2. Define the Network Architecture.ipynb

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    "## Define the Convolutional Neural Network\n",
    "\n",
    "After you've looked at the data you're working with and, in this case, know the shapes of the images and of the keypoints, you are ready to define a convolutional neural network that can *learn* from this data.\n",
    "\n",
    "In this notebook and in `models.py`, you will:\n",
    "1. Define a CNN with images as input and keypoints as output\n",
    "2. Construct the transformed FaceKeypointsDataset, just as before\n",
    "3. Train the CNN on the training data, tracking loss\n",
    "4. See how the trained model performs on test data\n",
    "5. If necessary, modify the CNN structure and model hyperparameters, so that it performs *well* **\\***\n",
    "\n",
    "**\\*** What does *well* mean?\n",
    "\n",
    "\"Well\" means that the model's loss decreases during training **and**, when applied to test image data, the model produces keypoints that closely match the true keypoints of each face. And you'll see examples of this later in the notebook.\n",
    "\n",
    "---\n"
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    "## CNN Architecture\n",
    "\n",
    "Recall that CNN's are defined by a few types of layers:\n",
    "* Convolutional layers\n",
    "* Maxpooling layers\n",
    "* Fully-connected layers\n",
    "\n",
    "You are required to use the above layers and encouraged to add multiple convolutional layers and things like dropout layers that may prevent overfitting. You are also encouraged to look at literature on keypoint detection, such as [this paper](https://arxiv.org/pdf/1710.00977.pdf), to help you determine the structure of your network.\n",
    "\n",
    "\n",
    "### TODO: Define your model in the provided file `models.py` file\n",
    "\n",
    "This file is mostly empty but contains the expected name and some TODO's for creating your model.\n",
    "\n",
    "---"
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    "## PyTorch Neural Nets\n",
    "\n",
    "To define a neural network in PyTorch, you define the layers of a model in the function `__init__` and define the feedforward behavior of a network that employs those initialized layers in the function `forward`, which takes in an input image tensor, `x`. The structure of this Net class is shown below and left for you to fill in.\n",
    "\n",
    "Note: During training, PyTorch will be able to perform backpropagation by keeping track of the network's feedforward behavior and using autograd to calculate the update to the weights in the network.\n",
    "\n",
    "#### Define the Layers in ` __init__`\n",
    "As a reminder, a conv/pool layer may be defined like this (in `__init__`):\n",
    "```\n",
    "# 1 input image channel (for grayscale images), 32 output channels/feature maps, 3x3 square convolution kernel\n",
    "self.conv1 = nn.Conv2d(1, 32, 3)\n",
    "\n",
    "# maxpool that uses a square window of kernel_size=2, stride=2\n",
    "self.pool = nn.MaxPool2d(2, 2)      \n",
    "```\n",
    "\n",
    "#### Refer to Layers in `forward`\n",
    "Then referred to in the `forward` function like this, in which the conv1 layer has a ReLu activation applied to it before maxpooling is applied:\n",
    "```\n",
    "x = self.pool(F.relu(self.conv1(x)))\n",
    "```\n",
    "\n",
    "Best practice is to place any layers whose weights will change during the training process in `__init__` and refer to them in the `forward` function; any layers or functions that always behave in the same way, such as a pre-defined activation function, should appear *only* in the `forward` function."
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    "#### Why models.py\n",
    "\n",
    "You are tasked with defining the network in the `models.py` file so that any models you define can be saved and loaded by name in different notebooks in this project directory. For example, by defining a CNN class called `Net` in `models.py`, you can then create that same architecture in this and other notebooks by simply importing the class and instantiating a model:\n",
    "```\n",
    "    from models import Net\n",
    "    net = Net()\n",
    "```"
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      "--2019-11-24 16:48:00--  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>\n",
    "```\n",
    "from workspace_utils import active_session\n",
    "\n",
    "with active_session():\n",
    "    train_model(num_epochs)\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the usual resources\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# import utilities to keep workspaces alive during model training\n",
    "from workspace_utils import active_session\n",
    "\n",
    "# watch for any changes in model.py, if it changes, re-load it automatically\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Net(\n",
      "  (conv1): Conv2d(1, 32, kernel_size=(5, 5), stride=(1, 1))\n",
      "  (pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (conv2): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1))\n",
      "  (pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (conv3): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1))\n",
      "  (pool3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (conv4): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1))\n",
      "  (pool4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (conv5): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1))\n",
      "  (pool5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (fc1): Linear(in_features=18432, out_features=1024, bias=True)\n",
      "  (fc2): Linear(in_features=1024, out_features=136, bias=True)\n",
      "  (drop1): Dropout(p=0.1)\n",
      "  (drop2): Dropout(p=0.2)\n",
      "  (drop3): Dropout(p=0.25)\n",
      "  (drop4): Dropout(p=0.25)\n",
      "  (drop5): Dropout(p=0.3)\n",
      "  (drop6): Dropout(p=0.4)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "## TODO: Define the Net in models.py\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "## TODO: Once you've define the network, you can instantiate it\n",
    "# one example conv layer has been provided for you\n",
    "from models import Net\n",
    "\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\") \n",
    "net = Net()\n",
    "net.to(device)\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": 5,
   "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",
    "data_transform = transforms.Compose([Rescale(250),\n",
    "                                    RandomCrop(224),\n",
    "                                    Normalize(),\n",
    "                                    ToTensor()])\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": 6,
   "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"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/workspace/data_load.py:39: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "  key_pts = self.key_pts_frame.iloc[idx, 1:].as_matrix()\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": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load training data in batches\n",
    "batch_size = 10\n",
    "\n",
    "train_loader = DataLoader(transformed_dataset, \n",
    "                          batch_size=batch_size,\n",
    "                          shuffle=True, \n",
    "                          num_workers=0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Before training\n",
    "\n",
