qLodGe9H8kmUS5zeH0V1mFQEpdHn6nqflM7AhzP9w3JpaE03R2fbIOA2JCj11ALAkekTbSE85LaTy//nRHzLkdH0j6189n7o4H/PeOTrWj3k6bHWFg/d+GNYeGHN3E9HV9ZCVHAxw8WzJ1iDwu3Bfp0TJykxXMOicSxh06SSHh47B14x495+Zj7hl3MGwKM3dpgFt8KaaQs3Dp2YsADiY3iV6T1XjIjjH3ILWB54gohwtXzsPvvers0D4XLmvDJWctxLyCpYbtcto4l0Y0w8bwSiPoGyujxQmmrltEOH/lYgDAteeLT8uiIKMkLLS1CJ2TIg5VCcnc5ZJ10dWbLPbAsFS0gLhqwrh3RhcmAXDxmSLPdu9ofCHjl874nYDd+GMZiTTj3lqwQ9Ey0WFudPkyPTfHwHgZQxPRWas1mB6CFaukg7FYceH4erGVSw7hkwxS19x1ViknTUkQhJznWjmxHwfPE4PVC64zYMdjf/ZmIB3osoxgfxS6vxsPDeKLj+5X3ycq4VmnSRq1w4Te0ZJgkCmyjhwRFHLCGEX7Lt3oiYltwfkBQNmLvDCWWIQ6xGxVndIQi3dVl1Jiz0P0fOTDY9noGi7i4d3C16QMMdnqGVk8cQSW52Bf12hsIl4lQXMXn5pxl/MCfMKlhxYHxyW3T9/qeCLzKYuG+ucZ/O55HCKUs2KZvVkHFsObkuNBPp4WSQZNuHrNEqw+b5VY1ccth407Mxa05VGsCIP6ijMW4MRQET0j4iWW6YTZEawubwFtetoIACvm2ciX83j1siXAeL+qFxDSRmuehOTjMcaGB2FFQ6AswusvORveyFJM+O0nAKuXtoMBvGXtWThrHiHnLkK/U8a5B76NQS9YgxQAXCrAcxkTh8VaqtGl1AgumAhlF2jTtiZqqpByVhwDbeehnJsPaC+FHhbGkVmKbXkbF6yYhwWtOaX/s94Jk9Tgg/rG80vE8Ln6cpUA4hEHOuTLUnY9uFYBO5e9Gbnu7QCgokIAYRikMQ9kGRdAPibL9I1XAG3hqrxtIWdZYBkto63SdKx/AoeGJ+CsdMKyjCtlGU9j7uTLMZKhhl/26ISzw/4EM2VkEi4Dk419XYJEuPkCDiy6Ghf2PxbaRxrngkwNoZWTS5Fljg5MYFGxgpachbFKRLIiG4HJDTdKkgiVkTMataZA6X03iTkksBeFjmOysfOkcKq2VwbQXhnABOBPLiO/RYSKSxALNaV0HlrrZSipPmLyWHTYocPVrGTxYMsRi+NPcmRoczxUq4VPzsgyNSEiYTryL8OwJ8K0JHMnEK5cvQjLy8eBPb8ERjpVTnELANjDZecsxqVnL4RNhPZCDi9bMV+9jHbbAhFv7ghdMEccSgf76guWIU8yyDvYrpKZeewzd9HOXR0nYpkYLQJyLW0o2La6+BYRCraFl62Yj1ecuRgL21rRXshhZanDT2MsIoEkqCBmxsr1TaOyjPhOGNMIMJOVOkuViHDZ2fFVcmJZ8Qjo1QrVWQ4gOseFrXmfiUqHKqsYePlw653Mi2e9W0V6MDiW7VdUKxlveLu38Jzgb+3HgdZzcZDPFjN5AbSwxsY0Vqsc8rrmXiXPVkvOgmUhCIXkQJbp9WPgR0tuSH92PE2W4eB1V9PlOCxjAWGWqyOI7Ej/DRDheYNtq4Pf/GqlrJKzwgEJF66Yh5ULWhPj3O9//gRe7BRhlxui+XV85i5yrSeDYfmMNtnUqNwzALD+D0X2zbalfvkiL77yuch0ximO3cHxirq/0lt2qC9IixzNwqqjknDNSxUPdz2yH7/c3hk6P5eBQ72jqHiMfE78UnRcNbGNNVnm5IK18KwcKq4XMe6pTWka5iRzXz6/Bb25l+Oppauw+uTdirn7O4TGQ2pxD39b3raQbw0n0WKyQOzCKswXzlqnHEghnourzl0cGBYvPC0dQERLIxARiDlISKaBQCK8jCzlIArdaLJVKNyyFg9dA8Jwt2qRGy0tbXBHg3O0vbCRJV+W0Y1oziYsam9VSz1GsaA1j1ecuQB7Oke0gmTDCAtbczgJC6NlL7Y+7FXnLsbAeEX4GhatAoaOhRyq+7tH1bUBhMNv/XlLNO0/0DuPDaQveRdLCDf/DBSGj/v1BNvly1+22/3T0GP2NSMq53qlhEIiYrIKOQsEgp3Li8gX9oJIHn+fYsVFiy7LuIEso88gluF6skW6XU0zXkHUTNxI6ccQ4nMJXGaUXJ9cWIQJLZR4xYIWf7+4AWZYeOHMG9Fe6VNzPmSZchFqEZDF2HL2++LRQ77UxSG2rJevmdwFZwDrtCWbtWgZ8X85RkhqJynnpqXSHlvoHCqqZR/T1pNlcDhtbw30jJbVyKQwjzC/JYeRkQJauCicp36fzwyMtIgIpVLFC7J+wjD3FAgd/YxFbTjhX+CczmWYoefnlr2l6z+A0RcWCG663TpPhAH6a47miAF20ZKz0SaNq2TspcAI5iI9sgXxIMrY+ba8jcXtWodiWeIh8DVzS7fuRMq4L20JXuLiJTfiqnMX45VnLoDd0h4yQvlIymJh3CnEUgtwcOGZi1ANS9sL6lxetnJ+oCcTwbZ8vT2B3dkW4cyFrSK01F/cmuYtB4BQXvsgWsQKsRj54qcadm3ou2xeARcsF3MUXM3Bp0s2nn/9ji+8CscXXonBBUHiJ2kYPE8LpWV/FaSILBOFfAYsywZHZBn5XI2XHLXNJqHFOrCVQ9UicX9U9BHE7nL0J9qfnMFTzpiM5ikR5YSZe4juMLDl8AB2d0+o1BCe9HQTQf6XJNsxAeXcfAy2nReqg1nIPCeHZGSZh0puHip2eCm9WsxdWUEfPSMlnBj0nwMrYOFiz0Bzj7fTDv9G6n+B4zwt/TEnM/c0kO6QJWDZ/AKGnILywxwdKGJoohIKjSy7ngpxlsdNNeaecfcfhHMWt6kHpiUX0fO0VYqkEZFSSeyqkqUeGqvQDotIhQpaEVkGQPw7oHT98fwSwdqJAPaQ9/y4X49x8RkLcM35S0P1ylHF4jbtZdZCwNqtoJMq5HJoydlY3F6AnWtRhmLFghbYkZWkLHb84W5wrnmuoCWfvMxZEmyLdHMhriMl67IhFpJrAa6/FVi1HkA4ekE5nskO3QYGoX88ZUgBqHvm+OcsO0NHc/CFjLsvP3lWDkcXXx1ZBk3WqadYYDWLMro0m8R1L1uOsxe3+c2RmrXG3P1yx7UQTelwf2hPP472j8JjRs6yRBSUmr0sGtWa1xyXduAp0cEQ4ZLbIpFCglAEDSd4oU6Y/Y7FIxuteRuWJdilBwZbCdknQ3Xq28LvzrMdg3hiXy9KTni2tQ6huUfLDox0dGGP/3rmML63yV9FjUT0m+z8x/LLcPaiVpTt8AREQIRfulYh/Fv0XU/puBmIpO2tBhIzUDUsnVfAkNci7q9XAiBmrDOCzqZUcaG5zUxumWpY1F6AfNgKlnbTmEOrFC1qy2P1kjasWTYvwrR86A9dfh4sK8iImLMIsfU8E4Z2Fslol4CREgCLhXFeNr8A8mez6vW25whXrl6Mc5dGFg72Zz7mtRc2n7OBpWL2aqFtHiYqru/0JeQ0TdkmyXDCzN3yyii0pOd0V6cHYLB1tWirigQJmHuSuhq6pGQBlgVrxUWoWG3omv9KAIL1nrlI+EhE1sTQQWE5SEPZbseBpddrdQXXUaV3gJihKK99VLNepHWeirlzOJbfgxhNhSdvEZa05/FnN1yIa85fGhAFlSpAXOfe0SArZqkSZAOUjupdnaM41DMGj/28OxDx7CcGJxRzb8kFbWbKKalER/doGSMlJ7bdtihk1OTITZ2bGiUIQmH5k7WYAU/PPploDjS2HmHuwyUPrM28TYQ/Skh6bjzKa9E2CbDsEAM/svgadF/0HowX4mvXepaNrWe/F33tFwIAcrZ2PVXgQlgaCX5HbVmGg2dNJwAWiYmJexe9Dr3zX47h1jPBEBo8c9CplZwwc58OzFnjvqC14DNJimtpGnMnEFYtaRfsOkmWsSysWdaOJe15wM7B1l5wGwnMPQUilIy0yBORl2btOYtwXtR4A0J68Ry05u3kXpys0PZCPg9c9rvAq/8MF52zHM+t/D0AIsbe8io4MyeMo2SLTIT2fHB8Dg7yuRTm/tqPAzd8ym+20MQlU5dY0JoXkQCRyJzzlraHDbX/MOfbFmHLqverWPOFrbnAyRodAlNypwEAXWdcj1JugTKslbaVwd+uWAj9mYN+1JI2Mrh6jaj3D645V0hMPpSTmBEy7uxLOVFd3+Owv0Ocop+nx2fu+7oTVmkCMHHW1dh+xjvFMb6ByVnieR0uOjisZdLUmTsALGkX90of7XkM5b/4/9s70yBJjuu+/18dfU5P91w7Mzv3zM4Ci70PYC8ssAsCIACSIEGCFEiJBHhBpkiZohVhk3ZYDvubvlgKORSS6BAtyVaQCll2GKLpoGmKNj8oSAIkcZEgFkucSyywi2Ox5+xMT6c/ZGZVZlVWd3Vvz9G9+YuYmO7q6qrM6qqXL9+VkguZIVzomcayk8EzQ3eLc9Viq2cBfEbDcya4QKsxpiTU6dp1IAPraJg1ES3z3OkLiVpxDW7MLCOvRdXJILpWgAa5wpIe/q7Vno0J9woFDnQAmpYsWXIKyGdc7JnSV4ICAxYV4f58/xE833+LsUn/79kzWh4GgaGQ8bDo9eAXlVvByAMjB1eW9LBYXu8qqaMrQ+cJd3ETlYQ2xtQVbviW+EIC6mdRBuYxWs7zWO/yBGrZ3uABd4XNPQ0yxE9qYMRqcGpL6Ml6ZuEthHsike/4nlidJ881/Ss+t5+7DuHATB/uvPj3AEJtkcFBMaMI99oikAmFnKbA+PngfFwflWYYfqzdU30Y6sly23vEeVsuRGYDMtMvoiGRkldgmv47CVrN/HAJt2wewmCRP7jvjOwPSo4889p5fblEqbmTi8ObBvA7t89jpJyL+zTABdtlNctVCnfVnk1mY8MbgzfhheUN+P7ZAaPwk2bCbLGCC1m5XqgQ7q6jJcbJDNVMJBxVCgLVV2PSrM8UN+OVsffwz0nee7oSw8DD8Bg5mOgvBNejVmNgjlI7poE40AYMyEK4uj08BiHmUJXhzK+VtuH5/puNjyVjTKktE35X9kMiB6eowHcjRc5e7DuIi9kheA7FIlWqtZooGMg53bMFp3uuh4kzFxahXlsCQzErlvi8XA22VmsijFK0lYdCWs29AfxOyPn8YeQauSLcl5eAapJjLmKWOfhbwPXvCd/7eZwevyuYxrrEYIzNMyBv4KzQwLI+oT/v6CVTVVxPWeDXQEQAZvwwsMlXVACXKIjQAUKnH0Bh2CaAqlcA8jxpqurkMHLLJ42nVWPS5QPT68lFQrJwhbNa2hLjXn/ze16qmYx9mx0qhoWvIkwPFLB3qg++J23bjqjcR3j9vD7QuIpwJ+WauJpQcoJ+LiyFGczSTl+NaKDGyJRsL57ZcA9+fPKSdj9JATdQ4MfKZzPKZ6GpL1rSQjo0d01UlL6E7ZQCPkljlTkQMpSRr74UzqdqjF+D+ZEK8r4b/A7VGgPTFuJWtWsTYbIdGDR7eL21Ws8vVGOJay4RLvl9eKswC2aQ7v/r6dfwytnFWL9dRx+EwtckfFjhDEnlQobXL/LcqEmQJ+8t1NKJQqn8SByEBebCrFz++cJSTeu3S4SxitmfshJ0nnBXUnp/ff8kdk72B8lEAHgUS0LNEzWjEACQK2vp1XAzWFQ0GX6vGDT3nCkmnP+oUnP+zOFp7N5Y0EumqjhefZNPRACqNkTJj8d+A5enjwEA+go+5oaKmBAmIEYU+CIuZIbwqw1HgVwZnkO4kBmC0zdlPC0DYmYZ5wqPp1/wSoEfQdp4YybMaF0TeQwRcQMAxbw+4BUznlFw7ZmsBOYJWeRtsVrjpjMnh6gICoWcHuGrau6yPQzc0S01ZmbS3GFOnNJLV1DgGAeA/YoZxfU89OZ9HJgdCLRTzyUtL6HGwl7kfRdvFrhf5fCmAWwZ7cXm4RI+eXgmMPtFqZEbmqMgy1kwrV0vvHERi8s1OK4ckPl3l2sMNc2hGt5jpgWyghkdpLeBYcHjM8jLXl/8C+I7UYEIyISe0EQW5dnXzuO5MxfFLcj3++3bNgUrRvkuidlkGNGlzpDVpD9pcQcgfA76uao1vgzlcG/Cs4rIzET/+QPNPcrlxeUgwgngyscH94zhc0fnEs/TThoKdyL6GhGdJqKnEz4nIvojIjpBRE8S0Z72N1NFxjURNvTmkEXEtPHak8DbL9b5brINEa6P15TQdNcULQMYBfYNo72Y7C8GIW28Lk2VR4+YaGiW4ceZ39CDwZ6MKCOqs+QW4OT4w0UgbCjllOw4wnAP1/jeKM5j/+YxIFvC7FARRVxCT4Jzta/gAyDNoSqF+xW3BLe2hJnBYhBDHtPcYyaXULhX8j62jJRwYG6Dtkch6yaYN1w+w0GYr7AgavVX3WxM2KlmGRX1YY4OIr4oTcDEIL9cY5opxOQrVPMalmoUzKR2TJRxaNMgmJhJOo6HT988g4NzA4rN3Yn7iOTh5u/E7R/6LD53dA6FjIe7to2Egw9Td+RIE4U8v2buItIEHG+PE3xP9pVpDlXhy3CStEtSBhIu8M7mJ/HU8AcCx7npO9G2kWy7+Oyt6x8A9nxcW+YSAC5VSTP9eK4jvufAJcJwbzbmvzndswVnivM4u2Gftp3BQX8xg7u2jsbMpAtLNdTIjZnGktD9B9BmQ2qfLy8tg5ET+HyIeB+iPpyVIk1v/gLAXXU+vxvAvPh7GMCfXH2z6qBo7kbOnQJ6hoD5O+t8NwE3g6NbJ4O3tVrNrLmrAntsL7BhC8qze7FlYzitxg//jM8g1IHA9YHdv8FfpxTugz1ZzG8oGYSmPEzc7LNnsoJD80OY6sti92QFH90/zW+wfB8GilnccuhwYOaIMj9cwrEtI/wBkMJdXOorXi88toSR3hyWpOYe4BQhDAAAHz1JREFUVYOiSSzSJks80qVSyKCY09uc992YvffV0g7+G/bNAAjNFFeqXOtbcnJahAmgO1RVXCf+4ElkrfclxkvTVmsMPVkPuycqqDo5s+auHG8hOwjGgMGeDMoiOa4mfTbKrFCaLVQtP9puuB5yGT/x4Y8OTFnPAQOFNnTSNXM38ttIAUTKQKYJd1I1f14kbWpADwZwNbMM3+9idoNxcNbbHvUphPtfKYyC9Y7hOz/Xq5teXOT5AOogLjV3fn+GgwYD9+ksOxn8cuAY3Ew8Au3dW0dQLvgxzf2ti4twXM8YSWPui26KIyJtYJDtlcJdRmtFK5muNA2FO2Ps+wDeqrPL+wH8FeP8AECFiEbb1cAYjitWaanT9NljQDEeLsVt83UEvJvBzFAJO8fLKOd9VPIJAlhZwACFAWDrB7jtPtomVtOF+6bbgYpICzcJ9+0fDl9Hj5Xw8HiZ+CyCJ135oKHrkPPcILEIXhY48rvA9JG4UJZdI0IxG0lc9vmD8pu3XYd903z6HWrukQMkDUJaGKguvHK+GxNcu6cGgPG9Qb+luavk1+AQYc/sKO7erhd1cxM1d9Uso7fPF7H///jCOXz9Ry+DieOwif04Vdpm1AdUs8z58jyOD9+laYPDJX7MHjV/gZltwQAfwAHUv6eB2D2Q810wcrGwKBeQkXkfqrYf4rgOsP83cXnrxwCIdYgNmrs8TVmalILzh+YOBpaoXyU0XnnN+CAnZ5mMaQusS5bh4kp1Wfuu1NyJhFIQXDPSbOmqX4rna4XmGFOAg+tQ4jMR64eyW1D2Wxm0mQiJXha1ZnrXq3BPwRiAV5T3J8W2GET0MBE9RkSPnTlzprWzDc4Dh79oFt6SypS+ansAgylOPUAmD737X+OGqVH4DsxmGVd5aFUBbVymTBkI1NKzJpv74KbkY0Xey/vTTUpMIgIG5oBjXwFEtihvDw8hJRDeLMziF0OGSZnywADA0u6HgBs/g5zvBmanqvBNRJ1T0XZGtUn+Rhe+Wc8JtB25/+yAPmjlfBfXj5Swf5ybDIb6+5D19UEotLkna+7RQSSTyQTfkVUxc54LGtsNRq7Roaoe76cvn8UbtV6oCvn0QAE3TvehlIubvgJHKTmY39CDbWO9iiBqpP3GNfcaObhwJUy359tFOGyk7Q45QKEfjlAIuOYeX2mpX0QmOUSaCYqBjKWyo/RpET5xswzAB7S9IiSxxoBzC/HggrAWvC7c5V3nOQ5u2zLKM6TLuilJXYhHxtmrC/dE8V1qoLmr15Ji26PfLfhhxmxvjt+nlw0D2ErSDuFuuiJG9Zgx9lXG2D7G2L6hoSHTLlfH1vv4Ki6uZxburIFwV5Ehlqb9E4W74VJ4yk2nCjXH08okGM+vvY/YW8V7z09wAjXQAomA5wZvx9n8pOlTAMD1wsyULZa5qQvgZqW5Y4n1T+LCnd/Yuuau78Nj80nb3zSo9hUyyAphjEJ/7JrId/U1d/2zjDDLqE7YnO8gI5yPN88r9+nEjcD4jZrAO/76eU1wADwU0XMcrZ9PD38AVScXTN+XnBzKeR+lzbeGg36D32wpkrk60JNBpZANYuFlv6XmHvUXyIHFE8K/xmDU3F2H8PjoR/DGzPuQ1Rz5ofa72DOBt0vmcMFKIUGxkkcRhca2buT+oudeP49zl/VZbCnnwffEcxYxy0B1njoubprux9axsnY7+FHhTk4Dzd2J+48S4KU9hG9Nfl+dKShmGgYKNHeTk3olaYdwPwlgQnk/DuDVNhy3eYpDXFsFdAEsOf5tPbJGsuMjwPTN+jYiPhjUluMPnaqBNxTu9TT39HHu0TbIKaSXSXDYNtACjTdyaYSHS4pzzQ314Et3bNYdc5UJYPJAYoXJ6HlD4a7uEtfcpU06SKqJ+joyIgNzeBs3gY3fpJ3rV7274fiiAFY0WoaAt/LTeLV3J04MHAuKOXkOBT6LcECgIEX/S3ds1sITsel2YP5247XTNdq4X+hidggv9h2A5xC2buxFeWonHyQG58P7oIFwOT54By754YzVdx3ct2ccG2ThL2mW8c2au0yb9xQ/jR4Kyb/nOoQFv4KlylwQ2st7xVPva+Th7HUfxrJnSM5LBbdTy3v41DsLOP66nqG8ZbQXg7354LwSNQOcvwh/N/XqqbOroG6/QXN/avg+PD18b+DkVc0rnzhojigDKLZUph/x64RObhc9WQ/7Z/vx/l0bowdaUdoh3B8B8AkRNXMAwDuMsVNtOO7VkVB8yWhmGZgDZo7o2wLNfTmI2AjQqvCrAtok3NXV45sR7vXNMuNiKT8vWyfUsg7GGei+TwIHPhcKmTqaZFQDNrWzv5gJBIamLYkZzI6xMj64Z4wLd3FNl0kMVtFrc+BzwJF/xhNbRneI4mvA9rEyZgaL2H7obvRmzWYZIsLxoTvxcmU/rnglnBg4CoBPxaVzUT6sGa+xBhfV/KQzL9wQRnTp+zlwHF4WedfWG0DH/iXQu5ELeIBX1KzDklvAqdK24D1vZ2gDlqGQwboEke/LAcjNhclsUEIhERF+RKTX+ieCI/rL6/SnUEWlE9ewr3od1czbXZMVHJobQCHIE1CEuzye3BQsxedol1u9v5dFGYAwVyL8cMEv40KWlwwYq+S1bGbVrBZu5w7dZSnc5QRCnHDvVB/u2T6qlbd2HcKhucHQt7JKNCz5S0RfB3AUwCARnQTwbwD4