4  Face Generation GAN/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": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "# can comment out after executing\n",
    "# !unzip processed_celeba_small.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "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": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "# necessary imports\n",
    "import torch\n",
    "from torchvision import datasets\n",
    "from torchvision import transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_dataloader(batch_size, image_size, data_dir='processed_celeba_small/',num_workers=2):\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([transforms.Resize(image_size), # resize to 128x128\n",
    "                                    transforms.ToTensor()])\n",
    "\n",
    "    dataset = datasets.ImageFolder(root=data_dir,transform=transform)\n",
    "    data_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,\n",
    "                                         shuffle=False, num_workers=num_workers)\n",
    "\n",
    "    return data_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": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define function hyperparameters\n",
    "batch_size = 128\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": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3bf1a9c320>"
      ]
     },
     "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": 170,
   "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",
    "    min_,max_ = feature_range\n",
    "    x = x*(max_-min_)+min_\n",
    "    return x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min:  tensor(-0.9529)\n",
      "Max:  tensor(0.9451)\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": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "#helper functions\n",
    "def conv(in_channel,out_channel,kernel_size=4,stride=2,padding=1,batch_norm=True):\n",
    "    layers = []\n",
    "    \n",
    "    conv_layer = nn.Conv2d(in_channel,out_channel,kernel_size,stride,padding,bias=False)\n",
    "    layers.append(conv_layer)\n",
    "    \n",
    "    if batch_norm:\n",
    "        layers.append(nn.BatchNorm2d(out_channel))\n",
    "    \n",
    "    return nn.Sequential(*layers)\n",
    "\n",
    "\n",
    "def dconv(in_channel,out_channel,kernel_size=4,stride=2,padding=1,batch_norm=True):\n",
    "    layers = []\n",
    "    \n",
    "    conv_layer = nn.ConvTranspose2d(in_channel,out_channel,kernel_size,stride,padding,bias=False)\n",
    "    layers.append(conv_layer)\n",
    "    \n",
    "    if batch_norm:\n",
    "        layers.append(nn.BatchNorm2d(out_channel))\n",
    "    \n",
    "    return nn.Sequential(*layers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "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",
    "        self.conv_dim = conv_dim\n",
    "\n",
    "        # complete init function\n",
    "        self.conv1 = conv(3,conv_dim,batch_norm=False)\n",
    "        \n",
    "        self.conv2 = conv(conv_dim,conv_dim*2)\n",
    "        self.conv3 = conv(conv_dim*2,conv_dim*4)\n",
    "        self.fcn1 = nn.Linear(conv_dim*4*4*4,1)\n",
    "        \n",
    "\n",
    "    def forward(self, x):\n",
    "        \"\"\"\n",
    "        Forward propagation of the neural network\n",
    "        :param x: The input to the neural network     \n",
    "        :return: Discriminator logits; the output of the neural network\n",
    "        \"\"\"\n",
    "        # define feedforward behavior\n",
    "        x = F.leaky_relu(self.conv1(x),0.2)\n",
    "        x = F.leaky_relu(self.conv2(x),0.2)\n",
    "        x = F.leaky_relu(self.conv3(x),0.2)\n",
    "        \n",
    "        #flatten\n",
    "        x = x.view(-1,self.conv_dim*4*4*4)\n",
    "        \n",
    "        #fcn\n",
    "        x = self.fcn1(x)\n",
    "        \n",
    "        \n",
    "        return x\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "tests.test_discriminator(Discriminator)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generator\n",
    "\n",
    "The generator should upsample an input and generate a *new* image of the same size as our training data `32x32x3`. This should be mostly transpose convolutional layers with normalization applied to the outputs.\n",
    "\n",
    "#### Exercise: Complete the Generator class\n",
    "* The inputs to the generator are vectors of some length `z_size`\n",
    "* The output should be a image of shape `32x32x3`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "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 conv_dim: The depth of the inputs to the *last* transpose convolutional layer\n",
    "        \"\"\"\n",
    "        super(Generator, self).__init__()\n",
    "\n",
    "        # complete init function\n",
    "        self.conv_dim = conv_dim\n",
    "        \n",
    "        #fcn\n",
    "        self.fc1 = nn.Linear(z_size, conv_dim*4*4*4)\n",
    "        \n",
    "        #dconv layers\n",
    "        self.t_conv1 = dconv(conv_dim*4, conv_dim*2)\n",
    "        self.t_conv2 = dconv(conv_dim*2, conv_dim)\n",
    "        \n",
    "        self.t_conv3 = dconv(conv_dim,3,batch_norm=False)\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",
    "        x = self.fc1(x)\n",
    "        \n",
    "        #flattern\n",
    "        x = x.view(-1,self.conv_dim*4,4,4)\n",
    "        \n",
    "        #conv layers\n",
    "        x = F.relu(self.t_conv1(x),0.2)\n",
    "        x = F.relu(self.t_conv2(x),0.2)\n",
    "        x = torch.tanh(self.t_conv3(x))\n",
    "        \n",
    "        return x\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "tests.test_generator(Generator)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize the weights of your networks\n",
    "\n",
    "To help your models converge, you should initialize the weights of the convolutional and linear layers in your model. From reading the [original DCGAN paper](https://arxiv.org/pdf/1511.06434.pdf), they say:\n",
    "> All weights were initialized from a zero-centered Normal distribution with standard deviation 0.02.\n",
    "\n",
    "So, your next task will be to define a weight initialization function that does just this!\n",
    "\n",
    "You can refer back to the lesson on weight initialization or even consult existing model code, such as that from [the `networks.py` file in CycleGAN Github repository](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py) to help you complete this function.\n",
    "\n",
    "#### Exercise: Complete the weight initialization function\n",