    "Take a look at how this model performs before it trains. You should see that the keypoints it predicts start off in one spot and don't match the keypoints on a face at all! It's interesting to visualize this behavior so that you can compare it to the model after training and see how the model has improved.\n",
    "\n",
    "#### Load in the test dataset\n",
    "\n",
    "The test dataset is one that this model has *not* seen before, meaning it has not trained with these images. We'll load in this test data and before and after training, see how your model performs on this set!\n",
    "\n",
    "To visualize this test data, we have to go through some un-transformation steps to turn our images into python images from tensors and to turn our keypoints back into a recognizable range. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load in the test data, using the dataset class\n",
    "# AND apply the data_transform you defined above\n",
    "\n",
    "# create the test dataset\n",
    "test_dataset = FacialKeypointsDataset(csv_file='/data/test_frames_keypoints.csv',\n",
    "                                             root_dir='/data/test/',\n",
    "                                             transform=data_transform)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test the model on a batch of test images\n",
    "\n",
    "def net_sample_output():\n",
    "    \n",
    "    # iterate through the test dataset\n",
    "    for i, sample in enumerate(test_loader):\n",
    "        \n",
    "        # get sample data: images and ground truth keypoints\n",
    "        images = sample['image']\n",
    "        key_pts = sample['keypoints']\n",
    "\n",
    "        # convert images to FloatTensors\n",
    "        images = images.type(torch.FloatTensor)\n",
    "\n",
    "        # 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": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/workspace/data_load.py:39: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "  key_pts = self.key_pts_frame.iloc[idx, 1:].as_matrix()\n"
     ]
    },
    {
     "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": 12,
   "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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XR6lU2vw9uK079v+R+Hw+4Eb9xuPxKDpfaxrcWuO5VUrdyliTomG9Xqdard6WsrQit+IOdQtRLpexLIvu7m5GRkZUATCRSNDX10dnZyfpdJpisYhpmgwMDHD58mVM02RhYYHvfe97ynIYhkF/fz8dHR2KbyLB8MLCAp2dncoCy7V2dXUptyRKJviUuKlsNsvw8DB333030Wj0piLsG8mWsCiVSgW/368ieb1QBzdSYz0VlmLcRgw48fdwo4osNR8JguXfjYqQIror1C2SfLcooChrd3e3St3D4TAdHR2KxLR7925GRkaYmJjg/PnzLC8vKyh/eXkZ0zRpb2+nu7ubVCpFvV6nv79ffW88HqdUKpHJZCiXy+zZs4eLFy/S09PD/Py8CpTHx8cVkam/v5/JyUksyyKTyWCaJh/96Ec5d+4cHo/nth6WLaEoYrpbXYle9NtoIXU8Ql7XK82maSpFkeMICCbB4q1EFFOUUc9uBI0tFotkMhls26ZUKlEqlZqyK8MwCIfDxONxMpkMMzMzDA0NYZomzz//PNlslsnJSdrb2/F6vcqNOY6D3+8nGAwqyECefsuyKJVKtLW1qUB1fn5e3YfOzk6ee+45wuEw5XKZXC6nMqY77rhDBc5vZI03ki2hKFLlFXejiw48wY1YQywENKyLfFZ8s5hwwTneKN5pFb3yLO/RCdnyZMt3S8oqPBLTNCkUCqytrWGaJj6fTwWSUkG+++67WVtbY2Zmhunpabq6uvB6vczOztLR0UF7e7sKcBcXF+np6SGXy1GpVNi7dy/xeJyf+qmfwrIsHnvsMXw+H7Ozs4rwLZC+BLyxWIx6va4qxrrib0a2hKK82UnrCyqL5ThOU1uCxAs6pwVo4rPqiK7usjayVq1VavlOsUjZbBaXy0Umk1GV2vb2dqWswlOt1WoEAgEsyyKbzRIOhzFNk2g0SigU4vjx44rllkwmCQQCxONxRWCS4l5bWxulUom3ve1t7N27F5/PpwJUwzDI5/MEAgGKxSLxeFxZQdM0SafTjIyMsLi4SL1eV3UnvXj6ZrIlFMUwDEWE1msl8pow0oSMpJf9RVH0gpzurqR6LJZIT2k3Eh1Ik+ouNALucrmsEOSenh4qlQqjo6O43W5lDQqFArZtK2DL5/ORSqVU4XB1dRW3201HRwcul4tYLEY0GiUWi2GaJvfff7+iOU5MTLCwsKBoCoVCgf379wMouF6uV6rQ6XS6iVRVLBaVFbt69Srnz59XbmmjB+RWsiUURRhoOt1Ah+Fbi4KtwWUr001nw8kxWwuArX/TlVB+F0QznU4rRQYoFovMzc2pTMrr9dLT08OhQ4cIh8O0t7cTDoeJRCL4fD7C4TD5fJ5araZI1t/73veYmpoinU4Tj8cpFAr4fD4WFxcJh8PMzMyQy+UYGhoimUzi9Xrp6OhQ2UqpVGo6/y9/+cs4jsPBgwfp7u4mGo0yPT1NMplUccn73vc+pqen2bVrFzMzM8zMzPDoo49uao22hKLI09vKP5XXWuMK+Vf/m+4eWplsojR6lbdareL3+5s+K3GIuKharaayjPX1dVWhrVQqxONxdu3axf33308oFFIBrhT3TNNUC2TbtmLZ79y5kzvuuIO3ve1trKys8MILL/Dcc89hGIaqaQ0PD9PT08PXv/51AoGAKiiWSiX6+vpYWFhQcdvS0hKxWIypqSlWVla47777KJVKzM3NqTpSb2+vsjAul4v19XXOnj17Wwy3LaEoYrpb2WeygBKltwJpunVpfU0sgyiKLL4w4nWOq3y2Xq9TLBbJZrOsra0ptlipVGJ5eZmlpSV2797Nu971Lh544IEmlBa4yS3KNcj1iTJUq1UCgQC9vb38/M//PIcOHeKrX/0qyWSSYrHI4OAgP/jBDzhy5AhLS0uKLW9ZFvPz85TLZTweD9evX+eVV17hPe95j+pi/Pu//3uuXLmi3Ft3d7eKZTweD8PDw5w+fZpqtXpbgNuWUBQJTCWVbZWN3AU013FalUbeFwqFgGYWnSiQHtBWq1WuXLmigDOfz0c8Hmd8fJxkMsm9997LE088QTgcplQqqeBbqrGtWIwokMQmOlgosYv0WT/wwAM89NBDpNNp/uzP/ozJyUn27NnDwMAAV69exe12k8vlyOfz5PN5vvGNbzA3N8fJkycZHx9XLSChUIjx8XH8fr+y0D09PfT09JDJZBSi+573vIcXX3zxtkhbWwKZlZuoB1i3gvP1n3XagY6c6u8RIpP+eVlciXNqtRq5XI50Ok0gEGB1dRWAfD7P0NAQjz/+OB/72MewLItUKkWpVKJYLFIqlZraRPQUuvV8a7WaQoZbXxMFikajfPzjH2d4eBiAY8eO8b73vY+ZmRlSqRTXrl3jT//0T3nmmWdUoU+CbEAhuWIxxR06jsPzzz9PvV5namqKfD5/E3XzzWRLWBRorvfciqb4RrUgWXixKPKzPL0iEqO0WgCJYwqFAj09PbhcLnbs2MGjjz6qUlcx7/K9Ho9HuRsd+dXPS5RSrI5gPjrIqFs6j8fDHXfcQSKRYG1tjZMnT1IqlfijP/oj1ZMciUSwLIv19XV27NjB8vKyIk+L0orVjEQinDlzRp2rKFg+n1cdiJuRLaEo8lTBrbmv+nuhOY3V/yYZjyycYC16w7uugIVCgUqlwvr6OgMDA4rUNDQ0xH333Uc2m2V+fl4V9eS8gsEgwWCQtrY2pXjSkiEEJVF6nV1nmiaVSkV9rjWtl/MVwG1xcZGHH34YwzC4cOECo6OjJBIJMpkMPp+PWCzGd7/7XdWcJpY1EongdruZmZnBNE0OHDjAN7/5TQYHB8nlcrS3t5NMJje9RltCUfRaj95CsBGC2upadIhdFmYj36sXwCSolffPzc3h9Xppb29nenqaWCymbqQEfeVyWRXa3G63wkukgFmv15UyiVJI3CXKUC6XVXAtNa1W/q4OCEr9J5FIsH//fjo6OtRnK5WKcjuiZOJqUqkUXq9XKZoE0H6/n2Qyyblz5xTlcrOyJRRFcBR5YnXqou6K5HdoHrTTyqHVA1sx/a0jNOT9qVSK1dVV7rvvPjKZjEJShfgsmZAQk+S79BhIshk5JyF3S5deOBxWJl/P7qRsoWdFuuuUY4dCIV5//XUCgQCxWIy9e/dy+fJlwuEw8/Pz7Nq1i8XFRYLBID6fj5WVFXw+n0JedRe3sLCgEgAJpjcjW0JRLMtS/0NzkCoWQk9zZZF0V9LqkgTWl9fhhnkXpZmZmWFycpJ9+/aRy+WYnJxUKGgymSSbzSoGm9SM5LNS8PP5fE3UBTlHj8eDz+fD7Xar9FYUSQbxOI6jRnToramSLQmu4nK5OHjwIGfPnsXtdjM0NERHRwepVIpCoaB4tYZhqABXipSC35hmo7VVrJ9pmqptZFNr9H9gnf+3RSyAfsNbsZFWEVOro7BAk5XRzboonFRis9ksqVRKPc1jY2NN1WZ5GmXhS6WSOi/heUDDbYqVkYBc+KxiaSSdFkg9FAqpgp9OqZSHQaySTh4PBAJ0dXVx+vRpNfbL4/FgGIZSaPmMThgXVyX1JmjEZf39/aysrGx6jbaEokhZXbcGupuQC4bmAl65XG6aKNDa5JXP59WTpn+uVCqRz+fJZrNEIhGuXLmioHYx04IQC94ivcH1eh2fz8e+ffuUmzIMg0qlotJlSZkl7ZVCYqVSwe12K1cmi10ul9VTL8rj9/tVQCzvu+OOO5idnWV8fFwpgt/vJ5/PEwwG1XX6/X4WFhYol8tEo1GGhoZ45ZVXAFTryNLS0lvP9Yi5lWC2FdCqVquqMChWRp4eeYL14FBEbqZYBykVhMNh9eRKxVYgcokVXC4XtXqNglkg7oqrGWuGaZCtZxVZemVlpQmRlcKjWAjps5EYRs5RJzxJU7rMZBGMBm7EZKJsBw8eVNVk6csZHx+/KVgX6zQ7O4vf71dE7XA4jN/vJxQKMTY2tuk12hKKIqMdAoEAlUqFRCJBqVRSFyjcDwn+xKRLS4Q8nWJR9KYv4YiIGxLrINmE8EgkrlAzVGpVnnSeZKY2w0h9hE93fJpgMMhvXv9NRvOjHFo7xF/+yF+qeEDqR4Byo3raL+cn7qter5PL5bAsi2g0Sjabxefz0dnZqbKZXC7XNAhIsqxoNEo0GmXPnj289NJLqgtR5qvIOSSTSWzb5tq1a7S1tREMBlWNyHGcJnzpzWRLKIpYhBMnTlAqlahUKk3QezAYbGpckiwGUBeuZ07COKtUKup1vUK8vLysGPA6mUiCz3q9ToECM84MNjbjlXGmFqYIzAe42n4VG5tL2UtMrU0RDATpjfQ2MfyLxSLr6+tAY4HFrbpcjTFb4mpkodxuN9FoVCmdxCjyAMh1itUdGhoiFotRLpc5efIkR44c4bd/+7exbZtUKkUoFGLHjh2Uy2XVBBYOhzEMg1QqpcoQbzlkVjgiQviROEGePokPbkUraG0VFV8v6KdOXJKuPGmR0LMT0zRxcCiYBdqcNoYYYsqeor/ez8lPnMTA4PAHDnNx8CK73Lv4z2f+M9dL1zkcPcwX7/siNU+NTn+nWhA9zZegWA80ZZSonia3kqx0yqKktPl8nunpabq7uxkbG+Puu+9WXBTLsjh69CjQCFqnp6dVw5egxhJgv+X4KDqaCjS5CEA1LOkTlDaiGMix5O+iPBJ3yOtifcTkqwYsE550nmS2OsugMcivhH+Filnh4O8exMTEwOCJv3qCVCDFmV86w2eDn8XG5kLqAh984YNcL17naOwoT/2bpwgEAirYliBWRo1K2iuuyuVyUSwVWcgs0NvWS92uk6wkCfvDKiiHRu1pamqqKePq6uoil8vR1dVFMBhkcHBQWclAIEBPT496EADVUCax32ZlSyiKZDs+n09VW+EGiVmf5AjNvNdbpdH6kylPsQ6lQ4O1FgqFiEQi1Ot1ymaZWWaxsZl2pimZJdrW22hLtGHwQ+zGcRHLx3j4/36Yp3//aa5Xr7PLu4uxwhg2NucT55lenSbuizcVI3UCtmRJlUoFAK/Py6+//utcyl7iSOQIhmlwIXmBE10n+MZPfYNIJML6+jrZbJbl5WVCoRAul0vxbNPpNMePH1exUblcJp/PK/K1KIZY1IWFBYrF4ltPUSQ70FNQPYiTvhT5XVeMjeD6VlBOzL5eOJQR6JIJ1Ot1LNtiJzuZYYZBcxBPxbNhO4iBgYnJp+xPkdyZpM1o4zPLn2GsNMa+4D6sskXVVVXnImCaxB7QiGXyhTzZehbDNLiUuUSdOhdSFxrXic2Z5TNMJ6YJt4VxGY0YRWa5dXR0qDYMqQtJ8KsXR23bJpFIKNRWssfb7ZTcEooiIFgrTC83WETnmug8FP0z+o3S+R/SjiqZB0A8HlewvASKHzQ/SMlVIuKKNL4nblCOl/EmvMqqODjY2JS6SoRo9Ph+svuTlF1l4r64Atvk2vQipfxuOzafWfwMV/NXORA6wKHwIS5lL7HXtxeX5eJK7gp7fHv48Hc/zMXURY53HOe/3vVfVWAu3YXFYpHOzk71fZJWi+UU5Nbr9aoa0EbK/2ayJRRFnnBBOSVT0S1A6/t12YiSADcyotanR7KkQCDA0tISoVCo6fvCZlhZII/Xw/oT6/T8+x74IR/b6XJY+3/WcJUbT7mYdr/vRkYjqXErX0ae6mQ1ydX8VerUuZK7wpfvbHBeO3wdWG6LqqeBUD/y/UeoO3XOrp3FaDPwxDwqEJa2UiF8S4qup+c+n4/+/n6WlpZIp9OEQiGi0aiybJuVLaEoEkfoLRvCKoc3HnbTanF08pBkNwLMyUhyAa/S6TT5Qp6CWcAsm7gtt+KEiAWybRssWP/rdfzLfqxOCyNuEKwGcVfcyupJlyOgvgduuFV9WkG9Xsdv+9nj28O10jX2+PdADlymC3wQjURvTKtuP8r55HlOdJ3go89/lFdXXmV/YD+fO/Q5MpmMGnpcKBSaSiH6wyMpv8/nUyQtsbyblS2hKPIkiAuSEnoryVr/91YkJllcvegm3yGL99hjj/HII4/w0qmX+Cvzr0hUE+x07+RD3g9hGjdaNXSidckuYe4wwQcr6RVi3phaGFGCdDqtBuXotE7JcERZ5Rp+Z/h3sH02w93DakpBuVxWGFCTOoh+AAAdGklEQVRbWxtPv/dpkpUkBgZ3f+Vu6k6dq4WreKIegskGH2Z9ff0mwpIogiQDvb29pNNpNYQHbripzciWURTdNcgCbdQb28qJfSOSkx6r6O2eKysrXL9+nUhfhLXcGg4OM84MOSdH2AirY0uBMp1JU7EqBAoB/nj9jxkvj7PXu5dfHfhVgk6QSrVCupbGKTtN2E3r9APJeqAxxjwYDBKNRgmHw6pIaBiG4sY6joPlsugOduM4Dnd138Wry69yousEe/v34qk2EGcZnAM36mKCF8l5eDweVT8SJPktF8zqqKWkj7qS3CoGeSMRZRIwS8+aZBOmo0eP0l/vZ8FaYMgcIkQDDRbuCMBaYo3/VvxvzBgzuOou6q46GDBaGuWXrv8Su1y7wICp+hSDxiAfDnxYURAEkRW4vlAoqMF+5XK56RplhIVMXBKXpV/L0+99mtX8Kh3+DjUvZW5ujlQqpTLDVsK5fIcojWA5eia5GdkSigIoYEyi9Y1ogrcrghtI9iPHSCaTxGIxzp49y+MDj7OcWybujTdxYNxuN4lEgqtzV5kdnAUDpST88FQcw2GiNtE4P8Nh2pmGQKPIF4lE8Hg9pKop2t3tZLONQmKpVKJYKrKYXaSarLKWWKPkKnF46DAn7jqB7bcZjA82YhQDVvOrdAYaPcQ40BnoxOPxkEqluHTpEplMhqWlJRzHUf1FrRQJQLl2PTl4y0H4OrVAh6x1LAWaFUZfeL3JS2e7ye86N8Xr9Sr6QSaTwTTMRqbxQ18ux/N4PHR2dtLb28u55DnGa+P4LT/lepldvl3MlecoUcJjeOg1eplllv3B/ezfsb+Bvnrc/IeX/gNnE2c5Hj/Obw39VuM6TYMnEk8wa8zSnmunZJcotBcITYSIrEVYci9xZ8edfOVdX+EX/vkXOL10mhPdDeDNclkqq5mbm2N+fp5oNMru3bu5cOEC0Wj0pi5JvdyxETdns7IlFKU17oDmWWsbWZPbeSoEw5B0WXCFaDSqWjnl6dP7cUQ+Hv84eSePq+wiU8tQKpX4nPM5MKBm1Hh86HFw4MjwEVVqSNfSnE2cbaS2ibNU9lQaCLCvztz6HI7hsB5ahxBgQK49R44cOHBm9QyjiVFOL52m5tQ4s3yGteIavW29KlAfvTbKyB0j/LtH/h2hUIjPf/7zXL9+XVXTdVcLKPhBlENXos3IlujrkaddHzOho6s6jUAAM93KtIrusmTxpbLscrmYmprCNBtTpdcSayTKiZsqyMKBqVarGBhE3VF6unvY27+Xg4MH2evfiwsXe/178VQ89LT1qIq33++nJ9TD8Y7jmJic6DzBUMcQ0WiUwY5B9gb2YmKyy9yFz2hUkP2mH7+rkRJ7DS9Tp6c43nEcy7A40X2CDl+HolFWqhX+vPrn/OrMr/Luv3s3V65eUWCeNNILGTwej9PT00NbW5uCD+Sh2ChZuJVsCYvicrkUbnEr1FAfmNPKztcJySJyIwSf0UsB8nfTZfIVz1dYKCywy7WLj/s/3lSQlGkGwnovlUrKKn08/nFqnhpOzlGMdslU3G43pusGyIYBlttSlu13h3+XsYUx8vk8n6l8BoAKFRy7cf5Vqpx48AQPex4mT55Ofydww4ou55Y5nzivrM302rTamk7qSB6Ph46ODsX1ldQ7kUg0odOblS1hUfToXE+Ndbi+NR1uLRJC85QkUTq9CKg3aXm9XgpGgQXXAjY2k/VJMvWMSqelb6enp0fVgpLJJEtLS2SzWWZnZlmbXlO0CNmAUr47VUnx2upr1J06r628RqaeUe0eju0wEB1gZ2wnu6xdmJgMmUMMu4dxGS5OdJ1goH0Av89PV6BL3SO5B3FvnBNdJ3AZLoZcQwx3DSugUO6fx+NRpG6/3088Hqevrw9o5hVvVraERRGLIFMMdaXQrYbud8XUQnMRUH6Xz+rgnf55t9vNvq59DGYGmban2eXaRcAOqM95vV7FoO/o6ODAgQNNrRmCctq2rQYDS/zg8/kIe8Pc1X0XZ5bPcKL7BL1tvRStxgjPtbU1CoUCbrebT3R9AlfYRdSK4va4WS+t0x3qZn5+nt7e3qZyhgTqHo+Hv3333zK+OM7YuTFFHRXOrvRPSx1Ld8H/q7IlFGUjZdC1/c0C1tZAWER/unTk1nEcFhcX2blzJ78U+CUq7gr+ul8poO7GhP8qLkXIz7Ozs8zPzxMOh9XmA6ZpqtK+2+3ma+/9Gulampgnpha7p6eHQCDA2NiYGqPhFB1sf4MJF480+Lk+n68JrJOfRSHtuo1ZNNm3bx/nz5+/aTx8rVZTbDd5+NLp9E28nM3KllAUWVD9xgiEvlGdRxa7FZRrtUTCMZX4Ryq3wkIHsFwWPtNHsdwA2KTABqit1+AGJiE0xwsXLtDX16cmLxUKBdWGIdxbORdJvWXeSiAQ4K677lJjvdLpNMFgkHA4TFdXl+Lw6mPc9W7DWr1GqpIiFouRyWSYmJhQNSSdhC7lELlfKysrqruglYj+ZrIlFAVurlHoSrORvNFreoArovcK2bZNsVhsbJ/W1cn44jgBI6BQW0FSATWrTT5TKpVYXV0lGo0Sj8cxTVPNbpP5aLVajXwhz4d+8CHOrp3l7p67efq9Tyv+y+joKC6Xi/7+foaHh1XArDP3xQq2YiKZbIZf+B+/wJX8FY7Hj/Pw3MNUK9WmzFF6eWTI38rKCleuXFFzbHXMaLOyJRRFL+LpA4dbC4K6tBJ0WkVXFv14Yomq1SoY8Pvzv89oYZQhc4jHrMcwDEP1CEvjt5xjtVpVkxml/1hGlOtUTbfbTc7J8dpaI5g9vXSa1fwqMW9jXtvIyAjPPfcctVqNnTt30t7erpBTsSStIoo4vTbNlfwVbGzOJs5yr30vQSuo7odACIZhqL4lKQaKVdQ5MpuVLaEo+ugIfYCdFNX01lA9FW5NpVtBOJ2gJOmxPKmVSoW2njZGr49iYzNlT5EnT7vRrthv0ubhOA6FQoFkMqm2se/t7WVsbEw1fjuOQ29vL5FIhEg0gjfmbaIIRN1RtVVcMBhUHJFCocCdd97ZhKBKrCTfLWChy+VidXKVA6EDjOZH6Xf6iflipFNpRZ3QUWqZbbu4uNjEImwlp29GtoSitN4cuAG9ww3/3NpLLNJqUXQ+ikwjkCdd3m8YBp3+TgaNQaadaYbMIdqMtptiH3nSq9Wq2g0jGo2qDZVOnz6N2+OmbJVZzi+zu3c3n1v5HKPFUY53HOfZdz3L6sQqp189jREyOLLrCOvr65hmY3RGuVwmm82qXU3189PTfpFoNMq3fuJbLKYX+eZXv6mwF3Hdcp2FQkGl7PK6jtq+JQE3MYV6c1frhMg3I1Prot8EyXp0tpwEm6lUit8Y+A0WM4t4KjcqzPp7dUWRfiNhxI2MjJBKp/hi6Yssexpde925blY8Kzg4nF09y/Wx6xRWC3w+83mm7Wn2n9/PJ7o/gdfjVYQj6YHWZ8G1XqPco6NHjxIKhkhdTmG5LMq18k1Brwz8kUqxWGwdtpdAd7OyJQA3uUhRFFEGvV9HfpcdJ+Q1YerrYJvunkTx9G5BwToWFxcbpXwHdSw5HqAANEE3pT9ZhufZts2huw+x6l1tVJUNWPWs0kcfBgaD5iC1VI0cOabtaWxsRguj1L11dS2maapxX2+2ywgG2IHG3j+jo6Oq3CCpNDQXUHUytzwc+ky52+HObgmLIpCzXqNpHc4HzTGILJ5pmk3EaaDJt8t7oNHT4vV66ezsJJfL8c//4595ceRFltxLDJlDfMT9ETzuG+O2AKU8MttElEgwlYgvwpHoEc6nzgOwy9pFtdZoL6nbdTJOhi8Xv4xN41wOtB2gM9Cp0lghE22EljZlbY7Nz/zjz/DK0isciRzh3+b+LS6zoWyiDDqKrWNSOlKtYye3QzPYEhZFUkrpNxZwSyceyfQi6XiTAp4EeXp9RhTO6/VSKBQol8vEYjE6OzvV9vWpVIr51DyL1qIKZgtGoYn8IzfXcRzFTBMTLkpbLpf51M5PqUKf4TKYYQYMmGaa31j7Da6VrzU+i8lv7v3NpqdbAnh9sUV0K5koJnhl8RVqdo0LyQsUjIK6L3L9urQGxHrcJuTyt1yMUi6XmZqaYnl5mbGxMQqFAnAj1tAZ+nDDvOrFPkkfDePGBEYZuSlbxi4tLSnIvVqtYjs2/hE/pc4SO42dDRhdI3RLOlkqlZS7kprP2NgYDg7LuWVqtRpj7WM4hsP18vWmaxNLYmJyZ+ed7O7drYjdkrJKE5ds3aaTo0Vh+yJ93N17N6cXT7ODHfSEGkCfbDIpgarewiIKLRZSL77eTp0Htoii/OEf/qF6ssQ8CnTe2sahuyE9C5Kbq/NFZQZKoVAgnU6r8rsOZHn+xsMvPvaL7Gjf0cRxFUxHnjzTNKnVa8wtz7G6toqn5uFroa8x1zZHR6kDy7GoUsWNG9MwKVNWfUD7A/v5ncO/w3DXsNosW9h3pmk2bUop1wPNRK1arcY/PfpP/OXf/SVzo3M40YaVWFpaol6vqyb0VgabXrqQ9Ph24XvYIoqiI6J6uwVwU0lcf2LkvfrOXo7jKIuRzWZVG4WY21YznM/lKa4WKQduFND07W7l+3x+H787/buM18YhAD31HpZdyzg4rPnXlFLUqKmfAR7vfJw9nkY7xrq1Tiweo2SVmshUfr9fKbLf728a/yUW5+y5s7w2+hrXzl6jPdquCpFyz/TPiNK1Wo3WlPh2YpQtoSjin+UpgxujscQ6SMwiJla/QcKWLxQKavyVxDxSUZUF1/ksbrebtrY2Xj3zKoGuAB2lDlUVFhGlS9fTTNQnEB1Ydi2z27+bidIEx2LHADi3fo5D4UO43W7Orp3Fb/l5Yu0J9gX28YV9XyCRSPCxsx/javEquwO7+fXor6vdP6SJXa5JFMlxHFLpFB987oNM29P0xnr5lbZfAW7sU1QqlZr2C4IbuIq4UgmeJZ65HQY+bBFF0Vsp9GKaAGaCZwBNlkFiEbEYAqxJgUy651oDOslkDMPA7XFz7eQ1Lgcusyu9i094P4HjOGqfYglerZLFiDXC9VojBukodvBj6z9Gzs4x5Axx55130vX2LiJWhO6ebl6deZX3/MN7qDk1rhau0t7fTtVd5cr5KwCMFcb4WPFjfGPoG6yvNYb1yfgPHV73er28fOZlpp1pbMNm0b1IzVPDi1dhJbIPsm4hNsr6blVl34xsCUWRxddjB1lcneMpLqp1bpv8LMeRmy2fleNKWi1YTLFYpGgUqfZWVcvFWnGNmCemFFBPKR/vepyMnaFWreFua6CxI90jfOeZ7/AvF/4F8o2F7d7VTcAI0BvpZc6YY8AZ4Ft/9y2ujV0jEouQDqTBgJJTYo01CrkCO3bsUIqiu51qtcozX3uGNn8bmUiGHcYOwq6wipvC4TDpdLoJbdWD2tYsSpe3HHFJrIV+oboFETOpZzfyN7EUugsSpdN9sqTVYklEmbx1L55lD5WuCt31bmqpGvV4XVkol8tFsVhsNFD5/HhtL3k7zx/n/5gpe4rdhd2wB65XrjPsbmxO/U/2P7HT2IllWBh2AysZ2jVEfDDOewPv5WfP/yxFu0jAFaDH7GEtvEZ3d7dq54jH46qJ7Nvf/jZP//3TWIZF1BcFC/4q9ld0d3dz8OBBotEonZ2dJBIJhTrrGSI0d1HK72/JrEfqEXp1U+9NEZehuxx9GtEb1S3EIuhKooN5pmHS8c0OnIDD/cfuJ9mXVKPAhKBcq9XU3j4ulwvH7zBlT2FjM1Yew8DAxmaiOoHzw/9mndnGk43NWGmM31v4Pa6kr3A4cpivHP8K07lp9rXvY2VlhYGBATWzTe/NWV9f56mnnlKQPFVIkSKTbhT7Zmdn2bFjByMjI01TslsLjDrfp5VXvFnZMoqiX5SeurXyY3UXI0UwafLSlUr4HWKCZVSXFAkF2hdALWgGeeD+B5ianiJZTRIkqMaC67t/eTwe2s129vj2MFYaY8gcwjANJmuTjLhHcHCYqE6wL7gP0zS5krvCofZDXFy/iI3NxfRFKmaF/bH9rK01OLeyPYogtaL8//2b/51r89cItYUwDVMhxOJy5+fnmZub4/z58+zbt4+RkRE6OjoUrCCTmuT9Oh3ydmmRW0JR9Oqx/jddeeRvoiQ6r0LHD+Sp112PPnNeD/IkQ5AA+PCRwzxlPsW5xDkGXYP8R9d/VDUifQQpwKd2fIpkOYlZMnki8URjZsoPj+vgNF2HZVgcix/j/Pp5jrYfpT/aTz6fJ5VKsWfPHlVzam9vV1nW+MQ4f5L6E9bev4Z7yY3/b/1E3BEOHTpEtVolkUgoIlK9XufSpUuk02kOHTpET0+P2vxJxqzrsYokDG+53mM9M4HmTSXl941EV5RWJROATqyG3FBJv/WAUVxeupbmQuoCtmEzbU9TNIvKbYki6tlFh9nBirPCZH0SB0f96+Awmh9VA3fOr5/nWz/6LUxMwq4wAOvr63R2duL3+9UWtpZlkc/nWV1d5evPfp219jUcw6HaXSXU1mgHmZ2dZefOnRw9elRNfczn8wqHuXTpkmrVkOBY4jFJiwW9fsvhKJK1wM3jLfSahJ7u6TNIWq2RVJOFDCWbGEjsoy+2KKVlWVTWK9zbey8vL7xMb7WX4lqRbCSrtjTR60jiAvsj/exP7Odq4Sp7fHsAuF66zsG2g2rk1pHoEaKuBgF7ZWWFcrnM0NAQ+Xyea9euEQwGSafTLC4tspJb4dLpS2SzWQJOgEK8QEepg572Hlarq2SzWc6fP08wGOSOO+5Q+/RYlqWGGRcKBRKJBN3d3Wpoci6XawIr9Xu8GdkyigI3K4l+Ia3Bql5O1wtirRG9KInulvT3SQHStm2mpqZ45qPPML44zvTlaZ57/jlm1mc4GDp4E+dDUk+Px8NnD36WtcIanqoHB4esnWVnbCfBUJB0NU3UfWPCkbSyBoNBNQgnkUgwOzfLNyLf4HrlOofuOMTuU7s5du4YnnYPcV+clegK1UpVTS7IZDJcvXqVZDJJV1cXvb29GIahNn2CRpwWDAbZvXs3+Xye9fV1dS82w+vRZUtUj/VF1+H1W2Uy4irg5pRPrEgrr0MvsOmzQYQdb1kWly5dwrEddsR2cPTYUb7d/W3+oPQHfHry0+QLeYX2Cr6iMgwH4r44oVCIYCBIV6CroYS2Q9QdVQpaqVQIBoNEIhGCwSChUIhCocArr7xCsCvIWHmMml3jUvYSH/6/PozbcjdmyWGohjTZi1Aq4ysrK8zMzLC0tESxWFSpsWRBck+PHj3Knj171H5I4n42K1tGUcT96ArSahr1+kUrMVinI4joDLVWQpQEqXLcarXKxYsXgR9uPu2pMVZsVIQnq5OMLYwpyoIsiB7fSBYl40j1pjMhOglMn81mOXXqFM88+wxXZq7w6KOP8vC9D3Oi8wSWaXGy/yQP3vkgX/jCFxQ+5PV6CYfDKl3XXaDELjLhYH19nVwup6yPvqvZgw8+yPDwsBrVulnZUq5no7/pXXKtnFoReX2joHcjq6JjDTIMUJ4wyYp6w73c03cP/3PhfzLsGaa4VmTFXCESjVAwCnQ73epzwsyT79dZcvIdUtybmprC6/Vy8m0n+dP8n3Jm+QzPTT/Hk7ue5Ml3PInZZtIXbrR+7t69mw984AP8xV/8BcFgkEAg0NTSAajCoPBvZYuZWq2mODvyv7ja48eP09fXx8TExKbXaEsoim45mlhdLUHsG4lO1NkISNJ9su62JDuCRiYiGyO5XC6e/blnuTR5Cb/t57vf/S6zc7N8ofgFZpwZ9vj28MnuT+Lz+pTF0rkwOjG6VquBAZdnLvP6y6/z2L9/jEw9w6vLrzZ6k9deo+KuNIb5OIYC2FwuF4888gizs7O88MILwI3ZLZK1iKWUeo/s2yMPg5QEBNSUB6qrq0t1OG5GtoSiwI2F1PGTWyGuG/29tRdIpJVOqb9X76ERvEXHbgwMBtoHWFhY4MEHH+Rv/vFvmLancQyHseIYV2euEiTYGO7na7iriBVp2v6tWq1yZfQKXyx8kTXfGod3HuZD7g8RMAKc6DrBayuvcVf3XXT4OtR5VioVstks4XAYy7L4yEc+QqlU4oUXXmhi8cngQEGrK5WKomxKG6z0HwuuIvwXvbK8GdkyiqKnqzoFUaSVk6KLTjuQBdcJTq3Ea50pJxmPxDiC9gry297ertho77jrHTz92tPMODN0ljuZmZ6hXqvjslw8N/Qcy55lusvdvC//PvK5vGoo79ndQ4IEDg6Xc5dZK67RZXTx1L95ivXSOl3BrqZzk4ekWCyq4PMXf/EXeemll4AbO22Ikku1W+IXOX/Z1FOuRRrXpDTxloPwW4NWAYd0hWi9KL1e0yryed3ytIJyegFSvksY9hIs1mo1XnjhBU6dOsX09DQDAwP84Z1/yHppnfXZdebDDQg9WUmyvGcZx3BY9i4zenWU4lqRwcFBhoeHuevOu/jW6LcayGzsKFErqrgkXcHGWAs9k5LzlNYLGYgzPDzM/Pz8TcBf633UCWA6oetWNITNyJZQlI1EAsONgld4Y3bWRu5HiNpyHHki9emIJ0+eVJnSP/7jP/KlL31JVWRXV1c5deoUX//614nFYtxzzz0cPnyYnTt3kkwlmaxOsuRZoqfSQ9QT5Y577+DAgQMcPnyYer3Ofxn5LxRGCuyM72zi9rYqq7iD1szG6/XykY98hBdffLEp6JZ/xTLqCqHzecSt6kT2t1y7xkZP+puJ7j5aLZJ+o/S0WC8NiGuTgLZWq3HkyBFqtRovv/wyp06dolarEY1G8Xq9igJQq9VYW1vj6aefxu12k8/nG1Ma0yl+5OEfYf/O/fQc7uHYsWO0t7ff6PN1WbQ5jT389BFkci0bNWnJAkNDWQYGBtT1iXLdyrIK3KBvjqnf3zfCqTaSLaEoutwqFtE5JnrluPWCN2JxtZbXxSTLgumck5mZGU6dOkUgEKC7u1vt5BWLxahUKuTzeVV9lu1h8/k8+/bu4z995D8RDAYpFotNGYdsVq1bkNZzEtBPD+T1QqRkOz/2Yz/GP/zDPzRxjHXLoVfXpYqsb6QANzCo2yFYbwnAbTOid+vrflyeRv3/NxMd5q/VGptD+nw+Tp06RV9fH0ePHmVwcJD77rtPtWr4/X41hlzwCJm9YlkW73jHO4AbqLFkI2IpfD5f006setCs/yvXpFe6dUzpx3/8x1VGJRZTrvuNqI6iQGI9b6fOA1vcorT+XYf3RTaK3G91DFmA1kBQfh8cHFTzzrLZLF1dXU0bSodCIdLptFJaySw6OzsZGBhQaasoiTyxuhKL+9PTeR1Kl8VvzdxECe666y5CoRC5XO6mLEneK6JXvHWl2+g+vplsKYsiF7xRq4Hur1tBNd0FbVQ8FFMsT5VeRZXvSiQS/MzP/AwrKyt0dXXxoz/6o9x5553s2bNHDdWRidTS6ys33e/3q42VxC0JL1ewC3nyxU2Y5o3NuOUcdEvXykoTy+f3+7n//vuVe5P36d8j75cYRd9BTXdXtyNbSlHeKK/XuSNAk8luFXmSNqpG64Ce/C9paG9vLx6Ph0AgoPYGfuc736kWUIJZXdG8Xq/abrdQKDS1nujuQI4hXFj9WnQr18qrEWui18PEym3kPnTXovcxtaLct5sebylFeTPRF1hvk2wVfXFaf5e/yYK3psiWZak9+6TCKwvUutmABNWA4rnq+/aI6NmXTr8UKynXolfARVoLobVajfHx8ZuoFfpnpQa1kfLp5347Yy+M2w1qtuX/n/KWsijb8q8n24qyLZuSbUXZlk3JtqJsy6ZkW1G2ZVOyrSjbsinZVpRt2ZRsK8q2bEq2FWVbNiXbirItm5JtRdmWTcm2omzLpmRbUbZlU7KtKNuyKdlWlG3ZlGwryrZsSrYVZVs2JduKsi2bkm1F2ZZNybaibMumZFtRtmVTsq0o27Ip2VaUbdmU/L8bwHCnykFJFAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95efbda828>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95efba7f28>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95efb04b70>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95efac6c50>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95efa89f98>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95efa43e10>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95efa0f390>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95ef9d2940>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95ef997a20>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95ef9531d0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the output\n",
    "# by default this shows a batch of 10 images\n",
    "def visualize_output(test_images, test_outputs, gt_pts=None, batch_size=10):\n",
    "\n",
    "    for i in range(batch_size):\n",
    "        plt.figure(figsize=(20,10))\n",
    "        ax = plt.subplot(1, batch_size, i+1)\n",
    "\n",
    "        # un-transform the image data\n",
    "        image = test_images[i].data   # get the image from it's Variable wrapper\n",
    "        image = image.numpy()   # convert to numpy array from a Tensor\n",
    "        image = np.transpose(image, (1, 2, 0))   # transpose to go from torch to numpy image\n",
    "\n",
    "        # un-transform the predicted key_pts data\n",
    "        predicted_key_pts = test_outputs[i].data\n",
    "        predicted_key_pts = predicted_key_pts.numpy()\n",
    "        # undo normalization of keypoints  \n",
    "        predicted_key_pts = predicted_key_pts*50.0+100\n",
    "        \n",
    "        # plot ground truth points for comparison, if they exist\n",
    "        ground_truth_pts = None\n",
    "        if gt_pts is not None:\n",
    "            ground_truth_pts = gt_pts[i]         \n",
    "            ground_truth_pts = ground_truth_pts*50.0+100\n",
    "        \n",
    "        # call show_all_keypoints\n",
    "        show_all_keypoints(np.squeeze(image), predicted_key_pts, ground_truth_pts)\n",
    "            \n",
    "        plt.axis('off')\n",
    "\n",
    "    plt.show()\n",
    "    \n",
    "# call it\n",
    "visualize_output(test_images, test_outputs, gt_pts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training\n",
    "\n",
    "#### Loss function\n",
    "Training a network to predict keypoints is different than training a network to predict a class; instead of outputting a distribution of classes and using cross entropy loss, you may want to choose a loss function that is suited for regression, which directly compares a predicted value and target value. Read about the various kinds of loss functions (like MSE or L1/SmoothL1 loss) in [this documentation](http://pytorch.org/docs/master/_modules/torch/nn/modules/loss.html).\n",
    "\n",
    "### TODO: Define the loss and optimization\n",
    "\n",
    "Next, you'll define how the model will train by deciding on the loss function and optimizer.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "## TODO: Define the loss and optimization\n",
    "import torch.optim as optim\n",
    "\n",
    "criterion = nn.SmoothL1Loss()\n",
    "\n",
    "optimizer = optim.Adam(net.parameters(), lr = 0.001)\n"
   ]
  },
  {
   "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": 15,
   "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": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/workspace/data_load.py:39: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "  key_pts = self.key_pts_frame.iloc[idx, 1:].as_matrix()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1, Batch: 10, Avg. Loss: 0.29435441046953204\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Epoch: 7, Batch: 100, Avg. Loss: 0.03634467832744122\n",
      "Epoch: 7, Batch: 110, Avg. Loss: 0.045773119851946834\n",
      "Epoch: 7, Batch: 120, Avg. Loss: 0.046457168832421306\n",
      "Epoch: 7, Batch: 130, Avg. Loss: 0.047065744362771514\n",
      "Epoch: 7, Batch: 140, Avg. Loss: 0.04015912655740976\n",
      "Epoch: 7, Batch: 150, Avg. Loss: 0.048727349005639554\n",
      "Epoch: 7, Batch: 160, Avg. Loss: 0.03829620815813541\n",
      "Epoch: 7, Batch: 170, Avg. Loss: 0.04916878920048475\n",
      "Epoch: 7, Batch: 180, Avg. Loss: 0.03593768812716007\n",
      "Epoch: 7, Batch: 190, Avg. Loss: 0.033670165576040745\n",
      "Epoch: 7, Batch: 200, Avg. Loss: 0.050453852489590646\n",
      "Epoch: 7, Batch: 210, Avg. Loss: 0.0375280849635601\n",
      "Epoch: 7, Batch: 220, Avg. Loss: 0.0430409774184227\n",
      "Epoch: 7, Batch: 230, Avg. Loss: 0.03543774019926786\n",
      "Epoch: 7, Batch: 240, Avg. Loss: 0.0307779373601079\n",
      "Epoch: 7, Batch: 250, Avg. Loss: 0.03855326743796468\n",
