2. Define the Network Architecture.ipynb

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    "## Define the Convolutional Neural Network\n",
    "\n",
    "After you've looked at the data you're working with and, in this case, know the shapes of the images and of the keypoints, you are ready to define a convolutional neural network that can *learn* from this data.\n",
    "\n",
    "In this notebook and in `models.py`, you will:\n",
    "1. Define a CNN with images as input and keypoints as output\n",
    "2. Construct the transformed FaceKeypointsDataset, just as before\n",
    "3. Train the CNN on the training data, tracking loss\n",
    "4. See how the trained model performs on test data\n",
    "5. If necessary, modify the CNN structure and model hyperparameters, so that it performs *well* **\\***\n",
    "\n",
    "**\\*** What does *well* mean?\n",
    "\n",
    "\"Well\" means that the model's loss decreases during training **and**, when applied to test image data, the model produces keypoints that closely match the true keypoints of each face. And you'll see examples of this later in the notebook.\n",
    "\n",
    "---\n"
   ]
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    "## CNN Architecture\n",
    "\n",
    "Recall that CNN's are defined by a few types of layers:\n",
    "* Convolutional layers\n",
    "* Maxpooling layers\n",
    "* Fully-connected layers\n",
    "\n",
    "You are required to use the above layers and encouraged to add multiple convolutional layers and things like dropout layers that may prevent overfitting. You are also encouraged to look at literature on keypoint detection, such as [this paper](https://arxiv.org/pdf/1710.00977.pdf), to help you determine the structure of your network.\n",
    "\n",
    "\n",
    "### TODO: Define your model in the provided file `models.py` file\n",
    "\n",
    "This file is mostly empty but contains the expected name and some TODO's for creating your model.\n",
    "\n",
    "---"
   ]
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    "## PyTorch Neural Nets\n",
    "\n",
    "To define a neural network in PyTorch, you define the layers of a model in the function `__init__` and define the feedforward behavior of a network that employs those initialized layers in the function `forward`, which takes in an input image tensor, `x`. The structure of this Net class is shown below and left for you to fill in.\n",
    "\n",
    "Note: During training, PyTorch will be able to perform backpropagation by keeping track of the network's feedforward behavior and using autograd to calculate the update to the weights in the network.\n",
    "\n",
    "#### Define the Layers in ` __init__`\n",
    "As a reminder, a conv/pool layer may be defined like this (in `__init__`):\n",
    "```\n",
    "# 1 input image channel (for grayscale images), 32 output channels/feature maps, 3x3 square convolution kernel\n",
    "self.conv1 = nn.Conv2d(1, 32, 3)\n",
    "\n",
    "# maxpool that uses a square window of kernel_size=2, stride=2\n",
    "self.pool = nn.MaxPool2d(2, 2)      \n",
    "```\n",
    "\n",
    "#### Refer to Layers in `forward`\n",
    "Then referred to in the `forward` function like this, in which the conv1 layer has a ReLu activation applied to it before maxpooling is applied:\n",
    "```\n",
    "x = self.pool(F.relu(self.conv1(x)))\n",
    "```\n",
    "\n",
    "Best practice is to place any layers whose weights will change during the training process in `__init__` and refer to them in the `forward` function; any layers or functions that always behave in the same way, such as a pre-defined activation function, should appear *only* in the `forward` function."
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    "#### Why models.py\n",
    "\n",
    "You are tasked with defining the network in the `models.py` file so that any models you define can be saved and loaded by name in different notebooks in this project directory. For example, by defining a CNN class called `Net` in `models.py`, you can then create that same architecture in this and other notebooks by simply importing the class and instantiating a model:\n",
    "```\n",
    "    from models import Net\n",
    "    net = Net()\n",
    "```"
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      "--2019-01-04 13:38:36--  https://s3.amazonaws.com/video.udacity-data.com/topher/2018/May/5aea1b91_train-test-data/train-test-data.zip\n",
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    {
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    }
   ],
   "source": [
    "# load the data if you need to; if you have already loaded the data, you may comment this cell out\n",
    "# -- DO NOT CHANGE THIS CELL -- #\n",
    "!mkdir /data\n",
    "!wget -P /data/ https://s3.amazonaws.com/video.udacity-data.com/topher/2018/May/5aea1b91_train-test-data/train-test-data.zip\n",
    "!unzip -n /data/train-test-data.zip -d /data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">**Note:** Workspaces automatically close connections after 30 minutes of inactivity (including inactivity while training!). Use the code snippet below to keep your workspace alive during training. (The active_session context manager is imported below.)\n",
    "</div>\n",
    "```\n",
    "from workspace_utils import active_session\n",
    "\n",
    "with active_session():\n",
    "    train_model(num_epochs)\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the usual resources\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# import utilities to keep workspaces alive during model training\n",
    "from workspace_utils import active_session\n",
    "\n",
    "# watch for any changes in model.py, if it changes, re-load it automatically\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Net(\n",
      "  (conv1): Conv2d(1, 32, kernel_size=(5, 5), stride=(1, 1))\n",
      "  (pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (conv2): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1))\n",
      "  (pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (conv3): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1))\n",
      "  (pool3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (conv4): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1))\n",
      "  (pool4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (conv5): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1))\n",
      "  (pool5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (fc1): Linear(in_features=18432, out_features=1024, bias=True)\n",
      "  (fc2): Linear(in_features=1024, out_features=136, bias=True)\n",
      "  (drop1): Dropout(p=0.1)\n",
      "  (drop2): Dropout(p=0.2)\n",
      "  (drop3): Dropout(p=0.3)\n",
      "  (drop4): Dropout(p=0.4)\n",
      "  (drop5): Dropout(p=0.5)\n",
      "  (drop6): Dropout(p=0.6)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "## TODO: Define the Net in models.py\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "## TODO: Once you've define the network, you can instantiate it\n",
    "# one example conv layer has been provided for you\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "import torch\n",
    "from torchvision import transforms, utils\n",
    "from models import Net\n",
    "\n",
    "net = Net()\n",
    "print(net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transform the dataset \n",
    "\n",
    "To prepare for training, create a transformed dataset of images and keypoints.\n",
    "\n",
    "### TODO: Define a data transform\n",
    "\n",
    "In PyTorch, a convolutional neural network expects a torch image of a consistent size as input. For efficient training, and so your model's loss does not blow up during training, it is also suggested that you normalize the input images and keypoints. The necessary transforms have been defined in `data_load.py` and you **do not** need to modify these; take a look at this file (you'll see the same transforms that were defined and applied in Notebook 1).\n",
    "\n",