    "\n",
    "* This should initialize only **convolutional** and **linear** layers\n",
    "* Initialize the weights to a normal distribution, centered around 0, with a standard deviation of 0.02.\n",
    "* The bias terms, if they exist, may be left alone or set to 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "def weights_init_normal(m):\n",
    "    \"\"\"\n",
    "    Applies initial weights to certain layers in a model .\n",
    "    The weights are taken from a normal distribution \n",
    "    with mean = 0, std dev = 0.02.\n",
    "    :param m: A module or layer in a network    \n",
    "    \"\"\"\n",
    "    # classname will be something like:\n",
    "    # `Conv`, `BatchNorm2d`, `Linear`, etc.\n",
    "    classname = m.__class__.__name__\n",
    "    \n",
    "    # TODO: Apply initial weights to convolutional and linear layers\n",
    "    #if hasattr(m, 'weight') and (classname == \"Conv\" or classname == \"Linear\"):    \n",
    "    #    m.weight.data.normal_(0.0, 0.02)\n",
    "    #    m.bias.data.fill_(0)\n",
    "    if classname.find(\"Conv\") != -1 or classname.find('Linear') != -1:\n",
    "        torch.nn.init.normal_(m.weight.data, 0.0, 0.02)\n",
    "    elif classname.find(\"BatchNorm2d\") != -1:\n",
    "        torch.nn.init.normal_(m.weight.data, 1.0, 0.02)\n",
    "        torch.nn.init.constant_(m.bias.data, 0.0)\n"
   ]
  },
  {
   "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": 177,
   "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": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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",
      "  (fcn1): Linear(in_features=2048, out_features=1, bias=True)\n",
      ")\n",
      "\n",
      "Generator(\n",
      "  (fc1): Linear(in_features=100, out_features=2048, bias=True)\n",
      "  (t_conv1): Sequential(\n",
      "    (0): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (t_conv2): 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",
      "  (t_conv3): Sequential(\n",
      "    (0): ConvTranspose2d(32, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "  )\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": 179,
   "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": 180,
   "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",
    "#     criterion = nn.MSELoss()\n",
    "    criterion = nn.BCEWithLogitsLoss()\n",
    "    batch_size = D_out.size(0)\n",
    "    labels = torch.ones(batch_size) # real labels = 1\n",
    "    if train_on_gpu:\n",
    "        labels = labels.cuda()\n",
    "    \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",
    "#     criterion = nn.MSELoss()\n",
    "    criterion = nn.BCEWithLogitsLoss()\n",
    "    batch_size = D_out.size(0)\n",
    "    labels = torch.zeros(batch_size) # fake labels = 0\n",
    "    if train_on_gpu:\n",
    "        labels = labels.cuda()\n",
    "    \n",
    "    loss = criterion(D_out.squeeze(),labels)\n",
    "    return loss\n"
   ]
  },
  {
   "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": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.optim as optim\n",
    "\n",
    "# Create optimizers for the discriminator D and generator G\n",
    "# params\n",
    "lr = 0.0002\n",
    "beta1=0.5\n",
    "beta2=0.999 # default value\n",
    "\n",
    "# Create optimizers for the discriminator and generator\n",
    "d_optimizer = optim.Adam(D.parameters(), lr, [beta1, beta2])\n",
    "g_optimizer = optim.Adam(G.parameters(), lr, [beta1, beta2])\n"
   ]
  },
  {
   "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": 182,
   "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",
    "            batch_size = real_images.size(0)\n",
    "            real_images = scale(real_images)\n",
    "\n",
    "            # ===============================================\n",
    "            #         YOUR CODE HERE: TRAIN THE NETWORKS\n",
    "            # =============================================\n",
    "            \n",
    "            # 1. Train the discriminator on real and fake images\n",
    "            if train_on_gpu:\n",
    "                real_images = real_images.cuda()\n",
    "\n",
    "            d_optimizer.zero_grad()\n",
    "            \n",
    "            #on real image\n",
    "            d_real_op = D(real_images)\n",
    "            d_real_loss = real_loss(d_real_op)\n",
    "            \n",
    "            #on fake image\n",
    "            \n",
    "            #generate fake image\n",
    "            z = np.random.uniform(-1, 1, size=(sample_size, z_size))\n",
    "            z = torch.from_numpy(z).float()\n",
    "            # move z to GPU if available\n",
    "            if train_on_gpu:\n",
    "                z = z.cuda()\n",
    "            fake_img = G(z)\n",
    "            \n",
    "            #fake loss\n",
    "            d_fake_op = D(fake_img)\n",
    "            d_fake_loss = fake_loss(d_fake_op)\n",
    "            \n",
    "            d_loss = d_real_loss + d_fake_loss\n",
    "            d_loss.backward()\n",
    "            d_optimizer.step()\n",
    "        \n",
    "\n",
    "            # 2. Train the generator with an adversarial loss\n",
    "            g_optimizer.zero_grad()\n",
    "            \n",
    "            #generate generator op\n",
    "            z = np.random.uniform(-1, 1, size=(sample_size, z_size))\n",
    "            z = torch.from_numpy(z).float()\n",
    "            # move z to GPU if available\n",
    "            if train_on_gpu:\n",
    "                z = z.cuda()\n",
    "            \n",
    "            g_out = G(z)\n",
    "            d_out = D(g_out)\n",
    "            \n",
    "            g_loss = real_loss(d_out)\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": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [    1/   10] | d_loss: 1.4879 | g_loss: 0.8720\n",
      "Epoch [    1/   10] | d_loss: 0.1736 | g_loss: 4.4940\n",
      "Epoch [    1/   10] | d_loss: 0.2193 | g_loss: 3.6944\n",
      "Epoch [    1/   10] | d_loss: 0.4565 | g_loss: 2.8662\n",
      "Epoch [    1/   10] | d_loss: 0.7987 | g_loss: 4.3544\n",
      "Epoch [    1/   10] | d_loss: 0.4810 | g_loss: 2.0606\n",
      "Epoch [    1/   10] | d_loss: 0.5510 | g_loss: 3.5640\n",
      "Epoch [    1/   10] | d_loss: 0.7501 | g_loss: 3.1005\n",
      "Epoch [    2/   10] | d_loss: 0.7802 | g_loss: 0.5416\n",
      "Epoch [    2/   10] | d_loss: 0.5699 | g_loss: 3.5514\n",
      "Epoch [    2/   10] | d_loss: 1.1655 | g_loss: 3.2440\n",
      "Epoch [    2/   10] | d_loss: 0.8897 | g_loss: 2.6395\n",