      "Epoch: 7, Batch: 260, Avg. Loss: 0.03685543909668922\n",
      "Epoch: 7, Batch: 270, Avg. Loss: 0.029157826490700246\n",
      "Epoch: 7, Batch: 280, Avg. Loss: 0.044666893780231476\n",
      "Epoch: 7, Batch: 290, Avg. Loss: 0.03716830369085074\n",
      "Epoch: 7, Batch: 300, Avg. Loss: 0.045620817504823205\n",
      "Epoch: 7, Batch: 310, Avg. Loss: 0.03289272841066122\n",
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      "Epoch: 7, Batch: 340, Avg. Loss: 0.03313686596229672\n",
      "Epoch: 8, Batch: 10, Avg. Loss: 0.02891931189224124\n",
      "Epoch: 8, Batch: 20, Avg. Loss: 0.03351779635995626\n",
      "Epoch: 8, Batch: 30, Avg. Loss: 0.04634635504335165\n",
      "Epoch: 8, Batch: 40, Avg. Loss: 0.034068671986460686\n",
      "Epoch: 8, Batch: 50, Avg. Loss: 0.03206225000321865\n",
      "Epoch: 8, Batch: 60, Avg. Loss: 0.03486007284373045\n",
      "Epoch: 8, Batch: 70, Avg. Loss: 0.030808434821665287\n",
      "Epoch: 8, Batch: 80, Avg. Loss: 0.04202190740033984\n",
      "Epoch: 8, Batch: 90, Avg. Loss: 0.03814318384975195\n",
      "Epoch: 8, Batch: 100, Avg. Loss: 0.03268812261521816\n",
      "Epoch: 8, Batch: 110, Avg. Loss: 0.036556182987988\n",
      "Epoch: 8, Batch: 120, Avg. Loss: 0.03325566817075014\n",
      "Epoch: 8, Batch: 130, Avg. Loss: 0.03311832286417484\n",
      "Epoch: 8, Batch: 140, Avg. Loss: 0.05618414971977472\n",
      "Epoch: 8, Batch: 150, Avg. Loss: 0.04245166722685099\n",
      "Epoch: 8, Batch: 160, Avg. Loss: 0.0395680608227849\n",
      "Epoch: 8, Batch: 170, Avg. Loss: 0.040172523632645604\n",
      "Epoch: 8, Batch: 180, Avg. Loss: 0.03555039837956429\n",
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      "Epoch: 8, Batch: 200, Avg. Loss: 0.033234122581779955\n",
      "Epoch: 8, Batch: 210, Avg. Loss: 0.03154648067429662\n",
      "Epoch: 8, Batch: 220, Avg. Loss: 0.03385943807661533\n",
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      "Epoch: 8, Batch: 280, Avg. Loss: 0.03198291845619679\n",
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      "Epoch: 8, Batch: 300, Avg. Loss: 0.0419558685272932\n",
      "Epoch: 8, Batch: 310, Avg. Loss: 0.022750291880220173\n",
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      "Epoch: 8, Batch: 340, Avg. Loss: 0.03898795330896974\n",
      "Epoch: 9, Batch: 10, Avg. Loss: 0.02944679269567132\n",
      "Epoch: 9, Batch: 20, Avg. Loss: 0.048818086925894025\n",
      "Epoch: 9, Batch: 30, Avg. Loss: 0.03165969364345074\n",
      "Epoch: 9, Batch: 40, Avg. Loss: 0.032202025130391124\n",
      "Epoch: 9, Batch: 50, Avg. Loss: 0.03215926438570023\n",
      "Epoch: 9, Batch: 60, Avg. Loss: 0.05584547603502869\n",
      "Epoch: 9, Batch: 70, Avg. Loss: 0.04302518237382173\n",
      "Epoch: 9, Batch: 80, Avg. Loss: 0.050808916240930556\n",
      "Epoch: 9, Batch: 90, Avg. Loss: 0.028924463875591756\n",
      "Epoch: 9, Batch: 100, Avg. Loss: 0.0397494638338685\n",
      "Epoch: 9, Batch: 110, Avg. Loss: 0.0258002826012671\n",
      "Epoch: 9, Batch: 120, Avg. Loss: 0.04397916067391634\n",
      "Epoch: 9, Batch: 130, Avg. Loss: 0.025139121524989604\n",
      "Epoch: 9, Batch: 140, Avg. Loss: 0.03125216523185372\n",
      "Epoch: 9, Batch: 150, Avg. Loss: 0.02748911939561367\n",
      "Epoch: 9, Batch: 160, Avg. Loss: 0.02934491280466318\n",
      "Epoch: 9, Batch: 170, Avg. Loss: 0.03683619601652026\n",
      "Epoch: 9, Batch: 180, Avg. Loss: 0.02961447760462761\n",
      "Epoch: 9, Batch: 190, Avg. Loss: 0.03375701140612364\n",
      "Epoch: 9, Batch: 200, Avg. Loss: 0.06791237499564887\n",
      "Epoch: 9, Batch: 210, Avg. Loss: 0.03061336362734437\n",
      "Epoch: 9, Batch: 220, Avg. Loss: 0.0356647384352982\n",
      "Epoch: 9, Batch: 230, Avg. Loss: 0.037880152091383935\n",
      "Epoch: 9, Batch: 240, Avg. Loss: 0.03116624113172293\n",
      "Epoch: 9, Batch: 250, Avg. Loss: 0.026855764165520667\n",
      "Epoch: 9, Batch: 260, Avg. Loss: 0.02405970711261034\n",
      "Epoch: 9, Batch: 270, Avg. Loss: 0.026454580295830966\n",
      "Epoch: 9, Batch: 280, Avg. Loss: 0.03236592337489128\n",
      "Epoch: 9, Batch: 290, Avg. Loss: 0.034166484232991935\n",
      "Epoch: 9, Batch: 300, Avg. Loss: 0.02919183988124132\n",
      "Epoch: 9, Batch: 310, Avg. Loss: 0.024700475856661797\n",
      "Epoch: 9, Batch: 320, Avg. Loss: 0.030005292035639286\n",
      "Epoch: 9, Batch: 330, Avg. Loss: 0.025267345178872348\n",
      "Epoch: 9, Batch: 340, Avg. Loss: 0.03381191343069077\n",
      "Epoch: 10, Batch: 10, Avg. Loss: 0.031076253205537797\n",
      "Epoch: 10, Batch: 20, Avg. Loss: 0.025617930479347704\n",
      "Epoch: 10, Batch: 30, Avg. Loss: 0.0588422279804945\n",
      "Epoch: 10, Batch: 40, Avg. Loss: 0.032968361396342516\n",
      "Epoch: 10, Batch: 50, Avg. Loss: 0.03298996016383171\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 10, Batch: 60, Avg. Loss: 0.062583895213902\n",
      "Epoch: 10, Batch: 70, Avg. Loss: 0.03855631835758686\n",
      "Epoch: 10, Batch: 80, Avg. Loss: 0.025056897941976786\n",
      "Epoch: 10, Batch: 90, Avg. Loss: 0.03150013964623213\n",
      "Epoch: 10, Batch: 100, Avg. Loss: 0.02905651442706585\n",
      "Epoch: 10, Batch: 110, Avg. Loss: 0.04101450052112341\n",
      "Epoch: 10, Batch: 120, Avg. Loss: 0.03190358020365238\n",
      "Epoch: 10, Batch: 130, Avg. Loss: 0.02677675001323223\n",
      "Epoch: 10, Batch: 140, Avg. Loss: 0.023883145954459905\n",
      "Epoch: 10, Batch: 150, Avg. Loss: 0.02447187528014183\n",
      "Epoch: 10, Batch: 160, Avg. Loss: 0.03566633835434914\n",
      "Epoch: 10, Batch: 170, Avg. Loss: 0.04004751387983561\n",
      "Epoch: 10, Batch: 180, Avg. Loss: 0.02817896120250225\n",
      "Epoch: 10, Batch: 190, Avg. Loss: 0.02639421969652176\n",
      "Epoch: 10, Batch: 200, Avg. Loss: 0.023883712477982044\n",
      "Epoch: 10, Batch: 210, Avg. Loss: 0.023787080496549606\n",
      "Epoch: 10, Batch: 220, Avg. Loss: 0.023005729354918004\n",
      "Epoch: 10, Batch: 230, Avg. Loss: 0.04172956272959709\n",
      "Epoch: 10, Batch: 240, Avg. Loss: 0.0243528563529253\n",
      "Epoch: 10, Batch: 250, Avg. Loss: 0.0288102800026536\n",
      "Epoch: 10, Batch: 260, Avg. Loss: 0.04444281794130802\n",
      "Epoch: 10, Batch: 270, Avg. Loss: 0.027025605924427508\n",
      "Epoch: 10, Batch: 280, Avg. Loss: 0.03480590656399727\n",
      "Epoch: 10, Batch: 290, Avg. Loss: 0.023020967934280635\n",
      "Epoch: 10, Batch: 300, Avg. Loss: 0.023825152218341826\n",
      "Epoch: 10, Batch: 310, Avg. Loss: 0.04585458617657423\n",
      "Epoch: 10, Batch: 320, Avg. Loss: 0.034845652244985106\n",
      "Epoch: 10, Batch: 330, Avg. Loss: 0.02931973375380039\n",
      "Epoch: 10, Batch: 340, Avg. Loss: 0.03117009047418833\n",
      "Finished Training\n"
     ]
    }
   ],
   "source": [
    "# train your network\n",
    "n_epochs = 10 # start small, and increase when you've decided on your model structure and hyperparams\n",
    "\n",
    "# this is a Workspaces-specific context manager to keep the connection\n",
    "# alive while training your model, not part of pytorch\n",
    "with active_session():\n",
    "    train_net(n_epochs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test data\n",
    "\n",
    "See how your model performs on previously unseen, test data. We've already loaded and transformed this data, similar to the training data. Next, run your trained model on these images to see what kind of keypoints are produced. You should be able to see if your model is fitting each new face it sees, if the points are distributed randomly, or if the points have actually overfitted the training data and do not generalize."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/workspace/data_load.py:39: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "  key_pts = self.key_pts_frame.iloc[idx, 1:].as_matrix()\n"