    "To define the data transform below, use a [composition](http://pytorch.org/tutorials/beginner/data_loading_tutorial.html#compose-transforms) of:\n",
    "1. Rescaling and/or cropping the data, such that you are left with a square image (the suggested size is 224x224px)\n",
    "2. Normalizing the images and keypoints; turning each RGB image into a grayscale image with a color range of [0, 1] and transforming the given keypoints into a range of [-1, 1]\n",
    "3. Turning these images and keypoints into Tensors\n",
    "\n",
    "These transformations have been defined in `data_load.py`, but it's up to you to call them and create a `data_transform` below. **This transform will be applied to the training data and, later, the test data**. It will change how you go about displaying these images and keypoints, but these steps are essential for efficient training.\n",
    "\n",
    "As a note, should you want to perform data augmentation (which is optional in this project), and randomly rotate or shift these images, a square image size will be useful; rotating a 224x224 image by 90 degrees will result in the same shape of output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import Dataset, DataLoader\n",
    "from torchvision import transforms, utils\n",
    "\n",
    "# the dataset we created in Notebook 1 is copied in the helper file `data_load.py`\n",
    "from data_load import FacialKeypointsDataset\n",
    "# the transforms we defined in Notebook 1 are in the helper file `data_load.py`\n",
    "from data_load import Rescale, RandomCrop, Normalize, ToTensor\n",
    "\n",
    "\n",
    "## TODO: define the data_transform using transforms.Compose([all tx's, . , .])\n",
    "# order matters! i.e. rescaling should come before a smaller crop\n",
    "\n",
    "# testing that you've defined a transform\n",
    "\n",
    "data_transform = transforms.Compose([Rescale(250),\n",
    "                                     RandomCrop(224),\n",
    "                                     Normalize(),\n",
    "                                     ToTensor()])\n",
    "assert(data_transform is not None), 'Define a data_transform'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of images:  3462\n",
      "0 torch.Size([1, 224, 224]) torch.Size([68, 2])\n",
      "1 torch.Size([1, 224, 224]) torch.Size([68, 2])\n",
      "2 torch.Size([1, 224, 224]) torch.Size([68, 2])\n",
      "3 torch.Size([1, 224, 224]) torch.Size([68, 2])\n"
     ]
    }
   ],
   "source": [
    "# create the transformed dataset\n",
    "transformed_dataset = FacialKeypointsDataset(csv_file='/data/training_frames_keypoints.csv',\n",
    "                                             root_dir='/data/training/',\n",
    "                                             transform=data_transform)\n",
    "\n",
    "\n",
    "print('Number of images: ', len(transformed_dataset))\n",
    "\n",
    "# iterate through the transformed dataset and print some stats about the first few samples\n",
    "for i in range(4):\n",
    "    sample = transformed_dataset[i]\n",
    "    print(i, sample['image'].size(), sample['keypoints'].size())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batching and loading data\n",
    "\n",
    "Next, having defined the transformed dataset, we can use PyTorch's DataLoader class to load the training data in batches of whatever size as well as to shuffle the data for training the model. You can read more about the parameters of the DataLoader, in [this documentation](http://pytorch.org/docs/master/data.html).\n",
    "\n",
    "#### Batch size\n",
    "Decide on a good batch size for training your model. Try both small and large batch sizes and note how the loss decreases as the model trains. Too large a batch size may cause your model to crash and/or run out of memory while training.\n",
    "\n",
    "**Note for Windows users**: Please change the `num_workers` to 0 or you may face some issues with your DataLoader failing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load training data in batches\n",
    "batch_size = 10\n",
    "\n",
    "train_loader = DataLoader(transformed_dataset, \n",
    "                          batch_size=batch_size,\n",
    "                          shuffle=True, \n",
    "                          num_workers=0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Before training\n",
    "\n",
    "Take a look at how this model performs before it trains. You should see that the keypoints it predicts start off in one spot and don't match the keypoints on a face at all! It's interesting to visualize this behavior so that you can compare it to the model after training and see how the model has improved.\n",
    "\n",
    "#### Load in the test dataset\n",
    "\n",
    "The test dataset is one that this model has *not* seen before, meaning it has not trained with these images. We'll load in this test data and before and after training, see how your model performs on this set!\n",
    "\n",
    "To visualize this test data, we have to go through some un-transformation steps to turn our images into python images from tensors and to turn our keypoints back into a recognizable range. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load in the test data, using the dataset class\n",
    "# AND apply the data_transform you defined above\n",
    "\n",
    "# create the test dataset\n",
    "test_dataset = FacialKeypointsDataset(csv_file='/data/test_frames_keypoints.csv',\n",
    "                                             root_dir='/data/test/',\n",
    "                                             transform=data_transform)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load test data in batches\n",
    "batch_size = 10\n",
    "\n",
    "test_loader = DataLoader(test_dataset, \n",
    "                          batch_size=batch_size,\n",
    "                          shuffle=True, \n",
    "                          num_workers=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply the model on a test sample\n",
    "\n",
    "To test the model on a test sample of data, you have to follow these steps:\n",
    "1. Extract the image and ground truth keypoints from a sample\n",
    "2. Wrap the image in a Variable, so that the net can process it as input and track how it changes as the image moves through the network.\n",
    "3. Make sure the image is a FloatTensor, which the model expects.\n",
    "4. Forward pass the image through the net to get the predicted, output keypoints.\n",
    "\n",
    "This function test how the network performs on the first batch of test data. It returns the images, the transformed images, the predicted keypoints (produced by the model), and the ground truth keypoints."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test the model on a batch of test images\n",
    "\n",
    "def net_sample_output():\n",
    "    \n",
    "    # iterate through the test dataset\n",
    "    for i, sample in enumerate(test_loader):\n",
    "        \n",
    "        # get sample data: images and ground truth keypoints\n",
    "        images = sample['image']\n",
    "        key_pts = sample['keypoints']\n",
    "\n",
    "        # convert images to FloatTensors\n",
    "        images = images.type(torch.FloatTensor)\n",
    "        \n",
    "\n",
    "        # forward pass to get net output\n",
    "        output_pts = net(images)\n",
    "        \n",
    "        # reshape to batch_size x 68 x 2 pts\n",
    "        output_pts = output_pts.view(output_pts.size()[0], 68, -1)\n",
    "        \n",
    "        # break after first image is tested\n",
    "        if i == 0:\n",
    "            return images, output_pts, key_pts\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Debugging tips\n",
    "\n",
    "If you get a size or dimension error here, make sure that your network outputs the expected number of keypoints! Or if you get a Tensor type error, look into changing the above code that casts the data into float types: `images = images.type(torch.FloatTensor)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([10, 1, 224, 224])\n",