      "Epoch [    2/   10] | d_loss: 0.8815 | g_loss: 1.9260\n",
      "Epoch [    2/   10] | d_loss: 0.7233 | g_loss: 2.2938\n",
      "Epoch [    2/   10] | d_loss: 0.8236 | g_loss: 1.2458\n",
      "Epoch [    2/   10] | d_loss: 0.9495 | g_loss: 2.0815\n",
      "Epoch [    3/   10] | d_loss: 0.9073 | g_loss: 1.9949\n",
      "Epoch [    3/   10] | d_loss: 0.7955 | g_loss: 2.1072\n",
      "Epoch [    3/   10] | d_loss: 0.8175 | g_loss: 1.3183\n",
      "Epoch [    3/   10] | d_loss: 0.8738 | g_loss: 1.4760\n",
      "Epoch [    3/   10] | d_loss: 1.0481 | g_loss: 1.8864\n",
      "Epoch [    3/   10] | d_loss: 0.7403 | g_loss: 1.5736\n",
      "Epoch [    3/   10] | d_loss: 0.5964 | g_loss: 2.0900\n",
      "Epoch [    3/   10] | d_loss: 0.5979 | g_loss: 2.0415\n",
      "Epoch [    4/   10] | d_loss: 0.5425 | g_loss: 2.1536\n",
      "Epoch [    4/   10] | d_loss: 0.7436 | g_loss: 1.9083\n",
      "Epoch [    4/   10] | d_loss: 0.9139 | g_loss: 0.7351\n",
      "Epoch [    4/   10] | d_loss: 0.8104 | g_loss: 2.6222\n",
      "Epoch [    4/   10] | d_loss: 0.8579 | g_loss: 1.3070\n",
      "Epoch [    4/   10] | d_loss: 0.6836 | g_loss: 1.5718\n",
      "Epoch [    4/   10] | d_loss: 0.6333 | g_loss: 1.3911\n",
      "Epoch [    4/   10] | d_loss: 0.8395 | g_loss: 2.1016\n",
      "Epoch [    5/   10] | d_loss: 0.8453 | g_loss: 2.0770\n",
      "Epoch [    5/   10] | d_loss: 0.5921 | g_loss: 1.4550\n",
      "Epoch [    5/   10] | d_loss: 0.9325 | g_loss: 1.7897\n",
      "Epoch [    5/   10] | d_loss: 1.3691 | g_loss: 1.2481\n",
      "Epoch [    5/   10] | d_loss: 0.9255 | g_loss: 1.5236\n",
      "Epoch [    5/   10] | d_loss: 0.6852 | g_loss: 1.4388\n",
      "Epoch [    5/   10] | d_loss: 0.6501 | g_loss: 0.9354\n",
      "Epoch [    5/   10] | d_loss: 0.8436 | g_loss: 1.2784\n",
      "Epoch [    6/   10] | d_loss: 1.4645 | g_loss: 0.4327\n",
      "Epoch [    6/   10] | d_loss: 0.5111 | g_loss: 1.4368\n",
      "Epoch [    6/   10] | d_loss: 1.3559 | g_loss: 0.5890\n",
      "Epoch [    6/   10] | d_loss: 0.7686 | g_loss: 1.0414\n",
      "Epoch [    6/   10] | d_loss: 0.8689 | g_loss: 1.0754\n",
      "Epoch [    6/   10] | d_loss: 0.5862 | g_loss: 2.8867\n",
      "Epoch [    6/   10] | d_loss: 0.4607 | g_loss: 2.0217\n",
      "Epoch [    6/   10] | d_loss: 0.4860 | g_loss: 1.0308\n",
      "Epoch [    7/   10] | d_loss: 0.5829 | g_loss: 1.2205\n",
      "Epoch [    7/   10] | d_loss: 0.5371 | g_loss: 2.0791\n",
      "Epoch [    7/   10] | d_loss: 0.7223 | g_loss: 0.6945\n",
      "Epoch [    7/   10] | d_loss: 0.8945 | g_loss: 1.4825\n",
      "Epoch [    7/   10] | d_loss: 1.1866 | g_loss: 2.7587\n",
      "Epoch [    7/   10] | d_loss: 0.2476 | g_loss: 3.5218\n",
      "Epoch [    7/   10] | d_loss: 0.7094 | g_loss: 1.5086\n",
      "Epoch [    7/   10] | d_loss: 0.7984 | g_loss: 1.0041\n",
      "Epoch [    8/   10] | d_loss: 1.5482 | g_loss: 0.8265\n",
      "Epoch [    8/   10] | d_loss: 0.3904 | g_loss: 2.3363\n",
      "Epoch [    8/   10] | d_loss: 0.7344 | g_loss: 0.8932\n",
      "Epoch [    8/   10] | d_loss: 0.6905 | g_loss: 2.6427\n",
      "Epoch [    8/   10] | d_loss: 0.4442 | g_loss: 0.7267\n",
      "Epoch [    8/   10] | d_loss: 0.1062 | g_loss: 1.5150\n",
      "Epoch [    8/   10] | d_loss: 0.4790 | g_loss: 4.7758\n",
      "Epoch [    8/   10] | d_loss: 0.2315 | g_loss: 2.1615\n",
      "Epoch [    9/   10] | d_loss: 1.1639 | g_loss: 2.2890\n",
      "Epoch [    9/   10] | d_loss: 0.2945 | g_loss: 3.0992\n",
      "Epoch [    9/   10] | d_loss: 0.9457 | g_loss: 0.7147\n",
      "Epoch [    9/   10] | d_loss: 0.4924 | g_loss: 3.0202\n",
      "Epoch [    9/   10] | d_loss: 0.3637 | g_loss: 1.6980\n",
      "Epoch [    9/   10] | d_loss: 0.0809 | g_loss: 4.2423\n",
      "Epoch [    9/   10] | d_loss: 0.1350 | g_loss: 1.9894\n",
      "Epoch [    9/   10] | d_loss: 0.1336 | g_loss: 1.4277\n",
      "Epoch [   10/   10] | d_loss: 0.8384 | g_loss: 0.4013\n",
      "Epoch [   10/   10] | d_loss: 0.1440 | g_loss: 2.5547\n",
      "Epoch [   10/   10] | d_loss: 1.2250 | g_loss: 2.4459\n",
      "Epoch [   10/   10] | d_loss: 0.2354 | g_loss: 4.6197\n",
      "Epoch [   10/   10] | d_loss: 0.4712 | g_loss: 3.9177\n",
      "Epoch [   10/   10] | d_loss: 0.0314 | g_loss: 4.1991\n",
      "Epoch [   10/   10] | d_loss: 0.0757 | g_loss: 0.4481\n",
      "Epoch [   10/   10] | d_loss: 0.1395 | g_loss: 1.7799\n"
     ]
    }
   ],
   "source": [
    "# set number of epochs \n",
    "n_epochs = 10\n",
    "\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL\n",
    "\"\"\"\n",
    "# call training function\n",
    "losses = train(D, G, n_epochs=n_epochs,print_every=100)"
   ]
  },
  {
   "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": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f3c0049e7b8>"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GNwJgXWXWrl1b27dYQnV0dBTvete7cPr0aVQqFWzevFk6nsceewzf/OY3AbCa23/4h39Y+9873/nOs9toA43P6nJ2UJoVpm+nB9jfpTnGvnsQy8RwL/hvb9gF/pfzjN/r8GHG7cK2bdvwr//6r7W/P//5z2NychK7du3Cxo0b8bd/+7e48cYbGz6zZ88eaalVSim2bduGxx57THossYTqRz7yEdxxxx24+eabsWfPnqZmC24Qqxb2fEnWKCAa6+UcEmgagB4HUrbhLs72rOHuXY3bKZX4wbDrHC/nG0dBite97nUolUq4++67a+/xMq033ngj7r77blSr7Lq/+OKLWFxcdN3XJZdcgomJiZrhrlar0uYGAGPv69evBwD8/d//fe19sSQrwJoG33fffQCAr33ta3jlK1/ZytdcvhB17U4k4RSm25OSblYAPSEw7t7VuXvXcPNZnJBgIYEi41Y4q0AIwbe+9S08/PDD2Lx5M6699lrceuut+Iu/+Av81m/9FrZu3YqrrroKl19+OT74wQ96RnAkEgncf//9+MQnPoErrrgCO3fudI0QufPOO/HOd74Tr3rVqzA0NFR7/6abbsIDDzxQc05+9rOfxZe//GXs2LEDX/3qV/GZz3wm8nPQ03Bq3O1EOQ/87P8Cky9Gv2+ryqSSWIpp2z3soOzdsq4nfw68+J9AKgekV/mXWfzxZ1lZxl2/CfSPLO3YCjX0RNlQBVf0xPV58h9ZcAAAXPnrwMB57TvW4iQz3BfdAGy4Otp9P30fW7lffRtrqJDoA3b8Sv3/pgFMHABGLmeEMmKcHWVd+fIrvTIc41ZSiYJCZ2GZgG67y9r9/HFG347jmFUmlQBAakUz4z79NHDgu9FHVrWA3jXcXCvjhttrZWAaZ0/2loLCcoNlALG0/XubNe7ac17x3q6lfVcBLc5+Tw2wqBLR7kw8bx+7+zamhw23fYHSK5lhrhbdtzWEBqzLORypR9EOOU1h6eiZ62KZ9Xjndhs1zrTbcRyzyqJKAOagtIx6RFs5D8zZchDtfhXE3jXcVGDcgLdcwmUSoCdmw7MJqVQKU1NTvWMkFAAwoz01NYVUKtXtobBnNWaPo93EyWxjQSvRcKeEWG6AOUP5M9AD5Wt7O45b04Fkjv1dmgf618i3bWDcynBHiQ0bNmB0dBQTExPdHoqCA6lUqpZg1FVYhmC428242yiJ8nBAoE4YS7MAzgMmXmD2yDJ7Iq0/sOEmhOgA9gI4SSl9W/uGZIOaANFYVAngw7gFw60Yd6SIx+OuGYMKCgAapZK2a9xcKmmHxm2wcECAEUZCmIOyUgBmjwODFwCTB3tCjg0jlfwugAPtGkgTLJOdxHiGvXoZbvEiKsOtoNBZWAYQt52T7X7+2iWVWKYdHWNLJXoMSPYzxj35ImPZq7fWt+0yAhluQsgGAG8F8HftHY4ALpUQYp/AgIxbSSUKCp0FN3iEtJ+Ntksq4fvjUglQDwmceIE5K3Pr2PvLyDn5NwD+EEDnxB1qAsSuOZLKBXNOEqIYt4JCJ0EpY6NEZyvjtmvcbZJK+P40QT1ODQCFSWDmKDB8Sb0G0nJg3ISQtwEYp5Tu89nuA4SQvYSQvZE4ssQqf8mcP+PWdOYg6QH9SUHhnAF/3rSYbbjbbNRqCThRSyX2/kTGnR4AqnbV0eFL60SyB5yTQRj3bgA3E0KOArgPwOsIIf/o3IhSeg+ldBeldNfw8PDSR2YZdcOdygGVvHthGV43V48rxq2g0EmIhrsTzx+fGNomlcTr7/GQwFQO6F+7vBg3pfT/oZRuoJRuAvCrAH5IKf31to+ML78AxrgpBSou5V2NEmPbeqI93mYFBQU5uBHTtM5KJVEfh9sN0XDzKoHDlzAZlssoPbCq790EnAappJ+9usklnHFrsZ44qQoK5wy6JZVETdBqjYIFw923BlizHVhvF7MitrnsAedkqAQcSukeAHvaMhInLENwTq5gr24OSs64LUNJJQoKnQTXe7nhbrtUwqNKDLYKj6pKnyyqRI8BlwkpK4TUk3C6jN5l3NRsDIYHvBm3nmCzpQoHVFDoHGp183UmM3QqHND5+1Ih07hlIFpPMO7eNdyiVBJLAPGUP+PWOzDjKygo1NEklXSorKvz9yXvVxIOKIOmt6f7Tkj0ruGmVl1TAhjrdus9aZRsjbsDM76CgkIdTc7JDpV1BaLVuWXhgDIQvScYdw8XmTIaZ79krl6pS4RpsJ9Yis3AinErKHQOHQ8HFPYfqVQiiSqRoROTUwD0LuMWpRLAPXvStLMma1KJCgdUUOgYaow71pmoLrGeSKRSSZXZG9HmyKDpPbGq713DLaa8A4xxV0uA4TDMPN1dlEpU7WgFhc6AywZE70xwgFllheeAaI/lXOG7QTknfSBj3EAz6+YFpmIpNhNT2hNLGQWFcwINzskOhMpZ1fZUIhRrcXtBOSd94DTcPAnH1XAn68HzKiRQQaEzqBluHg5ottewWUadcUctlfjp20DPOCd713DLpBKgOZbbcGjcgHJQKih0CjWNW29/SjilLBAh3oZuO0ENt0rA8YBlsYvkZNyENIcEioybL3V6wHmgoHBOoME52eYVL3+u28G4xQ7vXlCM2wNiNhaHprML1mS4BcbNT7yKLFFQ6AwanJO8el6biFPNcLdL4w7CuHujHlJvGm4qzOIiUpIkHKPEPL16XEklCgqdhjOOG6i3F4sa/LnmhrsrUommpBJXiNlYIpL9EuekXRmQEO+l2rPfBA4/HPz4p5/pCe+xgkJPwzIYceKZk/y9dh0LsOsS6W2I4w4qlXTfLvSm4RaXXyKSkiQcXqcE8J7x50+xpp9BMHMUeP7fg2+voNANVApAtdjdMVhmnWDVDHebNW4tHr1kYVVDhAMqxi2H6KkWkcyxBByuawN1xg14M26jBBSmmhN4ZOAz+dRL4catoNBJHPg34IXvdXcMllDFU2uzVClW8Iu6aYpZrUutXlDOSQ9YLhq3rKGClHE7bhzLZO9RCiyO+x+fX5ipl5RcotC7WJwAyvnujkHMOOTPX7sYaa3ZgR5tXRRK2fdQjHuJcJVKJEk4IuN2M9wiQ1844398vgSrFoGFU8HGrKDQSVgmUFnsfgSVmG/RdqmEE7qIpRJeJiOIxq0pxu0OV6mEG24hskRk3G5SCY/1BsIZbgCYPOi/vYJCp1GasxNSuhxBJTLudjsnm6SSiL57rTJgAMZNFON2h5hGK0KWhGOUWaMFvj0h7oxb04GF0wGOb1+Y/jVK51boTfBVZ7cZt8w52a7JpCaV8BKyEX332oQQQOPmUkmXC9n1puF2k0o0HUhk6zct16454+admN0Yd249c1D63Vh84hi+FFicBIozrX8XBYV2gNem7zrjNjuocbep2w4/h0HDAYGuhwT2puHmDkFZbdxkf51xi1mTHHq8ORyQb7fyfDZT5sd8jm+wSWD4Evb31KFw41dQaDe4g94yuutAb5BK2pzyzp9rPS5/zlsFH29Q5yTQdbmkNw23G+MGGluYiXVKOPRE843Dmy0MnM9eFwIYbk0HMquAzKDSuRV6D2I3qG5Ww2xwTtpSZdsScESpRPKct4qaxh0wHBDouoOyNw23uCRygrcwo1TOuDVJw2C+XXYYSGT8dW5x+Td0ITB3ojEyRUGh2xAjq7qpc4uMm5DoMxqdxwLqBa0i07gD9psE6nq+YtwSuKW8A0wqMavMkEoZtyS+0yixmyqWBPrXAnmfyBLxZhy8kI1n+nBr30VBoR0ozdWbaXdT53bWzW9nT0aeJEOI3aYwYqkkUDhgmyNnAqI3DbenVCKEBEoZt6R9klFmsykhQN8IsOjjoLSM+rFzG1j9XxVdotAroJTd/5lV7O+uMm6n4W5j+zKnnk6taCaJoI2CAeWc9IRbHDfQ2MIsDOPmxr1/LTvpeY8MyoYbRANWXcAclCqLUqEXUF5gz0hmkP3dK1IJ0N6yp5ZRZ8Vc1ohitSE6Pf2gnJMecEt5BxqzJ900bueNI2ZX9q9hr16JOE4WsfJ8lkVZmg3+HRQU2gWub2eH2Ws3pRJnpypd4mOKCmLp1VoJ5wgmrbAJOIByTkrhJZUkhCQcrl2LJ1yWUSUy7mS/v4PSySI4sylMhf8uCgpRg0eUZIfYa88x7jbGcXNCVQs9jIDdi9EqflCM2wNeUommAYm+usbNa3Fz6DKNu1Rn3IT4OyhdDfd0+O+ioBA1eAx3phcMd6c1bs64XeoStQKzUnd6+oE7hBXjloDPrG4nMtnPbl6RSXNoseYb2ag06uB+Dkqn4Y6n2Y9i3Aq9gNIcux8TWfZ3t6QSSl2iStpYq0R3GO4oJgkzYGVAQIgqUYa7GdSsz2wy8OxJUbvm4BlVYi0Bp4H3c1A6b0aAse6iYtwKPYDSHHPSR+mgawUyX1Q7NW5ZlmYUq42gjYIBJZV4wrLkMgkH74QjZdwO7YtSdnFFA98/wl7dHJRiAg5HZlAxboXeQHkeSK0Qiqp1SSqRJcoFYdwLY8Do3taO56yLEkUsd9BGwYByTnrC6al2IpVjs3ppTs64gfqsb5SZ8RYNfDLHbnpnGzQOp1QCsJjZSgGoluSfUVDoBChl0U3JFbZjPsKGAqHHIgki0OL+hvv0U8BLPwhfYU+USqKsi2IawQ33cmHchJAUIeRnhJCnCSH7CSF/1vZRyQynCDEJx8m4ndqXLNa7VkXQ5QaTGm4VWaLQA6gWmaFJrWB/R93CKwxk5ZdlJSecKM0zqTJsEouUcUflnAyocS8j52QZwOsopVcA2AngTYSQl7V1VDKNWQQ33EAz49YcSyhZrDcQ3nCn7Sw1ZbgVugkeCsgT0bpquF00bj82WvYpSVuYbqy5XzueoEVHabjPRo2bMvDGdnH7p71VxH2dk7n6726Mm9/MMsbNt3ONKpFMHOkBNibloFToJri8V2PcXZRKZGG7XCrxkkG4UXaTOZ77NpNSnDBlcdxRMO6AjYKB+iS1DBg3CCE6IeQpAOMA/ptS+rhkmw8QQvYSQvZOTEwsbVR+zslEXz1UUHcybkffO864ZdvJGDdvHOpk3JrOjLdi3ArdBGfcyV5g3C7OSfF/ThiVup/IbcKpFlk/zYZj2dJKTeN26XbVCsxqCKlkmTBuAKCUmpTSnQA2ALiWEHK5ZJt7KKW7KKW7hoeHlzYqP42bJ+EA/s5JXou7SVJxMdxe6faZQZWEo9BdlObZPR5Ps7+7abhrzknBjPhJGKIE4ipVVgGj2PweUGfa3DEbCeOunH1SiQhK6SyAPQDe1JbR1A7kE1UC1HVuv3BAN43bbYnpVQs8swoozqpiUwrdQ2mWySS1FWeAKI52Qcq49cb/OVEWGkC4GXez2hy9VWsvJh4rIpnIChFVslzCAQkhw4SQAfv3NIA3AHi+raPyc04CdeeMbzgg17gDOidrN6Pk1KRXsf+LN5+CQifBY7g5es056VdDpCSE4MrYMpcqmxg3r+AnOkIjMNyWyX4ChwNqdpefHjfcANYCeIgQ8gyAJ8A07u+2dVRiPWw3uDFuWTigHm82xG7NRj0Zt6pZouDA5EvAoR927niluUbnfJSdYMJC6pz00bhFqUSWPMM7qJtGo1GuPZeCgY2iYXCtw3tAjRtgtqnLjNvXlUopfQbAlR0Yi3BQH+ckwBIQANbkQIQsHNBp3AH3ZqOeGrcQEjh4gff4FM4NTDwPjD8HbHltsCJFSwF37DUw7m5GlUhIjq/G7cO4xUmIky5xfw3HSiw9czJMZUAOTeu6XBpitB1EEKlkZBvbRmQfgDwc0CmnAAGkEsmpiWfYRKEYtwKHWWH3a7VQL/rULjhjuAG7aa4Z7JmJGtIEHD+Ne4E5VqtFFx+T8F61VF9Zu0olS1xttMK4tVjXGffyTHkHWE3t9Vc1sxxNZ15uMRxQxrjdyk96GW5CmM6tQgIVOLjhkCWMRA1nDDcgFJrqglzilvIOeGvcXHKUMm7hc6LOLesLGUUJ2ZrhDqhxA+z7qp6TEviFA/pBbCTqxrh1tzhuj1rggKoSqNCImuHOe28XBXgHJnGV6VxhdhJS56SHxk0pc+zXWq7JVrwOxs1hymSZCBoG8/MWSirRl4VzsvNY6rJPTwiMu+IulTjLvwLejBtgN10hAXGIAAAgAElEQVQ5Xw8zVDi3we8Dt4JlUaI0b8uDQsmHbpZ2lTknvTRuo8SeOe4rkjJu4b0Gxi3pCyk+563CWp7Oyd403H4p734Q4ztlpV/5NkAzM/ByTgKCg1KxbgXU77OOSCULjVnDQHelkrBx3CVB6tH0YBq38/2mOO6oNO4QUommKcYthV/Kux90O0yIUnmzBcB9SReEcQNK51Zg4Jm5lQ5IJUapnjHJEWWxpbCwDDaJiCTLS+Pmk1sy5x4c4KZxt00qaZVxdzeqpPcMN69JsBSNmzNus8r2JQ0H5F2iHTe8zFMuIjXAblalcysAnWXc1WJvGW4eRCCuADSX5wqoJ66lcu5hjCKDbmDcEqmEF7RaSmheS+GAbWyIHHQIXT26DF4d3oOC3xRulQEBD6nEh3HrMWa8FeNWMI36A9w1w91NqUTii9I9GHdpnrHzeNYjqkuQLqRRJY44brdjBUVL4YAqqqQZXh3eg4LfFG51SgAPqcRH4wZUGzMFBm4sidYZw20UgZgb447YcM+eAMae895GFv1FiG3YZIx7gTlWNc29NyWXPpK5ZsZNNIcjlLP7JXz3VsMBlXPSgUgYt619eTFutyWmH+MGgMxKoDgTvvXScsehh4Dpw90eRe+A69vplYwkGG1kvZbFjuHMFG5XVMnoE8DhPT5jcon+cpMSyvP1iBi3Fmfc4Cf7mzVuZ83sKGpymxX2HcIQRRUOKIGfxhwEPEzIrRY34O+c9Ox5OcBuJGfN4LMdo3uBiRe6PYreATeWPNKonazb5L1TOySVmBX/fbrlW7jp1+UFoXOPi+E2q4y1J7LNUSXO0qtRNAxuJWeEaMo52YSopJIGjTuMVGJ32ZBVB+RIDbDX0jlUJdCy2LmpFv23PVfAiQGPNGpnLDc/706Nm7PFqA23UfZn8W5hu7KIEUptqcQ23G4lWa0q+3w80xzH7TSwUUxaYTq8cyjGLQGfyZYslVTdmygA3lKJXww5TznmmWznApyt4BTq5yQ7xF7bGRLoZriB9hSaMit2xIaHgbJMOVuVVe2r5Nn2tc49HuGAepxJQkalfnyxw7t4HCCcVHJ4D/DMv9RZepjuNxxK45YgiMbsB66fVVth3C43o4j0Oci4uZFSjLsOfk5qjLuNUonX6jGKDEIn+HfzmhDcZAaZxs3PTSoA49YTdUmIf28p425BKpk/BUwdAl74nl0+thre1vRAOGDvVQeMQirhF7SSZydZ1gjUbbYOonnpcabBFRXjPqfBzwmvGtlOw10t2MeSMe42NFPgMpApcYhyuDknZSsAnjXJnZNubcfMii2VpOrjSGTlz2Urzkm+/7H9bMK1WmDcPRAO2HuGW9bHLiz4BS0vyGUSwH22DuqsSK04N6USxbjr4FEksSRLRW+r4bYnTJnh1lxC61oFpcEZt1tWsrOWj5g1ybdxCwfU4/WVBb/fzKpHt6sQk5ZZBQa3MBtx5BEglgBy64N/HrCdk0oqaUTUjFu2tAT8nZN+SA+cm1KJWV16mvHZAu5D0RPMILVVKinaDXJl/pqIGbclFF/z2q/bsyLTuMtzbOVba3IcbzxObZ929EjcKZVIJI1WpBKuaV/yFmDFBjb5tuScVFEljQiSAOMHfiHKeXfG7SqVBEy3T61gy79zpXGwGKOs5BIGvuzmFfvazbhjSXm0U9SGW2TLXvulljyIQKYBl+bZ5MbT47W43V/SsR13QjoZt8z31IpUwicGPQZc/j+YYzm9KvjngXqtki7mcfSwVLLEcECAxVlzj78ThMg924GlkgF28crzdWfl2QxnS6lkX/fG0isQ2Vqyj+nQ7epEUy0wLV2GqKNKxGvdinNSNh4xFJBvA9gas7APvk9uuDlJkEWVtFKnRQz/S2SAXe8P33KuVgHRlPvPOoAeZtxLDAcE7AJTLowbQK0md8PxAxruWmTJOaJzNxT/UTo3AHZO+P2V7GcMrF0hgW7liYHuMW5PqcTxXIlZk0D9c06j68q4JVIJJ19BvztvQtxQqEprwXBz+9I9nbv3DHeUzknA/Wbn27USVQLUY7nPlciShgL3SioB0MjeOJtsl1wiKzDFEbXhblhdeTQMCRrHbZls9ZtydKcHJCteW4PWNDYpeoUD8v0EjfBopaCUDERg3F1C7xnuKOK4xRnVj3G36pxMrmCTy7nioDSFB1gxbgazUncWJmzpqF0tzDwZd1zezckLi1PAU/8sr68SWCrxqVXCx1NeYL9LW65JGDd/9nlTYRlTFvcTVCqR9a1sBdzP0MWQwB403LazL4qoEsCbccsqlAVJwAHYxUv2y6USq7uOi7bArNSXlIpxMxjlOnvjMkDbGLeXxp1gsmAYBjh3HJg5yoqlORFEKqHUvam37nA8lh0x3IA8OIBSZgz58xtLsXvNi8yFaRjMv0vYKBIn+HdWUomAKJ2TgA/jliyzwhSdcQsJ/Pnf+1dWW24wq8xwEE0xbg6zyuKAAcYOtVh76pVYJmPGbokwrcQzc+Msm4TNIIbbJiduUglQN6iLk+y1oTu9hHHz8ED+/HLGXTPcS2Tc3J+1VMNdc052L6Ks9wx3lHHcgI/GvYSoEoBFljg17tIcsHCmfrOeLTArdipyUjFuDlNg3ISwyJJ2MO5aurubxr0Uwy3RsMUa1W5G0ZMFCzkSpXngyMNA/0hj2J1M43bWxuaMu/Z+iEqEMtQYd0Qat2LcAqKqVcLhKZVILnpQjRtgDKKy2LiP2RPstRM9CDsJo8LYJWdBCo0aN8CkgHZc91qBKY+oEiBcWFzNcEuupVGul1Z1mwy8CJbIpg98h2279R2NMehSxu3octPEuJcolbTSpkyG2sSkDHcdvFRk2BAdEZrQKcNrdpUy7hBxuLJiU7PH2CuvLXG2oMa4U4pxA/UCReLqrl1JOF6VAYHWypvyayhl3Pa19opW8aqbzw3bkUeA2ePARTfUa5Y7txGNrlPKcDLuJUslEUWV8AlIMW4BlinPDgsLfmOEkUrCNiqWhQTOHmevlcLZ5aDkD7Ni3Axm1W5sIDDuRB+LKon6urdVKpFMwkaZfa8ghluaOWmPZ/wAMLIVWLPdY8zi8+cwrPE0O5fVxcbPOPcT2nBH5JxUUSUCgkZ1+IFffC/npPOih5VpnA0VSnPMiKdWsH15xcD2AiyrXrXND4pxN0KsU8KRzLWn2URbpBIvxl32Z9y8br5UvrANW3oAuPhN8tWzLF3ddEgZnHTxVYyU3XchqkRTcdzNcAsxCgtxueWGJsYd0nAnssxhUrJDqjjbXn0pe+1lucSygGfvB372xWAt2BTjboRs2d2ukEB+vl0Z9xKkEtm15IWYYonWnJOZQaBvNbD17QGqc4qG22FYuTRUM9wyxh2iMqJXdEoY1JyTKqqkjqhqPWgxppV7za6uhjvg8QlpjCyZPc5Y0cD57O9WelKaVRaV0m4cfogVlDcN/27egINxl1sLhepy8flIYUi6K/H6LVEbbqPI7mU/Ixi2LjUgZ9yBpBIP52QqB1zzfiC3zv34hEgyLB2Gtca4bYevVCpJyKsMyhBVVIli3BJEybhjSW8npx5nJ58boVYiWlJCLPfMMWBgYz2Lzs1wL07JDZ9lAb+4H9j399HILJYJHH2Udf0QcfoZ4MTPgPVXA/1rgDNPe9/4oiPOWW4zKEb3Ao/cxb7b4YfZuVrOhly27OaMuxLCcAe5ztUSIwRu97ImYa++xy01vooQnZNu44ukqbejVpBTg25i3C5RJTxxxw+8EfFSiaEKB5QgTDieF7S4t0wCNMabAt66nRt4Q4XiLDPgA+czCQWoO1VEFKaBn90DHPh2s+E69AOWzUZDaM9emDkKHPkRM5a/uB/ITwBzo8CL/wGs3ARc+AZg7Q72fn7MfT+iI85ZtS0o5k+xB5IQ4PhPgaf+CXjm661+s+6jZrgdzklCgjPu4izw48/UJTY3GEV3mQQIL5VYVj3V3dM5Ga9f+6Z9RNVi0CMcsMa4592PFaZCICcfS4lYA5YH4yaEnEcIeYgQcoAQsp8Q8rttHZFlRWO40yvrvQDd4HSQtHIzpgfYQzD5Ivt7YKOdYUhYZIkTPMV4/HlmTPkNd+opxkoHNrK/o8jAW5xgrxtfxsIU934JePo+5kTbZsfVrt7KzveZX7jvR2SXnAWF1blLs4zdX/U+YPfvsmiD+ZPLN/LGkDgnNZ1d+6CGuzDFHv7CtPd2XgWmADv8NUSVPDEz0i8c0C2VPpKm3o7gAFk4IFCPjZdJJWEaBvNa3EuFtjwYtwHg9ymllwF4GYAPEUK2tm1EUUklF9/ICqV7QXcw7laWfzwk8PTT7OHKDtuVzVJy5yQ3yJt2AzNHmCGdfAk4+F/Aqi3ApW9l/4+ieFV+nC3fL3gtcN3/BM67lk0022+pG4J4Ghi6CBh71r2TiKgNtsq4ebQNwJb9Kzaw47Wz+UA7wc9JzKGXhonl5tv5ySV+hhsIFxbHr1083ZyAw9uWccMNyCeESBi308fkiNfWY+x7cQIkZdx8jEGkksrS9W1geVQHpJSeppT+3P59AcABACGbtIVAVM5JQvzjwfmNYDoNd0iNG2Ap7gMb68uwRFaeRVdeYNucvxu47GYmIfziG8yobX273SVEi45x9622x5MBLngdcM1vNTeXWLOD6ahTL8n3U9Mek60xbrNql/UUGk7w9OeiD9vsVbg5usJkT9YMt8+59KoMyBGmtCufKFIr2L0vGiBRFgtkuKNk3FV274v7FL+3p1QS4LubjqYNrWI5SCUiCCGbAFwJ4HHJ/z5ACNlLCNk7MTHR+ojC1ApZKpqkkhbapondb3g0CWAbbhnjXmD/03QmF2y/hRn8y29hTLRWdXCJhtsy2VI8O+y/7crNLCLCTS6pxSzHW2PcfPUgniueSecnE/Qq3Ax3Ihs8mogb+KrPuawW3GO4OfR4CMNtH4+vgMRrKcane8WHR1FTSKZx67FGDZp/by0m16Z5pE2Q0Fse5rhULCfnJCGkD8C/Avg9SmmTVaGU3kMp3UUp3TU8HMBYuCEqqSQIajPnEhh3LFm/ubg+DTCtUyqVLDSWtxy8ALjyPUBW0ONTuaUz7sI0e7g44/aCpgEjlwPTh+X1pMWYZWdnkiDg4ZJidbhkjj2ky5Vx87ZlTmOSyNr1NQKES9YYt4fhNg3241bSlUP3iLl2gjsma4ZbrAYoXOsam5VIOVH1hnUybqcGzZ2ybkw5HYIARK1x9zrjJoTEwYz21yil32zriKJKeQ8Cp0e6Vd0uNWDr24IE4SWViAXlZUjmls64F8fZaxDGDTC5hFrA2P7m/4ns0tmZJAg44xalEkKYA7kgqQe9HCC2LRORyDamaXuBT85e55LLKIGkkpAaN78e4vHF+HQvqSSS8ssOjVvWV7LGuF0MbiLLtikEqMYpdixaCnhIYS8zbkIIAfAlAAcopX/V9hG5dY5uB5ylJVvV7TZcA2y5vpF9JbKM2TidJuV5f8OdyjEDH4S1mYZ8u/w4+x5+kTUc2UGWMDEuMdy1h1moIRGGcZdmGGPiYZIc6VXLl3GLJV1F+MXwiwjCuKuCI9ELoaQSQeMGGq9lYKkkok5VTuekc3+ccbsdhxAgMxSsjLJbF51WQLSeZ9y7AbwXwOsIIU/ZP29p24g6qnG7RZWEPP6ay4F1Vza+x5e2IvOqlpgxF6USGZI5NoH5ObkoBfZ9mWVBOrE4wXTkMJNQ/xp5NIszvTtsvZLiLGN3Tlkhs4r9bzkm4rjppXxy8jPcZrVulL00bi63+TLuFqJKeA9IUSrhMgqP4wba55zUJOGAbozby6mYHWaM2y+0NKqoEoB97y7et74WilL6KIAlRqyHQFRRJUHAbwbT4ZyMgvGLDzBnNpxh+Rnu2vbzjQ1WnSjNMaZhlFnEiGgY8+ONmnsQxNP1dHZRrnI64kIz7rlGmYQjvcpONpprLvvZ6zDcGDe/7j6TLr8X9LiPVMIZdxCNOwTj5rXVxWMAjtDPZON7IvhzupRkFt2Z8u6hcXtp09kh4FSJPWu87IAMUWncALMRvSyVdBwddU66SSURMH7+oImRJbLeezJwKcVP5+ap7OWFerINwIxqeSGYY1JEPMNYizM8zaywh5RPqPF0cMZNKUu+SUsM93KOLHHTuOMBGTc33NlhZkjd2KJfZUCOsFElsZQQISQ6J0NElSyVYGmOJseyVYwYVeIGLgd66dyy+ulLQZcZd+8Z7qhS3oPAVSqJkHGLUklgxm0bbr/IkvmT9e8gxmDnQzomOdxitJ1LzFgIxl0tsuW3GFHC4RXL3esNl92W3TG7qp4sFFSEaLip5W50/Wpxc4QqtmSvFrQYu9cbnJOCVMJD8GQJQlFImrLnzymJ1KJKfBg3wGoAuYF3nY9M41aMuxFRpbwHgSyqxC1eNCxqS2aRcdvJN36GO5ZkP36Me24UWLGeadOi4ebsuxXGDTSHMTqNVNzWuIMYCVlECUciwyYLJ+O2LODxLwCjTwQfe6fhpZcm+oJLJfwauU2E1QK7J/0Mjp6os0o/GGXGtglp9leYZTsJxn4O3LTzKFbGzufPrDRLGUEYd6KPPS9ejDuqyoAcMsZ9ch9w4LvR7N/v8B05SlBQ2tmoEt4irTbjR6iv63E7XdfBuHnyjR/8YrmNCmPWufXA4IVMNuGTRH6cGcSEh94nQ81wyxi32MfT7kwSpLJdyY7hlkklgO2gdBju+ZPM4PsVX+omeA9OGYIk4VTy7PN8Enc7l36VATnCZBCKmZhOw82/Fz+em3YeKePmhlsiZfhFlQBsrFmfyJKo+k1yaHpzPe75Ux27Z3vLcEeRjRUGzprAUUe0JPocUsm8P9vmSK7wrleycJrdONxwUwpMH2L/46nuYVcONanEybirjXouZ0F+qdqAPPlGRHpVM+OeOcJeg4R4dQOWZS/rl2C4eVhozOdcGkX/iBIgXIVAXv0PsGPyRY3bsZLQkx6GOyrGzYmTJBywFlXis+LIDPkw7oj6TXIQCeMWz2ub0WOGO0LnYFDoceHGiahtGkci0yyVBDXcfox7/iR7za1jUkkiy+QSSlnyTTakTAK4a9zOCArOgvxStQE2+cTT7jd0ZhU7L4ZgHKZtw12aDVdjulOoOfBcvlNQqSTZL2SiupzLIAWmgPr1CVKX2otxO+PT3aSSKCRNseQEpfJwwCBRJQBj3JWC+4QZVb9JDk1vPtdBr1UUh+/IUYIiimyssBCzt6Jm3PFM4wMcJPmGI5mrx33LMH+KedMTdgnZwQuZwStMsQegr4WyA1zekWrcwg0fhnG7RZRw1ByUdgZltchWE33D9iTUg6zbr3dhIssmO69JhxvuuE/tl6DGoFYwzYdxU2rLIS6M23BEy7RTKhHDcd3aiulxdo97hfkBQmSJi4Myqn6THDLnZJBiYBGhtwx3TSrp4LDEQjdRR7QksnUjaJSDJd9weEWWUFp3THIMXsCOcepJ9ncrjBuwa6w4Ne5q64xbLOcqQ8YRWTJzlH2/DdeyvxeXULCsXRAjL2TwS8KxLDvmWGDcbobbKPlHlADBpRKzyiQ2PnZnTL5ZblxJ6Al5rZIonJMi43aTMghhFS3X7/LeVy2yxGWij6rfJIfMOakYdwcZtx6rX4DIpRKh4FAtFDAE4wbkOndxhu03JxjulZvYzXT6qbqzphXIkmucD3NQxm1ZdhKRF+NeyV65zj19mBmV1VvZtehFwy3r8C7CL5a7kmeTU6KP7YNocsNNad056Yegnd5r4YUOxt0QSy0YN1epJArGLWjcnDzJMiR5U24vJHNsf76MOyqNW2tk3JQqxt1RjVsTukRHLpXwgkOF4Mk3HF6Me26Uva7YUH8vlmSZkqbBjGGrS8J4ptngOB9mP12Wo7LArqmXVKLH2XctTtsO1iNsEtJjjI27PYjdhJ8R8GPc4iROCDPMsnPJJYRAjDtgVEmt7oygcVOr/gw4HWyeUkmUjHuJjNgvssT0mBhagRZrrBFkVtm9fk4y7k5HlQASqSRi5yTADHcppOFO9LNZXRbLPX+SPVzOAlKDF7LXsIk3IpyM2zLZj7NFVyzhz7j9Iko4eGRJYYoZtVWb2fvZ4d5k3EZQw+3ioHQmYrnVfuHnN4xz0tdwSxi3+L5zdRVzM9wROCdFjTsKDToz5H6/RB1V4qwOGLSKY0ToLcPdFakk7nBORqxxA4x5BU2+4dA05pBxY9y59c3hfoMXsNe+kdbH7Kwj7sYuY+lGlrg4CTz6N/XVAOCdfCOCM+vpw+zvlYLhLs0H09I7Cbe2ZRyBGbeP4Q5aGRAIIZVIGDdQT6gyq43fi5eLdSZbRRLHLZSciCLOOjvEzrksmcnZFm2pII6okjDXKgL0luHuCuPW2xcOyLXOaoE9rPFMuO8mq8tdLTEjJzomOdIrgZ2/Bqy/egljTtsMyD4nzpKute0cxub00+yBObyn/pCXZtnkEoRxG2VWCzwzWJdW+MohSK3lTsJPKtF0dh5dNe4Fdp/xh9yPcQeK4/aWSuZLVXzx4UOYnp9v3KfIuLmB1h2GW5aRaRnRZk5GEa7H7xeZXBJ1VInTOXlOM+4oa4UERSekEs64g7JtDlks98Ip9iDlJIYbYPpwEGeWGxKOtHe3JaZYr4RSYPwAM+6zJ1hkCMCkkmS///XkkSULZ+oyCSBECjiWv/lx4LHPA2eeDfy1IkXNCHgkW7g10gDse6GvvmJy07hrBaZ8KgMC3unpAMbnyyhUTMwt8I7pvEQvrxBYljtd3SYEGgHJETMno4j68Co2xftNRlHOAmgOB3SuZNqMnjHclFI8eWwKs4VKl+O4Izx2LMX2V1n0L9EqQ9JuqCAuU+dOspsvty66cYpwpr27sUuRcc+dYOO88A3sOx55xK4K6FLO1QkeWQLUZRKAMXU93sygzjzDViIHvgOM7g3+3aKCwet5eNwriT5vqUScxF2lkoCVAcX