     ]
    },
    {
     "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": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95ef8aaeb8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95e10e44a8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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5XFaviz8jFXCjSUEp5ywWi6p8UrihqakppTETiYTSrFEUcfv27aF7eliI/2ZyJIDyRjet23vDMFQ4LKtP1LSoZ+ElhPEU1SwrMJFM8ItXf5Er9Su8Jf8WPjnzSU6YJ1gL1lg0F/ns1c+y6q5yIXMB0zK52rjK02NP8/yPPo9pHDifei5oFCzCnUixlB59SPQmtTASyorJk/SBPrFyHdd1lRnN5/O0223GxsbIZrPMz8+rIqtMJkOv16NcLlMul2k2m1QqFUW+Sej+xGkUnUN5VL5HTIFenTb6eVnR8nsURaqWVSa46la5UrtCQMArtVf401t/yrvq7+IpnsL1XP70/J8SGRFXW1cxMAgJeXn/ZVZ3VlksLiqNoIeZ4he02211D67n0gyaZM0srudS7pSJh3EwwLVdnLijQCFpBsn5jD6bfI9e4Sdc0OTkJFNTUzz99NNks1kVPgdBQCaT4c6dOzx48EAlBcVsP64cCaA8yhMXgOi5nSiK1GqWQQVUqR8cqNZCoaAKsg3TYKuxRdAImOxPsuPssGgskjWyBH4waNfvw0RzgnK2zHh3HNu22ba3OeWcIqgH1MzaEFmnk2uu67K3t0e72aa32+OL/he5F95jgQUiL+JB7AEzwQyGYbBpbTJbmOX9vfeTm8mxc3dnqIZX6nMlRyVlCN1ul83NTTqdDvF4nLe//e2cO3eOubk5pqenVfZatK7jOLzjHe+g1+upjLNUx+nJ0sPIkQLKwyIciRTkn5QAwkEvi2gZWYUSOk9MTAxqV+wYn1v/HDd7N5nxZvjq//VV/F2f7FyWKIxwbjk0Fhq4nstH//ij3D15l5X3rmCNW6TyKSZTk6pKTbfzMNBcXt+jvdlmt7XL/G/M4+/73Pv4PUIz5H54f+AMGyEb5sbgGYl4YD3gTyb+hL25PZKLSea/No8TOkOlAvrqF4AKf9Rut8nn82QyGeWTiEkTsSyLUqmkfCQpYHpiNcpoVCN2XBw8iRx0Yso0TdWTIzQ1oFhZx3GYm5ujVCrhxlxu9m4SErJlb+Hv+pQaJaJmBAYYoUHufm7we2RwevU0p9dO01pqcft/uq2YTvke/X57nR6x/yXG2I0xYnMxMhsZjNDg8v3LvLr4KpfvX8bA4OrCVaa9aXL7OW5O3eTkzknuzNwhMiI6pQ5BPMDre6qrAFCRitT1JhIJVSO8sbHB+vq66hmS9hWpOZFFJWWjvV5PaT4xdXq0+WZyJICi13roDKSEmxLaSjQjpkjoaz2ZJqswnU4zOztLNpvFxOTizkVWxle4uHeR1HSKqBXRPdUljELSd9N0T3XBgOTtJEQD8GTWMsS7cXz7IFUgWk5lbPcDkjeSg88/yNCaa5HZzPBLL/wSL/y9F/jbX/vbFDYLbJ7fpP/BPqe+cIp6sk6uk+OnP/bT1At1Lq9f5qO3P8pvzP/GkJ81qlnEIW+1WliWxebmJpOTk6TTaZaWlpTJ1X2ncrnMgwcPhthqna099Bz94NP8g8vDVKFeD2JZlkqp61XzwgfIKtKTdGNjY4qAsjs2z/2r56gn6xS6Be5+/i6u59KwGniuh9Nz6Ix1KFgFvIrH1B9NMX9jns7JDmE+xOAgQQcooHY6HZpWE2vZInknSXOxydWfvUrCTRDmQpaNZe7//H3KbhkvPci9uKddxm6P0TrZ4gN7H+BvPf+3KLVK+IZPwkkQhIHiQGRcxIGNxWKKCuh2u6yvr5PJZFTppWSVxfzWajXu3LnD6urqUMQIPJk+yqMIJfFPxLHTu+1khQFKq8ggJZNJFQWYpkmYC/HOeIzdGqN5oslaY41Wp8V2YxujZ/C1ua+x195joj9BGIRUfrrCbG+Wn0v+HEkjSSE2qPxvtVoqIZlMJWl1WrQ7bbY/vI3RMDCLJlkrq55FclA9q0cUDLiQV37mFeK9OK1Yi2QtSdfp4hs+H/3QR7k1f4tUJcX575xX5lU0gN4DFAQBrVaLtbU11b1YKpU4c+YMAOvr66q147vf/S67u7t0u13FM0l7yxNnekZLHfUcjABA2i11Z1bMk6hZUb0nT55kcXFRgSmKIh78wgNaGy022hus3VzjhYUXqM3VKHQL1JI1IiNiL7E3WGUGbMQ3+Nf+v2anssOys8zHMh87yNp22vz8Sz/Pi3svctI6yYfjH8bMm9gxWxFhunMNDBUqtWmDBxjwe+/4PXa3drk1fwtM6JQ69GN9Yn5sCChikqTbEWB/f19pi29/+9usrq7S6XS4ceOGIvIk19PtdtV96ObtsHIkgDLKo8ggixYRh1Yn20RtSuQjvAnAqVOnVIW+XNcLPJqxJq12i3KnTC07AEctXeOUc4q77l0WWGB/b5/meBOn4rA9vk1ExB33Dh2jQ8bMDJqrHI8X914kiAJW/VU68Q6FWIF4PE6n26Har1LoFGjbbZpBk0QlQcNs0DW7mD0TIlSLqWmZhFZIqpKiU+qQ2ktBG4J4MAQUeQ4hGgFV5Xb//n3K5TLJZFKZJSHVYrGYitikxVXM6BNHuInooafnearwWEyNAELvwRXNE7Nj9GN9CnaBp556ilQqpXyJIAjI5/MUCgXOnTvHW6tv5d72Pe5H91kIFohvxzEmDLIbWbJhlmtcY3JsEgeHNdZYtpcpxUuDnuDJKTJ+hmfGn+F75e9xLnmO2fws/W6fxnqD56Ln2DK3uLhxkTARcn3yOhc7F+kYHe4s3CFbznLxry5y+fJl1XxeLpdZ/tYynu0RtSOMmKEa4IzIoBAVqFFT7bNw4LdIz1ChUFARk57wE1PTarWU76KP9WHlSABlNAurk2wSLnc6HfWQAh7lm4QBzf+6iT/jY+6b2M5ANduWTbKXxJlyMC1TtXREUcSn+BTJryRJ3kvyDz7+DwisgJW5FSIiQjNkiy1+/0u/j3vSpfGRBvl8nmwmS/d/7FJ/uY73jzyMcYOYHWOsMEbqiym62122Pr5FYAZcXbg6eDYz5MrCFQwMMKE93ubv3vq7fGf5O+SDvIrQgiAg7IUEaJXyIXzk5kdYai1xN32XLy99GYwD8yFaQS9SMgyDYrGo+n2kPvdhIfHjRD1HYvtQ/YZFvepmQ+dI9PcEOFbOwp/xwYRWsTVQ8ZjkfzlP+mfTOJ9wsAxrKIpIe2nmV+YptUpcWr+EFVhcWr908Pv9S5SaJWauzJCLBoVPdsvGf8mnFq/xauFVgijgWvMabtcluZqk2Cxy6f7g7y+uXyThJQZmxk0Sd+MQQcJN8Lv/5e/yx2f+mP84/R+xYgOSTOh83UFP9BMstZawsDjZPknaTw/llXTfRVjcQqHA7Owsp0+fZmpqSo3TaKXb48qR0Cg6GISBFCZWdwZ1B0xPy5tdk0Q5QW+iR76RJ5VKYbUs7Bs2RmjAFTAaBrFCTDnFlCC4GGBds/jS//4l6qk6hXYBDKhmq+Rn82BBcCFg6tzUIAs8nSN8NmTsr8a4XLvMq6VXuZS7xPzSPNFbIqJXI379O79O/d/XIYS//wt/HwzoO30iIvX7tcVrREZEOV7GGXOw9wb7sEguRjRlw2ywllljqbXEWnaNVqylHHvhS4IgoN/v02q1yGazWJalms9836dcLuO6rtrL5fuVIwMUOOjCA4ays6PObr/fV7WxURRhx2zO/fk5omTEeGocpiHIBnjnvQFYLhvYk7ZiLg3DwDANjC8ZBNUA659bjF0dw0gbRN2I0skSxnMGYSWEHPCaOQ/8gIkvT2DGTJzvOhhlA6LB+/H/LY7dtmEM7P/BJng14MLeBVYmVri0fgkv4XFj6gYLLIAJ97nPorFIMV6kmWkqJ9QPfHzbx+pbxOwYv33pt0l7aRpWg5SRUhvj2LatHFbJlAtTLWZmenqaSqXCgwcPFNstzLU+roeRIwMUvTJMtIpkU6VSXQ+TR5uu404cAojCAZA63Q71z9RJ9BPEp+LEosGjSlWb8DT2mI35b02spoUxZuCVPXrJAeNrTppY0WvlCfEEzX/YpPJXFTrv6vDijw2inqvNq5Q7ZabSU6QmUySTSXJ/kKO72eXfpP4Nu81dnLJDP9Wn5tUwOgZ9t89ed490lKbT6bCT2BnUklgmN991k+5El2Q5yeXvXSaRSlBr1kjEB/U0ujMvzy4NZK1Wi263q3w82S1Br2eRAqxHZeofJUcCKGKb5cH0SnkpGNLzORL9wIE2CsIAI2NgWgdFRX7o42U8rOD1ZYxDYLFtrNRr1WslE6NrDPE3lmVh1A3c77kQQPLPkzzz08/wvf3vcTF7kZyZUytVOeInYiS8BOlUmmB8YB6KbpFutzuolQ1CFcHIffWsHt2JLpjQnejixlyiMCLjDUJywxxuldVrXCRRKNuTjY2Nqf4eSZHoXNPjgASOEFBEQ6g6Vi2UkxUhpul1pJEBN37kBq1ii1wtxzvddw4GI4gwagZMocJtnfnVGV5ZZSLyna7rDtL+qT7W0xbBywHOWx2e/7HnubJ9hTFvTIFaTxwKAG17YPJkpych5PTia5UacG2cHQd3yiW+GydqRnzw3gdZbCyyll3j+cvPqw4EvTJfvrvb7So+pVgsqjJIKQEVp/dxQQJHBCh6GSCgquclg2xZFqlUSrGMYoZEDZtZk1axBSY0Cg22q9vMTM5Q+uclnFsOwfkA/4u+yqrKxMpKk4Rfr9fDMAyVbJRwen9/f1Ca+KkETsfBmrD4uT/+OW71bnEhfYHffMdv0u8NShCksDuVSinA6Nt/Goah2lAFjGJeozBi7I/HIA2x/uC5FxuLWFgsNZewO7byTYRngeEKu0qlQr1eVzs1SD3LaHvJY8/RD2Oif1ARMEh1lqhSvfJ9NCsq3IDjOMT9uGI24ztx+u0+RsPAuelghAbWdYteuQdTB/ua6FVjwJAG6/V6dDodqrUq337p2/h1n7nZOZZPLpMJMlSDKrd6twgJWWmt8L3r3+Ps7FngoExiNGelmwkxtfpEi3n1PR+raYENlbDC7cRtTvdOczd9lxo1peVEq+hF31EUqWTl3t6e6mnSC8X1e3ysOfqhzPQPKPIQo6UDssMADPe9yGDLAGQzWc79+TkaQYPGVoPOuzu0Yi16p3skbifwz/kEuQAzGN5nVa+YHy1v3K/u82s7v8ba+BoTmQl+dO1H+ZH/80cIKyFz43OM/71xyokyk94kze0mr/Re4UTpBBMTE0pzycSJP6FrFr0bQDcdYRRCAizfwogMPj/xeSbjk3ScDpF3oE11J1bf+Ccej7O9vU2pVCKbzZLNZpW2FhOn75NyWDkSQInH42pLTdEUgKrckpWutzNIXYquZayeReAP0vRhFLL1i1sk+gnsCRsrtDADU6lssde6CRLfASBRSnBv8x6hEVJOlnnP33gPn01+lisLVzi3cY6P9D9CbGpQnvnVva+yXl3nKZ7iXy7+S4hQSTiJUPS95GC4XbTRaNBut2m2mrT+mxbetIez7ZD/f/LYMZut3hYx/6DZTW8+F79Nf6YHDx7wzDPPKK0mkaIswIfVG7+ZHCmgSPUWoGy8XrAzavNLpRKnTp3CNE0ePHhAo9EYkFqxQbtlSIif9rGrNsFYoFaUmDLxhfQ+HWE4S/ESF3MXuda8xoXMBYpLRa5UrhCaISsLK/xe4vewA5vre9cHZBrwau1VmkGTZJhUjqt8h3yv+EGSqJMEXqPRwI25eNMemOBOuxgpg1w3Ry2ova7YSG9Kk8Wi53dkk+bd3V31ecm2y709jhwJoEhkINpE1xJwEBVJz4tMaC6Xo1AoKHUrOZ+vT3+d1XCVT4efZvJ/nSR+K453zqP6S1W8wBtKD4iI+ZN6F8dx+OKlL1L36hTsAoRwbv8cK6UVMGC1v4qBoUBiGRZPFZ4iZ+Xo9Dv0ej0FcklMxmIx1bMjHYT1el21f4btcOBjTfVxth0+s/EZznnnuG5d55fzv6wSLro2ELCILyR9Pfv7+0PtIHrbq5jFJ65wSXag1rv5FT2v1ZxITkQAdeLECbWFZ6fToRE01Iq8z336nT7xW3GM0MC+YRPVIsz861eS7tzKAPqBz35/n7yRp7Xdggie++pzfOxDH+P2zG0uZy4T2AErjRWeyjzF5579HAW7oP5+lCA0DEP1BLuuS6VSUZsMu65LREQsH2Ph6wuQhHA95Jx3jhgxzgfnyQSyZ2q3AAAYt0lEQVQZmkYTGC5nlA2BGo0GMGiOM02TtbU15fcAQ2ZIgCtm8DByJICii57H0HkCceCk40/KAnO5HK1Wi0ajge3axHfiuNMuJ6wT5MZyuGddnJsO3nkPY8wgZh5cHxiivCWUrDfq/OrGr3Krf4uLOxd57l89R3upzS/8w1/g1tQtAG73b9Pv9jlfOc+/+JV/gf2UTfhcqDSMTISAX6IqKSba399Xm9303T67P7lLf7JPY7fB4tcXqRk1VqwVLgQXWLFWqFHDCI3X+RdSqyP9xULlh+HgPABAmZp4PK6eVdfYh5qXH870/nBEWg10Ukh3YEedz0ajgW3bVCqVgcqNJ5j41gQnLp7gb771b2KYBpV/UsFu2xjFAUgkpNYbrzzPo16v0+l1qBgVvKbHLfMWoRGyMr5CJVnhgf+AqxNXVd6nE3TAgJXCCtVEldIrJYKdgHaxTdwYPMco0CXp57quCsE9z6NNm/5kH0zoTfZohS2CMODT8U+TJ0+NGnHrYFtzmWCJAOPxOPF4XJ3Qoe/KJN2Bui8j5vtx5EgARTgEvQFc9+aBoYMGpFXj7t277OzsqO79VCqFHbOJ+3Hqfl1tN2EVLExMDA52MNCduyiK2N3b5TOtz+CZHo7hMBvNsskmZ3bO8LMf+1naiTZGOMI/RBCZET/zsZ/hq7/zVT718qfYsrc4FTvFJ+c+yUZvgxlrRtX6yn22Wi2l9j3PI2pFyjdJlpN4VY+Mm6ESVahbdSzzoPRAGOkgCIZqdpLJJP1+H8uy2N7eJpvNvq4fWkD6/bCzRwYoegp8dMcBfVKFRRX7K2rVtu3BjtExi28tf4s/4U84vXuaT858EiuwIASjZRDlD3ZjEhNkmibddBevNZg813D5KX6KQrxAY7bBV6KvgDEABTDQKjLOBrQTbf7gv/oDNmObRETc9m7z0XsfpU+flJni+bHnhyI3eVbRjDErxvwL82QmM9Qe1PhE4xOccc+wYq7w2dxnMazhbctEk9i2rah5vapNanNlpwThcPQ63icyPNbzHaNFS