AMAY+1MA3wJwD4ATAC4B+ORKNbYh6q9rtLk3cywh3GvLYqBQEmZUU436upHNXZ1NOG5TSUzR93dvG8VbFxeR9X3g0G8DL/8AOPlouEOPHm4YO3xdARY83Yl7JJtlwu88cNME/u57rwEwm2WKWQ/FgSKWlmuB5n4+O4zC0ptBlEyAaSZGDnqyHnqyHkZGerH0C1a/bQLpDPZdByQGQfmw9khncp2+m4Sa5owzmPJunh/Ez594IRQ6qvKx9YPyIHXbDQBvFDchXz2L3oVXQSIMOCjrIK5rPuNi10QF19VKePJkGF4oBY6aKcncDGQ0sQwrDGv31LT7hMlzEXeomtbiBcyXrqDEgvMIk4TZI3ipCiKC53tYQiQ6RfPdeKHmToShUhYXr1wSb6PHDm3q/B8BCO35jDHcsLEXN2zsxTOnziv7cVSfStXJokYeMsuXgntBflzIuBgo5LEQhIw6qaNw2k2aaJmPMsZGGWM+Y2ycMfbnjLE/FYIdIkrm84yxOcbYdsbYYyvf7BQ4dR5wP8cXBqgL//G55h4VLAmau0m4Z5QqdeoD7Xj1QzMbCPeM5wSFuJDtife3gXCve7+l0NwTzTLKQ5X1XBTzBm0l0lbPoaDCyMXMIB4dfxAY2Rb/Xvxk2nnlMZIWu5BIs43vOkFb5EwkTGSpI9xjzaCIoJKae3j9bpzux4OHZ0PjgTobdJxUgl228+XKfiw7mVBrjPxeLhGOXb8BxUzcPAVEQiRVE01gRxbXMTZGkRDSXHM3FQzrK/g4el1470nheXTzEHaMhyWnCQAl3F+yP564RmoopAxNfWXiXuCmz2rO/7u3jWJUFqeLZhorphIZTtkI9TdVXx8fvAMvVvii7kxcpCAbWjh8A/ORQw0UqZWj8zJU00LEbbOm9P+Beb4wQKPvB5p71CyjautqOVnD5SyNhK/VQcKkiWrnry/cY0iBKYV6eSJ5X4Q34/RgPbtp8k3ZXyqgkDEI0WiGqsk8ZjCbyNn/e3aN46FbzY66+HF0O2dYSiBdkgg3y4RZrZ84OIWBICyxzgPJom8jWY8JZpmYQ70JPrRnXNMACcxoQwbi8e3BKaMmDUCPlhHiQM5CorXt7987gZ6sF5hlTJr7u7YMo6SF0vJjVQo+btkcOqfrCTzZ/kxG3juqcOf/L5cmuX9IqT+T890gQzsKOa5ibgoVgGABEIOepc7Ggug0h7DkFlB1c6I9uubuibUagoF0jQQ70G3CPXohR3fEp/dAY0Ep95HRNUnCvTgIzN6afH5Aj4mP2twbnb+ZNkuBNjDHKzzmK/X3B/Cpm2fw3h11nDx1zvm+XWPYOV6J9yMhFFLDMKvyxWpa5WIevYYQwoQGKi9DDSnqUE3Cdx2wAhc4xJaDEMDYsSPEa6FEFomePcrzH0qRa6vdC2n7yJkcKGDXpP6bUvBfb2uScHdFOQpZ1wWAZjYMSkWI97WI8C7lfXGNCQzMqLmLYJY4TNd+Se6sIH0Fsv3S8VvMeXj4llneJrnASjBbia7WFJxOw3XDKqsO8aJlQGjKMtW30b6vaOJ8/9Cco3ZFau6BCWiNTDJAtwl30wOZM6w+lEq4k4iWqca1bDnMTx2O2NQjs4RCf6R2TcTmXvf8LWruteX6WqdCOe9rIWPxNqQ4Tnmcx9L7MkxP/870EK/Dozvm4uf0HK6BUjMabSTdXmJaC1Rlsr+ARZeXgWYlnm+XXb6Qevps0vK0TNbyGLD/4fisMcn/khJVaL/es4W3tzgYD+ZK6Ifc7jqEn294H342fK824AQZqkp5guj5ubGSUGO8Ds+W0XidJd1OH4pb2fzjA+9CdXQ3UNyg1eKRUVmyn77IY8h6bpBYN9VfwHUjpdD044Q2d/4/bMdgj1oCOLxIRMAr5X34wcRnA0WgkW84SROXA6Dc7InkLCnU11Bx78A1VJslZ9Bg02ruV87xQmR9M8BZJU9Lau7RX276CHDuV7wEAsDt+poLP2Jzb3R+7X2Du0RqMGnj+FNR1zCfsFlv9+7pISy92KcllUTNMoAoqEXUeNDTzmVuw9xwXOBIvnTHZiwsLeMbFx7A1vkiasUhnCptDzS5NM5kkyAw1X6JoYXFNl9fRF2K783CHHCM1293lAU8ZgeLicLdDbpGqHl5nHdzGNcKbPEdhnqyuL6/hINzPOxytJzDqXcWQhs5iaJjjGkJS42QQv+KX8bSHI94um6khB++oNfjl+2XOQhVpWKo5zq4Z7uSAB/R3IO+MODjB6fxs+c9nFuoaoX3ZKx8EhP9hVjtdZJRb0KBi2ru2rEdNxgEVmOt1CS6S7ibLmSPKVkqTQiXA1wQQnroOuDUE/z15ncDbz0f7qPi54C524Cf/jV/H9XOrsLm2hD54KdcXCQVaQbB4FrGnYgAQK6vC3bA6DwM6myntJeLo+tv93