      "torch.Size([10, 68, 2])\n",
      "torch.Size([10, 68, 2])\n"
     ]
    }
   ],
   "source": [
    "# call the above function\n",
    "# returns: test images, test predicted keypoints, test ground truth keypoints\n",
    "test_images, test_outputs, gt_pts = net_sample_output()\n",
    "\n",
    "# print out the dimensions of the data to see if they make sense\n",
    "print(test_images.data.size())\n",
    "print(test_outputs.data.size())\n",
    "print(gt_pts.size())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize the predicted keypoints\n",
    "\n",
    "Once we've had the model produce some predicted output keypoints, we can visualize these points in a way that's similar to how we've displayed this data before, only this time, we have to \"un-transform\" the image/keypoint data to display it.\n",
    "\n",
    "Note that I've defined a *new* function, `show_all_keypoints` that displays a grayscale image, its predicted keypoints and its ground truth keypoints (if provided)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_all_keypoints(image, predicted_key_pts, gt_pts=None):\n",
    "    \"\"\"Show image with predicted keypoints\"\"\"\n",
    "    # image is grayscale\n",
    "    plt.imshow(image, cmap='gray')\n",
    "    plt.scatter(predicted_key_pts[:, 0], predicted_key_pts[:, 1], s=20, marker='.', c='m')\n",
    "    # plot ground truth points as green pts\n",
    "    if gt_pts is not None:\n",
    "        plt.scatter(gt_pts[:, 0], gt_pts[:, 1], s=20, marker='.', c='g')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Un-transformation\n",
    "\n",
    "Next, you'll see a helper function. `visualize_output` that takes in a batch of images, predicted keypoints, and ground truth keypoints and displays a set of those images and their true/predicted keypoints.\n",
    "\n",
    "This function's main role is to take batches of image and keypoint data (the input and output of your CNN), and transform them into numpy images and un-normalized keypoints (x, y) for normal display. The un-transformation process turns keypoints and images into numpy arrays from Tensors *and* it undoes the keypoint normalization done in the Normalize() transform; it's assumed that you applied these transformations when you loaded your test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qFUAFtwLU3YzsGkWB69nMdnFiKtvT47cKyAQraDabXLp0iXK5TCKRUGZYgkFN0/B6vbhcLiYnJzl06BCRSIRoNLolrQaUyRaX4na7lWtzAlziRi3LIpPJEAwGqdVqeDweNjc31eO73a5KWS3LIpfLqbRWMh9N01hbWyOdTtNqtdi3bx/Hjh1TVkcUr91u0+/32b9/PxsbG/R6PWVRJdjXdR2/339TXBTYRYritBLOm78dQ9luTd4qNe73+7RaLXRdZ2lpiXw+zzvf+U663S6maWJZlko56/U6x48fZ35+nmw2q9yWXI8U1OR3+ZvEPWJNxPXItUqcYxiGgv/D4bDKVvr9Pi6XS7mVQqGgakdzc3MUCgUikQirq6tUq1UWFxd56KGHmJ2dpVwuYxgG+XxerYXT3QppyVmbMgyDaDRKpVK5qXu0KxRFzK/k+GJZ3gpo24n78Xq9lEolBoMB7XabQqHAM888w3333UelUmFhYQHDMKjX64yMjHDXXXeRSqWo1+uq2urz+VR5QZRDSvWCcAr/Q4LEbrerXFSv18OyLKUwqVQKj8ejFFjgdYEIJNXVNI1Lly5x5MgRotGoKiEMBgPlnsLhMK1Wi1KppGIueb9wOIymaQQCAZUeBwIBCoUCwWDwDSjuj5JdoShOJXg72W5iwlutFgcPHiSfz2MYBpcvXyYajeL3+2k2mwQCAQ4cOIDX66XT6aiveDzOyMgIgKrvbDf1Ho+HcDiMYRiYpqlSaGf8IZ9NgLNAIMDY2Jh6jmEYRCIRisUihUJhS73G7XbT6/Vwu900Gg2FlUQiEQBVt5J4LJ1Os7Gxof4vFkWsjVjA9fV1/H6/AhJ/lOwKRYHrWMpbBbM/jkjxbmxsjH379nHy5EkABcFL4JhKpWi1WrRaLZLJpAK5YrGYAtHktXq9HtVqlY2NDVZXV9nY2FCxhGEYimoowbJlWdTrddbW1nC5XAQCAUZHR4nFYhQKBUURGAwGxONxZVl9Pp9K1w8ePEi9Xle0RlHwVCpFu90mkUhw6tQp8vk8tVoNr9fLJz/5Sc6dO0c2m2V0dJQjR45gGAZ/8Ad/QK1Wo9Vq8Vd/9Vc7WsddoSj/XOw2sU7VahXDMPB4PGSzWbXzNU0jlUqRzWYJhUJomobP58PlcjE9PQ3ASy+9pKgAwsoPh8MKNb3tttsYDAY0m01qtRrNZpNWq6UUQNJYXdcZGxtTgF8oFFKZh7itTCaj6k5ut1uRpKPRqErBpQbkcrlYX19XQfrU1BRf+9rX6Ha7LC0tcfz4ceV6YAjzl0olFTR7vV7m5+d3vJa7QlHEikiRzikSn9xMbOJ8rs/nU4SdRqOBz+ej1WqpanEqlSIajRIKhWi1WrTbbXw+Hx6Ph9HRUUZHR7e4D+kPKhaL1Go1XnvtNZUiC04RCATUe8jrNhoNKpWKKg6Ka5IgVoJlKe5JzJJKpRQJWsoPnU6HYrFIpVLBtm1Fh/D7/XS7XUXP7PV6KrWX1xU6gsvluqmAdlcoClzPfN5O69Lr9YjFYiwvL1MsFrntttvI5/Osrq7idrsJBAKKVFQqlUilUti2zcWLF9m3b5/q8Wk0GliWpchEuq4TCoXIZrNMTU3RbrfZ3NzkwoULKki0bZvNzU1WV1fVJkilUltqLnLjxM1JICucFIk5NE1TcL7A9u12G6/XS7PZVAF5q9VSiO/FixcVSCfP/4d/+AfVjySf5XOf+9yO1nLXKIr497czRun1eqo/59y5czQaDUUtlNYIyTykbpNMJkmlUly9epUrV64wMjKidqv01Ag1sVqtUq/XVcFwenpa9dI4sZJqtUq326VarbK5uYnf71d0RklhnfUrp9VcW1tTbigQCNDpdFQtSdd1Ll26RLPZVO+j67p6306nQzqdJp1O02g0VC1K3vNmqsi7QlFkod7urMfr9eLxeBgbG+PVV1+lUqlQLpfp9/uYpkkwGFS81FqtxujoKNVqlWAwyGOPPaZI19KUJWmvBKb1eh3btpmYmGD//v1cvXqVM2fOYJom5XJZAXU+n49gMMjIyIiC9/1+v8rKnOULy7JUZiNFvnQ6TTgcplwuK2be1NQUuVwO0zSp1+sqmwFUPOOkFshGFLnZdd4ViiIXLW2lN2Ktvdlz3spNSUFsMBhw3333cerUKWzbJpfLqd4YaV2Q2GP//v3kcjmeeeYZBdNblqVoijBMlbvdLl6vF7fbzdLSElevXh0WF/fvo+PqEHfH6Q/6lFolBrUBA2tAoVnA7/cTi8XYrGxSbBcxOobCZ3RdV8w5EcuyFN6j67pi2pumSS6XU7FOrVZT1lgshRMwdPZhC8/llqMZiDXx+Xzq5uzUurzVY5ys/nA4zKFDhwDUTfF4PFtaGnr9HueunePBn3iQaCxKuV2mnq+zXl8nYAUUvmLbtuoj7na76sY2W03+69p/5WrvKmP9sSHJ2btBtB5FR2czvEl2kOWXtF/iz71/zrJnmbHAGD+18lNbKrpSi5ESQTgcVg30+/btU33Rzv5kwzBU/CONXhIrOYuukurfrHvfNYryo2gFP67IThQTPDIyonahjU3b1Sbij+Dz+/jdyu9yoXqB+RfnGQwGnG+ex6f56NgdDvoP8p9m/hOBQEC1k2zHftYb61ztXcXCImfk0AwNW7OphCrDm6XZrBqrvLL8CsvZZSzNYtW1ysA3gO5wHYQhLwGvuItkMgkMrYPP51Mdgc5SggB4ggI7q8fbke2dToQQ2RVUSFGSm2kf2Ik4za1kBEePHh2y131enj/4PP9x8z/y38v/nabe5HzjPAN7wJnaGc6b57GxaVktLCwuti6Sq+RUCiyUgH6/r2KNECGmtWl0WyfTzTDaGUWzNbKDLBPWBLqtkx1kifajJJtJNFsj083g6rroerp4vB5FMJf6kcRFkhlJjUroBIFAgHQ6DaAQXKkxCaAo7lWCZ7ieau9UdoVFEXm7LYoszmAwUDUbYFiCd3fJe/LY2FxsXeTMmTMkXUmKgSKJZgIdnUKggAcPffpM69N4e17VYSc3UCrU0tr5kd5HWK+tY5uvm3y/zb70PgbWgK67i92waYQbvOvyuzBtk4Q/wVNTT1HwFRhpjTC3Ngdc3/kyIUHTNCYnJxV3pdvtKixIsjDJfsSlOhmDb9bOslPZFYoiwdbNaPhORVo0RGEE3NItncwgQ96VZ0qbIh1M86ueX6VgFmgUG0N/b3QYi41h+2wS3oR6rgScktVIUbBcLg93fNuiUqlgGAZJI6nYbZJhBYNBMunM8PGWScFXwNZsiv4ih2OHCbQCqh3D5XIpBt3GxgYTExMKqZUmMpfLpSic0s6xuLioUGZxT2IJYWcMQafsCkURfyno4dspoiRSAa5UKvT6PS688wJVo8o44zzJkyTTSTqdDi7DRbE7RF37zT4b6xtDvkmgq6iNgUBAZRdut5tisajqPs1mU1ENms0mp06dUtiLUC6F+5JOp2m1W6RaKQr+AqPtUVKBFIPAsCQga+Pz+Thy5AiBQICNjQ3a7bZCgCcnJ6nVaoRCIRKJBMlkkkuXLhGNRpXVgWHNy+12Y5qmWpObWetdoSiA8v1vp2xfCJla1PP0qEaqw+DSXmXgH6j6jDSDC14i9EWhFAwGA1KpFJlMhl6vR6PRUKCbDN3p9Xqsr69j2zbj4+MkEglFBzh79qyqSssck0fWH6FjdAgQoOPqqNknzWaTRqPBxsYGV69eZf/+/Rw5coRGo7FlMsHo6CiLi4uqw1Cg+e3YiVgfSZ9vuUE6cH0819tZOYatTWDtdpt8Po/W1IjWotSiNaa0KVxtFyulFRYXFykWi6rVU9BPr9erWGjhcJiRkRGmpqZotVpcuXKFQqGggDDTNKlUKsMBOAE/K+UVrp68im3bHDh6gJAWomk2FRYjpKUgQ1KT5tNUfUhiDnEbCwsL2LZNOp2mXq8Tj8dptVqqpnXixAlOnz6tFEIQXcMwFJtPrInQTncqu0JRnNH42ymiJOKP5YZ7PV6OnjrK8RPH8fQ8FKoF1Qvc7XV56LGHCBKk2WxSapcwN0zW19YVhG7bNolkgvXautq1Ml5L+KrhaJgfHv4hG+4Nkq0k7Vab78a/S2gzxDsvvJOm3iThSWBbQzqkNKHLTRc2nJOuEAqFWFxcZGRkRCG74XCYwWBAOp3m7NmznDp1akv7rFhBwVKcG+dmZFcoivBOhfwMWxn0EuQ6sZad9v/Ic5x0wHK5TCaTYW5sjnPnzlGpVNjc3CQUCXHp/kt8yfUlfAUfWlCjNd0iMZHg7tfuxrRMDmQPkEgm+Njff4yTGyfZ597H+7zvI5FIYJomU1NTbBQ2eOXOV9iwN0CDkr8EfkCDRqLBC+94gXq4Tqqd4pG1R/B6vFtSblE8Zxwh/7Msi+XlZebm5nC73ayvD5X11Vdfxe12EwwGCQQCLCwsqOBZ3LoAcfI6t1x67Mx63m5einOhfT4fmUxmywKJ6dZ0jVePvUrNXQMN2qk2aIAG5XCZk0dPUvaVKXQKvDf4Xl6++DIDe8CV7hW0oEY2myUcDg9bIXwDfqPwG8Pnw/Xvr0stPHyPor/I9OHraXe321UWQAhKlmUxsAYQhKA/yNGjR6lUKtRqNdrtNjMzM5w7d05VsYXSIPGH0904P/ctCbjJzhew6O0St9uNy+VSpltugFAMpSDX6XQgcP0GYoO/7gD/bCh7y9iazRX3Ffg4zBXn0NGZMWYIMCQHxePxYZzR1JjVZtVzx/vjZPtZNFtjwppgSptCR+eA5wBxdxyv16t6huR6ZXKC1+dl7ZE1Lv/sZS7cd4FKtUIkEkHXdXK5HDCkRUoh0eVyUS6XVe+OM+b7p8APu8KiiKK83TiKc8cohdDAm/RiuIwtkxHjepxpfZollkCD0ejosN3TXuYdS+/Axubs5FmOXDvCbz/+21yMXeSQ/xA/3/55OgxrQDLgr9vt8gntE2x2N+n3+ujdoQs1eyZBLUgykiQwEiBIkHK5/IaBQbJpgsEgPW+PZrIJOlQjVV5bfI1jtx0jFAqxtrZGtVpVG6zdbqsYx9kU78SoJEC/ZdNjwzC2uJ7tnYE/joh5FbPeH/T5Lyv/hfMPnifVTvFE6Ak1SK/T6ZDZyPCfzf+MhcU1+xq/Zv0amdMZ7v5/7gYNKoEKNja/8Ou/wMAYcKF1AStgEfVGsSyLUqlEJBLB7/eTMlJEu9HhTg8Oe36dAapkSEJhkGuUmEriFL2rE6/HqYQrxOoxJhOT1Ot1ms2mUnLTNInH46o9xbl+wuUVYrfb7VZg4C0Xo8jiSCPV9v/drMiOkSBWXqelt7jQvICt2RR8BbquLulQWkHyM9YM+7v7udK7wrQ+TcaXYWZ2Bg0NzdZImAlsbA5fO8xrk69x2Do8pBO83sIBqJsdi8UA1Jw2uQ6JHSR7gusKLRCB7HZJbR9ceJBqv4q768YVHN4yGRMWDAYpFotcvXqVffv2kUqlKBaLamCQUDckRRaXe7NI+K5RFJG3I5iVmyJYgUybHHWNctB/kIuti4z1x4i6omrnBoNBotEoX5j+ArlKDndnOGFpJDOCxXU/b2v2UHHQsN02A2ugAmJnX9JgMMDv9+Pz+VR/sjNYlYKiEIwA5bZE5Cb3+33axTaWxyKYCG7p+otEIqytrQFDkna1WqVYLG5ZWyd1wxmz3HKuR1jhsjN/HGVxuilhdMlNkd01NjbGZ8zPcG75HL1OT1VWpaFbwLWxzJh6ncFgwHpwWOQD2AxscmbyDJZh8RqvsVpdJaJHFEfE5XKpCUm6rqvWDeGtOKccOBVIWkUEnhcLOxgM6Pa6w4Ji93rf8JEjR5ienubKlSu4XC7VsirWxAleStAu6yOKc8sVBZ1ciZvtiRVxmm9nT7EsmG3bhEIhup0u/oEf27CVS5ACnPQZb685+Z/xc+25a1hFi3aujVfz0qKFx/bQrXRpB4b9P9ImqmnD8V0izv4e4Y5I9Vm4J04ClHM2f7fX5Ye3/ZBysEykFmFiZUL1DrdaLRYWFhRPVprGZCS6cziRE2i7GSxKZFcoioBqztMgALUznB/MSYGU5x04cIB0Oq3oibVaTbHRpRH8woULwybw1RyrlVUm4hPUajU1vqLVaikcBK43owvlsJ6pU/aUOT84T1t7fSAt+/DlAAAd5UlEQVQgXZ5/5flhOjw7y2233YZpmng8HnXTpedHFMM0TUzTpNlsKssiDfPymcUSWpYFASiHhql5LVLDxERvDJX4ueeeIxAIKMUuFAoKSxGAURRDOClCi5CNs1PZFYribL0UBXCayTcrFgqd0bZt1eLZ6/XY3NzcslskZbx0+RJ/1P0jlvxLzPZn+fftf78leJSmcSf0XyqVVMHvqaee4vkfPI/vSR/tVJtoLcprL75Gp91heXmZVCpFpVLB7/eriq6UJ5wjR50dh2IBnbRN5+cPakEy3QzrnnUSzQQxd4ym2WR5eVnNfpPPJ/GMWBHneso6OoG2W25ytYjzQ+wEORTlajabxONx2u02GxsbW1BemfHa6XS4tHaJZXsZW7NZHCySb+TxeYfdeNJ9J2Qk5wBj0zTVwL5QMIT3614OHz+M1tSoHK2Qz+dxuVwsLS9h+S3uiNwBwMbGBt1ud0uM4pynIoriVJLthVFrYPHI+iOYmCR9STatTaUQcp1jY2PqemF4GFSlUlGxiUD5zvV0Zmo7kV2jKKLdzh31VjxaJ19U5oLI0Bipk4j7kO688eg448VxckaOaW0aT9ej+nmFZyKUSdmBooSTk5Ocee0MPU+PdCRNnz4D74CZ2Rlm981Ss2q8MPkC5WCZBW2Bz4x8hmqlqvpy5OY2m80tU5ok6xGr5rRmwp9tNpvMTs4OM6qERqvVolqtkkql1BDk2dlZZmdn1VzZAwcOsL6+rpTJiU8JFeKWy3qcrge2zkJ5sw+z3eI46xky0E4yD7g+KPjf5f8dHaNDSBvSBmRSpK7rahQFbK08RyIRxrJj/O87/zcXbruACxc9hjFFppdBQ2PdvY6tDa/nUvsSA9+AZDKpDliQHe/8EquwPXWVoH5gDahZNQbdwRaykqTjMpOt2WxSLBaZmZlhaWkJl8vFysoK0WgUQKXs0qYqceDNKMquqPVIz62ktW9mSZx/k2qrE2Hc3uIh0LYElI1Gg/HsOMvnlvF4PIpzIrtbFEfGS4i1CYfDFFtFLrUvYWs2Pa2nCoZ5T37IvX1dSXR05rxz0LiuZEKVdGY3MkJDKJUSR8iX4TL425G/5a+n/prvzn6XTrej3JKMSRfOSSgUUgc+HTx4EI/Hw4EDBwiFQqpKLJvK7XarIP+Wm4XvhJrhjY3p8jdBGJ0i1khMrPM1nYNtBNs4d+7ckAjdbqGHdJql5hZXJ7vOsobHsqVSqeGhTVqY+dA8Zxtn8eChzTDzmfMMydCXu5eZ1qf5WORjJL1JNf1ApjzKHDVn0O78jM5aj2VZtPQW656hlSoHy6RH0/Q2ezz33HOsrKyQSCSUokgMksvlSKVS9Pt9zp8/TygUIh6Pv6G+44Qjdiq7RlFkjOVbBVjbMxknVC+RfjgcxuPxbEEkxaqI5YnGonyh9QUWzUWydpZ/Wf+XihMi7RfyHGkcd7vdfPbgZym1SvhtPyuVFWrVGhOxCdBgvbZO3BMfjv3qm8rVSYuFE8+4UaAu/5PPEnPHGO2MsuHdYLQzSiad4a+e+ivW19eHeNDr/cUC429sbKjGe4BMJqPITVKRdiK622mSP0p2jaI48RKn3MgNbScwSQoqgauIZVkqixH00+/340v4WLQWsbBYNVapdWrEesPajHOUuMyMFdfhdrsZ8Y9gY5NypxgdGUVv61R7VWZHZ4dN4oZOpVeh3+5j6MaWRjEnRrQdBnAqj8vlot/r8y/K/4JKr4Jv4ON/fON/cPnyZZLJ5JaDrxKJBHfeeSdnzpxhcXGRRqPB3XffTTqdZmFhQbHbRCmcFepbUlFkJ8D1eawSjMrhSc7FdSKvMpNMAkSPx6PiARlu0+l08Hg8xONxZnozhC+FqcfqZAdZjLbBwD9Q7ks4q9J5Z5om/oAfgtCsNvmNs7/Bqmt1eK2WwUAfEDNjPF5/nG+NfoucnmPCNcEnAp/A0/Wo4pyMIJXGLmcA68RzhIn2w+//kKXiElFXlEa9oUabC7O+3W6zurqqAtrbb7+da9eu8fDDD/OOd7wDwzD47Gc/q2bEOK3azR6asCsURbRcglkJ7mQi0XYgSZREsA+JU5xsLgHhcrmc6hNOpVIYhsHq6iq/4v8VXjnzCv/2l/4t3/rKt+gaXeWaJAtSBzb6vPzW1d/iSu8KsWaMTf+mYq0N9MGQghCs8KXul+hoQ87LNesaX//O13G73USjUdLBNAF/QKHP23ENaQmVv4UjYc7ec5bB2IBOoUP26Sxmw1QuZPvYr1wuRzqdZnp6mueee47l5WUefPBBNQpMQMibZbaJ7BpF8Xq9b0AMReuDweCWScsCb4sVEdMuNQ9n0a1UKjE1NUUqlVK9NlLZ/cfJf+Tp7z/N5Ogkj5UfU0NngsGgwmXK5TKFVoEr+hVszR4qCYDNdYrj6z934h0yVoa8nSdcC3P64Glq8SHzbMKa4F9Z/wqX4VL4iJMADddjMJfLhWmbDMYGYEBntEPfc72BSxRFepAvXbqkzvC57bbb+PCHP0y9XucLX/gCPp+PWq22pdjqDPR3KrtGUSKRiBobJYz2hYUFdR6w3Ph8Pq96aZLJJKOjo/zqr/6qsiqCoUjb5ebmJvPz88pCuN1uCoUCy+Vlqulhb8+KtoIRNvD7/FvS9OVry5xeOE0oFGLq0BQr2gqzoVkuNy9fv/htYdV7N9+LaZl4LS9fTn9Z/X9FX8H22vTMnoLcpc4jcYzTnfaqPfSOjjVh4cl7cHVd9OzeG0Z6SRehkKF+5Vd+BUAN/BPlcB4WBdfhhZ3KrlEUWSBnq4LMIpPHWJbF2toam5ubiskVj8eVJZCbDMPmbbFUMgwnHA6Ty+Uol8uE9TDhSpharMYEE0RcEfU+brcby7b4evLrLCWXANjv2s+nvZ8mraf5D/wH2rTx4iVDhiV7+JiR1gh/6/9byoEy6W6a7CBLzhjyWjPdDCFPiLq9dejN9pRVfs/lcoSfCxPNRjHaBv1eX7lbObtQmtQymQyGYbCyssJzzz3H0aNH+frXv87Y2BiLi4vKAot7lve45SyKVDKlRO7ccdszA9lN0WhU/S7mWIAlsR7xeJz5+XlarRbxeJyLFy/y53/+5xw4cADDMHjPtfdw4QcXOHboGMHDQZVBmaZJgwbLLKtrvNK/wu/0foex/hgRK0Lb0ybYDFIZVOD1A8lt3abkLYEGG54NPlb/2DB4HFiMBke3nLYh0L0AhnIjxVJ8//vfJxKO4B/4MXwGJbOkaJQSrMuRtnKO0Hve8x4+//nP8653vQvDMMhms+RyOcVRkRPFRkdHqVQqtx7DDbZiJM40UkyyszPfGXRKHON0LYJ4ejweNSJTRmYdOnSIe+65R1mmpDdJYaOAPX+9n8i2bYIEmdFnWLAW1LXYms2aaw0bG7QhiUl+Bih5S4wNxsgbeTK9DAErwKD/epM813Ed9Xr2jRuy6vU6/UGfrqeLH7/aDBLbyBrJlO1Go6GOrzt48CCGYeB2u2m1WjzwwAM8/PDDaljhxYsX+cY3vsEzzzxzU5nPrlCU7SisDOcV9yJMM8mIpKgFwxmyKysrpNNp1TguHBRBLOX5AoEfOHCAUqlEKBRiZGSEpeUl8maepDepuCl+v59PJz9NuVumZbb4X93/RU7LkelnsGyLvDtPwkyg6RpF/5B6mB1k+XDnw5i2id7ScflceD1DfotUieXLSbRybpJ2u02xVMT8kMlmepN6sU7yG0kFEWx3F2JFU6kU99xzD+fPn6der/Pwww+TTqf54Q9/SKPRUJXxw4cP84d/+IdqfXYqu0JR4Hqcsri4yGuvvUY4HOb2229n//79irEljU2C5EqFNxqNqgptIBAgn88P+a4jI2xsbHDu3DnuuOMOFhYWmJub41Of+hTz8/ND4o4Gl951iVOuU0zb0/zrzr9WpCNd1/FqXqy+xZM8Sd/dJ+QJ4XK7CIwEmIhP0DAbrFZXh8CcqdF39wnbYZp6c0vJQagFghjDdSvqtJadTodKt0I33QUdmiNNRiOj+PpbzweUgw/kBNJyucznP/955ubmiMfjfO973+P48ePk83lWVlZUQ7ycv3yzLby7QlGcJ4PHYjHuvPNO6vU6L7/8Mi+++CLdbpdAIEA0GiUWi21pPRW8RbIB4WEkk0kqlYo62fwHP/gBa2trXLlyBV3XOXfuHBMTE5i2iRk3sTWbZXsZExNDN1RwLdmBz+dTrHi/349hGEpxU/4UfXefjtFRRT5n7Unmqgh+4sxyRAzDoD/oY2Li6rlwr7vpZXqEyiGSviSddkdNK9B1nWq1SigUUpa1UCjwnve8h1gsRrFYJJPJqPqSZFcS9/04fd67QlGcZB2ZIzI6Osr4+LiaZCT0QTmgQGopEgTKPBGZAxIKhVhfX8cwDF555RW8Xi+BQGDIxh8d5fLlyywuLjI+MU6sEaMSqjCpT+K3/XTsjiI9CVgnnXxCwhY2nJMDC9d5sULoFnfotBoiziB9YA34WvxrrHvWCQaDxP86TjwbJ+aJEYqGqOt1xWWR58hJpXLzr169yiOPPKLm3Mv4rna7rQJmuUa4BYuCku0AasiMpKnCaU0mk+qUz2q1usWEA4p+GA6HuXjxIhcuXCCdTvPCCy/QarXUkbWTk5NUq1VcLheXL1+mWCjyIA/SdXeZiE/QSXSUJXESfuS7KIZpmkqBnaUD4b/Kd4lJnPC8fGaxKrquU+vXVLW4kWgQjUSJuWN4Pd4tkL9QJgKBgDrMslqt8sEPfpBXXnmFr3zlK8zNzSkX4xzR5XSFTsXZiewaRQmHw8qHwxu1XoJbyXQkxZTHSBAreEkqlVJzS7rdrjq+VtM0BeQ1Gg0ymQzZsSzRaHQ4xy2ZpOvu4tE8qpgoM+aFu2JZlpqqJDu83+9vOVXM2a8jsiXjwaapNfFZPnrdHlbbImEmKAfLhDZDRFwRVZiUQFyG+cjnFHfY6/U4c+aMooO2220ikQjZbJZLly6pbFCq6oAak75T2RWKAqiBwOJKnNiIILbi+2XB5bHOSmixWGRsbIxut8vKyoqiChjG8FStfD6vgKpoNEoymVSLXd4s8yf9P6HkLTFpT/Kk9iQ1q4Zn4FHn9Mg1bmetOV0CXM/kZESp3/KrDMeyLb4WG7qZdCfNuxffTb/X54ErD9Dz9Nhc2aQ0KKl4xMlvlbjEtm3i8bhqK5Vze+666y5isRjz8/Oq6V1oBlJLctINdnx/3p7b/E+TeDyuyviiJE7TLGcOy+EF24G4fr+vMiPTNDlz5gz33nsvXq+XqakpXnnlFVZXV1Uw/O1vf5twOEy32+X06dOkUilGRkaIT8QpBorYDAPbP+FPWG2uMt2e5t/o/4am2VSkJhkQLDGI7Hgn5mPZloo7Mt0MP1P6GTRbw9RM5Wby3jxNmnjwgM2w2tzuqJNHxaUePHiQjY0NnnrqKfx+P16vl0KhQC6XY2pqSlm+n/7pn+bMmTOk02nW19cBVGoNbCF130x6vCuokNspjE6RRZd2BGeTlLghsQgyX61YLLKwsECv1yObzaoz+c6ePcuVK1dUSu33+xVdsFQq8fj7HueesXswNINJfZJVVrGwWLKWaGpNTEwG1kAF0U7+qVyr072YXFeIdc869UGddruN3tIZaY2g2RpJM4ne1rfA7IPBQI0Bm5yc5M4770TXdR555BFuu+02ddKpjDuXOEXI5dFoVFlNy7JUtuN04zcru8Ki3CgbcFaSRZGEMC2Pkaqx1+ulUqkocz83N6cmNMqpEtlsltXVIYckEAhg29cnOfl8PmKxGI8++ign2if4xv/7DSq5Cn/U/yNWtBUmmeTLvS+z7Flm3D3OhzsfxtV1KcBsYA3ouDq4cQ97koWA1HEpllqqlYImdO0hM+3dC++mpbdwd6/PhHFOXZJUWo5ikULoJz7xCX7/939fQfm6rrO2tsb4+DitVovFxUXcbje1Wo1UKqX6qp3iZNLtVHaFomynNG6X7fUeZwFNaIuSFuu6zv79+3G73WxubqoeH13XicVi5PN5NfnR2T76u7/7u8NAsNVm/co6sViMj5Q/QrU/PNrkf/r+J5ZmscIKxWYRV28YO9nYfHP0mxR8BdKdNO/Lvw9rcD2beDj3MA27gbvrpm9txS883evlB2ALxiHo8MjIiDoSbnNzk5GREX7t136Nv/zLv1TWZ2RkhGazSTAY5Omnn8YwDE6ePMnY2BjJZFINBhSWnQSxb0U73S67RlFERGGcQaEzTZW/wdZJkqIkUjDTNI3Z2VlcLhfXrl2jUqmouELMsLzvRz7yEZWif/vb36ZcLhOPxwkGgvSrfdrNNhkjw5p7jXQ3jbs37Anu9Xo09aYaKJz35mnrbTyD675fQyNoBxnoAyws5Z629wI70335v8vlIhwOEwwG0XWdRCJBMBjE6/UyMTGhug5lAPH8/DxHjx7l2rVrfPCDH+Rv/uZv+MEPfqDQbdk0tywfRUA0oes1m00KhYLa/YcOHVJWQvpZxPpIxiGprChKoVDgypUrasHFzRSLRdW9JxOfT5w4gWEYFItFjh07xu/93u+RyWQIBAJDpLc/4H3596kZJU1XU7kdT8+jBgqn2qmhS+J6Vubs4JPnSOnBycAXBZLWU8MwSKVSwwmT4TCNRkNRPmOxGB/60If47Gc/q8Ze3HvvvRiGwTvf+U5OnjzJxYsXiUajzM3N0Ww28Xq9W8Z1BYPBW7N6DNfBNuGjjI2NqRNBT506RTgcVnC/LLLUfIRr0el0OH36NGNjY6RSKdxuN6VSSXX3ywC9brfL6Oiomi8vO2z//v3qlFJnaWHQH+Bqu+j1e1iD62m5hsZP536ajquDbzBsTxVF2d41sJ1z4nyM00rK7Nl+v68sinBlJVWORqPkcjkKhYI6R3BtbQ3TNBkdHVUjvyYmJpient4CI8iXHG65E9kVWY9A43DdFMt3meQ4NTW1pRAou1COJxHS0sbGxnCW7OtnGEsFWuB/r9dLIpFQc0vS6fSWAYC6rvOZz3yGarW6pYvP2XPjxE8sy1JKZA22/g+20iTkd6erdfYUyeuLyA2VzAVQbsjj8TA7O0u/3ycajfLQQw8Rj8d55plnOHbsGA888AAzMzOK1ulsh3Ei3zuVXWNRtptht9u9JV2UHeaMV3RdV3PppRZUKBQYHx8nEAhw+vRpNE0jmUwSDofVwdQS5JqmyWOPPaYWTG7WPffcw7lz57h48aLidggXxDnjBLbOPrkRLuGs8WyPTZxxmPxPMh6/389P/uRPKkBNgDK5Hsuy+OhHP8rTTz/N6OgoCwsL6uCq2dlZNelApldLE5jwhZ1Z347uz03cy382kaqsYRhqJwuXBFBNXU4f79zRTtK12+3m6tWrlMtl0uk0tm1z5swZer0eyWSSQCDAtWvXuHz5Mvv27SMej6tdHQgEWFlZQdd13v/+9yuFknhCEE4ncUq+YOswH3m8swFLdvKNgnV5vnyW973vfUrhRZGdTVwSczz22GPMzs7SaDQIBAIcO3YMl8u1xfrK2okI9nTLxShSqxGzXKvVyOfzBAIB4vE41WpVpY0yecCJo4jyCLm6VquxvLxMpVJhenoat9vNpUuX6HQ6hEIhddrok08+ueU1xLxblkU6nebYsWNcuHBB7UwJgrdnKG8Vi4hsD17lfRSs/7o1sW2byclJfvM3f1NN9JbnS0Vb3kNOLk2lUqytrTExMaEq1xLgi6Juz7berOHuzWRXKIrH41GnhkYiETUJSBZPDhCQhXO2fopLkCqu9O5omkYmk1FM9XQ6zfLyMoVCgfe///3cc889xGIxRdoWJc1ms+rs4ccff5xPfvKT6pBssS5OKyaKJtcrN0Zu6nZcSG64sPScLRvCvvv0pz+tTuQQFyWPkYxP3luyIzmiJR6PMzo6imEMx5KK0sgmEIt0s1PCd4WibG9Qdy6mmE6ZziyWQ1yVcGTFf3s8HhKJBL1ej3g8rohEmqYxMTFBIBBgYmLiDRiCQlNfx1327dtHIBAgl8up6Y7yfttji+0Lvh0qfzMQUZTPWXD83Oc+h8vlYnl5mbGxMUXIdnJvVEtHr4dpmqRSKSKRCLFYjGq1SqVSIRwO02q11No6XSSg5s7uVHZFjCLijEFkIeF6C4VA+NthfCnMOX8XyyPEI5/PRzabZXJyUlkcZyuI0wwLCcrr9fKBD3yAUqmkRn8JLiMxh/O1ZPfLl7gb5+s7MzqJY3q9HgcPHuSP//iP1cDgU6dOKSsiiiibQd5PEFaJN3RdV9MMZOjw9p5subZbko8SCARUb46cWiUaLwstbDFns7cEa1L2d7lcijQt4JXH42F8fFwNldmemsrrOP83MzOjhgU++uijzM/P89/+239jampqy82Tm70d5ZRgW5RV/ueMEdrttnK5H/3oR3n00UdVw9ri4qJqiJPnOhFoAR4lppEsSd5Pqsn33nvvFpK6ruuKMyO0jp3KrlAUJ4tMtF98sbM46HRH0ncsu6xerzM6Oqp8sZjs22+//Q0BpzM13Y53AGoy5NNPP83KygqPPvqoil0kuHQO/XG6Q+frOTm38vmcOIppmkxOTvLoo4+qa6nVaqysrHDkyJEtLs1JKhdlFfCt2+2qvihN0xRwKYogTfeSYoui3HIWxZlybp+HL4sibZPO4E5ExlN85Stf4fTp09x999088MADvPvd796CYWyPFZzux3lTBGMRjkehUODIkSOcPHlSZUeCU4jrEAWRXS2v7SRVSeAqSqxpGh/+8If5+7//e44fP652viCy8hy5qYKJyGtIQO9EeZ2VaCekIFRT0zSZn5+nWq0qF7sT2RWKIrvRWQdx7jwn2cbZ5OVkmZ04cYKf+qmfIpVKbYk9nArijB9uFFfAdUvg9Xo5fPgw//iP/8jExIRaaKkneTweRQiSoNDpKuHG5XwB63q9Hvfffz8LCws899xz1Go1jh8/jmmapNNp5YoB5VadCnrt2jXVfC6b7M0+m9Najo+P89u//dtbqKQ7kV2hKKL5mqZtQTedxTNJN4WK6Ha7icViqu92u2u50c+wFdvYbnq34yFSrQ2Hwzz44IM8++yzxONx1dfsvH4nww2uxyPiDrYTh9xuN/l8nmw2SyqVYmlpidtvv516va4YbJLtAKr+Jdedz+dVDLM963IGzts3DWydwLlT2RWKImf6+v1+dSoFXI8f/H6/Qmenp6dVhC9yI4AL3vwABif45FxoZ3CraZrqJMzlciSTST70oQ/x0ksvqeAbUOmy81AqJ1tNsg5nxiap9uLiIo8++ijhcJhnnnmGfr9PsVjkvvvuU5iJxGLyHF3XKZVKw+N2Xx+444T3dwL+OUsgO5VdoSjSEioHB4g5zWazTExMqBOvtpvVH1fEFL/ZjhJ3JQ3jtm2Ty+X4uZ/7Oc6ePasQUeeul3gF2NKiIRVfp0V07uhSqcRdd93F+fPnFctevsN1BFWAPJnMLRbM7/dvcXE32iw3UpKblV2hKM8++yw+n49kMsnRo0c5fPgwmUyGAwcOvAGDuBlz+aPEGbM4RQJouG4xpJTw67/+63z5y19W1yEZkpPzIpbQOdhYlE9cp9AYCoUCuj6kb+r6kIXnTKfFrTlfQyrkEvhLG4YzMHf+fKNA/mZlVyjKL/zCL3D48GEOHTqkTKmT+SW0QGcm4by52zOXm1EmJ9FYnudEQZ3uQlLjn/3Zn+WrX/2qeqxkOk6cRM4ElPhEJlALdC84kUyAOnr0KLY9PNNYsCJn5Vos6sbGBpubm2oujK7rCn290dpsR5F/XNkVivLEE0+8aZwhscJ2uZEy3Oi5TpGbKDfOWS6Arb5brEo8HqdYLKo4QILb0dFRSqWScilO5RZ3IQioFBQlNpLGLkGOW60WmUyGSqVCPB5XKa/EYs7A+OzZsyrrk+5JsThOl+osLzhrU/L5nHHNTmRXQPhvddO374IbuYqdyJtVebdbpu3uTcjJpVKJtbU1crkci4uLTE9PqzZP4Yc4r08AMufoLU0bThMQeqOmaUxOTqrYTFpF5VqcUD+ghhxHIhFVRHW6vhut540+o23brK+v853vfGfH66f9U8zRnvz/R3aFRdmT3S97irInO5I9RdmTHcmeouzJjmRPUfZkR7KnKHuyI9lTlD3Zkewpyp7sSPYUZU92JHuKsic7kj1F2ZMdyZ6i7MmOZE9R9mRHsqcoe7Ij2VOUPdmR7CnKnuxI9hRlT3Yke4qyJzuSPUXZkx3JnqLsyY5kT1H2ZEeypyh7siPZU5Q92ZH8f28c7gycXiWlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5e1c7484e0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5e1c6be400>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5e1c68d4a8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5e1c64c588>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5e1c60a5c0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5e1c5c2f98>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5e1c58f4a8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5e1c562470>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5e1c50d198>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5e1c4d0278>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the output\n",
    "# by default this shows a batch of 10 images\n",
    "def visualize_output(test_images, test_outputs, gt_pts=None, batch_size=10):\n",
    "\n",
    "    for i in range(batch_size):\n",
    "        plt.figure(figsize=(20,10))\n",
    "        ax = plt.subplot(1, batch_size, i+1)\n",
    "\n",
    "        # un-transform the image data\n",
    "        image = test_images[i].data   # get the image from it's Variable wrapper\n",
    "        image = image.numpy()   # convert to numpy array from a Tensor\n",
    "        image = np.transpose(image, (1, 2, 0))   # transpose to go from torch to numpy image\n",
    "\n",
    "        # un-transform the predicted key_pts data\n",
    "        predicted_key_pts = test_outputs[i].data\n",
    "        predicted_key_pts = predicted_key_pts.numpy()\n",
    "        # undo normalization of keypoints  \n",
    "        predicted_key_pts = predicted_key_pts*50.0+100\n",
    "        \n",
    "        # plot ground truth points for comparison, if they exist\n",
    "        ground_truth_pts = None\n",
    "        if gt_pts is not None:\n",
    "            ground_truth_pts = gt_pts[i]         \n",
    "            ground_truth_pts = ground_truth_pts*50.0+100\n",
    "        \n",
    "        # call show_all_keypoints\n",
    "        show_all_keypoints(np.squeeze(image), predicted_key_pts, ground_truth_pts)\n",
    "            \n",
    "        plt.axis('off')\n",
    "\n",
    "    plt.show()\n",
    "    \n",
    "# call it\n",
    "visualize_output(test_images, test_outputs, gt_pts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training\n",
    "\n",
    "#### Loss function\n",
    "Training a network to predict keypoints is different than training a network to predict a class; instead of outputting a distribution of classes and using cross entropy loss, you may want to choose a loss function that is suited for regression, which directly compares a predicted value and target value. Read about the various kinds of loss functions (like MSE or L1/SmoothL1 loss) in [this documentation](http://pytorch.org/docs/master/_modules/torch/nn/modules/loss.html).\n",
    "\n",
    "### TODO: Define the loss and optimization\n",
    "\n",
    "Next, you'll define how the model will train by deciding on the loss function and optimizer.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "## TODO: Define the loss and optimization\n",
    "import torch.optim as optim\n",
    "\n",
    "#criterion = nn.CrossEntropyLoss()\n",
    "criterion = nn.MSELoss()\n",
    "\n",
    "#optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)\n",
    "optimizer = optim.Adam(params = net.parameters(), lr = 0.001)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "## Training and Initial Observation\n",
    "\n",
    "Now, you'll train on your batched training data from `train_loader` for a number of epochs. \n",
    "\n",
    "To quickly observe how your model is training and decide on whether or not you should modify it's structure or hyperparameters, you're encouraged to start off with just one or two epochs at first. As you train, note how your the model's loss behaves over time: does it decrease quickly at first and then slow down? Does it take a while to decrease in the first place? What happens if you change the batch size of your training data or modify your loss function? etc. \n",
    "\n",
    "Use these initial observations to make changes to your model and decide on the best architecture before you train for many epochs and create a final model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_net(n_epochs):\n",
    "\n",
    "    # prepare the net for training\n",
    "    net.train()\n",
    "\n",
    "    for epoch in range(n_epochs):  # loop over the dataset multiple times\n",
    "        \n",
    "        running_loss = 0.0\n",
    "\n",
    "        # train on batches of data, assumes you already have train_loader\n",
    "        for batch_i, data in enumerate(train_loader):\n",
    "            # get the input images and their corresponding labels\n",
    "            images = data['image']\n",
    "            key_pts = data['keypoints']\n",
    "\n",
    "            # flatten pts\n",
    "            key_pts = key_pts.view(key_pts.size(0), -1)\n",
    "\n",
    "            # convert variables to floats for regression loss\n",
    "            key_pts = key_pts.type(torch.FloatTensor)\n",
    "            images = images.type(torch.FloatTensor)\n",
    "\n",
    "            # forward pass to get outputs\n",
    "            output_pts = net(images)\n",
    "            #output_pts = output_pts.type(torch.FloatTensor)\n",
    "            #print(output_pts.type)\n",