9uPRenC8xg14pO5ihyLhlYY4y7dyyvFP+g4IQZkyjYtypFWx/smJTUfWb5NC0Rudk2Gu11MN35CgBQAjBc6dmMFusdpZx622MKiGkrhkHaVnmRCrHlmOcuVHKdODsUPtSa51p754at32zjj3HzuPwZcD5r2DJQdOH/ZNvOFIDdUMo1hCvRQoIjJtSYPx5YNUWYPhi4OB/A4cfDlVJ8OjkIsYXlqCbcx3Yi715pb27GW7nd+DnN0hUCcCkAl4Z0oGFEiMn1XLRLloVqx8bcDBumeEWmHxlgUWi+ElgQaDFGzXupRhu3g1HxrijrMUN1Bk3v2ZBqzhGhJ4x3ACQ1gHDpB0OB4zXy2G2o4lDIsPiro1yeKkkaT8YXOeeeJ4ZRWe3nSgRlnFbJhvX4EXMmK3ZwYz14YdY4+EgD7emsdZpAxubWVx2uJFxz59khm9kG7D1l4B1O4FjPwEO/lfgr/jfz43h8cNCfZS5k+EcoM7ICxkSWVanxmmMKWXnxWm4ZUW7yvPs/gkawtY3zPwfkjrR80VmGI1ysXHSF5s/y5yuMsZdi5AKSURk4PLOUsMBOdxCAqPqN8mhxeyiePb1rRaZ7YjyGF6H78hRAiKpUxiWxdhXp8AvgGW2p21aPMu0W6A1jRtgD7BZBQ79kBWPWttOw+2oV+Lm1Iml2TmbfJFtO7KNva/pwPm7gbzNkoNIJQCw7ZeBS9/a/H52mDFXzl7Hn2fXaOgiZvAvfhNzxp78eaCGDGXDRL5sYLEsOKSf+how+rNg4wSCGYFEHzNGTm24WmT3WUK4F9zS3kshSiQALJrIMqWx71wqMSqOolWaZnfhKcvDHGWGm/tdImPcEYUDAoxxlxeaJ8Eo+00CdYLHdW6efBOVhu53+I4cJSBSGmBYtMNSCXe+2Bc6asOdyNaXmaEZt9AJ5/hj7PWiN7a3JEAsoFTCjc3Jfez3VVvq/xu5vO5wDCKVAGx72fkRU5kpBSYOMAcmZ42EAGu2s9/zY76HmS2wa5HnhruyyIydVyVGJ5z1PGRwCwmUNdNwS3sP6xfhZQ4kcsl8kX1fs1JsHjvvaCR1Tnox7pD3swx6rJ456ec3CAI+prLDMRxlv0mg7ofj/rEOprsDPWa4k1wq6bRzEqgvlSM33EJEQNgbPZ5iy9a5E8Dxx4GRreH7SIaFptlRDpxx25OO82HnBn72BDB0SeMDp2nAltey75tpMfWeIzsMCornDx9GcfIYeyBXX9a0DYjmqu+KmCkwA1SomKCU1g0rN6hBEMQIuCXhBDXc3LmbDMFqeXNoxwRWNkyUqqY99JLkWiY9nJOSqJLyvH1vRuBnqTFuSfebVuB23qPWuDl5shyMu0PooJjsj6RuMamk0ynvQP2hiZrt81huoDVNMJkDJg+ym3rLa6MblxfEJByjXK9b3rCNcJOObG3ex/DF7GepSPQhb8bx/EuHoRVncLEWq2eIcvD0+CCGe5FNRKZFUTYspPj3tA1qsWLia48fw5u3r8X6ARcGZQYoFsabWDQxbglblZUQ4HHVYRi3pjc7c1F3TKbiOsyqzHCnwjknw0o4XuAyTVSGlZ93WU35KKUS4pBKqsVopKOA6EnGTTupcfOLabSLcWfrr61MCvxmOH93+HDCVhFPN8Zx6/Fm7Y4z7mQfsKKNqwBCMEtyyFSnUT1zABjcImd6fatZHXIfzBbqzDFfNurMrDwPUIrpQgULJQOj0x49I0MxbofhruTZuRS7E8k07lYdgNnVTYybOyZHcknQagmWJjPctnPSKVfIOr2X56IzUryRSVSGVcydEBG14ebnqEuMu8cMtwUKoEI7I/ADkDDuNkklreqBufUsWoCXOe0ERMbtZqS4njd8WdvL8E5YOfSXx1BenGPHkyG72k6P966fMlOoIhFj4y2UzfoDbhqAUULeZqfTix41P4J0OomnmRGUSSWJbOM5qyXBCIa75gAMabj7RlhDAUHjnbe/00guBd2qoKo5QzuTdeekM8xR0+3sYofGHSXj5hp3FIY1nrHPuycVqzkAACAASURBVNNwR6xxczvB25edy4Y7QQCAoBSgiUdkaLfGzaWSVg33pt3ArvdHV9UsCMRCU26hb8k+FgVy/ivaPpzTBmOnRVPDYv8m+UZ97o45nHoSOP44KKWYKVSwboA9YIxxN/YEzZcZO50uuBhunv7tZ2QIkcdyyzJo9XhzaddWGTdvoCGw7vliFTGNYFVag0ZNlCGJEOLOSdm1FjMyjTL7iWr1J2rcUUglhMjLDUSeoyEwbstkk16Hkm+AXjPcGoVFdJSrHWx7X5NKbEPVNsa9hBu9QyFGNXDD7Wek1u5odL62AaWqiTGzHwOZOGbT5+HMoiXf0M1wU8rivE89iULFRMWwsH6AjblQMRoZcXmhpgfPLFaY89IJy2Asyy+OGwhuuGWlXctz7F5MZBEKvMCYoHPPl6roT8WQ1dh3K1pOw51khscoy7Mh9UQ9vC7KGG6gPilExbiBxgYmgLzC5VKhCVElHU6+AXrMcCd1C5RoKFVdHs52oBPOydQKILc22v22E/Esu9HNSvRLzJCYWCgjHx/C0Oq1GO/firE5l0SZRB+bRJw6d3GGGZvyAmYWmfFZ3Z9EXCc24y7U9dryfC1MsGpSLJQlSz9DEjLnBlnvSbf2dc6095LdWLqVhs/J/oYJbKFkIJeOI60x1lx2xiTwJX55Xv69xJrcrUo4buC9G81KdFEfzjoxHun0hyfyGJ9vIYuW++GoKVQGPEcZd1wDLOgoGR1k3PxmaZdUomnAy3+nHmu8HCCmvZsBYpbbiNNzJZhaAv2v/hDiQ1sw5paqTojUMVdrPGwZmFtgkSMrswlkkzEUKrbG3beafb68gHzJqGngMzKd26+kqwgn486PM2Yr1mbhcBruVmrbcPSNNEkluVQcaY09V1LGDbDJwk8q4fHuUTFuLW6ThGp0cmAi29ik2yO554fPj+PxI/6JW00QnZMdrgwI9Jjh5lJJqaNSSTDnpGUFr4Wx7CGmvRsRpwqHxJn5EgYycaTiOkZyKZyZK8slDMCOLJlqLP4zc6T268LcNGIaQS4VQzYRY9mTFTstP5G1NW6jFgYodVCGaYHFDQgf77EfM4PPs0xFxFKNjlWJA3Dv0Wl895lT/sftG2ZZpKaBqmmhUDGRS8eRAjO+RcuFcVeLLlJJspFxE60xKmYp4PdWtRAd4+ZdnPh5d6k8SClFoWIyySwsauGAVsdrcQO9ZrhJL0glzYb7xbEFfPGRwyhWOjihdBNi2nuXpZKxuRLWrmCGZU0uhVLVxFzRpVlA32r2kPKUb8sCZo/V9O/F+RkMZOIghDDGXSwx6SOeAZL9sEpMKlndn0QqrkdguPvsB7vAMj8nXmDp+bIHPJ6qyzC8sJjAuCmleOrELI5OLrpPXLXzMMKOW5ishQL2p2KI0Qp0jWDRabjFJb4r47a/N5dwQkQSnZgu4EuPHpETMnHFG5nG3VeXXwDXa1Y2LJgWxWK5hee6VpJWMW7osKDpHWbcmiOOWxJDfnK2iFLVxKEJl/rKZxucUkmXGPdCqYp82cBIjj0QIyuYURmbl3QdBwTHnK3vLpxiK4Y1OwAAxYVZDGTYw5tJ6qiU7OuZyALJflQW50Ap0JeKYVU2Ljfcfm3LRIix3Md+wh52t7BOHksN1Ev5Cox7fKGMhZKBqklRMX2IjZD6zp2tuXQcMEqIaQQFN8YNuDsnRcYdUsI5M1/CfLEqn3BFeSQyw+2I5XbpN8mJWLEVeyPWKjnXGTeoBV2Pddhw+4cDcq3zxbEQadHLGVwqqRRs7bE7GveY7TRaYzPuwWwSMY3gjJszKTPYmPI9fYRp1yNbYYGgUpjDSttwZxMx0PIiTMvutp7MoVJg+m1fMoaVmUQtPb4BNY07SFSJfR7nTgDjz7Gqjm5RODx7kVKpA/DQeJ00+DLE9EpmpPLjteJSuVQMMMqI6xoWLYcDXlZ/W4RouFuI4eYO3wVZnK8oX0TpnATqDkoXxl2w7UzFsFAxQq7yiZDybhTt2uKde056y3BbBvRYHKWwJ3Ep0OxMMQ+phDOvE9PFc0MuiSXtRsW2I6pLUsmZuTI0QjDcxx4IXSNYnUu6R5ZoOjPevDLhzFHGwhNZlLUMYtU8BjLMOGSTOhJmAVXTqjHuarkI3arYjDuBxbLZTCI4Kw4qlQDAkR8xTfS869y3FUu71kLu6tmJL03ka07TRVm0S8N50OxyuOOYLxrQNYJsIsYYd0xHoeo03AGlEstiq4GQjLtgTzTScYssOyrGzXMnuIPSReMuCtp2aJ27IRywzO6HNieiNRzebwNCyL2EkHFCSPv7RFkmYrEOM26AGWu+nHIY7ophYaFk4KKRPliU4qXx3pJLihUTht/SOSx4EkPNcHdHKjkzX8JwfxIxvX6bjuRSGF8ouTuL++zIEqPMapfbrdDyJI2EuYiV2TrjjltFJjskmMZdMSzEzQL6k/HadjOFCrs3xvYDT3+dNW4Q+0V6gTO/apHVDU96OPTEtHcH455ZrGAqX8Fla1n892IQI2NH2MwXK+hPxaBpBDAq0GJJFJ3ESE/UGaSrVGLYZQGs0IybG+y8zHCLz1vkjJtLJfKokoJAwhbDEjKxVkmHKwMCwRj3VwC8qc3jYKDMcHc0AQdw3DyNhpvXtrh4pB8rM/GekktKVRP/8NhRPHJwwnfb0IinWQcboCuM27IoxuZLWLOikQGO5FKomhRTbinpfSPsgR0/wIzMyk0AgAVqG+4a444hzhl3PFsz3BlrEam4hlW2pDI3NQY89jnguX9jnVU2vhy45v3BJjM9wSQLzYdtA41p7yXeQIEd4yXbt7J9PSuR68u4+XmollBYmEV/qu7H0RPpBqYJgE3UXC6RMW7+P+70DVmnxFMqaWDcEYUDxjPsO1WdGre74S4EOaciaozb6ni6OxDAcFNKHwHQQqBjC7BMxGLxzkaVAPULSrSm5Q5PfV6VTeDikX6cmCm0Fj7UBvz8+AwKFRMvjef9Iw3CIp6pG+4uxHHPFCqoGFbNMcmxxv57zE3n5p2+R59ghiC3AQAwb6WQoQWkbbmBSSVFlGAb12Q/KqaFgVgJhBCsSMehawSlMy8w/8eOXwFe9jvAluvrtcb9QAg7/oZd/vKC2PvRkaRzaDyPkVwKQ30JxHUSLArCTn23FsaYvg0ARhmxZBpVk7IJq+H49nmWatz288ENdwjGzULu2PMinXDaoXFrWmPau0vD62LFrOU3nY2Mu3OwDNtwm9EbIi9wli3JmpxerIAQYCAdx0Uj/aAUPSGXFCoGnjw+i2xSx2LZdHfYtYp4ut5OqgtSCf8+axyGeyATRzKuuRvuWmTJJKtaaLO4GSOFPt0CsZfN6biOhFVCCfYDl2CMe4XGIlY0jWBlJo7KzClmdAcvaK30wM5fC1aOtxbJU7KbLDPjuFCq4vRcCRcMZ0EIQYbHn/uhbwQmNCTnDrGIEoBp3Al2PpsiKfjE4SaVAPU0+hAad9mwUDXZsyyVStqhcQONyU+8n6XWzLhXpOPQCGmBcYvhgD3IuIOCEPIBQsheQsjeiYkWl+7UQjweg2FR1gmnU6gZ7ual2myBZZ3FdA1DfQmszMRxcGxphjsKTfrxI9MwTIqbrlgHQoAjEy6NaVuFWCOjC1LJmbkSEjENq7KNxyaEYE0u5T5RJTJ1LXlVvWP8lMFis3ndbUII+rQSCoQzzRgWaQL9qCfBrMwmYM2fBvrXLO3LBDH4IuMWGigcsq/rhavZd+pLxoKxw1gShaHLMZx/AQPI1/YdS7AJouTcR41xy5yT3HBPsnGGWIHxSSaT0P017igNdzzbqHFrsabVdKFiIJuIIZPQW2DchP0sd8ZNKb2HUrqLUrpreHi4tZ1YJuIxdvE6mz1p3zAuESXceBBCliyXjM4UcPeeQ/jG3hM4EiSZQoK5YhW/GJ3DtnU5rF2RxrqBNA5NRmy4xRuxC+GAZ+ZLGMmlQCRGbySXwuRCpXm5z8FZt61vV00LM2YSqbhWd/wByKCMRcquLaUUeZpGH6kb7sEkBQrTMLNLNNxBwDXu0mxDA4VD43msyiYwaEfWZJJ6MMYNYGZoFyjRMTRp99M0yoineIGtMIybSyWT4SNK7OOM5FKoGBbKznIWehwnZ4s4PJmPtkONWGjKpbtOsWoindCRSerhn2dCmFzCi0wtV8YdCaiJeJwb7i5kTzoMN6UUM4uVWoQBgCXJJcWKif949gzSCR1zxSq+9eRJ/ONPj+G5U/OhDPhjh6ZAAFy3hWmtW4aymFwou2cUtoJ4BtOLZRZV0WGppGyYmFyo1DImnRjJpWBRijNuYYGDFwC5dTW9e7ZQRUXvQzpRZ9wAkEERi5Qdo1AxUdKzyNJ6jYthTIMCmI8PRvPFvMBLu/LiUMkcSlUTozNFXDBcj0bJJmNy5irBnJnCmf5t6Jt7kYVImmUkki5SCZ+opRo3Xw1UwrVSQ10e4b6KvNNBqbFEp8mFMiwSYZ2gRKYxjlvyvYoVE5mEbpc/aCUJR6tX0ew1xk0I+WcAjwG4hBAySgh5f9tGYxmC4e5CEo5D454vGTAsWoswAIChvgRWZRN48vgsHj88hV+MzuGl8bxvfDelFP/13BkUKiZuvmIdfmP3Zty4bQ1ACP5z/