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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      "--2021-05-31 11:18:16--  https://s3.amazonaws.com/video.udacity-data.com/topher/2018/May/5aea1b91_train-test-data/train-test-data.zip\n",
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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>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Code snippet to keep workspace alive during training\n",
    "#from workspace_utils import active_session\n",
    "\n",
    "#with active_session():\n",
    "#    train_model(num_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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\n",
    "#%reload_ext autoreload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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, 32, 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(32, 64, 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(64, 64, kernel_size=(1, 1), stride=(1, 1))\n",
      "  (pool4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (fc1): Linear(in_features=10816, out_features=4096, bias=True)\n",
      "  (fc1_dropout): Dropout(p=0.5, inplace=False)\n",
      "  (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
      "  (fc2_dropout): Dropout(p=0.5, inplace=False)\n",
      "  (fc3): Linear(in_features=1024, out_features=136, bias=True)\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",
    "import torch.nn.init as I ## For initializing model parameters\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 models import Net                          # Note: 'models.py' contains the CNN configuration\n",
    "\n",
    "net = Net()\n",
    "print(net)   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "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": 30,
   "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",
    "## Scaling to a particular size and final RandomCrop to desired input size of 224x224 px\n",
    "data_transform = transforms.Compose([Rescale(250), RandomCrop(224), Normalize(), ToTensor()])  \n",
    "\n",
    "\n",
    "# testing that you've defined a transform\n",
    "assert(data_transform is not None), 'Define a data_transform'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load training data in batches\n",
    "batch_size = 10\n",
    "\n",
    "## Note: We can use parallel-processing too. Using Windows, hence using num_worker=0 to avoid error\n",
    "train_loader = DataLoader(transformed_dataset, \n",
    "                          batch_size=batch_size,\n",
    "                          shuffle=True, \n",
    "                          num_workers=0)   "
   ]
  },
  {
   "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": 33,
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test the model on a batch of test images\n",
    "import torch \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",
    "        # 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": "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": 37,
   "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": 38,
   "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": 39,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f468d60f6a0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f468dd45390>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f468d53b9b0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f468d57ba90>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f468d625780>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f468dd02710>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f468dcc57f0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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/sfUTxrRI/+lyCN03ksLQ8tg6s8INSC1GFCjTwrwHgUV79mEYjs8bjPj6la9TzVQ5F57j19O/TiVV4VLrEtf71zkTnqF30MPCgjTEY3GWYksEK4Fxfnf9Xeq5OqE11javrr/Kucq5ibR+r9ej0+lQrVap+3X2VvbAAkKwQovl3jLFUZGV3gq7yV3iQRzf9lkZrNDoN7ievE5AwI3BDQb5AbRhu7HNlrtFaIVUM1V6Tg/Hc8wOBjqrrKMxwBB+muzTKYhZZS6AImoylUpNfK7rP6JqVV6nAUgWUXW7XeNY+kmfaqZKaIXcCe9QG9QoFUv8xhO/wd36XbwDj9uJ2+N61WKBQWxA0S2SXk6TGqWoVqvcWr/FK94r1FI14ntx1l9dJ3k5yfLyMvl8Hs/zqNVq7O7u0mq1iMVjlLolapka56xzfKT4EfJ2ntGJEVd6VzjsHUIPdlu7lBNlCoUCF+MXuTm8yZPuk8T7cQ4aB7T32pSKJWrZGuV2Gb/hY8UsisXiRP5HJpbuJx1t6SWympCbReYCKN/73vcmQBElhbSTdtwsmMax+L5vIhI34ZJ9V5ZWqcVSb4lhbUg32x3nT8I0Pas3XoOcTvE5+3PcCe7g9lyGG0POhGd4Zu8Zvpj9IrV0DYCEm6BYLBr+JZ1O43meMUGSQwqDo/vI2TkzqBYWxViRMBeSdMd+SCaT4VPLn4I0+A2fGzducOfOHTY3N1k+WGYptUR8GOeGf8OkFKIifaRrVHRgAEfBw6PIXABlaWlpwq+A6TH+ceUIeiZpPkMzr47j8I6X34GX9Xgi+wSjy0dZYinEPnHiBG3abHQn/Yd11vnm+jc5+KGDsRkB2qU2mZUMpVKJYrFIOp2m2+2STqcZDofE43Eaowb17NhU3Q5v8/ev/j1LwRKEEHfjkIFCoUClVDFLXeOxOOlYmrpTNzmbTCZj1kvLn47e9L3r7LdkuyWairK8j53puXr16kRx8LTwWNdTRIk4kWjOR3MQ/sjnhfwL7Lg73Ore4lJwycz8RCJBEIyXnibaCU53TnOHO1jcC7l9h3gzTq6Ro1VsAXBicILLpy5z+dJlisUitmMziA9MZV2j0SDZSVLulDnMHGKPbF5YfYFKp8J7dt7D3638HduxbRjCJS7xqcqniDmxI0e426XrdfEcj3whTyo5NstiYqScQgDQ7XaNRhNzK/W0kp+Co2o6XSM8i8wFUDR/IrmQMDyqNpdyxugsOC6XISK2GKBrd9lxdwitkIP0AQedA5a7yySTyftY1Z8Pfp6N/gZ/nv5zsCCIBeRP5HFjLhYWS4MlfoFfoFgsUqlUKBQLfPzfPs53a9/lQvwCH8p9yGR533fwPm6+cZMX3/YiWHCYOaRX6I1Bcq/pN4Y3aAUtSs7RzlNBGIyBvbTDUneJn2n/DGFwtPeLAEaXUOrIUROVUZZW6lEeOx9lf3/faA3P88yOQ7lcjkwmY3IavV5vgu6XHIpmIAU89y2zsC2W3CUOUgcUm0X8tk8j3zBAkdkYBAHxWJyz1lnOhGfYZJOT/klWV1epZsfO8KF7iJt2SSfHC8YG8QHfrX+XESNuDm8SpALyQf6oHdWQF3nRtDudThuQAFxwL1BwChMzvOk3J4DtdT2SQZJ+vz+x1EP6TQZfSkBlBYHmeeR3Ehk9dkDRtSJiW7VD2Gq1sG2bbrc7kUCDyZX7AhSpF5FOlPUvz+w8Q9fqMmqOSKyNVbEuN9SLwh3H4cPhh2mOmiRIYJdtztvnWQ/XOe+cpxQvmZLLJZZ4x9I7+Ob+N7mcukzBKeDFPRM2u67LueAcG+EGp8JTnE+d54J9gdvBbc7FzvFb537L9INsIxbrx1jtr7Kb2KXcLuP0HPqjvsluCzD0Nh5SdCV+STabNWuUW60W/X4f27Ypl8umb2eVuQCKkEfScNk6S+o+ZKM+qTyHyaUP2ocRMya5HfFTbNsmDEKSYZIgOd5gx3XdiQIiUePiBNq2TTlZNu38pPtJ2kGb5ChpfKput8vB4YG5HiHk8jnjE7iuS6VS4XeSv8NeZw/HG4ezn0p8Cs/2yJK9r7BJQt73HbyP/c4+8WGcxPL4fLLzgtaCtj3eiy6bzbK0tESpVDLnyWQyE9t4yXWk72aVuQCKrt+AyT3LAFMT0ul0jEaRY/XM0llT6RDRGHKdeDyO7dg0gyYZLzNRGyLglBS+FCsJcDudDsPWkGqtiud5Zt1Q1+rycvNlAgKu96/j5B0OvndAs9nEtm3OnDlDsVjkVOEUTvkor1UMi0YLSJt13isWi5FIJkjGj5zRYrFIKpUypqXValGv16nX6+Y7+V5KJlzX5eTJkyad0ev1jMadVeYCKDovEa3x1GtYpNAZjoAiGkVXl8k5RTPoQuVRMOJrF7/GYeaQU8EpnvWfhZAJP0d8FSkByGazxJoxsn+RJd6Oc+2/XIMMZluvlfIKP1j5Qf718F95qvIUK+kVbvm3xsC6N+NbrRYrKysUi0XgiO/Qpk9HI17f429Kf8OOu8Nyb5kPND+AxdG+uKIdZLtT3X9yfsk/ASZnJGuBNFM7i8wFUIQgilZ4aRDIFg9aO8isk/9hsjww6u2HYcjAHXCYORzT8PZdmqMmWbJT6zksyxo71rWAp//H0+b7D1z/AF/+8JfNrLYsi794z1/QpUtylGR9fZ18Pk+326XVarG7u0u9XgcwxVR6SYaYPp2/qfVq7CTGzux+cp9mrUkmzBiNIzUrMJ4UonV1pKed3WgmWXNMs8hcAEWXBmiGVdS/3sssCgyx7/JeIgCp75CZKw5eKpZiyRtHP6vDVRJBAmKTK+m0U2xZFst/Od4RyroXqoSEvPd/vpf1P1qn1+tRrVaxHZsgGZhSgKtXr7K6usrGxgbf+ta32NvbM22QUF+KvHW5gNnbpW9TDstUk1Uq3QrJUZKAwBSAC2+iq/P0ZIHJ/VmEctCh8WOnUaTcUNeKSuZTZl+3250oLNaigSJlgtHyQfE7bNvmxzd+nKE7pBAv0Cw1WXKXTFJPFx4JCHN3cxPXs7BwA5eSXSJVTOHEHH75n36ZV5qv8PbS23nuqefM4Enk4XkenU6Hg4MDE9WNRiOT2dbt9kc+X0h9gcP4IaVOiR+9/aPYxaM1SGLO8vm8ccrlPJoSEGCIphJCU9cVzypzARQJj/UaYblh4QGi9aYwucOBJux09KITYMZ5CyFrZfnKylfYje9yxjrDR+yPEPhH5ko6s9/v85X3fIUPvfAhQsIxW0tIq9yi5bRYzi7jOR6vNF9hFI74Tu07tEYtdrd22d/fp1arkUwmyefzDIdDdnZ2zH2JZjFO9r3XVthi2x4TcvVMnb5ztFBeiEi9v4qE1OJ/SH/E43Gy2azpY+lHuf5jp1EKhYJBfRiGJsoQr146RUc2Ep6K3ZW8hxREO45Dp9OZmD1y7Gg0omN12ImPfYCNcIOG3xhndu8RUq7rEgTjXSRHxRGf/5nP85Nf/EmSJKlmqnzpp7/EyisrjIIR5UqZS+lLXOtc42L8Iv/2T//GxsaGmfkSjdRqNZrNJoDZhkNfT3Z8spoWS84SB8kDiu0i8UHcgCSVShmfTqIXue9er0c8HjeLxYCJfXVl2YdMzMdOo8CRnyLeOxzZ2GjJn56BUrEle93L95ZlmQ6CSQe13+9DCKuDVfbcPU4Fpwi9kG6sawAp62hka4zk6STf+O/fwO25/HH6j9lgg9XBKh96/UN8MfNFNqwNTo5O8s677+Tl4ctkwgyxeIxRckQ6kzbaQ9oqDroARfyhdrtN9bDK2zbfRm1QIzlK4mW9iYXzwH2mQzSv53kmOpQ1QmLqovu7Rav4HyRzARQhkbSzpT3zaEQAk2toxbSIKpWCHkmz6wr3MAzpdDoAvLPzTtJLaYpukR49usF4kZjsKy9FSPJIlFEwopausWltEhKy6+5ybecadzJ3CBlHUZ+rfA6AU6NTOI7DprXJGc7wweCDJBIJcrmcaR9guBqpxdne3ub69et0Oh0KhQKOMy5Qqlar5HI58vn8fWuwpc5W+kUeOSP76dq2PQEQPYFmlbkAiq5wiwJCnEoBjk4cavJNgCO2WLSOrOeRiElm4mg0YjgY0q/26ea6ZLNZs0ZZ78YkKl80QcpOcdY5y53wDieGJ1h1VlnqLbGf3B83+F7/bzlb2NgEBGyEGxx6h6SClNEcehNBAbqYx3anTYcO5ViZeGycjpAlsJK30ctfNX8kD4LQS1AFWI7j3JdJnlXmAij1en1iSYJlWXS7XWq1GqPRiGKxSLFYNBpG15/IoOqwL5PJmB0K5BiAVCpFsVikVCqZnRbbnTZ+wqcYL1IpV8xSCQFtNpsFC5pBE2tg0abN+9330/N6ZMIMbtnlA8EH+DSfNiWPWIwLtEch2/Ftit0izU4TP+VTTpZxA3dCA+j2rayusPnfNmnkG+xX93nq20+RSqaMk9psNk3or3eI0psbFgqFiWyyaNdEIkE6nZ5YUzSrzAVQfu7nfs7sPSZRi9STOI5j9jqJLuzW7KaYINmwV4eRok1kNu3v73Pnzh0azQZfu/g1OpUOK70VfiX8FZNEEzIsFo/xfOZ5tt1tYkEM3x4PTtkq81ONn+L0mdP8JX85Bkh4j2sJodvucuJ/n+DgRw+orlR5Pv08MK5jeWbnGUrFkil2GgwGZLNZrl69ysoTKzz3zefAgk65wyA+4OzyWS5evMiZM2cMFa81qITGuiwSjqJCnRYA7gujZ5G5AIrss6ZZRTgqTMrn8/cVMOmb1dVxQnGL3Rb+Qj8UodlsUq/X2Wnt0Kl0wIb91D7VfpV0kDbayXVdBu6AXXcXLMYgude8aqbK9Vevs93c5s4P3DGfh2E4Dmtzdc6cOMNweThRUrDj7hCmQ5MN9zyP5eVl1tbWOHHiBIlagtPhae6Ed0jsJujud9kOtxkOh2xtbXH+/HnOnj1LuTyusZWCbnGOJVzWRekCHt1vAqxZZS6AsrGxYUJFYVGjdDRMLsXUjCtMbpcpf6Kida3JYDDg9ddfHxdA77WI78QZnhhS7pap5MdreER7xeNxcnaOtWCNLXuLOHEG4dhkLHlLlNwSfs8faxMt4bg9T519it3+LnvJo0r8NX+NklsyW3I4ztGj4oQm+NXcr/Ll//NlNl/fxMpaDAYD9vf32d/f5+DggNu3b1MoFMhmx5ln3/dN3gnu34lb/KzoCoVHkbkAyuuvv35f6aPWMDq7G9U4UQZWe/iy4k46s9FosLe3x/r6ugHR6f97mtKpEj9w/geoPFExM1I6M5lI8ovhL9IJOpTcEv14H3/gM0gNiF2JsT26V6kmzbrno4SErL11jV/zf43twTZZJ0t/0CdJkmFsvHPlcDikUqlQLpcplUrGCS0VS5TcEltsAUd7sPT7fdbX17l16xbAxEMmZVVhKpUyxV7ypxlv0aqPZXG13rVIP4ULjlb4i98iIlGNZlxFk0gNrC4olqhFIqyJDXJGSQjBTbj0nT6pVMqYhWKxiI1NLshhYZGzcnyaT7Nur7OWXuMj1kdIhAn69CfuKUmSE7ET/H7391kfrXMmOMMv2b8EcfCHvmmfDGwqlcJ27LE57HQmGGLbHj8wU0xLNDIUR1fvySIciaRBdNT4KIysyFwARXgKod0BE5rqIhvpBH3jgBl42fdEtEmv12Nvb4/Dw0NqtZoJnQFDznW7XXZ3dymVSzxXe45r3Wuctc7y7sa7adQbnDhxYmIWtoIW68E6AQGb4SafCT/DJ6xP8Cfhn7DL7jgkDgNWrBV2W7vjY61xiNy1uhTihYlseLFYZHl5mUw2w/uffz/fuPsN1vw1LvcvmzyOTBTbtikUCvR6vYklrI7jmGhN95uAJJfLsbq6apbAyuujZJDnAij5fN44qZ1Ox3jxcPSgJnmMmkQ4Et7JbIPxNqTNZpNqtcr29ja1Wm0CbGK3s9ms8XF8f/xM41t7t3it85pZXvrMyjMM+gMz80XFJ4YJToYnx2bBgs1wk5duvsTehbEfEoTjhe8b4Qb1Zp3TqdOGdMtaWeNsl8tlstksp0+f5ty5c/iuzz9v/zOjcMSmvUm5O66sE5DUajVD4eviaV3NJxNIuBrHcWg2m9RqNdbX183kyuVyJmk5q8wFUOAoZJM8jziqMvukfEAPsPQ0AAAQI0lEQVTvAZ9MjtcIHx4esr6+Tq1WM/6FcCB6zYvYaOEPxNwBDOtDznKWDTa4krnCueQ5hrWhCdN1KuDZ0bN8ZvQZtp1tKp0KyXaSpe4SB+kDYkGMoT1kzV+j7Jb5qPtRgmRA1hqvUZaneUhNqzyJLJvN8iMnf4RvbH2Dpd4SYWdyxZ+Yl2khrXZaowSl+Ct6p8l6vW408KwyF0ARZzW6JxpwX5G0fNZutzk8PDTrfPf29iaWkIpDrPcJEROiiSbRFoP+gJ9u/DRv+aG3cH7pPLdu34IMNJrjBztKEk/a82z7Wbar23QPujhph/fuvRc/4ZOxMpCG1ewquWzOML79fp9Op2OIsHK5bMg/WUj+pZ/8Ep//28/zj//rH3ll+MrE+ibti4lzKjKNUhARUy4Ovd4HV1fAPUzmAii6hlMzlfoh1JZl4XmeSd2LmdHPJBYHV4um4IXKFrMDR7UqvV6P9dvrXLxwkcz5DH/Y+kNeLb1KpVPhZ3d+ltWVVZaXl014nkqlyGVz1DP1CfMUdVDT6TSAcaTr9bopuD51+hRWdgxez/Not9oMqgP2dvfo9Xomm6wde3nVUaKI1noCCmFmpW16dwP924fJXAClXB7bY/EDzPqae4PveR7NZpPt7W02Nzep1Wom96FvXGsNbbokxa4jhuguz77vc3h4yMHBAY1hg+91vkdAwEH6gP2Dfez9cfmCFAsJLa75HzmXFGjrpKZmUNPpNMVSkU+8/Am+U/0OP3zyh/nC+75Au92m2WyaZwaKRAdUk4z6e62BhEsRikD66b7anBllLoDy9re/3fgQkosYDofcvXuXO3fu8C//8i9sbGwYqjudTpunegmYoglFHVKLAymmSEoIxQQlk0k6nQ53797l2rVrPP300zxVeYpvH36bC/ELnCqe4vDgkGuvXyO9lObJk0/iOI5ZYyzknoBRNKQMUqvVotlqUvfrrK2OH63rFl2+/cq3GYUjXtp+ia36FoPmwDw4U5NjYjplImgQaXBENYVQBXBkwjWL/dhR+L/9279t/BKZib1ej/39fdrttsl6SmWXzgKLSKFzlEjSPIqu0I9uAijPSpZC6M/+189i52wObh/w2muvcePmDT7LZzlwDzi1d4qPuh8l5sRMGWK73TYZXQGkcEK1eo2/Sv0Vu7ldnnSf5PdO/h5rpTXesfQOvnXwLZ5efZqCU2Czv2n8GO2TSfmiLpKO8kzyvwaBaGfRInJ81JTNInMBFCkXEFsts0ZCWf10DJkhcFTYJLUjMqP1Hm16GQQclS3oAm75XCrqDg8P6bQ7XFi5wF6wR7lcZmm4xOH+uHp/y9ni5VsvU3bLEwShRGTD4ZDDw0PThl6sx25hl8AKuDm4iZ8Ym4XPvPsz9GN9VjOrNJtNU3Tk+74xq6JR9EZ/OjTWzrnWEqJ9puVzHlvCTVYEwtHNR7d5ENHrefQaHOFIpINk1sirrt6X8FTnhgqFAtevX6ff73Pt2jVOnz5NuVwmmUxSKpV4S+ItPNl8khuDG1S8Ct39Lj2rZ4qskskkhUKBRCJxX4V9sVTkjHVmHHqnr5AYjqOgWCxGwRmXBLTbbWq1Gq1Wa6JvpN1RVlWH+VHRVEDU2X0zeR6YE6BIIVG0I0T0zUYdO53D0LNHh5Xyv3Z0ox2mVwNubm6aTK2UKwD87n/6Xe7W77J/e5/Di4fjDPTOzlGhUzBilBzhDl0cewzCTCbD6soqHy9/nHghzvnl84Y8lKhOIhTtZ8lnmjCM9kvUjAhA9EoG7a88ammBlrkAis4Cw/EgmSYPcsqi30ln6g6GIx+gUCjQbDbZ2dlha2vLPE5WShRWV1Y5tXaK7dI2u7u7bG1tGZKv63X5h4v/QDPfpNgq8mN3foxKucLS0hIXLlwwOzPI43Wz2ezEdqEa5NEwPmpOtKY4zozM4qw+inaZC6AcN2OOy3LqWSK/l86L2mz9vfzJQOitMSQyEj9BwtRLly6ZwWw2m+zv77O3t0e9XieVSnH27FkODw+pDqo0C01CK6SRb/DW//xWThdPk81mKRaL5knu9XrdLDUtFAqmLlaTiZpdlbbrzYyn9ZXcp/z+uGPerMwFUKapRT1jtC2O2lw9wzRdr0V3XrRoR+dNdCG253lUq1U6nQ57e3um6BngiSeeIJfLsba2xqVLl3jttde4+cZN1kZrbMe2uehe5N0/+G7zmFnP84zPcvPmTba3t+l2u6ysrHDlyhWuXr1qaly1EypmZ5rW0Fplmi8iIr99s76JyFwA5UHmRVSvBoJIlEMQEERNS1S0PdfHyRoYqUmp1+t89atfZW9vj0ajQRAEZDIZNjY2OH/+vNncWCKey+FlglTAucq5iWhIojZZTtHpdMbbZRwcEIvFODw8pN1us7m5ye7urikT0IMsfss0rkT3R9THexT29UEyF0DRQIgOfJQjmGZ2RKK+zrTrSGdK6KxJunQ6bSIW2Qa00WiY0kLJusri82azydLSEmfPnqVYLFKr1cz6m263axKXsmhre3ubZDLJE088MfGQ60ajwdbWFuvr6xwcHNxXoQbcRyZG70v6R9+nyLRg4LFkZmeR42hrEe3xTxPNTMrvJcTWAyAci1Tpr6ysmCUfmvqWheKDwYBEImHW28jCMaHypQRCPs/n86TTabLZrGmPFFnv7OwYk6P9E10iGi3qmqXf5Njvx/zMBVC0So2WNspnMP15w2JqdJTwoA7RJQfR8kAZIL0E5PTp0+NlHe22of6B+1L0kgwUnyqdTpsCoVarRaPRMMVKslJAO6y1Wo1CoWC0jPZPjqPcta913ASZpm3ejDmaC6BEOyDq2MrNRbfGkBvWmmEaCSXHifoWqj0a9cDROhtZIiIkmuwfqzOwuVzOPItYiD8NAskf7e2Ns8GyJliWv0oqYtoKBGm33I8unpJ2RjWKLqeI5nSm8U+PXa5nWjgXtbHTHNpZwmdd16J5Cc3MasBJUtL3fba2trBtm1KpRKvVolqtTjwOFzClB8KuAqYcU0CnH2qtn+4eXWst7UokEoZnETBHQRGl8R8m2jd7MyHzXABl2uyYNgOm+RnafEhYqbVKFHjSYbpeVHMq0g7hPHq9ntlFsd1u02q1zBPac7mc2T1A2id7pEkSMh6PU6lUTIlCsVicWLAOk2G5VKVJLQpMFpIL2KOTJDqJBBT6T/pJdoV4FJkLoBwnGizar4iq06jpiDpux5mk466ni50k4pFC5TA8eo6xcCPRlQM6ByUlj7lczuxFJ4lKHc1FnWo9GablvKLHCdCj2lauJfc/jbibReYaKDC5331UpnWeDoG176KBo9Vw9FoS+ciMb7fbRjNIbkafQ3aD0ktYpT5GNJxsP6H3fYnS89rJjk4C+f5hZkabWcBsPiQa8/uJfOYCKJooi2oR+V4fp2dYlHfRZkSvURbRzp5Wz9Ou2e/3uXv3LhcuXGB5eZmlpSWjLWQTYNnDVfZRkdV7vV7PzGRTwK0e/qTbIE6q3Iu0Wy/esqyjR61IO6dxIpJc1Fo0an4f1ZGFOQIKzEYza/MT9fAFHFJHGz2nDjmnMZdRZ0/KI/v9vlmVJ7sshOHR2iGJXESCIDA1vUEQGB9HKuL07k8SMkvYLNpMFy5J26Jhsn7ki/5cL+S3rKNVl/q+5ZyzylwABSYBANOZw4d9JhpHnqAe1R5w/6Nete8S7fAwDE12GJhgWmX9Ua/Xo1KpkMlkCILAJBYlCgrDkEqlYlIDOuqSCEd2dBSaPtoOMWGiJaaZIK2N5XsdJOjE6SxpjqjMBVCmmYbjQKNnge4EvVWmMKP6fLN0ilTYa6BIQVG73aZQKOC6rlly2ul0aLVaVCqV8UOh7u0a3el02NnZMYNSLBbNvrMS2QjXIsSdpA6CIDA8zTQ/RZdwanbZsqwJml9PDulj/f+sYbXpm5mP/P8o03iP42xoNMyNfmfb9sTWU9NAdhxodGgtlfkS8na7XfL5/MTSVVD7wt4zI6Ilcrmc+b1oDAlLdYiu26RzPFGTqkNcPcDa19J9Ny0EjmrXR5G5AMo0eRglrSl8LVKIHc2L6PNJmKtVsK4JkeMl49tut435kecHycY/kijUYapsQ5HL5cxaabmuhNk6KSi/m8bCas5kWp9MI+KiO1Npvyxa+zKrzAVQ9I2JRDmBabMhuumddMi0Lbyis/JhbZHz7+7usrOzQ6PRwLLGCbxcLker1aJcLnPmzJmJ7TolAkqlUliWZUJmCa+ljalUyjC4Yhrl8yhrrO9T+yqad9ERjtZyuh91lPWoOZ+5AIpINHyLmqGoGYluhSES7YBZTNpx0mw2aTQatFotRqPxA6szmQz5fJ5sNmvCZNEqYXj0DB19L3rwdM5Ga0Yh6aJphagPJ787zueI0gf6c11Q/igyV0ARmfUmHubHRAH3KOeGIzMm+RpJ+sViMVNvIqsAJWejIxrzDB8mC6ukDXoVo0RQssjtYe2cFt7L59EwWN4f9/ksMhdAOY4AioaqemZEfQ/dGVETNk1m6SQxNZ1Oh93dXTzP4+TJk8Z3kShJA1KHv7IuRz/4W68KkN9L7Uuv15uI+uR+5FWDQJsjHek86L6naaZZZS6AAg+vvNIRwLTfiURV9/fTHjEjUtEmtLwQbDoE16G1ZJ+j7dIL2eWz4XBoiq11CBv1y7Qp0W2cFtlolnsaGxs1e7PIXABlWup7lpuY5vBGf6sd2Qed57j/hcDTD78Wc6MdRg0MAY2m06VdGmCyEWGr1RrvuR/ZIEd+o+9LD/y0/JW0WScBxSGOnutRZC6A8iDz8DC2Vn5/HEH1qNc+7jeaEZUlqlo7aG0T1X46l6OjDph86oWYKH1Ps0rU/9DniGqWN3P+uQAKzDaoD7LBUS3wIE0yzaE77vrRdcyAedhU1DREQ3UpUJo28/XjYSTzrLPH0TZFIxnt70QnU7QmR5saDeLHLjzWW2UdJ9MG5GFrb6NAiSYDowM4LUpyXdfUzDabTYbDoSlUkp0V5AkWch45vzxaRbLJYoai2kj2tpc8j2igaUARUwJHe6BM6yvtMwnppvv7UX24uQBK1LmaluUUSv04TTJNter6Ev1UsDAMJx4YrR3H6DkzmQyj0Yhms8kbb7zBmTNnyGaz5pGxQpB1Oh1eeuklbt68yenTp7ly5QrlctnwJ7KxnjC1QpwNBgPq9bphf7UGk/brtkUX5Uf9u6hWE1IvSro9qswFUKI+SJQoEjlOXUZNiYBEh5hRtlMq7aOdGD2vmAjP87h165bZj75cLhueZTAYsLu7y4svvsjXv/51rl69SjqdJplMmrVCMkDRqrbBYGDYXKlzgaNQVqIlkah50psAynXk/2jOSO7pzVD41vcbRi7kP4Y8ug5ayH9IWQBlITPJAigLmUkWQFnITLIAykJmkgVQFjKTLICykJlkAZSFzCQLoCxkJlkAZSEzyQIoC5lJFkBZyEyyAMpCZpIFUBYykyyAspCZZAGUhcwkC6AsZCZZAGUhM8kCKAuZSRZAWchMsgDKQmaSBVAWMpMsgLKQmeT/AQmChghWmwn6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f468dc87940>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f468dc46860>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Ln2QAdR7FVvPaD7GZZnle29+y/RYdlQEMBgNT5ilhvR2Gi+nUYNDtfj9gWQmgaDCIedCJNhFtbiR0lPKAKIoWygh1pRjMq/RHo5EBF2DCaYlgfN+nXq8vzHStTZY5pDrxKKDTaQg71BeTo/0Re4CXDbqYISHfGo0GW1tbRutNp1NjHqVUQWsoDURJYywL4U+SlQCK7nQZbElm2WGmDJwMuFbP+kcKfICFHIvkgvR1xGSJJhG/YRlFr7WCtF2uo9uoQ1hpv9zDLkXUJkJ8KX1/GzBSHqGdY20WbUdX32fZ8dPISgAFFjOv8lmcOw0WmYGz2Yw4jhd8ETEV2nTZORDtBOpIRLTFsnyOVttyTCfmBDziKNokl3xHNKIGJRxrUmm/Ha2IZpUJFEWRqWOxzYvca1lUpP/X559GVhIo2gewIx5RnbKWZzQamUIjWAxTdRJNZ091WkC+px1PPcA2SOQ88ZW0gy3OotYMAjoBh/BE