    "            #print(key_pts.type)\n",
    "            # calculate the loss between predicted and target keypoints\n",
    "            loss = criterion(output_pts, key_pts)\n",
    "\n",
    "            # zero the parameter (weight) gradients\n",
    "            optimizer.zero_grad()\n",
    "            \n",
    "            # backward pass to calculate the weight gradients\n",
    "            loss.backward()\n",
    "\n",
    "            # update the weights\n",
    "            optimizer.step()\n",
    "\n",
    "            # print loss statistics\n",
    "            running_loss += loss.item()\n",
    "            if batch_i % 10 == 9:    # print every 10 batches\n",
    "                print('Epoch: {}, Batch: {}, Avg. Loss: {}'.format(epoch + 1, batch_i+1, running_loss/1000))\n",
    "                running_loss = 0.0\n",
    "\n",
    "    print('Finished Training')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1, Batch: 10, Avg. Loss: 0.003032584235072136\n",
      "Epoch: 1, Batch: 20, Avg. Loss: 0.002360285945236683\n",
      "Epoch: 1, Batch: 30, Avg. Loss: 0.001615319736301899\n",
      "Epoch: 1, Batch: 40, Avg. Loss: 0.0021281955540180205\n",
      "Epoch: 1, Batch: 50, Avg. Loss: 0.0025042983293533327\n",
      "Epoch: 1, Batch: 60, Avg. Loss: 0.002066782258450985\n",
      "Epoch: 1, Batch: 70, Avg. Loss: 0.0019224828332662582\n",
      "Epoch: 1, Batch: 80, Avg. Loss: 0.002157331921160221\n",
      "Epoch: 1, Batch: 90, Avg. Loss: 0.0018890595585107804\n",
      "Epoch: 1, Batch: 100, Avg. Loss: 0.002189477875828743\n",
      "Epoch: 1, Batch: 110, Avg. Loss: 0.002020704597234726\n",
      "Epoch: 1, Batch: 120, Avg. Loss: 0.0018041160628199577\n",
      "Epoch: 1, Batch: 130, Avg. Loss: 0.0017504974529147148\n",
      "Epoch: 1, Batch: 140, Avg. Loss: 0.0019875027760863305\n",
      "Epoch: 1, Batch: 150, Avg. Loss: 0.0021432219222187994\n",
      "Epoch: 1, Batch: 160, Avg. Loss: 0.0020850454717874526\n",
      "Epoch: 1, Batch: 170, Avg. Loss: 0.0017617641538381576\n",
      "Epoch: 1, Batch: 180, Avg. Loss: 0.0040932798683643344\n",
      "Epoch: 1, Batch: 190, Avg. Loss: 0.0019685904532670976\n",
      "Epoch: 1, Batch: 200, Avg. Loss: 0.002100829228758812\n",
      "Epoch: 1, Batch: 210, Avg. Loss: 0.0019612043127417563\n",
      "Epoch: 1, Batch: 220, Avg. Loss: 0.0017736973017454148\n",
      "Epoch: 1, Batch: 230, Avg. Loss: 0.0022830162942409516\n",
      "Epoch: 1, Batch: 240, Avg. Loss: 0.0016692428663372992\n",
      "Epoch: 1, Batch: 250, Avg. Loss: 0.002040472663938999\n",
      "Epoch: 1, Batch: 260, Avg. Loss: 0.0018738468140363693\n",
      "Epoch: 1, Batch: 270, Avg. Loss: 0.0017221066430211068\n",
      "Epoch: 1, Batch: 280, Avg. Loss: 0.002049865871667862\n",
      "Epoch: 1, Batch: 290, Avg. Loss: 0.0018983992636203767\n",
      "Epoch: 1, Batch: 300, Avg. Loss: 0.001876862846314907\n",
      "Epoch: 1, Batch: 310, Avg. Loss: 0.002086288169026375\n",
      "Epoch: 1, Batch: 320, Avg. Loss: 0.002110691949725151\n",
      "Epoch: 1, Batch: 330, Avg. Loss: 0.0016822484061121941\n",
      "Epoch: 1, Batch: 340, Avg. Loss: 0.0016807370707392693\n",
      "Epoch: 2, Batch: 10, Avg. Loss: 0.001981443800032139\n",
      "Epoch: 2, Batch: 20, Avg. Loss: 0.0018477160781621932\n",
      "Epoch: 2, Batch: 30, Avg. Loss: 0.0018364485800266266\n",
      "Epoch: 2, Batch: 40, Avg. Loss: 0.0019663674980401995\n",
      "Epoch: 2, Batch: 50, Avg. Loss: 0.0019483882635831834\n",
      "Epoch: 2, Batch: 60, Avg. Loss: 0.0016044456213712692\n",
      "Epoch: 2, Batch: 70, Avg. Loss: 0.002222041204571724\n",
      "Epoch: 2, Batch: 80, Avg. Loss: 0.002477765440940857\n",
      "Epoch: 2, Batch: 90, Avg. Loss: 0.0017900144904851915\n",
      "Epoch: 2, Batch: 100, Avg. Loss: 0.0016814825162291527\n",
      "Epoch: 2, Batch: 110, Avg. Loss: 0.0019583223313093187\n",
      "Epoch: 2, Batch: 120, Avg. Loss: 0.0017334000393748284\n",
      "Epoch: 2, Batch: 130, Avg. Loss: 0.0016402971670031548\n",
      "Epoch: 2, Batch: 140, Avg. Loss: 0.0014907649047672749\n",
      "Epoch: 2, Batch: 150, Avg. Loss: 0.001723266325891018\n",
      "Epoch: 2, Batch: 160, Avg. Loss: 0.0017029241397976875\n",
      "Epoch: 2, Batch: 170, Avg. Loss: 0.001924047365784645\n",
      "Epoch: 2, Batch: 180, Avg. Loss: 0.0017227167785167695\n",
      "Epoch: 2, Batch: 190, Avg. Loss: 0.0016697951667010785\n",
      "Epoch: 2, Batch: 200, Avg. Loss: 0.0015777119919657707\n",
      "Epoch: 2, Batch: 210, Avg. Loss: 0.0014572359323501588\n",
      "Epoch: 2, Batch: 220, Avg. Loss: 0.0021909747421741486\n",
      "Epoch: 2, Batch: 230, Avg. Loss: 0.0016193372085690498\n",
      "Epoch: 2, Batch: 240, Avg. Loss: 0.0017244932651519775\n",
      "Epoch: 2, Batch: 250, Avg. Loss: 0.0015876698270440102\n",
      "Epoch: 2, Batch: 260, Avg. Loss: 0.0015560485497117043\n",
      "Epoch: 2, Batch: 270, Avg. Loss: 0.0033277862295508385\n",
      "Epoch: 2, Batch: 280, Avg. Loss: 0.0016674790978431701\n",
      "Epoch: 2, Batch: 290, Avg. Loss: 0.002269907608628273\n",
      "Epoch: 2, Batch: 300, Avg. Loss: 0.0017042259201407433\n",
      "Epoch: 2, Batch: 310, Avg. Loss: 0.0016153100058436394\n",
      "Epoch: 2, Batch: 320, Avg. Loss: 0.001573856607079506\n",
      "Epoch: 2, Batch: 330, Avg. Loss: 0.0028570543229579927\n",
      "Epoch: 2, Batch: 340, Avg. Loss: 0.0014507741928100587\n",
      "Epoch: 3, Batch: 10, Avg. Loss: 0.002034837760031223\n",
      "Epoch: 3, Batch: 20, Avg. Loss: 0.0017670368850231171\n",
      "Epoch: 3, Batch: 30, Avg. Loss: 0.0017794758230447768\n",
      "Epoch: 3, Batch: 40, Avg. Loss: 0.002206102579832077\n",
      "Epoch: 3, Batch: 50, Avg. Loss: 0.001809946447610855\n",
      "Epoch: 3, Batch: 60, Avg. Loss: 0.0018364743515849114\n",
      "Epoch: 3, Batch: 70, Avg. Loss: 0.0016064921990036965\n",
      "Epoch: 3, Batch: 80, Avg. Loss: 0.0015686433240771293\n",
      "Epoch: 3, Batch: 90, Avg. Loss: 0.001332162857055664\n",
      "Epoch: 3, Batch: 100, Avg. Loss: 0.0014531051814556121\n",
      "Epoch: 3, Batch: 110, Avg. Loss: 0.0013377370610833169\n",
      "Epoch: 3, Batch: 120, Avg. Loss: 0.0017751225903630257\n",
      "Epoch: 3, Batch: 130, Avg. Loss: 0.0013815232738852502\n",
      "Epoch: 3, Batch: 140, Avg. Loss: 0.0013334048092365266\n",
      "Epoch: 3, Batch: 150, Avg. Loss: 0.0013880314901471137\n",
      "Epoch: 3, Batch: 160, Avg. Loss: 0.0013134685903787612\n",
      "Epoch: 3, Batch: 170, Avg. Loss: 0.0013525153398513793\n",
      "Epoch: 3, Batch: 180, Avg. Loss: 0.0015770718157291413\n",
      "Epoch: 3, Batch: 190, Avg. Loss: 0.00125010896474123\n",
      "Epoch: 3, Batch: 200, Avg. Loss: 0.0017842485830187797\n",
      "Epoch: 3, Batch: 210, Avg. Loss: 0.0016369011402130127\n",
      "Epoch: 3, Batch: 220, Avg. Loss: 0.0014279360100626945\n",
      "Epoch: 3, Batch: 230, Avg. Loss: 0.0013980918154120445\n",
      "Epoch: 3, Batch: 240, Avg. Loss: 0.0017582288533449172\n",
      "Epoch: 3, Batch: 250, Avg. Loss: 0.0013766553327441216\n",
      "Epoch: 3, Batch: 260, Avg. Loss: 0.0015802804008126258\n",
      "Epoch: 3, Batch: 270, Avg. Loss: 0.0014117457419633866\n",
      "Epoch: 3, Batch: 280, Avg. Loss: 0.001717139720916748\n",
      "Epoch: 3, Batch: 290, Avg. Loss: 0.0013666845560073853\n",
      "Epoch: 3, Batch: 300, Avg. Loss: 0.0016441715136170387\n",
      "Epoch: 3, Batch: 310, Avg. Loss: 0.001537620685994625\n",
      "Epoch: 3, Batch: 320, Avg. Loss: 0.0012475813999772072\n",
      "Epoch: 3, Batch: 330, Avg. Loss: 0.0012818490602076054\n",
      "Epoch: 3, Batch: 340, Avg. Loss: 0.0012987514175474644\n",
      "Epoch: 4, Batch: 10, Avg. Loss: 0.001511773392558098\n",
      "Epoch: 4, Batch: 20, Avg. Loss: 0.0011223755180835725\n",
      "Epoch: 4, Batch: 30, Avg. Loss: 0.0015529634281992912\n",
      "Epoch: 4, Batch: 40, Avg. Loss: 0.001102227471768856\n",
      "Epoch: 4, Batch: 50, Avg. Loss: 0.0012321285717189312\n",
      "Epoch: 4, Batch: 60, Avg. Loss: 0.0011294546835124493\n",
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      "Epoch: 4, Batch: 100, Avg. Loss: 0.0009536517821252346\n",