/I+HDC4+/v7KnSWHIoMWMfosJ/eB+C2e5t21Mb2bUqfL+HcdHDPutQ+UyN0DrYIjcfjPDX9FKlyik7YIWEkWEgsDGpfKRA34vTpkyBBZET0oz4JIzHIVNMnbsR559Q7WWGFu+FdTiROsNZfA6ATdnjQe8B4OD5UYA0H0Vyn0xksCAyKZpGz7lksLC6EF0j7aZo0h3wLGSM5H0iAIjWzYmIk8hrdSvX7SQweCaBIdBMEg0MCpLxAciCygvReFNEsuvR6PaJehL1t48/4nI6fJh/LYzZMRec7Nx2C/QDGD/ZEE1P0O5d/h43+BtPWNJ9d+yw3whssRUtMGVOsh+ucvn96wKPMrbC8sQwhXFu8xowxQ6lUIl4bRFhpO03KG4AuZaaYjc0O7XQt0ZA8p2wZBlA36tyK3+J0/zQ3YjeoG3Wi4KAVBQ62MNcjGNM0lfkZLcLWC9ZFnkigCNLFvIhK1pvQZSVI7axhGKoXWAp3YLCnq/k7Jh/47z/A+/6z9w14hlyoop/e6d6gGcsdOLWyNYZlWcTtOCdjJ9lp73Cjc4OQkHvhPSIjIjIjVhYGNbWRFXF9bkC0RWbEfe4T+3cxbv4XNwmtkOut6/zm2d+k4TaYjc0O5Xj0f57n0e11CZIBs/lZ3P6g0f2fFf4ZsVaMKtUDE8fwvnGyg5RoJd3vetguTvLzYb8fRo4EUKRfRzgH3dbq/TytVotms6kGR2duxXydPHmSGzdusPrqKu966l3qiLedT+wQ1SL8jE/ohVih9bqJM02TKIgoNoucM85x07/JpfVLRERcW7j26N8rl1j+7jKXzl/i6sJVLsQvMN2bZjw7jh/4Q6ZVL5zyfI+/uPQXdEodas0aCy8sqN0GQuOgK3FUE8iJYuKjiKMMA0ZbXtPTFDqjCzyZfT16n7GsCL1PRwZZzyTrf6eDRUoky+UyfbdP22gznhgcbhmkAqLgYNCll1hFWJFB6p+ksK5ZfMX4CtVSleJ+Ef+MT/PfNyk2i4RGOKiv7RSIiAa/dwtEZyO+9DtfolqsUtwrYmDgX/Apf7o8xFtImNxqDToVOxc7YEI9W6dIcWiTGz2xCMObCcJBJ6L4PgJ2vYpN50pGyxKeOI1imiaZTEaFbpKPkPfkp23bqgsPhvdlk/3chNLfLe/y5fqXeWA+4JR9in88/o8xDRPf9Ym1Y3iZ4f3nTdPEbJhY161BGB1ZlPZLdJ7rEC1EFP+7wYmhZmQy1h6DFJiYFNtFSEP4XEhwL6D0j0qD1gwgdiNGVIvoOT263S6dTkedDNJut2nttjAfmIRzIdl6FqNlUAgL1M3BEXSZTEa1sQpodIJQFo2uMSQKlM8Jgysgkr99XDkyQIHBIUSdTmdoH3mdQZTkoa42xfaLx7+8vEw2m8WP+9znPgCr3ioPqg+YSc+w9OUlkneStE+2Wfuf11TVuu/7ZCYzRBcjuPLatS9FxKfi4ILRHylE7nPgP/QgqkeE2ZDgQoB19bV2kjMubbtNvV6n3+8rur7ZbLK/vz84zu7/Nkg5Kd7/3vfzY5s/xnJvmRuxG/zT7D8FUM3qY2NjqplLWGQxS8JBGcbgdFJhr0ejx1EN88RpFMlHCN0tIaSEcfLAkhjUaWzXdYcG7f79+4MeXSc5WK3zIZPuJATgdT2SdwZbVKTvpulud3ll7xXlQJ89e5bSJ0vkwzyWaRH/QhzzvzXhEhiXDaIrESSBDsTe+lph9yshXAbzcybOtUE1Xeu3W/TcHo1YA7fnqsmVE0lbrRabW5vs/MQO0VyEu+VyIjrB6d5pLCzO+ecoGAXccDAG6XR6yBSLE6+f9iX1t2Ky9KpBvbVF95eeuCp8fdPcUZSLHdaLrQU4Emrqe+Nfu3btYNuIf2eSn8ozNT7FnQt36M50OXniJJl7GXYndvnL239JrV5Tkc/W1hYTExO89a1vZS45h3PVwQgMuArR+WigQUIg0tIMEeADK6hqOi/y6Ka69Ft9RQzKvXqeR6fTodwuE81FYIE/6zM7O8vd37/LUnuJFWuFptXEiQ6q7aUEFA5OIRVnVQ+RhX3Vq9/08fp+62aPBFAkM6wfHqAX2IjoJ1HoNSW1Wo1EIoFt26oRqt/vY8ds2rttbuzdYH9/n+J8kW9Pfpu5k3M0zAZR9WDL7yiKqNVq1Go11tbWOLF4gp888ZNk7mUIz4RYKwN2l9d2Lg1fCRVwousR0dkIboF31qNGjVZtsGt0u92mUqnQ7XbpdrtUKpXBsXdbdcyNgX/i7Dpk57N8YfoLNB80adtt0lZa3Zv0Druuq05ql82MJQIqlUqKPhBGVjS1rkHEz9GDgsPIkQBKPp9XPIhEBkLC6ZvjyMDpG8qMsp2dTkfVYUhE4Ac+W+/fYn12nXwjT/56HkLwbA+rN3BedfttGAYbmxv80Xv/iJ/8Gz9JfDJO5jMZjKsGJIAucHnQQ8Q1CC+EdH6lQ7/cp2k11a7R+k7asmtktVqlUqkQs2LE/o8Y42PjdL0ut3K3aLab1IwatmE/tPhb8jWiUfQ93nS/TcZMIiC9MEuv133ifBSpsJdaDXlovQB5tHJcDmYMw1A5i1KrIgMs9D5p8GYHdSqNXAPXdnnpzEvUMjWKtSJPX3l6aNDEadza3uJu7S6niqdo/0qbbJglzIQE1WDQieh69Hf7GGMGvuvTdbp024OaEwGHVK8FQcDe3h57lT06ZoeYFeNXu7/K+a3z3Ijd4Bur31ChMTCk6eBg/32pAtTDbfF/YHj3qdFSDT1XpJdaHEaOBFDk8KIgGBxWJE6tDJQcGmlZlsqLyIlYMABNq9VS2kZnby3Lgj6km2nquTpjrTFM06SWqRGZEbV8jZ+6/lP8h/P/gSA8OKFCNjp++eWXicViLC4uQhyCdkAv7NHfHZRbdnodzB1TmZVms6kiHNmsr1KpDMoKuh12f2IXd9rFeeBw9vmzxIhxzj/H18pfUwVa4o8J8AEFBKmQEwpf/Dq9KH201UWy7vr78ARGPZLHkJ+S19EfSLSLYQyOuN/Y2FBHkeiElmUNjmOp1WoqQ1wsFjm3co4gHmB0B6uoWC9Sy9W4tH6Ji1sX+fqZr1M360OV/zA4h3h3d5dMJqPKBfRyRvEJut2uKnmQFe753qD7r9cl8APMjIk77Q5qYudc/iL7F7yz8U5uxG6wVl8bygDLT12jik8m0Y5wS+LYwsEeuzrbrG8R8v0UVsMRAUq73SYej6uJ1ntjZCAktyG7Q7fbbRUSSyLMsix6vR6zs7PqsOyFhQUmJyeJWTHchqtA+Larb+PvXP87nN84z3Zpm36iT66bo5KsEHNjGBy0cNy7d49Wq6UOYRCTIsXRjUZj8Lrn0opaGJ2BRnj5bS/TGmuR2c/wvrX3sbO1Q2IrQW+mR2wrxheiLzCWGyPMhZSMEoCi5SX8FY2gk5CiIUYPCpeiJDig+vUSSPldGOnHkSMBFCmykZsXoOgrTESiI6HrRWvo7afNZlMxtjMzM0xMTKgOPjFlqVSKFzIvsP4T6ziTDh/8tx/k8z/+ea4sXCHbzPKOa+/AMi36/b6KWoTI0tP1SvX3e3z34ndp5BpkqhlOfu8krbGW2rOlfKXMh1c+zNJfLXF99jpfin2JptOk0q+Qj/JDftVoklQ0htTcCoCbzaYal0wmo7SHXhEoPovwKqONdYeVIwGUQqGgVLiIrjbhYG97WSmyosUW6yl1qcOQnauz2Sz3799Xp3AlEglmZmZwTgxWYj7ME2vFuLpwldAKaeQaRMkI+gfn57Tbbbq9Lp7t4fiO4lKkLrZjdqjn6mBCc6xJu90msZugN9kjV8uRbWQ51TmFhcWlrUuMLYzRMlpDbRdBEJBKpRTogyBQzKxEcHrKQSI+8WV0ok3vz5boSB/PJ9KZXV1dVaWOYk9hMBGS49DJItnbTRxbQLGzYRiysbFBsVhUJkp4FpF6vc43v/lNkskkCwsL5LI5xjPjXFq/xJWFK+TbeWL9GLFmjFqrBgZqS4pWcWBKzvzZmUF4zGsHJZkGqb0UnfEOid0EyUqSyf93kkQpwWxhluVnl+nUOmTWMjQXm1glC9oDIOZyORqNhiqrGB8fV869aBTZvbvdbg+FuWE4OHBStrcQR1/fwUDMEwy+L5/Pc+rUKebn5w89R0cCKHDQsiG22Pf91zVzw0GNilR4wcHmumK+Wq0WjuOwsLCgDq8WJ7XX6zE3N8epU6fY2Njg2rVrZLNZ7jx9h4VbC8TX4qSiFB945QPM1ea4m7rLr8/9OtaYpRrhW8UWXaOL5R1sTGMYBjP/3wy+7fOJ1U9wtn+W1dQqf3Txj3j7O97OqVOnqL27xubWJmWvTOGbBWIbMWVehL3VM8H6tUUTpNNpZX5G6QN9XxnTNFXeDIa3Wn33u9/NM888Q6FQOPT8HAmgyFEpsl+7DJCsAjjgAcSJk7oVnajTIx8ZoLm5OVKpFJubm8Cg9qVarVIulwcb+GWz1Ot1mmZzYPv7EQ4Oc7U5rGiwd1o2zNLr9oa3+GxBwktQN+qqMj8KIzL1DGf7g1LGU91TPLv8LKdPn1arP8yHWDVLnfohZkFYVDFzOtkmnIiYHr3ZSyIg13VVdWA6nVYdDeJoiywtLfHss88yPj7+5JmeIAjY2dlRQHEcRx3GNFqRpXMNsmvzaMmfOHu+PzgVTKreRWNNTEyQTqfZ3NykWq0qn0euXTbKrKXXWGovsZpcpRpVsfoW81+bJ0yExHoxPn7/4yx3l7mduM2vTf0aQfRaiB8LuJu+y6nuKbwzHk+99yn84KCcUzYLKhaLTE9PA6hoRbSpLArpNhB+ybIsBQLRunoorLdx+L5PNptVZySLv/OWt7xFRUhPnDMrR79Jyl9PZI3G+2JukskkzWZzqChbtImYoUwmw+XLl9nd3WVzc5OpqSkmJyd58cUXuXbtGvl8XuVSAOUEB0HAc4vPMZueZc/fg+ZrfEYEVs8i42dY7i5jYXG6d5qCUaBu1QnDwb5pf/af/xn20zap2RSGaWBhqay4PFMulyOXyykzIhEZoLSltGfoPloymVQVf8I9JRIJstms2pdF+J5mszlE4y8sLDA1NfW6grDDyJEAimmaiuYWv0Qn4USDwEG+Qt/FWt/TTMyRaI4gCKjVasTjcXK5HPPz80RRxMsvv0y5XB6iycXmS/i9l9gbbOT3mhoXdrNhNtS+JbcTt+k4HczQZHJykmeffZbz58+TmkqpSRKHVJ5JgDExMQEMnPl0Ok0YDvY+EWdUB77uuwm/ou+8JEfPJRKJocZ7WYSpVIpSqUQ6nVbj/sQxsx/60IfY2dnhD//wD1U7pEyg1IPqtaOxWEwRb9JSKaYpDAdtp5lMhvHxcVUMJfuv9vt95a/kcjnVdikA6Xa7ytY3Gg3i8TjZbJazZwcb5TQaDTqdDr878bsk3SSx8RhvybyFmZkZzp49q85WFnMgXYn6yk4kEuTzearVqkoSCpmnlz1KqC8mVzLQwo2Iwyt+joTrrVaLvb09pXl832diYoJsdnCgg37402HlSACl2+1y5swZUqkU1WpVJfiEth7dPEenpXV7Lp8Ts6Wn2kUly/YY0qOrh+PSF6wfZyJFQu12m3e9611Ki4ndz+Vyyu/QczPigI7WqYqIySuXy/R6PbUzkl63oje5pVKpoa0vRtsxAFWX0+12Vdmovt+MTmjq/z+MHAmgyGb+mUxGrXCZKFGvUkMh4Z/wLuL8yUNLal+Ob9nZ2VED32w21XEkUrsqEYc4gLovId/XbDbZ2dmh3+8zPz+v6mJ0FS+Rll5hJtcAhkAt+8Jdv35d9U3rUZ58Rt9LTk+WygISUykphbGxMbVQkskk9XpddTIIqyv+jiQMDytHAihve9vb1OGRumaQ0FcGRmco5bOAWvkCqFQqxcTEBFtbW8oBLJVKKqmmt1tKE7dcX19tMpDpdJparcb+/j7j4+NDZJYuo4XMkr/S2zqlMOqFF15QPIdt2xSLRXUv8oyy6Y6uPWTBCMjFR9vc3CQejzM5OQkchNWiyVqtFtVqlW63Sz6fV5r6sHIkgNJut7l79+7QpjO6mtYzx0JJC1j0JjDRCGNjY6peRXaUzOfztFot2u22ApRe7DOaudXJLCmM6nQ6Km0g4NJrO3SOQxJ24lzL/a6urvLiiy+qU857vd7ggEytZED+6R2FMFyppo+NmKmtrS0FMn0LMPHd6vU6+/v7TE1NPRaHAkcEKC+//DJXrlxRFLweJqZSKRUBAMruSlgpXIIMtOM4lEqDTKzQ2XNzc7RaLe7du8fW1tbQRsAyISJ6g9QofV6r1eh0OkN9RQIIaamQn/L9Asp6vc7q6iqvvvoquVxObaIjzyjPLVS8JD314iMBsIBH3pMMe6vV4s6dOySTyddVt/m+T7VapVarqQMpH0eOxOkaOzs7TE5Ocvr0aeBgfzI9NQ7D2z4AQypdVrbwDCJjY2OMj4+zt7enThXXK9H1RJt8h0za6DYckmPSN8kR2lzuV5hSMTnSnrGxscGrr77K1NTUIPTe21Oni8pz6TQ7DDgjCZVFWwlo9bOMLMsin88rh1gKplS/kuawy/Uft7fnSGiU9773vSSTSdbW1vjWt74FHKweWe0CBr3ASae2hWATQsmyLDKZDNPT0wO29bXoAlCOouxSpJsZPapStPtrPEYulxtyVIWz0Nlj3YcQn+Tu3busrKyoUsatrS11vrHcv/QTC2gkkhLzJmSaDg49jyX8SKPRUIc4jRZ8jX6HvqDeTI4EUCqVCp1Oh2vXrimVK0eKyIPq1W+jdhwGWmhycpJ8Pg8MbPfy8jLFYpGrV69SqVRotQaV8Xt7e0ojCCgMw1CrVO+BFvNlGAalUkn9blmWqh0R0YlBacuo1+tcv35dbXzz4osvDmXJxQzKpEquRl7Xz1DU+RP5G7lvMdvCNwm3ooNL2FsZzyfOmf2t3/otFQZKWYEkuWB4Mx2x4XrZgW3b6sRw0RDJZJLJyUn29vaoVCpDJuthe+yP+gGjJYTAUGO8/K3udMv1ZZK73S6bm5vKBN2+fZtut6tWtd4+IX8vYavUlIxqKX0vXD08F5pAj3bkeQxj0J6qk4GPw8rCEQGKrAadYRXSSCe/5KH1PJBM9OLiogKRbduKvl9fX1d9Mb7vK3Oj+x5yLVllup+hb7YjNLqo7NEKPOF1hPSSYutGo8He3h7ValV9TteUQgmMdhwI4OR59Qo2AZqYR4nGRu9JNJ9oaf3giMfxU4zHRdax/KcpRyLqOZajL8dAOZZDyTFQjuVQcgyUYzmUHAPlWA4lx0A5lkPJMVCO5VByDJRjOZQcA+VYDiXHQDmWQ8kxUI7lUHIMlGM5lBwD5VgOJcdAOZZDyTFQjuVQcgyUYzmUHAPlWA4lx0A5lkPJMVCO5VByDJRjOZQcA+VYDiXHQDmWQ8kxUI7lUPL/A+fajWdpSbTXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95ef7de588>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95ef7967b8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95ef757be0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95ef71a978>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95ef6dda58>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95ef699588>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95ef65a668>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95ef61f748>"