xk8cXQm0FAn82U8f2YeOzcO1KIFttgP9tEIWXfeiuPgeB4vnFnA4RmPpgJtwOhMERal2LhKnqyycVUGiZiGA6fnpf/H+quBq2+tSRSzhQqqehrJeKxuuI0KUsRE3mJGKV82UNGzSAuGe5XFfPLTWkBn5FLAS7tyw53K4aXxPCxKazIJwKSSimG5rzYELJSqOJPbyYz1kYcBo4KkzbhdNW4vqcQeVxgs1gx30v7bcVxNR8mwYFJguhShPJros5tjVKRt0SyL1hl3ogXGDTDGzeWYXmPclNJfo5SupZTGKaUbKKVfattoLBPxODOiTUuqdsJFKuEZkyuz9YtOCMGVGwewWDHwk0NTePDAGL7z9Cnc98RxT+36qROzODyxiFddNITVuRR0jWDruhx+/bqNuHRNP3780qQvi59YKOM/959BIqbhmk11Y7IyE8dAJs6WmxFh3ynmpMskdHzvuSmcnmvsLEMpxcRCuS3Ft45PFRDXiSvjTsQ0XDzSj4Pj+UD3yUyhCkp0pLK5uuGuLiIe07BgJWFaFAslZrhTtFyLQshVJ1GK5TBVrt8XozMFPPziRHuKjsVStfGZiX7sPTqNob5EzegB7HoAwUIC50tVpLJ90M67lvUtBRBPpqER0qxxD10CnHetPCRPXHG1kDUp+ioWyo2rwpJhoWxnco4vRvjM17J/89Ka8iXDBKVAJhFDNskYd2jZUtPrrL7DhrunmgVj9WWIZdYBZ3pDKhFDAUXs2DCAHRsGYJgWClUTp2aL+P4vzmDfsRlct6V5WT0+X8KPDk5iy3AWO88baPgfIQRv2DqC2WIV/7n/DFakz8NwfyPrKRsmfnp4Gk8dn0UyruGGrWuYo03Yx5bhPjx9YhYVw6pl2LWK8YUSXpg2cOOKFNau6se+RALffuoUfmXXeViVTeDEdAE/OTSJU7MlXLt5FXZf6N/JfbZQwf5T8ygbJspVC2XDwtoVKen5Oj5dwIaVmYbEGye2rcvh2ZNzODiWx+XrvZfv04ssCSWWFgx3ZREJXUOFZFCoGDXGnYhpbJvMKsQWx2BmRzBt1/Eemy/h20+dQsWwsGUoi/NcVgQtgz/8mo79EwZmClXcvHNdg87fl2T3aL5s1OquuGG+aLBQwPOuBU7uA6pFkFgKqbjWzLhXrGc/MiyBcRcqJrIJHVl73E7GPVuowiIx6LSK8YIJSdvp1mA71/P5eZTnFzGYlcdwZxI6KKWwKEWpajE5LSi0WJ1xd7AWN9BrGvelb0ViHatb3ZVCUxLGnYrrSMflFzOma8il4rh0TQ4XjfThiaPTWCg1MopixcS//+I0MgkdN2xdI3W2xXUNN12xDgldw789fQqFigHTojg1W8QTR6fxDz85hp8fm8G2dTnc+vJNDUtnji1DWZgWxfHppcslP35pEnoig/UDaSQSKfzSletBADzw5Encv28U9+8bxXzRwJoVKTx5fCaQ5vrQC+N44ug0Do7lMTZfwmS+jJ8enm5aos6XqpherPgaxbUrUliVTWD/qTnP7cqGicOTeaxdYTcY4M7JCmPchp7GYtlEvmTAiPchprO63KgsAqU5xAbWYXqRjemBJ08iFdcR1wkOjrchEcs23Ea8Hz89Mo31A2lsGWrsgJNJsHu0ybkoAet8E2cyyMaX146RTuiBPl+DaLhbYNyZZAxxXUMqrtdaw3HMFCqwNB0JXcOZfITPvG24nz1yEs8cGwdtSndnx0rHhUkltINSr6fVd7D7DdBrhhtAXCcs8SEk4z48kccPDoz5byiDLte4WURJXGpsnXjVRcOgFHj04GTtvbJh4ltPnUS+ZODN29d6zuZ9yRhu3rkOhbKBr/30OO7e8xK+/sQJPHpwEn2pGH712vPwhq0jrvtYN5BGMq7h8BLDAk9MF3B0soCdF6xFTNMAPYGBTALvuHI9SlUTU/kyrr9kGLft3oQ3X74GpgU8fnjKc5+T+TKOThbw8i2D+OD1F+C23Zvx9p3rYVGKF840GsDjU+xBOH/Q23ATQnD5+hxOzZY8G/s+MzqHctXCrk0rmdGpMe48Y9xaGosVA/lyFfHMChDYhnvhDAAguXI9phfL+ObPR6ER4H9ctR6bhrJtSnpihvv4Ilu6775oqOneExm3FxbLbBWRS9v39vqrgYtuAFZtRiquh6uIp2l1UtOCxs3H3JeKNWVPcsa9sj+F8TwjLJHANtyL+QVQs4oybXxuRMbNiVkhrINS0+rNgs9pxg32QKbiWmjG/dSJWTwzOifvWuKCYsXEsyfn8OihWfzsyBTG8o1sYLZQrVWT88OKdBxXb1qJ588sYHSmAMO08N2nT2N8voy37FhbK87vhZFcCm/evga5dAyXr1+Bt+1Yiw+8egt+7dqNjDF6QNcINg1mWw4xBJhu/aODk+hPxXDFxmE2odna4EguhdtesQm/+crNuGrjSsR1DQOZBLZvyOHZk/O10gAyPHl8FnGdYMeGukw03J/EcH8SzzsM97GpAvqSMQxm/c/7pWty0AhxZd1V08LPj81g01CGRTUk++vNbiuLiMU0VPU0CmUTCyUDyT57fOUFYOE0QAiyg+tRNSnKhoV3XLkeA5kELlrdj8WyiVNuUS2tIpZC1bTw/CywZTgrvWdScQ26RjyNzEKpim/sPYG4ruFCHpGix4ANVwN6HJlEC6WTY3Y9k0Twpte8UQFntH12spiIuWIF8UQS/Zk0TItiMu9SsjcsbI27UpiHZhlYNJpjuIG6xg20yLg5znXGDQCpeLj2ZYZp4eQMc54dmfJnnJRSfP8Xp3HPI4fx38+NYa7CDN/BifqDWKqayJeNhlBAP1yzaRX6UzHseWEC/7H/DI5PF/DGrSMN4Vx+uHB1P951zUa85pLVuGikv3ZTBcGW4SwKldazKF+0ZYxXXDDE9OV4piHKIGsveUVcu3kQusZCFGVYLBs4cHoel63NNa0WLlubw5m5OmO2LIrj0wVsHMwEWuVkkzFsHs7iwOl5KVPbf2oehYpZd+TyJr3lPFApIJHqA4iGvM1Os+k0kxU4484MYt3QAFZm4nj7znVY3c9Y1aahDGIawcGo69bEUjg5W0SBZF39Bix70iWZBcBcoYpv7B3FYsXEO65cj9W5ZiaYjutNUVCUStLgRegJltgUogJexWTlUrP2de9LxpukktlCFalkCn1pZvhcM2LDQtNhxdKoFvPQqYFFo/F+4unuqbhWc/i2XCFQj0VXZyXooTt6tIBIxfRQUsmp2RIMi0IjJFBI3KGJRTx/ZgGXr8/hPddtxFuu2ID1KzOYKlq16InZArvBgjJugGnVr7poGBMLZRwcy+P6S4axdV1EvfkCYNNgFjGNYN+xYKGFIiilePzIFIb6Erh0jW3gkjnfLu59yRiu3MhWGuOSfpBPj87CohRXbWzus3jJmn4QglpY30S+jFLVdA0DlGHbuhwWyyaOOK67aVHsPcp04hpzrRnueaCSh5bIIJ1gCS35koG+VKyugy+wjMmhviRu270ZG1bWx5SM6Ti/DXLJIo1jbL6E9WtGMNTnnvTUl5Snvc8sVvCNfSdQNizccvUG11Ve2pZKxLH//Pgs7t5zCP+1/4x81arHW6gKyCYHTj6ySaati5PsbLGKTCqJVIpltro2yWgBZS0F3ShAo1UsVBsNd8GO4SaEIBnTENMC1oARwRl3h9k20KOGOxlSKjk2vQhdY5rn6EzRsyg6peyBXpGO47WXrMbqXApEj+P/b+9MY+M6z3v/e8+cmTP7kMOd4ipRtiVZtmzJkrcEdVMnTpo0LZoCbosuaIp8aXHbiwJFgwIF+q0Fit72w8VFg95uFxdp0TZtEndNnbhp6tSxbGuzKFm2REmkKO6cfZ+3H845s5Az5AxFcg7j9wcI5AyG4sM5Z57znP+z9QUNdLfOO3fMwUp2FLixomQ7HhoIcvJQhI8c7W3orPYSr9vFucM93FhItt0gdGs5xUoyz5mJqDkCFODYZ2DqxW1/9vR4N163i9ffr4+6C6Uyl2ZjTPYGGt65BA2d8R4/0/NmA9JtS99ux3FP9gQIGK5Ncsn0fJxEtshTk9Fq9F5x3Fby0RMkYOispHIUy9LUYo2wGW3nkhAaavp7j/YHSWSLuzoj5sp902k9emR0y9f5Db1hdPi1i/colSWfOz2yaThXLV6PCynrK7c+WDTnfV+/n+DPvzvDP1yaZ6VWthg9t/2Eww3YFxdb4w4ZbqSsShLZQolMvoQR7EJ4IwyEjd2LuIE0XjylNJoskdrQm5YulPBZiV4hRNP3dEvsiHuf9W1wqOP2utvT4G6vpBmKeHloIGRVVqSbvnZuPcN8LMvp8e6qg9LMSXBjPWFuLCSJZwuspfNo1pS4drDL+85M7EPTRgNOj3fTFzL41rXFtt7D87fXCHl1Hhqo0TB9XS0lo7xuF09NdHNrOcU3ry1UPrDT83Ey+RKnx5tfwI4NhUlki8yuZbi9kqIvZLQlD2ma4PhQhFvLKb56YY73F5MUS2XOz6zSHzaYqE1yejY67gABj4ulhOmgQpWI25JAtphRMtkbMOW1B5xbYxPLFLgU89LT3U0oOrzla4OGi+SG6DCVK7KayvPUZHRTOelGbGnATlDmiiXmY1lOHorw+Y9M8tRElJmVFF95e65arz54EvoeautvSuWrc0rATE5CtXvSvqtl6kV49McZCHtZSW4xyqBNUtLAKCUwdI14od7VZfJF/DXVYgHPZv19W+yErYq4TbxuF7kWt+Ck80WWEjnGon6Gu3x4dG1LueT8zBp+j6tewrAOwOFBMzl18e46q6k8XX7ToR8kXJrgxeMDpPJF/vP95e1/AJiPZZhby/DkePeO/95To12cGu3i8mycP3t9hv+6ucI7d9YZCHu3TMwe6Qvi0TUuzcaYj2W3rSZpxNnJKKfHu1mIZ/n6xXv80bdvspYucHYiWq+Vu3RT+snFK47b79Er0+uChrsalQutOvOkAV63i7Gonxu7JJe8eWuVjNFL/yd/feuFC5hLILKF+gUadqS6VaRtY1dR2I67tlPV79F5bqqXT5wYJJkrMtNCzqgZ9gW8ViqpfX49Y97VdoX8oBuVUQa7laBMlj14ShnCPjeJjRG3JZXY7Cjiriyf2P9ZPs503LpGvlhuaYqeHV2P95gR0HiPn5mVxpUVS4kct5ZTnBrtqk+yWZUTAa/BVH+Qy3MxlhK5thKTTmIg7OXJsW4uzca4u8Xdh835mTW8bhePDrc3h6IW3aXxwiP9/Owz44z3+PnuByuspvI8Od61ZaLR7dKY6g/y3kKCUlkyHg00fW0zPLqZW/jF5w/z2VPDjEX9HO4LNE4KGyFzrnS5CJ5g5TYeqGrcAIGebWe0TPUHiWcKLCYezNHEMgWuzsd59FCYsHf7O7xqFUQ1QlyI5xCCypq3rag4bstR3Vnd3Kk62RvA73FxtdlYgRZI5kq4XaaGDKZUApDI1Ufc9l2t3SG6Wzp3vGRg6Bo+t4tkUauL5NP5El7Phoi73QmBFalERdwAla7AVqLuOytpvG4X/dbt4URPgES2yFKDq/Zbt1fx6BqPb+herO2cfGKsi1yhTCxTqJtRctB45kgPEZ+bV6cXuDwb4+Lddd65s8aVuVjdCbyWyvPBUpLHRiIP3HEJ5ijUTz82zMtnR3n+aC8P9W9fPnZ8yLz70TXBcNfO9UJNMztIP/P4MJ89dagqhdVia9gAHj9+KwrUhDBvnW3HHdpargDTcWviweWS8zPmTJRW5bVqF2I1QlxMZIkGPC0dQ9thZfLmeXBnJc2hbl9dp6pLEzw8GOLmUmrHe1bTuSIBQ69cuL1uOwlYddwhb7VSKWjoBAzXrunc60U3htuFoWuUhV4Zwla0ql1qpRK/RyezIXG6LZXkpNK4garj3k6jldLUs0ejvsqHdMLqNJtZro80Y5kC1+8nOTEcrmsXB+oc91DEW9kq3h3Y36l4u4nbpfFDxwaIZ4v82/QC37y2yGvXl/jG1QX+4ru3KxURb99ZwyXEplb8B2Uo4uOp2kTnFox0+wj73IxGt25z3xWMUHUjiidQibgDhsu01Wu9Dy3M4Pa6XYxGfVy7H29pS3gmX+KrF+Z4/f3lijOMZQq8e6/1aBuolNfV3tovxnOV4GU7aqWShNWp2ighfGI4Qqksub7Dssdkzpx3bSOEMKcbWhp3LJOva9sXQjAQ9u6a414rejB0s2OzJNwVx21vd/fX2GbLOG3JJR2MuJ01q8TC6zY/vNtte19N5Ulki5ybrN5eBw2d/rDBzHKKs5PVCOat22ZU82SjRFllyJRZHnRmvJtXLs1vm+RxOmM9fr7w0cMUSmU0IdCEYDmZ47Xri3z94j0mO2M35QAAEVJJREFUev3MrmY4NhRuKyG42wgh+IkzI+j7kU8wau4APEH8VtQUstd7BXrg5Oege7LBD2/mqYkof/v2LP/+3hIvHm+uiWcLJb7yziwryTw3l1K8c3edx0e6KnXN7SSzA5XuyZL11axDb1Sz3Qi3S8Ojm/NKbKlxrIFE1Rcy6A8bXL0X39GFPZUr0heqtyno1StSyVq6UG0QsugPebm1nHrgmTv5YplEyYPXjrgL1Yi70u7uqY+4wZRQQi1eQFXEvYFWI+7qSVcfLUz2BrgXy5AtlCiUynzj6gIX78aaRzUbRloeHQjxC89PVhouDjJet4uQ103A0PF5XIxG/fzUuXE++lAf99azlKTcsupjvwh73XUR0J5R57irEXfQqDkveo+23FAxGvVzdiLKlbkY1+431oPzxTJfvTDHSjLPZx4f5meeGWeyN8D526tMzyfairbBjJhr120ttpGYtPFaTTh3VtIEDBe9wcay4PGhMAvxbKXyph1S+VIlkrWxa9DtUsAuf/3fPRA2kJKGPQHtEM8WzFG+uobuErjcnmrEXdPublONuNsZBaAi7jq8euuOu8vvJrLh4E/2Bnjj5ioX7q5zYzHJciLHuckoTzeYRGf+wgg8/jJEqvWz7ZYBHiRcmuD0eDcPD4ZIZAsHNgm7I2qrRnQffit0sUvVdsLTh3uYXcvw6vQig2Fv3e1/sVTm6xfvMR/L8sMnh5i0pLxPnRzi3GSU6/cTnBprL5rVtPruyXYSkzZmE06RxXiO8S06VR8ZDPMfN5aZno/TF2p9s1W+aHVNbriTCxo6H2SLFSe62XHby6BzdU1P7RLLmI7bK1wIBEG/j3jFcdeXKZrfb84bbIumIu46DFsq2aJ7slSWzK5lGpaPDYTMCWjf/WCFVK7Ijz1xiGenerfWW6OT+9622mmChr7tDJTvO+zuP7cPNFMyeG6ql2NDrc/g2IimCV46OYgmBP9weZ5iqcxqKs87d9b467dmubOa5uPHBzk6UP87eoIGz0717uhOI2DolTrpdhKTNj6Pxr31LOl8actJjD6Pi8neANfuNx4rYHPtfrwu0KqUAm7424Jec6esvb0o4qsPGgKGTsirP9DMHTAdtxQuDJ95oQz6/ZWLhW2nb0NVCWyOuLdcXC5UVUkdhq6hCUFui4h7PmZ2SDZKqmia4PGRLiZ7A/zUubFKwlKhwGNpqjXLkM9ORh9YFgt73bx4fIDFeI4/+vZN/vz1GV67vkSuUOLjJwZ2ffRBoGZgk5mYbM9+n9tVSahu16l63BorcLtJTfdyMsc/Xb7PG7dWK8/ZF5Vgg4gbzNpx2Bxxg9lEdnc1zaXZrUf2bkUsU8Cja+he8ziH/D5i6UJl8JWuCTw1iXDdpWG4tbpBUyvJHF/69s1N4xQqdDDidmSIWZkQuMWgqRsLSVyaaHo79cyRfdgTqDh46B6zRdnT+uCvVpnqD/L80V4W4lnGowHGov5NMt5uEfDo3I9laxKT7SXS7XbvnqBn22TcRI9Z0z09n6isyavFdsLX78f5iHVna19U/A00bvNn0nWlgLWcGu3i9kqab7+3xKFu35ZzW5oRzxQI+9wITxDSq4QCforlDKl8iXTeXFm2UR4KePS6qYuX52KUypK7a5mGf7d516ZXt+3sI46MuMFOnjSWSgqlMtP340z1BzeX9ikU29H7kCmN7QFPTUT59GPDnByJ7JnTBlNSyBRK3LeGorWTmIRqSWArW3xcVn387dVUw5Vts2tphDCHStmdlslc44jb1rzT+VLTPJIQZvevR9f4p8vzO2qBj2cK5v/v8YPQiATM9yeWKVhLgjfHrH6PqxJxF0tlpufNMsiFZuN7B07CmV8wg4F9xsGOu/mgqQ+WkuQK5Qfq9FN8iHnkh811XgeYgGEOirq1nG47MQlVxz3e4kCv8R4/uUKZhQ3VHlJK5tYyPDwQquu0TOWK6Fq1a9ImaOj2DuctV68FDJ1PnBhkOZnnOy2Obqi1KWY7bl8UjGDlIhFLFza1u9f+TrtS5/2lJNlCib6QwWIi23jHqEs3y0c7gIMdd/OZ3Ffm4kR8bkajH7LEmkJhYUeut5aTbScmwZwpfmq0q+VJjKPdfoSgMsHRZjWVryQ47U7LbKFEOm+uLNsoR7isihhorG/X2xjgibEuLtxZb2lcs006X6JQkqazHn8WnvxZUzYRZsSdzhcbbpLy17S92z7m9Hg3hZJkObV742Z3A8c6bqPJTO71dJ67q2lODIdbGravUHw/EqiUr5V2lFgNed288Eh/y52qPo+LgbC3slrOxta3R7v9HB8Om52W9xMkcyWCRmMZ077odLVQcvv8VC8hr87F2fWW7AQq1SNhr7XByQjh0gRBQyeWyVtSSeOIO18ss5TIVXyMPb9lIaYcd0s0k0revRdHCPZ1QYFC4TRqG1vaTUzulPGon/lYtu5zObuWIeTVCft0+kNe+kIGV+fjpKw5JY2wde/tttSDWe3x0ECI2yvplscUx7P1w6tsIj43S8k8xbJs6Ljt5753axUh4MShCBGfG6/btatz13cDBztus1yptna0XJZcvRdnsjfQeluqQvF9iN9T1YrbTUzulLEeP2UpK1G2lJK59TQj3b7K3e/xYXMd3Xq6sKmG28YeL9Bqk9vRgSClsmxelreBmDV1MNzAcdvLIXzuzbbZ9t5YTDDZG7D0eMFgxFCOu1WqEwKrV9lbKymSuSInVFJS8SHHpQl8bteOEpM7ZShizru/s2o60LV0gVSuVFeS+8hgCE0IylI2jbiPD0V49khPy7r8YNhLyKtzo8WtTrFMgWCD/agRn7mBB2gccVt3MVLCo4eqPsZc8JBraZDYfuFgx725e/LKXIyA4aq0DSsUH2YChr6jxOROMfsmfJXJm7Nr5teR7mqRgN+jM9Hrt+xrrHEPRrycazZ+ogFCCI70B7m9nKoL5JpRqSjZQG15ZkON22PPrdGZ7Kn6mMGwFylpOCq6UzjXcdfMKzH3EaaYWU5zfChy4LbSKBR7wdOHozx7pPE2+L1ivCdALFNgPZ1ndi1D0NA3OUn7jridwVnbcbQ/SLEsN41rbkQsUyDs2xzt19rZqKrE53bh87g4ORKpG49hS1H3m9VzdwBHdk5CVSq5uZTiOzeWmVs3kyAnR5RMolAATLWwpGK3seu+b6+kmVvL1OnbNkf6Arx8dpTBXdTehyM+AoaLG4sJHh5s/neXypJkrrhJ34YNjrtB456mCX7+2YlNtecBQyfsc+/qIuMHxcGO23zz3pxZJeTVeeGRfh4dDu/9oH2FQtGULr+bsM/Npdl1krliw5ETQohdH16maYKp/iBX78UplMoNW+UBEtkCUjZOfPrcroqs1MyPNOvEHgx7VcTdCmGvm8dGIvSFDI4PKYetUDgBIQTjUT+X58wBULX69l5ztD/Exbsxbq+k6u42YhlzeJQQorKvspHjFkIQ8bl31EI/GDF4byFhNhY1qZaJZwssxrP7cifkWMetaYKPHWu+UUShUHSG8R7TcQcM17bdj7vJoS4fPo+LGwtJpvpDrKfz/Pt7S9xc2lwm2KxGfLI30FKCcyN2k9NCPMdkb73blFIyPZ/gtfcW0YRgLBrY84SxYx23QqFwJqNRs/19pLv5Aoa9QNMER/qCvLeQ4Ds3ls19qZrgmSM9hL1uylatn8/j2jTcyua5qZ0lcwfCXoQwE5S1VW2ZfIlXry1wYyHJoS4fnzgxuC9VPspxKxSKtvC6XXzy0aGO7GQ92h/kylyMN2dWOTYU5vmjvU2d9G7i0TV6Ap5KgrJUlkzPx3n9g2WyhTLPH+3l9Fh3S8uxd4OW/mIhxEvAHwIu4I+llL+zp1YpFApHs1Vlx14yFvXz3FQvI90+hrv2d8jcQNjLzeUUF+6uc35mlUS2yEDYy48+0b/v+2m3ddxCCBfwv4EXgVngTSHE16SUV/faOIVCoahF0wRnJ6Md+d2DES/v3ovzrWuLHOry8bFjA0xssa9zL2kl4j4LvC+lvAkghPhL4LOActwKheJDw9H+ECupPFN9wYb16/tJK477EHC35vEscG7ji4QQXwC+ADA2NrYrxikUCoVT8HlcvPBwf6fNAFpreW90Wdm0DkJK+SUp5Rkp5Zm+vr4Ht0yhUCgUDWnFcc8CozWPR4B7e2OOQqFQKLajFcf9JnBUCDEphPAALwNf21uzFAqFQtGMbTVuKWVRCPHLwL9glgP+iZTy3T23TKFQKBQNaamOW0r5j8A/7rEtCoVCoWgBNblJoVAoDhjKcSsUCsUBQzluhUKhOGAIKTeVZD/4fyrEEnB7hz/eCyzvojm7hVPtAufa5lS7wLm2OdUucK5tTrUL2rNtXErZUhPMnjjuB0EIcV5KeabTdmzEqXaBc21zql3gXNucahc41zan2gV7Z5uSShQKheKAoRy3QqFQHDCc6Li/1GkDmuBUu8C5tjnVLnCubU61C5xrm1Ptgj2yzXEat0KhUCi2xokRt0KhUCi2QDluhUKhOGA4xnELIV4SQlwXQrwvhPiNDtvyJ0KIRSHElZrnokKIbwghblhfuztg16gQ4ltCiGkhxLtCiF9xkG1eIcT3hBAXLdt+23p+UgjxhmXbX1kTJvcdIYRLCPGOEOIVh9k1I4S4LIS4IIQ4bz3nhOPZJYT4GyHENet8e8Yhdj1svVf2v7gQ4lcdYtv/tM79K0KIL1ufiT05zxzhuGv2Wn4SOA78pBDieAdN+jPgpQ3P/QbwqpTyKPCq9Xi/KQK/JqU8BjwN/JL1PjnBthzwg1LKx4FTwEtCiKeB3wX+l2XbGvD5DtgG8CvAdM1jp9gF8IKU8lRNva8TjucfAv8spXwEeBzzveu4XVLK69Z7dQo4DaSBv+u0bUKIQ8D/AM5IKR/FnKT6Mnt1nkkpO/4PeAb4l5rHXwS+2GGbJoArNY+vA0PW90PAdQe8b1/FXOLsKNsAP/A25oq7ZUBvdJz30Z4RzA/zDwKvYG516rhd1u+eAXo3PNfR4wmEgVtYxQtOsauBnR8H/tMJtlFd8RjFnLr6CvCJvTrPHBFx03iv5aEO2dKMASnlPID1taPL54QQE8ATwBs4xDZLjrgALALfAD4A1qWUReslnTqufwD8OlC2Hvc4xC4w1wD+qxDiLWtvK3T+eB4GloA/teSlPxZCBBxg10ZeBr5sfd9R26SUc8DvAXeAeSAGvMUenWdOcdwt7bVUmAghgsDfAr8qpYx32h4bKWVJmrewI8BZ4Fijl+2nTUKITwOLUsq3ap9u8NJOnW/PSSmfxJQJf0kI8dEO2VGLDjwJ/B8p5RNAis7INU2xtOIfAf6607YAWJr6Z4FJYBgIYB7TjezKeeYUx30Q9louCCGGAKyvi50wQgjhxnTa/19K+RUn2WYjpVwHXsPU4buEEPbCjk4c1+eAHxFCzAB/iSmX/IED7AJASnnP+rqIqdWepfPHcxaYlVK+YT3+G0xH3mm7avkk8LaUcsF63Gnbfgi4JaVcklIWgK8Az7JH55lTHPdB2Gv5NeDnrO9/DlNf3leEEAL4v8C0lPL3HWZbnxCiy/reh3kiTwPfAj7XKduklF+UUo5IKScwz6tvSil/utN2AQghAkKIkP09pmZ7hQ4fTynlfeCuEOJh66mPAVc7bdcGfpKqTAKdt+0O8LQQwm99Tu33bG/Os04mFzaI+58C3sPURX+zw7Z8GVOnKmBGH5/H1EVfBW5YX6MdsOt5zFutS8AF69+nHGLbY8A7lm1XgN+ynj8MfA94H/O21ujgcf0B4BWn2GXZcNH696593jvkeJ4CzlvH8++BbifYZdnmB1aASM1zHbcN+G3gmnX+/z/A2KvzTLW8KxQKxQHDKVKJQqFQKFpEOW6FQqE4YCjHrVAoFAcM5bgVCoXigKEct0KhUBwwlONWKBSKA4Zy3AqFQnHA+G8GJM7kCV8QkgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3c008af358>"
      ]
     },
     "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": 185,
   "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": 186,
   "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": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3c006f59e8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = view_samples(9, 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:** To imporve the model following measures can be taken:<br>\n",
    "* As, the data set is biased we need to add the faces of people from the diverse community\n",
    "* Using a large model with residual block like Cycle GAN we can generate high-resolution images while training with hundreds of epochs. \n",
    "* As, the images are not smooth traditional computer vision algorithms can be used for the image smoothing.\n"
   ]
  },
  {
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
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