      "Epoch: 4, Batch: 110, Avg. Loss: 0.001216182556003332\n",
      "Epoch: 4, Batch: 120, Avg. Loss: 0.00104659092053771\n",
      "Epoch: 4, Batch: 130, Avg. Loss: 0.0010293485149741172\n",
      "Epoch: 4, Batch: 140, Avg. Loss: 0.0023807658702135084\n",
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      "Epoch: 4, Batch: 170, Avg. Loss: 0.0013329869247972964\n",
      "Epoch: 4, Batch: 180, Avg. Loss: 0.0008530615530908108\n",
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      "Epoch: 4, Batch: 200, Avg. Loss: 0.0010276465639472007\n",
      "Epoch: 4, Batch: 210, Avg. Loss: 0.0009600224271416664\n",
      "Epoch: 4, Batch: 220, Avg. Loss: 0.000901151169091463\n",
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      "Epoch: 4, Batch: 240, Avg. Loss: 0.000760860625654459\n",
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      "Epoch: 4, Batch: 260, Avg. Loss: 0.001612386729568243\n",
      "Epoch: 4, Batch: 270, Avg. Loss: 0.0012408898808062077\n",
      "Epoch: 4, Batch: 280, Avg. Loss: 0.0009529099054634571\n",
      "Epoch: 4, Batch: 290, Avg. Loss: 0.0010150303989648818\n",
      "Epoch: 4, Batch: 300, Avg. Loss: 0.0009440671913325786\n",
      "Epoch: 4, Batch: 310, Avg. Loss: 0.001139551118016243\n",
      "Epoch: 4, Batch: 320, Avg. Loss: 0.0008533304966986179\n",
      "Epoch: 4, Batch: 330, Avg. Loss: 0.0009488707557320595\n",
      "Epoch: 4, Batch: 340, Avg. Loss: 0.0011907183490693569\n",
      "Epoch: 5, Batch: 10, Avg. Loss: 0.0012579541169106961\n",
      "Epoch: 5, Batch: 20, Avg. Loss: 0.000981910154223442\n",
      "Epoch: 5, Batch: 30, Avg. Loss: 0.0009175984971225261\n",
      "Epoch: 5, Batch: 40, Avg. Loss: 0.0008796183876693249\n",
      "Epoch: 5, Batch: 50, Avg. Loss: 0.0008642782717943192\n",
      "Epoch: 5, Batch: 60, Avg. Loss: 0.0009310599379241467\n",
      "Epoch: 5, Batch: 70, Avg. Loss: 0.0019160076081752777\n",
      "Epoch: 5, Batch: 80, Avg. Loss: 0.0013254938796162605\n",
      "Epoch: 5, Batch: 90, Avg. Loss: 0.0008401182293891906\n",
      "Epoch: 5, Batch: 100, Avg. Loss: 0.0012227046340703965\n",
      "Epoch: 5, Batch: 110, Avg. Loss: 0.0012380837313830852\n",
      "Epoch: 5, Batch: 120, Avg. Loss: 0.0010573165081441402\n",
      "Epoch: 5, Batch: 130, Avg. Loss: 0.00159764551743865\n",
      "Epoch: 5, Batch: 140, Avg. Loss: 0.0013544789589941503\n",
      "Epoch: 5, Batch: 150, Avg. Loss: 0.0007278372496366501\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 5, Batch: 160, Avg. Loss: 0.0008372727744281292\n",
      "Epoch: 5, Batch: 170, Avg. Loss: 0.0008955290950834751\n",
      "Epoch: 5, Batch: 180, Avg. Loss: 0.0008709894306957722\n",
      "Epoch: 5, Batch: 190, Avg. Loss: 0.0007176546975970268\n",
      "Epoch: 5, Batch: 200, Avg. Loss: 0.0008733422979712486\n",
      "Epoch: 5, Batch: 210, Avg. Loss: 0.0010467772521078587\n",
      "Epoch: 5, Batch: 220, Avg. Loss: 0.0011398990377783776\n",
      "Epoch: 5, Batch: 230, Avg. Loss: 0.0007716315798461437\n",
      "Epoch: 5, Batch: 240, Avg. Loss: 0.0008962933383882045\n",
      "Epoch: 5, Batch: 250, Avg. Loss: 0.0013126115873456\n",
      "Epoch: 5, Batch: 260, Avg. Loss: 0.0009111007116734981\n",
      "Epoch: 5, Batch: 270, Avg. Loss: 0.0029471269212663176\n",
      "Epoch: 5, Batch: 280, Avg. Loss: 0.0008395772203803062\n",
      "Epoch: 5, Batch: 290, Avg. Loss: 0.0008354391604661941\n",
      "Epoch: 5, Batch: 300, Avg. Loss: 0.0013250650949776172\n",
      "Epoch: 5, Batch: 310, Avg. Loss: 0.0013525293357670308\n",
      "Epoch: 5, Batch: 320, Avg. Loss: 0.0007933394685387612\n",
      "Epoch: 5, Batch: 330, Avg. Loss: 0.0008605400361120701\n",
      "Epoch: 5, Batch: 340, Avg. Loss: 0.0008350665941834449\n",
      "Finished Training\n"
     ]
    }
   ],
   "source": [
    "# train your network\n",
    "n_epochs = 5 # start small, and increase when you've decided on your model structure and hyperparams\n",
    "\n",
    "# this is a Workspaces-specific context manager to keep the connection\n",
    "# alive while training your model, not part of pytorch\n",
    "with active_session():\n",
    "    train_net(n_epochs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test data\n",
    "\n",
    "See how your model performs on previously unseen, test data. We've already loaded and transformed this data, similar to the training data. Next, run your trained model on these images to see what kind of keypoints are produced. You should be able to see if your model is fitting each new face it sees, if the points are distributed randomly, or if the points have actually overfitted the training data and do not generalize."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([10, 1, 224, 224])\n",
      "torch.Size([10, 68, 2])\n",
      "torch.Size([10, 68, 2])\n"
     ]
    }
   ],
   "source": [
    "# get a sample of test data again\n",
    "test_images, test_outputs, gt_pts = net_sample_output()\n",
    "\n",
    "print(test_images.data.size())\n",
    "print(test_outputs.data.size())\n",
    "print(gt_pts.size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5e1c501c50>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5e1c3346a0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5e1c37a278>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5e1c35c6d8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5e1c431a20>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5e1c3fb0b8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5e1c46e630>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5e1c4c0ac8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5e0de67ba8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5e1c655fd0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## TODO: visualize your test output\n",
    "# you can use the same function as before, by un-commenting the line below:\n",
    "visualize_output(test_images, test_outputs, gt_pts)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you've found a good model (or two), save your model so you can load it and use it later!\n",
    "\n",
    "Save your models but please **delete any checkpoints and saved models before you submit your project** otherwise your workspace may be too large to submit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "## TODO: change the name to something uniqe for each new model\n",
    "model_dir = 'saved_models/'\n",
    "model_name = 'keypoints_model_garima.pt'\n",
    "\n",
    "# after training, save your model parameters in the dir 'saved_models'\n",
    "torch.save(net.state_dict(), model_dir+model_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After you've trained a well-performing model, answer the following questions so that we have some insight into your training and architecture selection process. Answering all questions is required to pass this project."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1: What optimization and loss functions did you choose and why?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer**: Optimisation functions calculate the gradient so to calculate the partial derivative of loss function with respect to weights, and the weights are modified in the opposite direction of the calculated gradient, I used SGD optimisation function. After rigourous tests, the results showed that it is much faster and yields low loss as compared to the other optimization functions that I implemented.This function furthur got improved by tuning important parametes like momentum, learning rate etc.The implementation of SGD with Momentum subtly differs from other implementations in some other frameworks.\n",
    "\n",
    "Loss functions calculate difference between output and target variable, I used MSELoss(). It creates a criterion that measures the mean squared error (squared L2 norm) between each element in the input x and target y. As opposed to the other loss functions implemented by me, like CrossEntropyLoss() which combines nn.LogSoftmax() and nn.NLLLoss() in one single class, MSELoss() calculates the mean(L),sum(L) whenever size_average=True or false respectively. Here I've applied size_average=True where the sum operation still operates over all the elements, but divides all by n.\n",