+lp6RYGIHW4DJmUgS0ouXbpkuBYd+urlGXb+534iHS0rAxQ4nlkyODII+n898JPJhNFoZMyKZiWlwzRbqqMgPTAaDCdxHTKj9Xe0yRPg6WfR5kSAYifotC8h7ZbPOtzWKYIkScxCfDFlwMJ1pQ1yD+1UL4sy30tWAiiidoMgWAjhpPMllNUPK+WNOlEo39FRkWgg7cBJdKWXTmiCy76WDjHluO5sbTLgGCQSMclghmG4QKlrVtXzPGazmbmOdkRlIgjYJHIbDAZUq9UFsEiqAxZDcS12ZHkaWQmgCBhkFgkgbH4BMESaLHzS2kSHhDoa0dGGZGrtwZD7ye/yv232lh1f9jxaO2rmWZdRCsDsyEkDFI5NqH6GOI7pdrs0m00qlYpZq6TbrzWlPgYPKI+iZ6gmrPTf5LMMgADE5hBsckyuoxlU7Vfoe2lSTIuOROQ7tth+jF08tOw6ywZLRzw6whKnXK4PGBbajo60ltT3023SfXAaWQmgaOLJ/rETW3AcyWg/wI5AbM9e+wn27LI/23KS3/Ju58iAaDMqmkOvoQaMybVFzJc2o7rWRFYw6vBZTJAdQUobdYnBA0e4SR2IeO1CGGktoJ1P2/wACzPOpt4B4wNpUkuOyzXFsVymzWybvgyM2pexP8sAO45jUgdy/TAM7ymOln4RAs91XcbjscksS5GS3u6jXC7fsyTEphuEkLPD9/eSlQCKsKgiYrt1wg4WiTkp5pHqONvUyPk2ILRK1nISx2CbAfs7+pp25KXPE39KricLvsQv07S+3FdXvmmTK1pBa0udZ9LJTAG/9Nv9RjsiKwEUWb+rWVMdHsO9jmRRFAuJQL3YSb6/LA2gWVWbY1hmYuzCJbs9GiR2dCQi2kqzyXBcBmmDUBxWzfVIoZVoEHkuvdGOTdJJ++UZZAIuM73vJSsBFJlZgnibkxCVLfUfsq3ESeGrDLqYIkmoaZDYSTjNeWiVLLbctulam+jZqwfLcZwFB1I74TKwyxJ3AgCbe9HgtJ1YXbe7LHqUCaXlgXRmARMqwqLql84ToMg2EtoX0Q6wrviS2abvJQOvWVj9/ZOKe/TvJ5FrclyDR+4hXMlkMllIPOqqPjvNIDyMTAgBneM4ZkWBREW6MFsnNeWZlvlmp5WVAIruTPtB9OCLw1av12k2m9TrdeOriIh6l8Iiu8JdfB75jgyCXsMj97YBprXISRyLLn20n1HzPprD0fUy2p8R82M7ntIO2VNFtIf4a+LDyFppfW3d7gcu6rHV/7LoQf+4rku1WjX7rIkDJ50siUJRw7adtm2ztvVyz4UZV0DpsMTj33mcxrjBd3/zu7idxfIHfb5dLK5NoQ5zpcJe0/naVAlTrO9hm2HxOZaZJf288owPNFCW2ftlEYvQ044z36VoY2PD1MdGUWQGYjweUxQFzWZzgY/Ji5wxY4IsICIiJFzoSCmSXmA4C/j4//w4W3tbpr1f+Jsv8JX/8RVoLi6i1xpItIoeeP0jJRVSRyLHJeTXhdASLWlTIsth7f1StO+l+24Zy/3AObM2J6GJM5tBFYKpVCqxublp1sTIniNSFhkEAUEpYOJN8DOfZJbw1+5fs+PsEBQBqZdy3jnPi+mL1JwaqD7TGiWchGztbeGg0vYU/Mr/+hW++4ffNQOqZ6kegJNYXAF1kiRmhSBgyidkGYn4KGmaUqlUDM/U6XTMnnN6XZG0XYCxrE/l+APHo4jYpkFmgI4mtIPoeZ5Rv4PBgMFgYLRBqVzi78p/x03nJheLi3x2/Fl2GjsUTkFCAg7cdG/yF6W/4GJ+kT+I/8D4KdpsjctjJkyoUFkAS31QXxgQrfU0uSdtF9WvSwBEtJOpNY6uvRmNRhRFYUg12T9X71enTZAu0RCfyPab7sf0rMRLE5aRXNrLX8ZPSIfIeuJ6vW4yqY7jkJZSbro3yZ2cHWdnvllNvA0F+JlvNEjhFOy4O+yP941WEgc5TVOyPOPv/9vfz89VauelT78EHKcGtC+kSyvtemBdMiHPKs8uz7rM8RQ/x3VdUy+r99O10xP6f/te70dWQqOcFPHY2VA7Cyocge/7tFotOp0OR0dH9Pt9Gl6Dy+5lfpH9gvPpebyJx68f/TrdpMv4YMzUn/Kzp3/GsD2kHbU5vHEIFagUFUpBaSF6cFsuf/u7f8sXv/JFXFxmzDh69IhidqzKNTmmfQEb/MvUvi6qWlatpydSqVSiVquZlztIza++n76eyDLt8cA6s8uOido+yfHS+ZJms0m73WZ3dxcHhz8q/xE7RzsUo2LuxPoBtWmNxE2YjWY89u3HyMOccl7mWy98i0FrQGfU4ZPvfJLAD8y98zyHNvzNf/8b2oM2g/YAkrtRSp6RBAlhFuI6Ljgw9aa0ghbJNLnHN7AHRyc4bVpewCIiIJRyA3Haq9XqPXW+tjP7fmpQtKwEUJaJzbpqB1NmsN4eot1uA/PlqTdv3WTqT0mm8wGcFBPT4bIHimzFlaYpSZDQb/XBhW6tyyAdUEtrC/kXYVUH7gA3cpnEEyIiXvnAKwyaA5qDJh/9j4/yo2d+RK/RY3O8ySd7nyQrZ1SpmuvIvYWGL4rjDXp0CKwr+iXa0RxMmqYcHh6aV9HIzlF6DZFo3IXIT/kwDxwzazfYDo21zRb1Dschn6wpTtOUaq3Kl5tfZocdLqYXeTF78dg8uA5JKaEUlkhKCfWgTjpLSdKEer9O1Ipo9Bo4E4fESxb8DklEOo4DDnzvye/NweUADvSbfXazXbr1LjhwUDngnx/+Z7rVLmfiM/zmnd/EdRb5HP2stinS5QiiPQSssquDRHcaGMvYYW3u7uGITikrBxTdcZo0kr9pFldvGyEzduJO2HF2yJk7sVk5w8s90izla9tf407pDn7mk3kZnajDR378EV59+lWiZkStV+PqS1dJKglFcFzxJnZf2FytgSiAHKqHVTiA2lGN0caIer9Ot92lcAr2w31GjGjSvIf6lyhHNJ7wKHk+r8qrVquGJxKfZTwes7u7S61WY2NjwwDB8zzj4OrtLyTxqPePO02NjZaVAMpJYtttAZGuahPzJMe2wi2uuFe4nl3nkntp7ivkCb20x25pFxxIvRScuZk54IBeswcujNojpu6UMDuubdXhuZmhmUu9WyfqRNS6NR774WOU0zJe1ePZHz1LWk6pFBVe/dCr9Jt9WsMWk4MJTsUhzVLGzhif48LpOI7NveSZhFeRUgp5uZNOdm5tbbG5uUmtVjOTSTgV7YzrRKGdSzutrARQ3q3Ry1hbWL5/mtjkLzW+xK3+LZpOkxnzWVTJK2xPt9kt7+JlHpmb0Rg0CPqBGfTqUZUgDShVS2bPebmfXqDu4PDUD55iVpoZUHllb8FRdV2X5689z9SbUs7K5EFOmqV867FvcVQ7ohN1+K9v/dd7ViOKiUzDlHJYxnPnfo28jKHRaNButw3hJll0HRHa/besf5elMt5NVoZHse2r/Xftxdv1JTqjWiqVCMshDaexcI6Dw+d2P8eL11+kHc9fk+Li4ns+V1+6SqVfYbwx5ucf+zlhJTSlDPLWL7tc0/d8qvk82qAOQSkw0YhxQl2PSl7Bc+ffjb2Yo9oRhVPQrXdJS+kCgwrguA7ffOSbfPnKl/nq5lcJSnPmWULiZrPJxsYGZ86cMctfxfm1J5Uc10tg7D4/ray0RtH+ij5nmUMovkqtViOshIydMWEeLqhd3/Px8Mxg9Vt9gk7AfzzxH0wak7lT2u5TO1OjHbTnzOxkzrkEWUCQBAsaJifnhx/6IcPGkNawxUevfXSeZggSwjwkCzPKaZnAv7uwPnPYiDY4qh3Rjtr4iQ/zmi2ybP76FK/pcVA5oHAKbnu38Vs+7XKber1Oo9Fge3ubs2fPmvSFrrnRUZRmePVOUtqZfeCAYot+AD0b7NyPgECvzndchz/d/VNez1/nYn6RPyz+0Fwzy+YDtzXe4qB6wEa0QblcZtAYzKOXArbiLTYrm/ieT0HB19tfZ7e0y0a0wYde+RB5dtdMFDmvfPQVRs2RiXq6aZc3nn2DQXuAl3nkXk4n6vCrb/zq3QeDF157gaiIKKdlispilbzjOLSCFg+lD3Hbv82F4gINt0ERziOXVqtllmfoJa06tWH3n113Y/t7p5WVAMpJDdbZVjg59tce/FF8xOvx6ybqGcwGVPKKyctkWcanb32aUTEimAU4nmOAszXd4vd6v4dfnjOdERG7pV0Kp+CofkRaTvHju7UfpRmj1hwkIhMmczLOhczJ5g5zvcuoGFGezXM0RV7MQXI3wakZ1dlsxmQ84dPTT5MECe2gTa/UW9idQb9MUqcGdF5JR4zLNPL9ggRWDCgaBMse0i740SLH20Gbx8uP80b8BhfyC1SKysJSUgAv8wjyAIL59z7f/TzxIKYdtPHD43XJYR5yNjnLbmmXrfEWlbxinONqUaXZb86BATS6Depxfe4oO9k8bC6gHbUJZgEFheFGdOQCGF8oyzL29/eBeT1tVs5MnYy83k40ik4d6JoVHdVoDWz39QOpUURs+6m1ALAAEjvCgGNW8k/af8Jr77xGKS2RFdnCy5aEG9HX8jyPOnVDiOkF5p8/+jyDdABj6NM3bXMdl+dffZ4kmHMrQRIwa8zIvRyceWT0a6/9Gq1pC8rHqQjHcRYy1NKWUqnEJJ4w8SZUi6px0mV3KCl71Ik/uxZG0wSaPtD9qwHywAIFlteivpfoDp9/EcIsJJklRqXr17zZiTlNn+sEmx4IzUNogqzEnO+gNP/cjtpzCn+0yVa+hVde3LZD+woCHs/zcFyHbz/+bQ6rh2yON/nMrc+QpXPg6xdl6tyPtF3Arq9raw79c79kG6wQUGxA6IdaNoO036LtvOM45EVOVEQ4qUOeLa4DkjoWPWMBs/GeXmOUFzlf6XyF3dIuZyZn+NjoYwuF4DLIuu2fePMTzEozatQIy+ECWwqYN41q8qtcLjNyRhxWDymcgsPqIUE7oEMH13V57LHH+MAHPkCz2VyocpN26qSi1ih6Qdsy9vt+ZCWAsgwkIraN1SWC+m9in7M848+6f8Zb1bc4F5zjd45+B7h3P1a5llDdkgbQ54yKkXFm9yv7xF6Mg7NQmCRsqvx4jkeYhjj+YnRmaybxg0SLVdMqG6N56Lwdb9MpdTh39hy1Wo2LFy+a9xDq/rEHX2sKe6LZWvR+8z0rARTpNO20wvK1wVp9a8pbBnh/tM9b6VvkTs7t4DZjZ0wpL5m/6y0w5JjM+kajged5xysQU5ez07PslnfZmmxRmpVIiuPtM2xaXNonzyCgFnOli7DEhEjFW57nfOLNTxDlEdv1bfzzPpcvX+bs2bNmB0jpB5uJ1stL9HPZ/WT34wPto+jOfK/6CWNqVOlgp9ThUf9R3pq9xbnZOWrUmOb3FjDrAiFZC7yxsWHyKpL6/63932LMGDd26Wd9cy9Dt6tyCFvLSZv0sgu5JxxXx8lnAPoQDSNu5jdNpBOGoVmfLACwl4dK0lLnpnQ/Leu7+5GVAIq9VlY7YnbnishALas3/VLjS/zkFz+hnJZxSy5Zmpn76CwwYNbFyDt8pG52ls4YO2NqeY2aW2PqTM09xDRpJ9IORbWK15yHXRJpZrjrkFdzsm7G4cEh77zzDrPZjFu3bnHp0iUOOBrGAAAPYElEQVSuXr1q3pau62TF35JyC7vkQD7rPnxgmVl7SYMGjHSCzRnI96SzpfPieO5HNN0mqTs3CXpHIpn5gKk/lZV6okmSWcI/nP0H9sN9tqfb/PbBby8UKwML200s4yvgXhOqyTFNJM7SGd946BvshXs0ggadVzt0j7rcvHmTl156ia2tLZ577jkeeeQRrl69SqfToVqtUq1WFzLEun9EIy8rqD6pve8mKwEULSd55O+mZSQCka01RRv4hY+DY3wQ3/cXXs0ia2MkkhiPx/PXtORD9sN9Cqdgr7zHqBjh58d7msj54vPo19hLO+3Sw5NKEfM8pz/rsxfuUTgFg/aAijdflqHfyzwajbh+/Tp7e3sm17O9vW0AI8s2tGhnW9/PXl99GlkJoNiV9jblrP0BEc1M+r5PFEXs7+9z6/Yt/nz459yo3uB86TwvDl80wJDBDILAaBL9HuFoFBEVEUma0B616da6bI43YQzTbMrEmxiw2KG7jmSkfWLi5N3LJ4kzcWj2m/SbfcK9EHdy3N4kSYiiiNdff51bt26xu7vL1tYWZ86c4ZFHHmFzc5Nz585x6dIl44zDoubQLPe71R+/m6wUUGyAaPu7zAyJWt3b2+MHP/gBb731FjvdHd558p159tW/zcSZEGbhghmQNcyiTdI0Jc1SvnHxGxzWDgFws7t7wWUpvUGP7139Hv1an9agxfPXnp8XUoMpElpW+Q7cYzJtgizLMoaDIQ/900O0nTb+1IcK+J5v3l4mVW9xHPP2229z48YNgiDg+9//PhsbG1y+fJkPf/jDXLx4kfPnzxsGV9/b1iAPpOnRING0vRQY68VU2uk8PDxkNBrxzjvv8PLLL3Pjxg2iUcTGhQ269S4PZQ9Rc2okWWJS7bJo3eZNRozmILnbf0LF9xo9vv3kt4nqkckST70ppdnxLlHLRJaB6rpbnQiUWR7HMcPhkGk8xckdgjC4hxMRIlBErjuZTNjb2zMh/9tvv83m5iYPPfSQeT6JmqrVqinD0JsGnlZWAiga9bZzKLyGzKjxeEwcx0RRxI0bN+j3++zt7XHz5k16vR5JkvDhVz/Mw08+TMNrmAhFmzdNRkmo6aUe7eGcfrdFQEIBjWEDP5mXIGiVDovrZMQk2hGGrV0kwkrDFC/27uFk9AY5tlMs95xMJub5K5UK169fX4iEpDquVqvRarU4f/48zWaTZrN56jFaCaAIskWDwFylR1HEeDxmZ2eHfr/P0dER169fp9vtMhgM6Pf7hlkVnyDPc9JZSjktk3P8UgPJ+ehoSrSJ5IKeefkZkiAhKzKzOKzRmy+H6Df7NAYNnv3RsxR3/0mbNR+iwSKiTaqAJ8vmuydFo4if/5efM94aE+6FnP+n8xR5scC/2OG/+Br6WvLyb0k62sCUfVjCMOT8+fNsbW2xsbHBZz/72VON0UoABRa3iJAXRN6+fZu9vT1+9rOfsb+/T6/X486dO4xGo+Mln1lGqVSiWq2a2S2zdOpP5wVEatlCURzv+6rzPcksYeJOKMdl8lLO0z98mqycUUpLzIJ5aYE/9ReALCLXlR+tIXU0pP0v2fZikA0Yb43BhXg7JgsziJZnz7V2EoDaeTAxy8scVtk7fzgccvPmTRqNxj3nnCQrAZTJZLKQ4e31euzv7/PGG29w+/ZtfvzjH3NwcGDWs8Di3mqiLcxO1bOEr25+1fAgn9r5lDEF8o4eKWUclAZ4U4+ffOQnDDtDyAAPwl7IlTeusPP4DlE7ojqo8tzLz5HlGVk5I0/mxUuu6+Kkzj1mQjuPAmB9PEmS+T4ug4xwLyTejgn3Q7zYI3cW+Rdtlu0NfewaHSlJgEUNpCdGv9+n3+8v1X4nyUoA5R//8R/NCxCiKDJvPhdHbTweG4dQO3Wa7ZTfHcchKqIFHuRoekSYhkZdw3xx+r/8yr+Qeilu6s6dVxezoCvuxLz2y6/Nb+TAuDXm+7/2fcJByLgzxs1ccn8+oM1ek+dfff4eX2JZ1jsIArNEYzQaMRlPOPO1M/OlrVn5nu9pwC3bIVM+y331G8k036T9Gv2y7dPKSgDl61//unFShWKXGScEkU1q2apZ+wFFVNAatOg1e3TGHYJZYKIZKRIa1UdmfU/u5zipQ+FY6vquA0uBOW+8MZ5/dnJzzUFrQFpOKeUl81UJw+WznvlpljLIBozGo7mPlRe4kzlY7Qo+rUnsFIGdRDXXV5s1287zwmR50Cj8a9eumYewl0VowshOo+vPOgJJpglPfPcJ3vjEG3SrXf71sX+dFzjf/UqpVKLm1PBzn9RN5+WLXrYIDI4/O5lD4RdzzeMrPuLuec1Bcx4uq0hZ1LwmvoqiYBJP+OaVb9JtdAn3Qza/ukmRFwvnaBDoCFA/s13eoCMiDRTpTzv9