      ]
     },
     "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)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you've found a good model (or two), save your model so you can load it and use it later!\n",
    "\n",
    "Save your models but please **delete any checkpoints and saved models before you submit your project** otherwise your workspace may be too large to submit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "## TODO: change the name to something uniqe for each new model\n",
    "model_dir = 'saved_models/'\n",
    "model_name = 'krunal_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": [
    "Optimisation functions calculate the gradient so to calculate the partial derivative of loss function with respect to weights, and the weights are modified in the opposite direction of the calculated gradient, I used Adam optimisation function. After rigourous tests, the results showed that it is much faster and yields low loss as compared to the other optimization functions that I implemented.This function furthur got improved by tuning important parametes like momentum, learning rate etc.\n",
    "\n",
    "Loss functions calculate difference between output and target variable,I've used SmoothL1Loss() function which creates a criterion that uses a squared term if the absolute element-wise error falls below 1 and an L1 term otherwise.It is less sensitive to outliers than the MSELoss() and in some cases prevents exploding gradients."
   ]
  },
  {
   "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": [
    "I began with a simple two convolution layer network each having a max pooling layer in the middle and finally flattening it with a Flatten layer. The impact of this network architecture on loss was tremendoud hence, I have to increase layers with which the dimension of the image gets tone down to single number.\n",
    "Also, adding the perfect dropouts within the range of 0.1 to 0.5 helped alot having the middle layers dropout probablity set to 0.25. \n",
    "\n",
    "Model architecture:\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, 64, kernel_size=(3, 3), stride=(1, 1))\n",
    "  (pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
    "  (conv3): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1))\n",
    "  (pool3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
    "  (conv4): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1))\n",
    "  (pool4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
    "  (conv5): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1))\n",
    "  (pool5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
    "  (fc1): Linear(in_features=18432, out_features=1024, bias=True)\n",
    "  (fc2): Linear(in_features=1024, out_features=136, bias=True)\n",
    "  (drop1): Dropout(p=0.1)\n",
    "  (drop2): Dropout(p=0.2)\n",
    "  (drop3): Dropout(p=0.25)\n",
    "  (drop4): Dropout(p=0.25)\n",
    "  (drop5): Dropout(p=0.3)\n",
    "  (drop6): Dropout(p=0.4)\n",
    ")"
   ]
  },
  {
   "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": [
    "Rigorous number of attempts were made to set the correct epochs. Realising and visualizing the loss after each training made it achieve that number of epochs and batch_size."
   ]
  },
  {
   "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": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.03708604  0.14393698  0.21824703  0.21966095  0.21316873]\n",
      " [ 0.18654135  0.06825199 -0.09066105  0.07029258  0.03306517]\n",
      " [-0.08780232  0.14541276  0.07170275  0.09655026 -0.05332645]\n",
      " [ 0.14055002 -0.03346777 -0.18110183 -0.24199145 -0.08321024]\n",
      " [ 0.02158092  0.00628453 -0.30038896 -0.29379553 -0.23898384]]\n",
      "(5, 5)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f95ef4b6748>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95ef5e1b38>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the weights in the first conv layer, \"conv1\"\n",
    "# if necessary, change this to reflect the name of your first conv layer\n",
    "weights1 = net.conv1.weight.data\n",
    "\n",
    "w = weights1.numpy()\n",
    "\n",
    "filter_index = 0\n",
    "\n",
    "print(w[filter_index][0])\n",
    "print(w[filter_index][0].shape)\n",
    "\n",
    "# display the filter weights\n",
    "plt.imshow(w[filter_index][0], cmap='gray')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature maps\n",
    "\n",
    "Each CNN has at least one convolutional layer that is composed of stacked filters (also known as convolutional kernels). As a CNN trains, it learns what weights to include in it's convolutional kernels and when these kernels are applied to some input image, they produce a set of **feature maps**. So, feature maps are just sets of filtered images; they are the images produced by applying a convolutional kernel to an input image. These maps show us the features that the different layers of the neural network learn to extract. For example, you might imagine a convolutional kernel that detects the vertical edges of a face or another one that detects the corners of eyes. You can see what kind of features each of these kernels detects by applying them to an image. One such example is shown below; from the way it brings out the lines in an the image, you might characterize this as an edge detection filter.\n",
    "\n",
    "<img src='images/feature_map_ex.png' width=50% height=50%/>\n",
    "\n",
    "\n",
    "Next, choose a test image and filter it with one of the convolutional kernels in your trained CNN; look at the filtered output to get an idea what that particular kernel detects.\n",
    "\n",
    "### TODO: Filter an image to see the effect of a convolutional kernel\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95ef60f160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95ef733c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##TODO: load in and display any image from the transformed test dataset\n",
    "import cv2\n",
    "image  = cv2.imread('./images/obamas.jpg')\n",
    "image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
    "plt.imshow(image)\n",
    "plt.xticks([]), plt.yticks([])\n",
    "plt.title(\"Original Image\")\n",
    "\n",
    "## TODO: Using cv's filter2D function,\n",
    "## apply a specific set of filter weights (like the one displayed above) to the test image\n",
    "filtered = cv2.filter2D(image, -1, w[filter_index][0])\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(121, xticks = [], yticks = [])\n",
    "ax.imshow(filtered)\n",
    "ax.set_title(\"Feature Map\")\n",
    "ax = fig.add_subplot(122, xticks = [], yticks = [])\n",
    "ax.imshow(w[filter_index][0], cmap = 'gray')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "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": [
    "It is an edge-detection filter. It mainly identifies a basic outline of the entire object as can be seen from the filtered image."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Moving on!\n",
    "\n",
    "Now that you've defined and trained your model (and saved the best model), you are ready to move on to the last notebook, which combines a face detector with your saved model to create a facial keypoint detection system that can predict the keypoints on *any* face in an image!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}