    "\t  \n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2: What kind of network architecture did you start with and how did it change as you tried different architectures? Did you decide to add more convolutional layers or any layers to avoid overfitting the data?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer**: I began with a simple two layer convolution layer network eack followed by a max pooling layer and finally flattening it with a Flatten layer.\n",
    "\n",
    "After trying the above mentioned architecture the loss was still too much as the image dimension being fed into the last layer was still was huge number and this needed to be scaled down into singular numbers. On realising this and seeing constant loss of the model I finally increasing the layers one by one till my loss eventually started decreasing.\n",
    "\n",
    "My final model was-\n",
    "\n",
    "\n",
    "      (conv1): Conv2d(1, 32, kernel_size=(5, 5), stride=(1, 1))\n",
    "      (pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
    "      (conv2): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1))\n",
    "      (pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
    "      (conv3): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1))\n",
    "      (pool3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
    "      (conv4): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1))\n",
    "      (pool4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
    "      (conv5): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1))\n",
    "      (pool5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
    "      (fc1): Linear(in_features=18432, out_features=1024, bias=True)\n",
    "      (fc1_drop): Dropout(p=0.5)\n",
    "      (fc2): Linear(in_features=1024, out_features=136, bias=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 3: How did you decide on the number of epochs and batch_size to train your model?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer**: This was simply a result of observation of the loss values after each step.\n",
    "Started with epoch equals one and batch size 20 i started training the network. The loss seemed to be decreasing and so i decided to increase the epoch to 5 and batch size to 10 which made the loss even less."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Visualization\n",
    "\n",
    "Sometimes, neural networks are thought of as a black box, given some input, they learn to produce some output. CNN's are actually learning to recognize a variety of spatial patterns and you can visualize what each convolutional layer has been trained to recognize by looking at the weights that make up each convolutional kernel and applying those one at a time to a sample image. This technique is called feature visualization and it's useful for understanding the inner workings of a CNN."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the cell below, you can see how to extract a single filter (by index) from your first convolutional layer. The filter should appear as a grayscale grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.00326137 -0.11466072 -0.07545099  0.1121868   0.19854148]\n",
      " [ 0.17474623  0.02466397  0.05793892 -0.16296415  0.16106488]\n",
      " [-0.06231651  0.09188677 -0.19493487 -0.05962691 -0.1855251 ]\n",
      " [ 0.17659484  0.04501486 -0.12449556  0.12607627 -0.01104853]\n",
      " [-0.05342367  0.13118     0.0121659  -0.12007516 -0.03802654]]\n",
      "(5, 5)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f23274c3be0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f232956f4e0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the weights in the first conv layer, \"conv1\"\n",
    "# if necessary, change this to reflect the name of your first conv layer\n",
    "weights1 = net.conv1.weight.data\n",
    "\n",
    "w = weights1.numpy()\n",
    "\n",
    "filter_index = 0\n",
    "\n",
    "print(w[filter_index][0])\n",
    "print(w[filter_index][0].shape)\n",
    "\n",
    "# display the filter weights\n",
    "plt.imshow(w[filter_index][0], cmap='gray')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature maps\n",
    "\n",
    "Each CNN has at least one convolutional layer that is composed of stacked filters (also known as convolutional kernels). As a CNN trains, it learns what weights to include in it's convolutional kernels and when these kernels are applied to some input image, they produce a set of **feature maps**. So, feature maps are just sets of filtered images; they are the images produced by applying a convolutional kernel to an input image. These maps show us the features that the different layers of the neural network learn to extract. For example, you might imagine a convolutional kernel that detects the vertical edges of a face or another one that detects the corners of eyes. You can see what kind of features each of these kernels detects by applying them to an image. One such example is shown below; from the way it brings out the lines in an the image, you might characterize this as an edge detection filter.\n",
    "\n",
    "<img src='images/feature_map_ex.png' width=50% height=50%/>\n",
    "\n",
    "\n",
    "Next, choose a test image and filter it with one of the convolutional kernels in your trained CNN; look at the filtered output to get an idea what that particular kernel detects.\n",
    "\n",
    "### TODO: Filter an image to see the effect of a convolutional kernel\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2325b43b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f23274beda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##TODO: load in and display any image from the transformed test dataset\n",
    "import cv2\n",
    "image = cv2.imread('./images/obamas.jpg')\n",
    "image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
    "#image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "plt.imshow(image)\n",
    "plt.xticks([]), plt.yticks([])\n",
    "plt.title(\"Orginal Image\")\n",
    "\n",
    "## TODO: Using cv's filter2D function,\n",
    "## apply a specific set of filter weights (like the one displayed above) to the test image\n",
    "filtered = cv2.filter2D(image, -1, w[filter_index][0])\n",
    "#filtered_image = cv2.filter2D(image, -1, CUSTOM)\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(121, xticks = [], yticks = [])\n",
    "ax.imshow(filtered)\n",
    "ax.set_title(\"Feature Map\")\n",
    "ax = fig.add_subplot(122, xticks = [], yticks = [])\n",
    "ax.imshow(w[filter_index][0], cmap = 'gray')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4: Choose one filter from your trained CNN and apply it to a test image; what purpose do you think it plays? What kind of feature do you think it detects?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer**: It acts as a representation of the input.The hidden layers of the network carry out feature extraction from the image i.e. here the CNN detects the areas that have high or low content of brightness in the image. For example, on the green board behind Michelle Obama, the whole board is of similar color, the color varies as the content of brightness increases or decreases.This type of feature map helps extract those areas that are similar in the picture in terms of the level of brightness."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Moving on!\n",
    "\n",
    "Now that you've defined and trained your model (and saved the best model), you are ready to move on to the last notebook, which combines a face detector with your saved model to create a facial keypoint detection system that can predict the keypoints on *any* face in an image!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
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