cJIfc5KsBFCE15CcjH6BgADEfqeN/t+eaVmWMcyGdCtdUxgdezFhOl8b4bouLi5feOML7Bf7+H2fH1z9Ab1Gj9qwxmM/eYxSVGKwNWCzO6/IHzQHtEdt/v2Zf6ff7NMcNHn6p09T5AWlWQnHXSSw9KBr1naQDuYhuAvxmZgkSHDHx7N82aoDmzDTE8MGlP3dZfkxm9g8jawMUGBxT3y4t4bWrgG1/QGtrpNuQr1XJ2pHtKP2fMY7iy9PyPOc1rRFTMzzP36el55+iWFjyNtPvk1BQdSOaA6a4MCgMaA9bPPCay8w82fzNTnF3RUA7r1FRLrdC22cOFSPqow2RpTulHDGx2USWptoIm9ZptwWDRR9zM5B6b/dj6wEUOQhdIGPXe4n59n5CjFHeq1wnudkacbFf75IuBHSClpkYYYbuIa+F6DIRjQTb0LUiMCFYXs450lcGDQH5nOv0WPmz8scJTOtB3fZDJc2ySQYRSO2v7ZNd9rFm3o43rx0Uxdc21GTyLIqNVuzyHERXeGmfRYbVO8lK7Gk1DY1Nll0UmrcZllhsTNnyYxskBFP5k5xQUFSSnC94+1CxeQ13AbtYRsnd2hHbdrR3c9D9TlqE+bhwn1EdEGTnvkCEskHxXHMLJnBCFNWqZ/NBphd9rmMBbb7ZNnnZVWD9yMroVE0oaTBIE7Ye3WO2F2bgNLvOC6HZV566iV6jR7b8Ta/2/tdHByzEN1NXT7+5sfnOxHklXklnD+lnM5D1qk/z+84wfEs1vkcaYcQYdqZDIL5up6RM2ISTwwXJM9k+xVyfJkTexpZdr7WUverTWBFgKKBYM8srSrh5E4TbaSZU9nHNU1T0jClV+/NuZVwj6iIaDgNQ20LW1txKrjBfPBKRQm3PNc+depk/rHWEg5CQCoJRtEqcp7rurieyzce+gYHlQNK2yWaX24ugGvZTpHarCyLhvSqQC02h7Osv97PWzZWAii2prD9Ev2/Lbbza+9lIpolPoypHFYYb47ZGG/ACMbu2HxHIiu7LbrUUYsMkB2lAOYFBZKQS4KEg+p84fl0e0oe5rhjqxTyBMdc38/2Rd6tb2zA2Md0e08jKwGUZQ2WY3aeAxZX+cnvknvRtlg6Rqrut/73FrXtGpc2LzGpTBYiC6lW09eH49e76YhMNIe9NFSkYL6jQal8d50QDpvjTQ4rhwR3AorRvYXY7zbY+llsoNjgWdaf9t/fj5+yckCxO89Gvra1dhRgR0pa04ivQgHDYEie5Xi+x9Sf4k/ni8dl+aYW7VBK2h5Y8DNEfN/H8z1efvpl+s0+29Ntfr//+0zjKR9/4+O8c/AOv/jpL+bawbmXZNP3XJYOOM0An6QllmmQB06jiCwDiXx+r3NtjaMdNzE/ME9AjsdjPN/j2lPX5uUDvQZPvfzUnEq/C4i8yEmChGAS4LnHmwoKgzxLZ8xKMwI3wOE4lZ9XcvrNvskzzYIZxaQwNL3sfm2bS/0cJy3aWvb8+tmXmazT9PVpZCWAchIHIH+znTp7Ftrf12ymTXyJuZj60/mAuvOC5jFjynnZhNGvvfAaw86QerfOk999Et/zTY0IDvzbc/82Z2uHbX75J7+M68xzSCW/xHa8zV64x0PpQ1Sp0s/7pi7GNpu6bXYfLCvOXsZM64jpJN/D1so2Y/teslJAWfaQy8zPMpur+QptmsRRFZMhBVG1es0sKG8NW1SpMk3vmpMa85IDF6JORBIkODPlNFeLORHnFvQaPSbuBD+e0/StVosvJl+kXCuTjTPiPDZpBalBkeuImRPnV6I27Z9J7kZCce2XiN+0zMl9N7AI5XA/lfgrARQty3yS95JlM0TPLpur8H2fcqnMb/ziN3DqDkVUMKwNKfK7RU1Th0a3wbAzpNFtUC2qeCW1333qmrLJZr/JeH9MOkup1+uEYTjfjK/UYVgaks7SpSAXjSjtEZOjHWe9z/2yZ363PrrfqOa9ZCWBIrKsM97Lo7fNjlbJMmOlWLlaqdKoNkj8hDzL6Xa7AMySGY9+61HySk6YhQsJP5mJv/SDX2JWmjE5mNCNuguzPgxDOp2OKWlYtkRCO6d22l9nnYXxtRlcTQXAsWN/kmax77+sMv/dZKWAclrn9STRSUPdcRoscp7udL2AfcERHjlMOa6osxdY5eN5Tkm0AhybtqOjI1OeqUky3TbbxNgLs0TraMd8WTrD9sPeLfJ5v7JSQLHFzlUs8+ptG21nmOUcDRzNfUhl/3g8NltnyXUkaSjX1CZArzmSzffED5pMJhwcHLC5uWm299TLUWwzqdMPus5E94F+Rl3Zp59b+uj/tdmBFQLKMrTrzl2mIfR3bSDZIbIMmGSLxUmUQu5+v7/w2jU5V6rs7FC12WxSKpVoNBpUKhXDwkpb+v3+PA1QKs1LHu++mcwOZ6X9egWCLRq8mleRN2poQOvrnmSm9QrH08rKAOX9iK1y9bFl58qP1N2KTyGr6vQbKvQuRvJ3XWHn+z6VSoV2u02lUqFSqZi3gk4mE/r9PnEcG7DppSL6Rzvb9oJy0X5aa2p/RwPC8DgngGUZ9X8/shJAOanhmqG0nbl3E7Hjyxw2PShFUZjalFqtxmQyuaddnuctzHT5vdls0mq12NiY71WrVzDmeU4Yhka7CEhOGrSTQlwJq5cVLclxOXfZRDmpr0T73g+P4vzfODhr+f9HVqJwaS2rL2ugrOVUsgbKWk4la6Cs5VSyBspaTiVroKzlVLIGylpOJWugrOVUsgbKWk4la6Cs5VSyBspaTiVroKzlVLIGylpOJWugrOVUsgbKWk4la6Cs5VSyBspaTiVroKzlVLIGylpOJWugrOVUsgbKWk4la6Cs5VSyBspaTiX/B3awALoR8lXSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f468dc08fd0>"
      ]
     },
     "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",
    "    \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 (C x H x W to H x W X C)\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 +100    ## Refer class normalize in data_load.py or class normalize in notebook 1\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 +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') # Turn off number axes\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": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "## TODO: Define the loss and optimization\n",
    "import torch.optim as optim\n",
    "\n",
    "criterion = nn.SmoothL1Loss()  # For this regression problem, we will use SmoothL1Loss()\n",
    "\n",
    "optimizer = optim.Adam(net.parameters(), lr = 0.0003)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 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": 42,
   "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",
    "\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/10))\n",
    "                running_loss = 0.0\n",
    "\n",
    "    print('Finished Training')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1, Batch: 10, Avg. Loss: 0.16360252127051353\n",
      "Epoch: 1, Batch: 20, Avg. Loss: 0.12025235965847969\n",
      "Epoch: 1, Batch: 30, Avg. Loss: 0.10021275654435158\n",
      "Epoch: 1, Batch: 40, Avg. Loss: 0.10310096219182015\n",
      "Epoch: 1, Batch: 50, Avg. Loss: 0.09420291222631931\n",
      "Epoch: 1, Batch: 60, Avg. Loss: 0.10771445706486701\n",
      "Epoch: 1, Batch: 70, Avg. Loss: 0.1023484006524086\n",
      "Epoch: 1, Batch: 80, Avg. Loss: 0.09114122688770294\n",
      "Epoch: 1, Batch: 90, Avg. Loss: 0.09166164770722389\n",
      "Epoch: 1, Batch: 100, Avg. Loss: 0.10741566568613052\n",
      "Epoch: 1, Batch: 110, Avg. Loss: 0.0896807849407196\n",
      "Epoch: 1, Batch: 120, Avg. Loss: 0.10413974523544312\n",
      "Epoch: 1, Batch: 130, Avg. Loss: 0.09374217540025712\n",
      "Epoch: 1, Batch: 140, Avg. Loss: 0.11449380218982697\n",
      "Epoch: 1, Batch: 150, Avg. Loss: 0.1005661927163601\n",
      "Epoch: 1, Batch: 160, Avg. Loss: 0.09604039788246155\n",
      "Epoch: 1, Batch: 170, Avg. Loss: 0.09445689506828785\n",
      "Epoch: 1, Batch: 180, Avg. Loss: 0.08498869575560093\n",
      "Epoch: 1, Batch: 190, Avg. Loss: 0.07709028348326682\n",
      "Epoch: 1, Batch: 200, Avg. Loss: 0.10163504406809806\n",
      "Epoch: 1, Batch: 210, Avg. Loss: 0.10225512012839318\n",
      "Epoch: 1, Batch: 220, Avg. Loss: 0.07626309245824814\n",
      "Epoch: 1, Batch: 230, Avg. Loss: 0.09561376422643661\n",
      "Epoch: 1, Batch: 240, Avg. Loss: 0.1107070840895176\n",
      "Epoch: 1, Batch: 250, Avg. Loss: 0.08743543326854705\n",
      "Epoch: 1, Batch: 260, Avg. Loss: 0.0927788071334362\n",
      "Epoch: 1, Batch: 270, Avg. Loss: 0.08348089307546616\n",
      "Epoch: 1, Batch: 280, Avg. Loss: 0.10377094075083733\n",
      "Epoch: 1, Batch: 290, Avg. Loss: 0.08595583103597164\n",
      "Epoch: 1, Batch: 300, Avg. Loss: 0.10415311753749848\n",
      "Epoch: 1, Batch: 310, Avg. Loss: 0.09563775844871998\n",
      "Epoch: 1, Batch: 320, Avg. Loss: 0.08381204567849636\n",
      "Epoch: 1, Batch: 330, Avg. Loss: 0.08995776623487473\n",
      "Epoch: 1, Batch: 340, Avg. Loss: 0.07227199487388133\n",
      "Epoch: 2, Batch: 10, Avg. Loss: 0.10488746017217636\n",
      "Epoch: 2, Batch: 20, Avg. Loss: 0.07703486271202564\n",
      "Epoch: 2, Batch: 30, Avg. Loss: 0.09317758195102215\n",
      "Epoch: 2, Batch: 40, Avg. Loss: 0.07802236378192902\n",
      "Epoch: 2, Batch: 50, Avg. Loss: 0.0800964567810297\n",
      "Epoch: 2, Batch: 60, Avg. Loss: 0.07829003855586052\n",
      "Epoch: 2, Batch: 70, Avg. Loss: 0.07347716204822063\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 5, Batch: 210, Avg. Loss: 0.04336607288569212\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Epoch: 25, Batch: 10, Avg. Loss: 0.013854573713615537\n",
      "Epoch: 25, Batch: 20, Avg. Loss: 0.012255958374589681\n",
      "Epoch: 25, Batch: 30, Avg. Loss: 0.012555424217134714\n",
      "Epoch: 25, Batch: 40, Avg. Loss: 0.013907078001648188\n",
      "Epoch: 25, Batch: 50, Avg. Loss: 0.01326936697587371\n",
      "Epoch: 25, Batch: 60, Avg. Loss: 0.012003557151183486\n",
      "Epoch: 25, Batch: 70, Avg. Loss: 0.014864498330280185\n",
      "Epoch: 25, Batch: 80, Avg. Loss: 0.012735409196466208\n",
      "Epoch: 25, Batch: 90, Avg. Loss: 0.018904559733346104\n",
      "Epoch: 25, Batch: 100, Avg. Loss: 0.012133451458066703\n",
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      "Epoch: 25, Batch: 180, Avg. Loss: 0.014009852800518275\n",
      "Epoch: 25, Batch: 190, Avg. Loss: 0.03163088541477919\n",
      "Epoch: 25, Batch: 200, Avg. Loss: 0.018422130774706603\n",
      "Epoch: 25, Batch: 210, Avg. Loss: 0.03474380280822516\n",
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      "Epoch: 25, Batch: 250, Avg. Loss: 0.012167111877352\n",
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      "Epoch: 26, Batch: 80, Avg. Loss: 0.016966959368437527\n",