wCN049D2faXP1CJpPkfBcP3XoDAF5X5KW+g42PrWC6e/qKmYRPFK7CLAPoBctU1Jl/Ycd7gYrZma4KGteRkzxFyxbX3nOAuxUBet+eMVy6sgzU3g4esWC1soQ2SWoJCVrqLEIiZ5FyFiTLXNdb6CmquUdNVLJ/nqcK9/hh1FPcvzcekrowshcvvXwOlytbgcth7ktoc1c09XxfGJlkbe7twnAhK5PA9vv1bakEoDiW44bp+9kSMLYnvo/2tZRmDr/NdZ2lSagFjbAleoRDevKAvj3NjMMgwCv5DLd5N2WWiRy7PAZv1lw35/PHNuFBQ1KK3DNetjVZotywUZ8ZPHDThFJHvw7NONQNzA31YP9sf6z4mCbQRncBspzzwBxOlbYFtczVy+JEtGQgXt5AkvVcPnhJnwYIi1VZCM3Bg4emw7ZEfpOTvXtwsncPMLpT226K3JEDhVSGA829VkdAJmnuWqHfqFkmfryGIfuOh1O9O4IZQKi5y2gZZd9cxei8Xm26S3NPQiaJSNKsriSFlKlkb2CWMQnylL9mppRuv7Rs2ApcfhuY2N/e4yaRKXBbe5Q0d7Phug2Vsnzd0KbMMul1k6RyrjLiI1YsrQ5jlTy+dMdm/IfvPodqjcVnJkloJrrmH71y3sehuUFsHysH9WRih1Xf7PgIXnr9OHJLZzH+zk8wq0RuBeUr1EzOhrMmWZwrFO6ew0vpTg0U8NKbl2J3f83xcbKyL/a7kklzVxYoAXg9fCAetaMRWa0p6H4kYEnV3E13aCOHaugwjUSDRaYVjDGAwpo8alLUatNdwj3tMJlGc5eCw82GD2JBpH4n2dzV7zVqS7bRjy5umtGdYSXJejgOMGNe93FVSeusTqIpoXf1atFAMYNdExXsmRTRIy2oWn4uz2d3M7c23llyFSpdKedr2ZNaFIrhsAt+BScGb8N+RcCGCU0GzT1JmBLPxGUgLAqzTEYsKXjn1hE8efJsEGfeiFiMPgBfHEuaOnJCc988kuxDSYqWCc8jBjFlAJbnzPoOPrx3Av/lBy9hdrD+8yhnR/IwLDDL6OeRl86bvQU7Ry4gN78CNbRS0l3CPe3Dnkpzl0N+hici7fgI0CuKXTKzfVn7XjPOtXr0DAMj29Ptux5IpU3XE+5X71BtBsfhCT8xUqTW+56D6uIyXNflCTVrhGZuiVyT7WNlPCUWwdDqGgZCL25zT4aCf6pZBuCZvYfmBpO+aGhzfOawZbQXL75xKRggPM/HwdkBYMQQ8RYcSLY5mrSktRieE3GoggvsoVIWnzkyEyyV14igqmfELLN/ph+nzy1gvE/E2c8cQauVd9pF59vcN72r+e+kqYkhha90gg3McYcpUF9zDx6ANl3atTTaNUOaayqp65e4CodqO5gQPoSk5REV7t87joNzA+nNMitEvYiM228YDrJuTT+RWr0yyeYeQKF1PzDLtNh3k81dFqoLEskcj/+5dRQh+TstL4om6n2Qh1cXp4lerlLOT8wNkEizTdhdfXAa7s3hM0dmkTcl9q0Rna+5T9wEnPguf91Os0xGTNOatbkr4WJXRb3ZwXpmNc0yKzHwTe7nfykY7MmuejEoE4k2d8G2sV68fm4Bvfnw2soQPtchYOMu4Fc/SaG5Q9zXhCvLyStLpSF0OCoRPNG2ux6w96H6CXny+awuiOPxtyxiLvGcuHBv5vYJ/ACB5g7MDRVRKqQzQ60FnS/cNdpolhGrDxnNBHWFexOa+75P8ZruP/nPyfusVvRLu+gws8yqM3UIuFxvYbPm0WrJGy7J9rEyto+VjVEinkPApjuAmVvwgbNXkF0sol6kEIEAIixJs0xCTZyfbPwYqN5xDO00ZnMaQ5kV5HoJ1UVtc9RBqpqfQrNMegLhrjzfG0q5df18dpdwb6vmXkzet65d3fDZxI2AX4zvWre+TRdp7vs+BYABj/0n/r6edr7KDtVVZ7YJp2sLmDR3k1CvqZq74wBOHhMDWaCUS3YMK+sXX5HCPSEKadELHZRjlXjYb2wFr1aJau6Rj2U8/qiyUlMrYYra9UIK5/M6oDuEuyzPm5Y0mnsg3A0117e8D3jlUaBkSJYxae6bbk/fttjx1q9mYMT0xJSG9Ycgunix9v0mBrNO1NxXmLRXRP4cWvkJxzGHt4bf4ucgNc69/hm/cNsm44DTtpIrMl9E2NzlEoPSDzLRV0A5v4SR4XCwCQqINTHAyLs3tM2v/3uvy4R7nQu+9yFg8SLw1N+mGwhkkolJuOf7gM13JrSlBYNePTpGc28w01CvRz1nZVPXTYlosgBoYvIa2JBbuE+JAk02ySwjSapd1La0fHkvifZs3diLaq2GHePcTl+87iiKp57QLkwrJXhlf6UDNSwZbDX3lcVxgEbyuneUJ/kAXDg3Iitia4v166LHaSFahhxgaLO+rVMdqmk0GiJue1bLIbd0Kplotn6dWqtN6uJnCQs71/+SHmDoOdRy1cO2Tboiv73jEHZPKs/3zBFEV1lrzaHKguMD1iyzesiHvJFGnu8Ddv5aGK9ej0I/sOfjQE+TAihtnLvK0X+R/FnKRRw6jnbYnoO0Qau5NwtrSXOXCgf/12oYJNCa9mwkugRmCpqZNfzajROoLjOcPh+16VuzzOqwcQ/w0j+mq9fRP5v+uA3WtDRStzRBUwdq03FWid4x4K0XWo8eGJwH3niuue9IZ7fV3JtGaqJ1Sz5HEc7Li2KB8Xr29n3TfYEzc1XI1clijaCuEduIjcIZ/LoQ7o6q9k8e4EUF1yndIdxnbgGmb14fYUltN6esfw0BALD1PuDiGy0VxAIQL+6WhuoV/r/dRdiuAVrS3HNlYN+n8MpjZ4Fq/YHhyGqm3R/4XPqMb7QWqROt2w4AmDvW9HFWk+4Q7kTrJ6pEapFRG3qzJC3Vtl7xsnyRinpkCm0upSCuUTa91mbhSEtx04s3l4ZB7gWgutyaM9bAjvFy/bK+jUix6phKtGJkGmod9jgC3SLc1xOZAnD4i9ZUYOLwF9t7vIF5rj1t3NN4X4tGELfdQoYpKQ7VdvCuLQ3WM24zrehN4YDQOdLdCveVoF4cd2qipY8sMRwnXk/ekoqgemELAjosf9vZ92YrGaqd1GUr3C2WaxivhWgsUy32TqJS8LFrooKdE+nNOUEVyzUuEtcMVrhb4kin6HpwUFtWlFY0d/mdThXuRAllnuuwb7oPjDHsHC/j+8fPrFDL2kvnDEPXGmvpUJ06zBM/IkujWbqH/bP9ABKKdTVAFuFqReufGljrKuet4bsODm0atJq7pQ3M3Qb84n+2kCHbBlyfh5ZaupZDc4NNLa6hcjWa+327UyQQdgCl3PoXnamGISK6i4ieJaITRPRlw+cPEdEZInpc/H2m/U29xqhMAAf+ia2bYll3SI29FeFORO3LTl0jfuvYnLYo+Hql4fBDRC6APwZwB4CTAB4lokcYYz+P7Po3jLEvrEAbLRbLOiI0y3S2kG6VrNcZvqg0mvtNAE4wxp5njC0C+AaA969ssywWy3pFZqZ2qkP1WiGNcB8D8Iry/qTYFuVDRPQkEf1XIpowHYiIHiaix4josTNnOsPjbLFYdKTGfq1q7p1Cq2uiRZOF/x7ANGNsB4D/A+AvTQdijH2VMbaPMbZvaGgVa09YLJa24XR4nPu1QhrhfhKAqomPA3hV3YEx9iZjTFRxwn8EsLc9zbNYLOuNbslQ7XbSxPM8CmCeiGYA/ArAAwA+pu5ARKOMsVPi7b0AnmlrKy0WS10eOjSN6lVV30qP1dw7g4bCnTFWJaIvAPg2ABfA1xhjPyOifwfgMcbYIwD+KRHdC6AK4C0AD61gmy0WS4S+4uqHzK7fNYgsQMokJsbYtwB8K7Lt95TXXwFQb2Vdi8XSLaz/FeYssOUHLBZLk4TGGCvd1zNWuFsslqaQGaZWc1/fWOFusViaQmruVravb6xwt1gsTSHLD9hgmfXN+i9tZrFY1hX7pvpRXWbYMd7c2qWW1cUKd4vF0hQZz8Etm22G+XrHmmUsFoulC7HC3WKxWLoQK9wtFoulC7HC3WKxWLoQK9wtFoulC7HC3WKxWLoQK9wtFoulC7HC3WKxWLoQYmtU/YeIzgB4qcWvDwJ4o43N6QRsn68NbJ+vDa6mz1OMsYZZZGsm3K8GInqMMbZvrduxmtg+XxvYPl8brEafrVnGYrFYuhAr3C0Wi6UL6VTh/tW1bsAaYPt8bWD7fG2w4n3uSJu7xWKxWOrTqZq7xWKxWOpghbvFYrF0IR0n3InoLiJ6lohOENGX17o97YKIvkZEp4noaWVbPxF9h4ieE//7xHYioj8S1+BJItqzdi1vHSKaIKLvEdEzRPQzIvqi2N61/SaiHBH9iIieEH3+t2L7DBH9UPT5b4goI7ZnxfsT4vPptWx/qxCRS0Q/JaJvivdd3V8AIKIXiegpInqciB4T21bt3u4o4U5ELoA/BnA3gBsAfJSIbljbVrWNvwBwV2TblwF8lzE2D+C74j3A+z8v/h4G8Cer1MZ2UwXwu4yxLQAOAPi8+D27ud9XANzGGNsJYBeAu4joAIDfB/AHos9vA/i02P/TAN5mjG0C8Adiv07kiwCeUd53e38lxxhju5SY9tW7txljHfMH4CCAbyvvvwLgK2vdrjb2bxrA08r7ZwGMitejAJ4Vr/8MwEdN+3XyH4D/AeCOa6XfAAoAfgJgP3i2oie2B/c5gG8DOChee2I/Wuu2N9nPcSHIbgPwTQDUzf1V+v0igMHItlW7tztKcwcwBuAV5f1Jsa1bGWaMnQIA8X+D2N5110FMv3cD+CG6vN/CRPE4gNMAvgPglwDOMsaqYhe1X0GfxefvABhY3RZfNX8I4J8DqIn3A+ju/koYgP9NRD8moofFtlW7tzttgWwybLsWYzm76joQUQ+AvwPwO4yxc0Sm7vFdDds6rt+MsWUAu4ioAuC/A9hi2k387+g+E9F7AZxmjP2YiI7KzYZdu6K/EQ4zxl4log0AvkNEv6izb9v73Wma+0kAE8r7cQCvrlFbVoPXiWgUAMT/02J711wHIvLBBftfM8b+m9jc9f0GAMbYWQD/F9zfUCEiqWyp/Qr6LD4vA3hrdVt6VRwGcC8RvQjgG+CmmT9E9/Y3gDH2qvh/GnwQvwmreG93mnB/FMC88LRnADwA4JE1btNK8giAB8XrB8Ft0nL7J4SH/QCAd+RUr5MgrqL/OYBnGGP/Xvmoa/tNRENCYwcR5QHcDu5o/B6A+8Vu0T7La3E/gH9gwijbCTDGvsIYG2eMTYM/r//AGPt1dGl/JURUJKKSfA3gTgBPYzXv7bV2OrTgpLgHwHFwO+W/Wuv2tLFfXwdwCsAS+Cj+aXBb43cBPCf+94t9CTxq6JcAngKwb63b32Kfbwafej4J4HHxd0839xvADgA/FX1+GsDvie2zAH4E4ASAvwWQFdtz4v0J8fnsWvfhKvp+FMA3r4X+iv49If5+JmXVat7btvyAxWKxdCGdZpaxWCwWSwqscLdYLJYuxAp3i8Vi6UKscLdYLJYuxAp3i8Vi6UKscLdYLJYuxAp3i8Vi6UL+P+SZTuMP6eAvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5122233be0>"
      ]
     },
     "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": 36,
   "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": 37,
   "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": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f51222c0c50>"
      ]
     },
     "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:** \n",
    "\n",
    "- We note that since the dataset is biased, the output from the GAN is also biased; i.e. outputs are mostly white faces\n",
    "- We observe a blur in the output of faces which could be the result of a low image size and/or batch size, as well as the fact that the dataset which we trained on already contained some blurred images\n",
    "- Another possible way to improve the \"human\" quality of the faces could be to lower the learning rate further, but not too far -- so as to avoid local minima and momentum tuning\n",
    "- Training for a greater number of epochs could also help \n",
    "- Batch normalization was used, but, again, another type of normalization be an interesting addition to improve the \"human\"-ness of the outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Submitting This Project\n",
    "When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as \"dlnd_face_generation.ipynb\" and save it as a HTML file under \"File\" -> \"Download as\". Include the \"problem_unittests.py\" files in your submission."
   ]
  }
 ],
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   "language": "python",
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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