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      "Epoch: 27, Batch: 10, Avg. Loss: 0.013742349576205015\n",
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      "Epoch: 27, Batch: 30, Avg. Loss: 0.013595561683177947\n",
      "Epoch: 27, Batch: 40, Avg. Loss: 0.020100289303809405\n",
      "Epoch: 27, Batch: 50, Avg. Loss: 0.01230810284614563\n",
      "Epoch: 27, Batch: 60, Avg. Loss: 0.012472592759877443\n",
      "Epoch: 27, Batch: 70, Avg. Loss: 0.012587922625243664\n",
      "Epoch: 27, Batch: 80, Avg. Loss: 0.01174256526865065\n",
      "Epoch: 27, Batch: 90, Avg. Loss: 0.014819418219849468\n",
      "Epoch: 27, Batch: 100, Avg. Loss: 0.009797955583781003\n",
      "Epoch: 27, Batch: 110, Avg. Loss: 0.011863832548260688\n",
      "Epoch: 27, Batch: 120, Avg. Loss: 0.010732853692024947\n",
      "Epoch: 27, Batch: 130, Avg. Loss: 0.012020571669563652\n",
      "Epoch: 27, Batch: 140, Avg. Loss: 0.029974327189847827\n",
      "Epoch: 27, Batch: 150, Avg. Loss: 0.015287349093705415\n",
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      "Epoch: 27, Batch: 200, Avg. Loss: 0.014300287421792745\n",
      "Epoch: 27, Batch: 210, Avg. Loss: 0.012941204058006407\n",
      "Epoch: 27, Batch: 220, Avg. Loss: 0.010405308101326227\n",
      "Epoch: 27, Batch: 230, Avg. Loss: 0.013490496715530754\n",
      "Epoch: 27, Batch: 240, Avg. Loss: 0.01306030978448689\n",
      "Epoch: 27, Batch: 250, Avg. Loss: 0.01160393124446273\n",
      "Epoch: 27, Batch: 260, Avg. Loss: 0.01494148699566722\n",
      "Epoch: 27, Batch: 270, Avg. Loss: 0.011601548362523317\n",
      "Epoch: 27, Batch: 280, Avg. Loss: 0.012212826358154415\n",
      "Epoch: 27, Batch: 290, Avg. Loss: 0.010811603488400578\n",
      "Epoch: 27, Batch: 300, Avg. Loss: 0.010183113580569624\n",
      "Epoch: 27, Batch: 310, Avg. Loss: 0.010968312062323094\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 27, Batch: 320, Avg. Loss: 0.010788695374503731\n",
      "Epoch: 27, Batch: 330, Avg. Loss: 0.012909097271040082\n",
      "Epoch: 27, Batch: 340, Avg. Loss: 0.015333420829847455\n",
      "Epoch: 28, Batch: 10, Avg. Loss: 0.011591365421190858\n",
      "Epoch: 28, Batch: 20, Avg. Loss: 0.013117322092875838\n",
      "Epoch: 28, Batch: 30, Avg. Loss: 0.011265277769416571\n",
      "Epoch: 28, Batch: 40, Avg. Loss: 0.010622757021337748\n",
      "Epoch: 28, Batch: 50, Avg. Loss: 0.014835501089692116\n",
      "Epoch: 28, Batch: 60, Avg. Loss: 0.01662674602121115\n",
      "Epoch: 28, Batch: 70, Avg. Loss: 0.013802420580759645\n",
      "Epoch: 28, Batch: 80, Avg. Loss: 0.022276663361117242\n",
      "Epoch: 28, Batch: 90, Avg. Loss: 0.013607721729204058\n",
      "Epoch: 28, Batch: 100, Avg. Loss: 0.013495424482971429\n",
      "Epoch: 28, Batch: 110, Avg. Loss: 0.011897352151572704\n",
      "Epoch: 28, Batch: 120, Avg. Loss: 0.025646131485700607\n",
      "Epoch: 28, Batch: 130, Avg. Loss: 0.04404769949615002\n",
      "Epoch: 28, Batch: 140, Avg. Loss: 0.015433237515389919\n",
      "Epoch: 28, Batch: 150, Avg. Loss: 0.011000270862132311\n",
      "Epoch: 28, Batch: 160, Avg. Loss: 0.009951904881745577\n",
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      "Epoch: 28, Batch: 190, Avg. Loss: 0.014125766791403293\n",
      "Epoch: 28, Batch: 200, Avg. Loss: 0.009789237286895514\n",
      "Epoch: 28, Batch: 210, Avg. Loss: 0.014495594240725041\n",
      "Epoch: 28, Batch: 220, Avg. Loss: 0.01603715745732188\n",
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      "Epoch: 28, Batch: 240, Avg. Loss: 0.02271386575885117\n",
      "Epoch: 28, Batch: 250, Avg. Loss: 0.012891196133568882\n",
      "Epoch: 28, Batch: 260, Avg. Loss: 0.011511621065437794\n",
      "Epoch: 28, Batch: 270, Avg. Loss: 0.00975631969049573\n",
      "Epoch: 28, Batch: 280, Avg. Loss: 0.01094853999093175\n",
      "Epoch: 28, Batch: 290, Avg. Loss: 0.015931684337556363\n",
      "Epoch: 28, Batch: 300, Avg. Loss: 0.010385819943621755\n",
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      "Epoch: 29, Batch: 10, Avg. Loss: 0.0125261090695858\n",
      "Epoch: 29, Batch: 20, Avg. Loss: 0.011844777315855027\n",
      "Epoch: 29, Batch: 30, Avg. Loss: 0.01941113267093897\n",
      "Epoch: 29, Batch: 40, Avg. Loss: 0.012048093043267727\n",
      "Epoch: 29, Batch: 50, Avg. Loss: 0.010902543505653738\n",
      "Epoch: 29, Batch: 60, Avg. Loss: 0.012745973095297814\n",
      "Epoch: 29, Batch: 70, Avg. Loss: 0.01152296713553369\n",
      "Epoch: 29, Batch: 80, Avg. Loss: 0.013134392257779837\n",
      "Epoch: 29, Batch: 90, Avg. Loss: 0.01094507877714932\n",
      "Epoch: 29, Batch: 100, Avg. Loss: 0.012889210972934962\n",
      "Epoch: 29, Batch: 110, Avg. Loss: 0.00952569516375661\n",
      "Epoch: 29, Batch: 120, Avg. Loss: 0.011758604878559708\n",
      "Epoch: 29, Batch: 130, Avg. Loss: 0.01360386237502098\n",
      "Epoch: 29, Batch: 140, Avg. Loss: 0.010780575918033719\n",
      "Epoch: 29, Batch: 150, Avg. Loss: 0.012359806476160884\n",
      "Epoch: 29, Batch: 160, Avg. Loss: 0.012802487891167402\n",
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      "Epoch: 29, Batch: 200, Avg. Loss: 0.013130513112992048\n",
      "Epoch: 29, Batch: 210, Avg. Loss: 0.01599925132468343\n",
      "Epoch: 29, Batch: 220, Avg. Loss: 0.03267076527699828\n",
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      "Epoch: 29, Batch: 240, Avg. Loss: 0.017500661313533783\n",
      "Epoch: 29, Batch: 250, Avg. Loss: 0.01360330507159233\n",
      "Epoch: 29, Batch: 260, Avg. Loss: 0.010722968634217978\n",
      "Epoch: 29, Batch: 270, Avg. Loss: 0.010068024368956685\n",
      "Epoch: 29, Batch: 280, Avg. Loss: 0.013218637788668275\n",
      "Epoch: 29, Batch: 290, Avg. Loss: 0.022023058542981745\n",
      "Epoch: 29, Batch: 300, Avg. Loss: 0.011159136844798922\n",
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      "Epoch: 30, Batch: 10, Avg. Loss: 0.021176752680912615\n",
      "Epoch: 30, Batch: 20, Avg. Loss: 0.014311881037428975\n",
      "Epoch: 30, Batch: 30, Avg. Loss: 0.016426942078396678\n",
      "Epoch: 30, Batch: 40, Avg. Loss: 0.0302078808657825\n",
      "Epoch: 30, Batch: 50, Avg. Loss: 0.01036833254620433\n",
      "Epoch: 30, Batch: 60, Avg. Loss: 0.014664984680712223\n",
      "Epoch: 30, Batch: 70, Avg. Loss: 0.014202492590993644\n",
      "Epoch: 30, Batch: 80, Avg. Loss: 0.015523996483534575\n",
      "Epoch: 30, Batch: 90, Avg. Loss: 0.011248259898275137\n",
      "Epoch: 30, Batch: 100, Avg. Loss: 0.013510975521057844\n",
      "Epoch: 30, Batch: 110, Avg. Loss: 0.012044003093615174\n",
      "Epoch: 30, Batch: 120, Avg. Loss: 0.011415916169062257\n",
      "Epoch: 30, Batch: 130, Avg. Loss: 0.015905276453122497\n",
      "Epoch: 30, Batch: 140, Avg. Loss: 0.013547984790056944\n",
      "Epoch: 30, Batch: 150, Avg. Loss: 0.012125829281285406\n",
      "Epoch: 30, Batch: 160, Avg. Loss: 0.01061125285923481\n",
      "Epoch: 30, Batch: 170, Avg. Loss: 0.01074767904356122\n",
      "Epoch: 30, Batch: 180, Avg. Loss: 0.01042861002497375\n",
      "Epoch: 30, Batch: 190, Avg. Loss: 0.008771017426624894\n",
      "Epoch: 30, Batch: 200, Avg. Loss: 0.01063639484345913\n",
      "Epoch: 30, Batch: 210, Avg. Loss: 0.023191319592297078\n",
      "Epoch: 30, Batch: 220, Avg. Loss: 0.013028311729431152\n",
      "Epoch: 30, Batch: 230, Avg. Loss: 0.010660801269114017\n",
      "Epoch: 30, Batch: 240, Avg. Loss: 0.02934840340167284\n",
      "Epoch: 30, Batch: 250, Avg. Loss: 0.01803675964474678\n",
      "Epoch: 30, Batch: 260, Avg. Loss: 0.013041939958930016\n",
      "Epoch: 30, Batch: 270, Avg. Loss: 0.016508981818333268\n",
      "Epoch: 30, Batch: 280, Avg. Loss: 0.012755567347630859\n",
      "Epoch: 30, Batch: 290, Avg. Loss: 0.013186202570796012\n",
      "Epoch: 30, Batch: 300, Avg. Loss: 0.012644269224256276\n",
      "Epoch: 30, Batch: 310, Avg. Loss: 0.013182368967682124\n",
      "Epoch: 30, Batch: 320, Avg. Loss: 0.014451029803603887\n",
      "Epoch: 30, Batch: 330, Avg. Loss: 0.015971034299582243\n",
      "Epoch: 30, Batch: 340, Avg. Loss: 0.010693665081635117\n",
      "Finished Training\n"
     ]
    }
   ],
   "source": [
    "# train your network\n",
    "n_epochs = 30 # 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",
    "from workspace_utils import active_session\n",
    "\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": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Un-comment the following code to load saved torch model file\n",
    "\n",
    "# model_dir = 'saved_models/'\n",
    "# model_name = 'keypoints_model_1.pt'\n",
    "# filepath= model_dir+model_name\n",
    "# net.load_state_dict(torch.load(filepath))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13259c08320>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13219dea668>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1321630c710>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1321634fc50>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13217152da0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3fV+VSOigZNhHtOlIMb2/isRIxmVYYTRWUqlUmJubU9JEYh460l0fH0AYhez19w5ESseVwupR5rgnJKH9arWqmLterwMoOMM4w1XHBsfjUK+mbudhNBGMEtf7AlGUhBqgvBO99EEqCcXTkUXSPR25v5CewhcGyufzFAoFYBhLEYNSXFkMqHk19Ry36/Le597LD/+vH+a9z70Xt+ey3d1Wdo8uYfRni+ure0EizaamprBtm1arRb1ex/d9lXSUe+hFZbJRZJM8TB39fxHCF3UglXmtVoudnR3e8IY3kMlklFoR3IjOGOLF6PfSQUeiWsQgFaNRDNZkMsnMzIyKj2Sz2aFnk7BpBk1293b5xM1PcKN3gzPmGd5lv4v7nftcsa8QGiGXmpf40T/6UdZY4wnnCT765EfJpDNqXLrnMi5+EYYhMzMzKkko71mv1ymVSsqA1v/FPZl49DqeVH0taCIYRRetshvL5TK2bY94OCJBxIgVI1Ouk/yK7DaddL2uo/Kr1SqlUolEIqGYL5FM8JvGb7LaWmWps8RquEpIyC3vFjO/NcPt77rNWeMsN+ZucGrjFC/PvUxohdx0b/LitRd56vGncA2XufzcCA5YsDC6KtK9MckNGYahGFmvC9LVpq6CdRvlm+X1TASjACO7XnZ3u92m3W7j+76Kb4iolYnUGUh3g4X59L/pageGE760tDTS9QCga3S5F90jNEJW/VWezD7JtfY1Lq5eZPn2Mr0f6vG2T76NZrpJoVPg773r73F56TIXVy/SbDT5QO8DrEQrnM+f55+d+2fwyuY3DEONsdVoYTUtUtkUJbNE9ETEna/e4XLnMplsBtd16fV6KiwgEkfPTgtjxL25b4ZXNBGMohtj8rLdbldBGaU2R5cSopfFiNWztnHRK5OqA5iTyaQCHenQxkQiwXQ4zcWVi8PFv3eRj3//x2n9UovyV8sE5wLMZZPOyQ6llRJ783t88EsfxGpa5Ad5Wk6LT7/h0wRmwNXWVWqDGuVEWY0lnU7T7XRZ+uQS6dtp9sp7TNWmmE3NsmQtcTtxmy+Wvshd466CQ+gqJx5x1aVx/OdrSRPBKLCvvyUh1ul0aDQaACogJR6OiHAhyf2IrhaPJ47q0sW9eDupVArXdZXItyyL1EyKX/iDX6C32aOwVKD+A3X8n/TZqG2w0d1g7eYaL/yFF7DbNj2nh+M4TLen+e4vfDdTrakhk52+zPnceapOVZWFSBZ79+Yu33L7W3j/O9/P5aXLXFi9AMDXT30dgKfuPsX7/uP7+GV+eQS0HcfEjvOu9DnRXfGHBQMfRhPBKPrLCrhIEnkSnILhy6dSKbrdLp1Oh2q1qqSAZGgFySa/i3Eon4uKE0S8nn9JpVJks1ly+Rzr71+nv9XnKlfh+nBcEgVOp9M4aYed3A5mx2Sru0V6Jk37VJv83Tz//I//ObUfrjGbm8U0Tba2ttjd3aVer/P8889Tr9VJzaa4vHSZwAq4vHSZiAhemYZLpy7xs3/nZ7HXbQq/VyBhD910PTMu0jNu2MZJpO2jgqnjNBGMIlwu0VZBo8G+XhcdbZrDTgONRkOBnsXgk0kTcJKyUfyQrJelYTQUak3KLvT+J6VSCcdxht6H12ezv0mj0VCueCaTwfd8evd7fL76ebaL2yTzSTzTYyFYYPHvLLKQWSA9n2auMKea80gep9VqDSOyyQT/5S3/hUK9QK1cw9lyiKyI7nRXzUlgBUTzEZETEQ72gdu68Sq/x2ug4nOr06tVSxPBKEKdTod6va4a+urWvkiIWq2mAlciBcQ7kqo/HVIQ+iHvvvxuTrdP83LuZT574bMqxS8BLumRIrEUeZ7ruioC3G118UKPdzz/DjzbY+c9O2DAwByAAWvWGolygnQ1PYKlNQwDz/fYaG3Q3GtimqaKAH/f9veRClKcKJ2g1+vx7B89y+1rt5n987NcXbrKhdUL9Lt9GmZDMYUe4DsMh6LTwyTOUWkiGKXf79NsNtncHO5gHYsqu9nzPOr1uhLjtm0zNTVFOp2m3++zvr6uII06BjUf5jndPo2Fxen2aXL+EO8aBMMu0WEYkk4PF7dYLB7I1fi+z6XyJW6cv8HZ+2f5xMInuLx0mcwgg5t0cQYOXadLRMSv7f0anzr9KaJwH1BlWRYfvfdRrkfXmS5N84PhDxL4AbZlkxlkiMyIvJdn4bEFfjf/u2x/2zbFtSJf+MQX2PF2+FD+QwT+fn2PbBLdVrEsayTAeBjpbjo8GhxyIhil3W6zubmpMr/ASKBMcCRiJ4gkcRxH5X4kyKaDmgH2jD1ezr3M6fZpVgorWNVh3kWkjyDLcrmcgjCIupiZmaFdb3PjxA0CK+Da7LUhVMAM6CV7fPozn+ZXf+hXubp4FQy40rzCnrtHKVUaGt+GycZPbXDjL9wgtEK2nW0WZxeZqk5x6tOnSN9Js1PYYboxzfq5dTbevgEG3Fy8yS8+8YtsbG/AK+sfz+uMC+mLPRIHUcHBPNDrEjPbarWo1WojLyE7R7fsdRRauVxmMBhQq9VU8k8Ft8KAMB0S9SNSyRSfvfBZihSxqza5dG4ESplOp1WPEwEriQeVzWYp5Auc//p5rlav8uT2k1h9i8uLw5jJrDvL9fnrQyM0gnNr50jX0vjVV7C7bSj8cYGL54au9rmtc1z8wkWcqkN3u8vPvPNnVPzlFz/3izy++Tg3Zm5grVus76wDjOSG9DnRwdy6/aJDQ2XOYL/Xvu71PEq8xfhmZBoflebm5iJJhMFwd5TLZQUXkMmREH+1WmV6eprt7W1qtRr9fl/FQwzTYOV7VuhOd8nsZjj35XNk0hkqlcpIOF2uOXPmDI899hjValVV/aVSqZEgljfwqO/WCaKA/IfzdNe7mBUTy7T4R9/9j4ZMsH6OTz77SfyP+piWqZjOe69H8PWAzfObfOxPfYxLS5dID9K4SZfIjBSTnV89z/WF61ibFva/tUlYCWXLyCLH4QZCoiIltSH2i55X0ovidcO32WweybqdCIkiEEchYYpx+E5J2NVqNWq1mooxSAKvZ/WG3oMJ7pSLXbTJJ/NMW9M0zAZe5Kmoru41CRRA4JAywXLvysyw14n9yzbhRkjdqGNbNh8efJheuUfpfAnzbSZJI6nG73ke5qdMoq2Izv0Ol7cuE5ohHaejGETo6sJVsCA4EVBxKrS77QM5onFlKkctO/1GaSIYRe80AMPEXKlUUpX+YqhJkm9vbw/TNGm1WuRyOZX/CYIA3/VJb6fpTncpNUuUE2X+xlf/Bid2TrBZ3eQ/fPt/UIm2SqWiUvmCYIsXqwsJug4gMZtgxpjBNE1ldEeVg7GKVCpFGIQMPjzg8SuP8+SPPcmVE1dID9L07B7OwMF1Xuls8ArjOJ7Db+z+BteN6/xj6x+P5PcF0a97PzJver5HfsarFL4RD2giGCU+eEn1xxNqIj6lzleyrVLuEEURtmVz9stnsfIWp2dOU8wUmd2ZxYxMZndmSbkpjKmhaBYgs+M4ZDIZBWrW9X48N6R7VLI4Am+EfWmo3qsO5lUTIzT41L/8FC/+wos0afLcnzzH1RtXefmHX95nBgO8hEcn0+F85zw5P0fDaqhn60BuvevBOKkiUdwH2SGvS8ysnj2Wkk3f95VHA4xEanUIpEyU1NvkcjnSlaGR2vN6rJXWWKgtsFpYxU25FO0ixWKRcrmsvCqJx+gAJ1FnsoPHQSxlXLq3Jjvf8zz8io/xlEF0KcI74+HkHH6l/yvcfeNdKmcqZHYzdKe6mIFJYAacXj1NvpPnqnmVulEfSSjqtclil+gMqv/TpYk+v6+WJoJR9JewbZtqtUomk6HT6WDbtgrDi/srDCLoL9M0FUg5mUry5bNfpp6vU2gUOP2Hp3lx5kWmF6dJz6WZLk5TKBQUo5TLZQUz0Cc5HtiSMLieN4Lx+Fs9p2RZFvwK9K/38ad82qtt7kZ3CY2Qvcweb2u8jQVzgdpGjd/777/HemOdH8n8CA2jQUSEwf7c6PaaPs7DirnisEhdZY1DzT2IJoJRdMpkMmSzWRU4k6JzfTJk8RKJBOl0mmq1yvz8/LDYPGpTz9eJzIhGscH99n2srkXuRI555kk7aeVi6zEaHUo4rqRB9xTixWcyJtiXenKNZVr4P+HDn0DuqRzOOx2WrWVuB7dZtpd5cuFJTNMkOZOk+b1NetM9NtY3SP+7NESjLVPj6Lm4oT+uriceV9Ejuo9CE8MokuQqFApkMhm2t7dH8CPxBUmlUkxPT1MsFimVSvT7fer1Ou1Om2QpSX+mj7Vu0dvtUcwV+YmXf4L5r86zO7/L83/teTVZ8R2p2yT6JMvu1FuHwX7r0XGqyXEc/G2f4E8CCIBL0Nvu4ad8DAxMyySZSBIGIV7CozfTAxPC+RAyQGf/XvJs2UC6anlQ0k8P0sl3X7fZY0B1jT5x4gT5fJ69vT06nY6aANkNMzMzTE9PMzU1RT6fV23IV1dXVS+1xLUEyVwSszv0SpYry8zfm8eMTKY2pigbZVKZfZtETuE4LBUvqk4YVg9e6b3m9EMjL126RKvVwht4nDtzjuT1JJ1THf7nrf/J6vlVQiPk1uAWftEnE2bIB3nyu3k6pQ4XVy/yozs/ys84P0NkjJ4NEAcsCSM8yJuJF5GNk5IPo4lgFHFL+/0+J0+eHOkZIpNi2zblclmh5ev1Oi+99JLKFEudDLwiFXoBEcNdbldt9ub3qGxUaJ1sQWn/mYKmg9Hopy7FZHH02ptxXRPu37/PysoK9Uad//HH/4P2/Tbzc/NU7Arzxjxu16WUKHHKOsXd8C6PJR6jaBUx7KGB/q1/9K2859p7qHaqBAQUKbIX7o31TvRgmjxfl4w6Sl+3aR6WbT6MJoJRLMsinU7TarUolUpsb2/TaDRU+aZEaiuVisoLNZvNkUCdnHohZNs2tXqN3GyOqekpvvqnv0ohLJCYTpB1sip2IoZnHDkmTBHfqToOREhhX02bfr3Ppxqf4uapmyycXOC2fZvPLXxOhelP5k/yZ6t/lsAJsHv7B1E6jkPP6bHj7VChMvR6qPMopKvKOMjpG6WJYJQwHJ4WIRnctbU1tra2eOKJJ5RaMAyDVqul/sE+RLBSqbC4uEg6nVa7Z25+ji+Wv8hKboVdb5eni0+TSe+fW6y3rxBm0e0NGde4qOi4HW5EBvkP5fFf9rn1d28RWiFr0RqEEFohl5YusVXYgrnh9T2vB/4wlSDtNhKpBD+39HMsWUtcvneZMDrYcFiYVHflD/N4DqtmfDU0EYwihqKUdMoRKLr9AMNJSqfTCmkmCbxqtUqlUqHb7eJ5HtPT0xROFKgla0RGxP3kffykTyFXwGyZhKl9A09PlBnGwQY944zEOFbVNE2CRgBXoBJUuLh68QDEMTRD3vPj78ENXZz7Dv2oz2P2Y7yTdw7tnDDghTe+QOM7GzR3m0S/EY2E+ONzFTe0ZVw607yWebyJYBQR9dIiXA5JMk1TdTxKp9MqXC4lntK3NZfLKXvGdV3K5TKL5UUqWxX2snuc8E6Qi3LMfmwW56aDd86j9wu9kVIIPQQ+zgZ52Pj9nI910cK4ZPDJf/9JamaNcqvMXm6Pt//U2wmsQOV4utFQZd72b9OgQdWustfdo1VogQndSnfYuHh3GN4XgzUePIvHScaRzlDfCE0Eo0RRRCaTYXl5ma985SusrKxgmsM2nd1ul8XFRWZnZ1XHAhi12KVLUa83BDo//fTT9Pt93vK1txCkAp4++zRls4xzy8EIDRLXE1i+hZ3er7jTdbsuZfS2WTomRBoQS6S41++R/FiSZDcJOchuZwn+aUDpaoknNp7gpbmXcHyHXqqHg8OAAcv2Mlk/O6wK2EnyVO0pBTsolos8t/OcGpPeukvwMjpzx4OEusTT/zYu5nIUmghGEVR8FEXK/tB7xOr2RLzKTgzJZrNJv99XYKZOp0On09mv75my8M/52NdsjKcMMguZkVJNHYIg9xZPR74zbnJ93ycMQoyagV/xIQuhF+J2XJrPNPn59s9zK7oFBkRWhInJUnKJHyv/GCkvRb1eJ4oiVpur/Pjv/DhVq8qOv8Nnzn5GMYU8RyodD7NNXivY4ziaCEaRAqx6va5gjvl8XkVkU6nU2LiGtAY4aK7uAAATUElEQVQdDAaqqD2fz3P37l22d7a5+q1Xqefr3AnucMG/gPvzLsWoSP5U/kA2NQ74Ee8mlUqprLWApKXbAEDgBfBTkLmcwX3M5YW//gLn/815ineL1Ko1bv34LXjF+ehHfTDg1uAWYbCfFW/UG2xf3+af5P4JhNC3++RreZXGECnX7/dHVKUwUbzx4DeDJoJR8vm8SgCGYahadUrfV/FmhKR2xzAMVavsuq6Kgdy7d49m2KReqBMZEevGOnWvTikqEZZCvIGH2TSxqvuusb4AusiWe8r/B70B7ppLvVVn1VmlsFXgTZfeBBF88Ns/yGX/MhfeegEDg0tLlzAxlfeS7qXpp/qcNE/S7/exBhZREPHm33wz37PxPVx+12VuLt7E2rBIfjGpEqMSBtAL2A4DMI0rpX0tpMxEMMrU1BT9fl/ZA1IyIUE0yezCaNVcEASqI5PYLgCNRoN2p83MmRm2UltM96cx3VdOqwgi+n+7T/T1CPONJunPpLETo9Ogp+117ycKI6yftsi/lOdvfuBv0nE62KHNv5r6V2TczEidjoFBaIVDz8UAMzBZ3lnm2vw1NqNNPtT6ECc5ybvq72JqY4p6ps6dxTtD8NJ8QN/ukzWz5PN5FbqH0fpjGJWEel+V+Ph1et2G8EUEi80htkgQBKpYPZ1Oq74pYj9Iu3JxpweDAVevXlWVhd9+89upD+oU7AJuYfj3YC/A+roFAYRfC4lqEcasodSLhOV1ySIT66672Nds7lTvKA/GN32+9INfonGlwfnV86rMQiRKepCml+zxxNoTXFu4RmAFBNFQZaxGq9y373M7c5tTnVOcXj3NraVbWOsW/b0+bsGlWq0ShiE7OzsHGCAOIxB1qXtJEuaXd9LTE6+77LFeXwyoInTpPR833sSFbbfbikGkM6RAE3K5HJZpkSWLwf4ZPb1yD/uCjXHFwHjKICjstyMHRpgjjhqzqhbeBY9Tl06R7WXpOB0yXoZ3f+7dDE4PsJ+1aWaalDqlYU+VTI2SW6KeqVPsFFUxuyDb5sN5ok7ER0ofAWDv3++xuLhIv9anbbRVp229UxPsG9/AgShxfF5frZcTp4lgFN0OkCpBgRHoATeRJOIKt1othb7Xd8pgMFDheblvs9XkXv3eEOr4EUj1hjkgcTWFRKroQS25dyKRgF+B5p0mv9r4VVZ6K3zbB78NQnC3XGYuzmBftfEzPnWjTr6fJyTENVyibMTHP/dxVqurFLNFvvSOL1HoF6hv1nG7Lp7tEaUikn4SktCzewowrR9YJfOge3xwMDAYD8bJdfK316V7LFBEYZLBYKA6HslP3fL3PE+VZ+p9aHVxrLvSQRjwefvz7NR3ONM9w4cf+zBROsLqWUO3NdrvDKlnisd5RolkguLjRQpRgZn2DN5Zj3/wZ/6Bin/8UvhL/PSf+2kulS9xYe0CURhx6dQlADK9DN10lxQp+lGfczvn+PR//DRvMt/E+9/1foL5gLsbd5n5TzMUCgXq9TqtVksdcrm1tQXsG616s55xpMMPDgM3HZUmglGmpqYA1NEoYi+USiUymYzCxcqL63UpIl1gfxfp9TlRFDEwB2w720RGxM3BTTZbm8yGwwCebdpQA6NsHNhpeomELmXkMyftsPZP17j8tcsEDI3Yu//5LpfKlwisgCsLV0aKzwVI3aMHBtyYvUEr02KOOYL5YGjIzgV4SY9KsqK6OUgsSfI744zt+O/6Z+OM10d1pyeih5u4v61WSzWaKRQKyuIX0gFFOtwwrodlt0kgL2/mmepMYUQGC+ECUWvYA79Zb2L9fQvz7Sb8FPjefmfKOMxQfuodBCzLYiY3w/nceazQGkZUnSIpPwURpLzUMN8TobwfYAhvjODMxhnynTybvU2s9aGBba6ZePVhSYn0b5P2qcViUZWS6FJOIsv64seTmLpb/WpiLhMhUQxjeKjS/fv3MYxhu6yFhQUMw1AekSygnuYXZhgHR3Rdl0qlMnxAH9569610zS6VVIX2mTb9QZ9oEHHqpVMYoQFXINwN4QQHpIheDQD7xrR8/pHHP8K93XvUWjX+21/+b3ST3WFOJ9Vl+8T2wReO4B333sFLz7/E3yr8LXaDXVK/kyJ0QqJOxMAYdmtYXl5WtdZTU1OUSiUV8NMbC+kqV0fexWGPMld6yuKoNBESRU70lKzw9PS0Kp0QT6bdbqvj7mFoT+itu0R6iP6WDkqqt71lkxwk8QYejWaDf9H9F/xs+LP85Ht+ksAMGDwxwE0NbaR+t09vvUe/1x8xagVfq1MURYRBSM7M0XN6FOwCi9EiZmRyIjjB/dT9oSTRpMn5zfM886+f4d1X381OaoeQcFjY7hoYGKozgwQfAfWe+XxenRckz4+78bqKlk30jcAgYUIkyvXr19nc3ARgZmaGcrmsdu1gMFB2C6AOF7Asi3q9ruIqklGWyZEJ1Nufyz223W3uZe8REnJl9gpf+chXKFaK5Bo50qk0pY+UMK+aRBcivF/2MK39sL5OOujJcRxmZmbo9/s803uGulcnQ4bfCn+Le+Y9AE4bp/krzb/C9//692Ng8Jm/+hk6Sx3MNZPEFxLDIJ1mnOptQdvttjrXUGwUOWNIpKyMTy9ce61oIhhlbW0NgFOnTnHhwgUl5ldWVuj3+yPHqEkHapE0+skYEnKXjC4MkW+ym6SxTSFZYCFaYI01lowlBuGAvdoebtcl5+WoXKkodeSuu9jT9kj5ha6axND0fZ/Z2VnlmRUaBVzX5ZngGWr9GpZtUTSKdBtdNsobOD2Hy0uXwYJwISRwAozOqGSo1WqUy2WmpqbY2dmh1WqpblGC29E3hs5k8dpjUTfjwvxHoYlgFIEZLCwsqFLRTqejTvQUi1+vMRYgNYyKVf07esmntLSAoTv+I+aP4OKSI6cmu9/v41s+axfWmL8yT//xPvWoTqo5jMmkEinsjk3qxBBtr9tKmUyGbrerdrjkZ1zXpWAWCL2QN/72GyndK/Fy9mU+4H+A6F4Ei2DcMw4g7oXZJaUB+xJEumNKiw5d8sTtNT0W9I3QRDBKKpXi7Nmz5HI51tfXlb2i9z3JZDJq4iQIJbsKoNlsqiCbMESv1yOKItWuXG9kDJAnD+wbpxERn/M+x92/fJfHeIz3ld9HupOm1+9hRAZzH5/Dum7hP+UT/VJEZOwfHhU/OErUoJN0oDb0qEprJczI5FT7FL7tY33eIspEikkiRkFGkuORoKMcFiEY21QqpY6bAw54NTpCX3Jjeob8UWgiGEU6KjUaDTY3N1X+RpBmEt7XOyOq5sFaz3txa8W2cV0Xz/PURItXMG6HGYZBh86wio+Q29ymNqgBrzQCdFMkrycxQoPocsTenT2Myv6ien0Pq20R5APCaAh46rk9zv3aOfIreXZP7LI1vUX1fpXL5mX2MnvggtF55QgX9luRyUJLkyBpGSa11vJOepmGbrTGsTO6mny1NBGM4nkeGxsbSg/rO1TEuqgfx3FwXZdOp6OAybJLdCYQdJqeK9HzRnp9jDzH9m1O+idZiVY4bZ4mE+2rk07YoXmySXYly+b5Tdp+m1RzCNK2DIvyz5VJXk8yODtg6x9uDbtQty3yK3nMcFhP9LE/9TG+OvgqO39pBxaBVbA+fzhS3vd91WxQOlDpbrouLXRJpFcQxN3gV4tbmRhGkX5q8eRcNpvl8ccfV8fUSxtOOfVLDEk9/yEiW440kTJPsVMkSBfPwIZhyPuC9+Hikg6H9T5RFCmj8X+//X/z2+Zvs5ZYY2lriWfMZ7Atm0Q7wbdc/xaM0CB5PUl/q4/bd6l36yxMLzC9Nc215DX+6xv+K+EPhMOghAEsQeAEmO7BhsO659PpdFQJbBiGylbRDWkZq955AfYxLLKJXq1UmQhGEXUBHNgZ1WqVQqFAFEV0Oh1u377Nzs6OAlULPDDuVopxGt9VhmEoxJz8TS9CT5IkQ2bk4AUZYytscS91j8iIWIlWuH3/NjlyGBicPnGa8maZe0/cY6O7QbfXxQ98fufP/A53v3aX69F1woVwiHaLGJaYroLhHr7DdckYV7OieuPFanGJ8f9VN4O49wLDF8vlcuRyOdrttjp77/79+8DQcymXy3iepww86W4gjBMEw07RxWJR7VbJxOpdlYRJxJ0U5hHvScaY8BKcsE6wmdyk2q3S2mxR84Z2zBe+9Qt8+fEvs5XbYsad4a1330qz0WTz/iZf//NfH+ZyJEm9Cvzu0D4xGN3lwry6Idrr9RSYW0hPbYiNJ3M4rtJRZ5g40PooNBGMEidZwLm5OTzP44UXXlAHUBqGQTabVYAewWzoky0SRULfYqeI6BWbRf7pkUyZaPlMPg+CAMu0+KHaD9HwGgStALe33zi5Z/XYym4N64hS9/nDr/whYTOkTZvgLwYKN2t+1iS6P9rOIu6dxJlF5kM+E3XoeZ5iCj3YGJcwevXAuOrHo9BEMYouWWzbVu3OxX4xDIN8Ps/MzAypVIr79++PHBenXx8EAfV6feTsYD3PoSPAdBihnp2VGI4wTDKZxPEdAj+gbbZVqiAIhnXO6d007pSLs+XgbruEwRAKaawZRAsRxj0DY/tgOwoZ+zjJAqPJUAFgiXGr21cPyh7LtTI/D4InjKOJYpQ4ra6ujohHy7LUWcESv4gnuKIoUpMowGRdFekegw78EYkj1YnihotrLnaUuOGSSxLPCmD5D5cpzhXJmTleTL04zPpikPxCctjOtP2KK8u+S/sw20HGIQsu7d6l6aHYUmKPybuJAS+bQscUy5y+7lSPzuEiTSQ4Jv+fm5vjxIkT+L4/7IPyyhElqnfbKyI4HpXd2dmh0Wiowyv1LpLyuy7udc9DbBpJD0i6P5FIUCqVANQZy71ej1KpxHd923exu7vLc3/8nHo/A0OF56NxdaKHkK6KdGkh5wIUi8MWY4AqJdE9R6HD4JCvO8yshMFN01Tuq3SoTiaTnDt3jqmpKYIgYH19nWazqRZMPAKBP4pUkAmW/inT09MqOSiYl7g3JDaAMKh4HGLQSr2zqCo5hhaGLVCr1SrlcpkbN24cUAXj1ELcFR73d1216Mg2kRq+PzwJrVarjUg7/Zk64+tR20cpm50YRpGSDH33Apw9e5Y3v/nNtNttrl+/TrvdVi8chwLqyTER151OR52dLE1uJAWgJ/nGgavjdoIsnJ7iF4kkjX329vbY3j6IQYlHg8ft5nGfyfuJfaHDNeWafD7PW97yFjUm2QhxLI0c7Sv0KFnmiWCUTCbD3NzcyMBN0+TUqVMsLS2xsrLC3bt32dzcVDha3ZXWbYh4UMmyLIVbmZmZOdApKd4KVNffenRXvq97RboHJbbD6uoqm5ubR9b/ulcDo1JG3wQyVlHPMjbZZBcuXOBNb3oT1Wp1pH2YDmTSVZhunx2FJoJRpEPj9vY2ruty5swZFhYWVGj/+eefVy+czWaVpxJ3BfWEXzylHhfZMtH6+YM6IFsYT5hXd5WjKFLxGNm5UTSsm5ZDuB9ED0v1y3jirrHuHgvJOCWvJQVxwtByHIx4gmKQP2pycCIYRVp/rqyscP78ec6cOYPjOPz+7/++8nxMc/9EDWEScRV1SSRqR/5JJ+pms4nv+ySTSVzXVYutu8gw2v5CFkXPEQHqxDFJ0knyrtvtqvOCjoL7iC+Uzhxx1zeTyahj7eQ7gn0xDIN79+6pjt5yaoiEF/S4jOM4KjBp2zbf+Z3feaQ1mghGkSNWpqen6XQ6vPzyy6ytrXH37t2RfIwwi96WIh6VjC+oHlzTQ94SF5EKxLj00ZNqIjnErpH7yQnvrVZL3TOuRnSGk89gFJerS0EdcCTnCklgTVSsLDqg3n11dVUx6UsvvaSglDraT38fGc+v//qvH2mNJgIzC6jOAclkkk6no3AWohL0ztLiAscb3kjEUg7UhtGiJx3zqkdc4z/133XS7R/B4sqiScsvSSnI92FUcoxzweOkS0/Bx+pnPEv8SArE5OhbMa5LpdIBJtGffdhzH0QTIVFEz+q4FF3P5vN55frK9+VnfDHlGkAFpgDV8aBUKqnJ6/f7uK6rAE9yf/1IE9HruhcksRXpZNnv98lmszQaDbWr44ujSxJ5nmBu4mMXu0JwwNIfRv4uklGkhlwLqPfVvZ64ax433I9CE8EosjAiWgW4JBMjEAM9ZK7r8sPgf0LynThkUCKdIvrlb8IkcaNRj3TGjUvLslhdXaXRaIyMSR+DkHgqMFofLH8TCSqqRy/4KpeHZyhblkWj0cDzvAMZ9HF5nvjmirvrD6OJYJR4VFGSeNJvVmCQujrQYwQwKkp1rIZ4NbprqC+MhOIlljJuYoERA1l2vTCKqLrbt2+r+8fTCvJMHSppGMaIZ6VfK336pURFDGhpepzL5bhz5w47Ozvq2Lt6vY5lWfR6vbF4FJ0eNTE4ESeAHdPk08QYs8c02XTMKMd0JDpmlGM6Eh0zyjEdiY4Z5ZiORMeMckxHomNGOaYj0TGjHNOR6JhRjulIdMwox3QkOmaUYzoSHTPKMR2JjhnlmI5Ex4xyTEeiY0Y5piPRMaMc05HomFGO6Uh0zCjHdCQ6ZpRjOhIdM8oxHYmOGeWYjkTHjHJMR6JjRjmmI9H/AcECCVZDn5ygAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x132163d01d0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x132164142b0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x132164577f0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13216492630>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x132164d6710>"
      ]
     },
     "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",
    "\n",
    "visualize_output(test_images, test_outputs, gt_pts)"
   ]
  },
  {
   "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": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "## TODO: change the name to something uniqe for each new model\n",
    "model_dir = 'saved_models/'\n",
    "model_name = 'keypoints_model_1.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**:<br>\n",
    "**1. For optimization: Adam optimizer was chosen.**<br>\n",
    "`Reasons:`<br>\n",
    "- For optimization, I chose Adam over SGD with Momentum. This is because Adam optimizer combines the advantages of two SGD extensions — Root Mean Square Propagation (RMSProp) and Adaptive Gradient Algorithm (AdaGrad), and computes individual adaptive learning rates for different parameters. \n",
    "- Learning rate was an important hyperparameter that required some tuning within the Adam optimizer. Adam optimizer seemed to have performed better for the best network architecture when implemented with an optimal learning rate of 0.0003. <br>\n",
    "\n",
    "**2. For loss function: SmoothL1Loss function was chosen.**<br>\n",
    "`Reasons:`\n",
    "- Experimented with MSELoss and SmoothL1Loss functions. Within PyTorch, omitting a scaling factor of beta, this loss is also known as the Huber loss. I built multiple models using the same. \n",
    "- The average loss computed after implementing SmoothL1Loss was lower than that of MSELoss.\n",
    "- SmoothL1Loss offers an added advantage as it is less sensitive to outliers than the MSEloss and in some cases prevents exploding gradients.\n",
    "- As this regression problem has a high number of dimensions, SmoothL1Loss works better on such a complex neural network. It performed better than the MSELoss function. \n",
    "- Hence, SmoothL1Loss was the preferred choice."
   ]
  },
  {
   "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**:<br>\n",
    "\n",
    "**Starting architecture:**`<br>\n",
    "- Started with a CNN architecture consisting of 4 Conv2D+ReLU+Maxpooling layers [32, 32, 64, 64]; limiting the maximum number of neurons in the fourth layer to 64. \n",
    "- Added two fully-connected (FC) layers in the end followed by an output layer to predict the  key points. \n",
    "- No regularization technique like dropouts or batch normalization was implemented for this initial network. \n",
    "- Performance was not upto the mark as the model was slightly overfitting.\n",
    "\n",
    "\n",
    "**Built multiple models of different configurations. Key decisions taken to avoid overfitting the data and choosing the final model architecture.**<br><br>\n",
    "Multiple iterations were performed. These involved\n",
    "- `Performing batch normalization (BN)` in between the convolutional layers. Improvement was observed but nothing significant.\n",
    "- `Performing BN` in between convolutional layers and `adding dropouts (p=0.5) after the FC layers`. Results were not remarkable.\n",
    "- `Changing the architecture`. Increased the number of neurons in each layer [32, 64, 128, 256]; limiting the maximum number of neurons in the fourth layer to 256. Model was clearly overfitting for the given configuration. A slightly simpler model performed much better. \n",
    "- `Implementation of initialization technique`. As we are using ReLU activations, hence I implemented 'He Initialization' to initialize model weights. Much stability in average loss over batches in the epoch was observed yet the results were not exceptional. Moreover, the default pytorch initialization worked better. Hence, eliminated initialization of weights in the final architecture. \n",
    "- `Experimenting with different batch sizes, learning rates (in optimizer) and loss functions in criterion`. Attempts were made to build models with MSELoss and SmoothL1Loss functions. SmoothL1Loss was chosen as it performed much better on this regression problem and Adam optimizer was put to use for training the final network. \n",
    "\n",
    "**Final network architecture chosen:**\n",
    "- The final CNN architecture consists of 4 (Conv2D+ReLU+Maxpooling layers) [32, 32, 64, 64].\n",
    "- Followed by two fully-connected (FC) layers in the end which are further followed by an output layer to predict the  key points.\n",
    "- Have included dropouts only after FC layers, eliminated batch normalization and limited the maximum number of neurons in the 4th layer to 64. This optimal model seems to better generalize the outputs (key points) predicted and gives results with improved accuracy.\n",
    "\n",
    "(The following code cell output explains the final network architecture.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final CNN architecture chosen:\n",
      "\n",
      " 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, 32, 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(32, 64, 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(64, 64, kernel_size=(1, 1), stride=(1, 1))\n",
      "  (pool4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (fc1): Linear(in_features=10816, out_features=4096, bias=True)\n",
      "  (fc1_dropout): Dropout(p=0.5, inplace=False)\n",
      "  (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
      "  (fc2_dropout): Dropout(p=0.5, inplace=False)\n",
      "  (fc3): Linear(in_features=1024, out_features=136, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "print(\"Final CNN architecture chosen:\\n\\n\", net)"
   ]
  },
  {
   "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**:<br>\n",
    "- The default `batch size of 10 worked well` for building the best model. \n",
    "- I did `experiment with the batch_size` and increased it to 20. However, for the current configuration the default size of 10 worked better. \n",
    "- There was a `trade-off` involved in this selection. I had to tweak other hyperparameters like learning rate in the optimizer, choice of optimizer, criterion, and the network was trained over a considerably larger number of epochs (in this case, 30). \n",
    "- The decision was based on experimentation and understanding of the relation between number of epochs, batch_size and other parameters that influence their working. `Higher learning rate caused drastic updates leading to divergent behavior` whereas `lower learning rates gave a considerably stable model yet many updates were required.` Based on the current batch_size, in order to attain convergence, an increased number of epochs were required.\n",
    "- `Hence, while training the best model, the learning rate in the optimizer was reduced to 0.0003, and the model was trained with a batch_size of 10 over 30 epochs`.  "
   ]
  },
  {
   "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": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.01505673 -0.19133131  0.02996238 -0.21378928 -0.18608022]\n",
      " [-0.14559066 -0.20854029 -0.19047242 -0.04640543 -0.09785476]\n",
      " [-0.09998132  0.10126282 -0.04931063  0.14606291  0.14605601]\n",
      " [ 0.17202024  0.1582928   0.15104967  0.22079813  0.02615002]\n",
      " [ 0.14382946  0.01594523 -0.12959418  0.07843349  0.11524371]]\n",
      "(5, 5)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f468da88fd0>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f468d5dff98>"
      ]
     },
     "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",
    "# Note: As the above note says we can extract any single filter by index from the first convolution layer,\n",
    "# We will consider filter_index 15 for this test\n",
    "filter_index = 15     \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": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f46ae03a550>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f46ae110860>"
      ]
     },
     "metadata": {
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     "output_type": "display_data"
    }
   ],
   "source": [
    "## TODO: load in and display any image from the transformed test dataset\n",
    "import cv2\n",
    "\n",
    "test_img_idx= np.random.randint(0, len(test_dataset))   # Random index to select an image from the test_dataset\n",
    "\n",
    "# Getting the tensor form of image and convert it into numpy\n",
    "# tensor copy of test image\n",
    "test_img_tensor = test_dataset[test_img_idx]['image']\n",
    "\n",
    "# Make a copy of the image\n",
    "test_img= np.copy(test_img_tensor)\n",
    "\n",
    "# Since we have only one channel, just retaining the HxW\n",
    "test_img=  np.squeeze(test_img)\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",
    "filter_index= 15                    # Applying the filter index 15 on the test image\n",
    "kernel= w[filter_index][0]  \n",
    "# Filter an image to see the effect of a convolutional kernel\n",
    "filter_test_img = cv2.filter2D(test_img, -1, kernel)\n",
    "\n",
    "\n",
    "# Plot all the images\n",
    "f, (ax_1, ax_2, ax_3) = plt.subplots(1, 3, figsize=(20,10))\n",
    "\n",
    "ax_1.set_title(\"Original Test Image\")\n",
    "ax_1.imshow(test_img, cmap='gray')\n",
    "\n",
    "ax_2.set_title(\"Filtered Test Image\")\n",
    "ax_2.imshow(filter_test_img, cmap='gray')\n",
    "\n",
    "ax_3.set_title(\"Filter\")\n",
    "ax_3.imshow(kernel, cmap='gray')\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**: <br>\n",
    "- From my final trained CNN model, I have explicitly `chosen the filter with index 15`. \n",
    "- After applying it on a test image (please refer to the above mentioned plot), it is quite evident that the filter helps in detecting most of the horizontal or almost horizontal lines/edges. \n",
    "- On that basis, I can conclude that `the purpose of this filter is to detect horizontal edges`. "
   ]
  },
  {
   "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!"
   ]
  }
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