2. Define the Network Architecture.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 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",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 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",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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": 9,
   "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": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NaimishNet(\n",
      "  (conv1): Conv2d(1, 32, kernel_size=(4, 4), stride=(1, 1))\n",
      "  (pool1): MaxPool2d(kernel_size=4, stride=4, padding=0, dilation=1, ceil_mode=False)\n",
      "  (drop1): Dropout(p=0.1)\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",
      "  (drop2): Dropout(p=0.2)\n",
      "  (conv3): Conv2d(64, 128, kernel_size=(2, 2), stride=(1, 1))\n",
      "  (pool3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (drop3): Dropout(p=0.3)\n",
      "  (conv4): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "  (pool4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (drop4): Dropout(p=0.4)\n",
      "  (fc1): Linear(in_features=9216, out_features=1000, bias=True)\n",
      "  (drop_fc1): Dropout(p=0.5)\n",
      "  (fc2): Linear(in_features=1000, out_features=1000, bias=True)\n",
      "  (drop_fc2): Dropout(p=0.6)\n",
      "  (fc3): Linear(in_features=1000, 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",
    "\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,NaimishNet\n",
    "\n",
    "net = NaimishNet()\n",
    "net.cuda()\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": 11,
   "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),RandomCrop(224),Normalize(),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": 12,
   "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": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load training data in batches\n",
    "batch_size = 62\n",
    "\n",
    "train_loader = DataLoader(transformed_dataset, \n",
    "                          batch_size=batch_size,\n",
    "                          shuffle=True, \n",
    "                          num_workers=4)\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": 14,
   "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": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load test data in batches\n",
    "batch_size = 62\n",
    "\n",
    "test_loader = DataLoader(test_dataset, \n",
    "                          batch_size=batch_size,\n",
    "                          shuffle=True, \n",
    "                          num_workers=4)"
   ]
  },
  {
   "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": 16,
   "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",
    "        images = images.type(torch.FloatTensor).cuda()\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": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([62, 1, 224, 224])\n",
      "torch.Size([62, 68, 2])\n",
      "torch.Size([62, 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": 18,
   "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": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AcCfj0ZiNjQ2ebD6J5VnUa3Vycu579402mvoz+n4ZMZam6UI0pzvo9WfFD9LMaRkrIxhX4ari1TJkoITUqkpiibmRmH80GjGZTMjSjM43OgRbAZve5mz1ThPu9e8Rn8fceekO6X6K+8DFd30muxMapw0+8/3PkKWZqSIXR1AEXIqQo2FE97hrmp0838PreIwnM4d3a2uLra0t6o06+9N97nv32Q13Z8JZiipEO5bzM7qSSxaXdkR10rAKHwrB0BJftZOMrKByzqHcVS6DpdsFZCVLFlO2QrAtGydywJ35Dz/+Lz9msjvBO/ZI91JwIN1PyawMLJjsTBjlIzp5x2gkuR5pLsrznOFwaKj5ZJqQBinRVyKGnSGtXov4b2NTgLO/v8+X+TKDbEA+zDmyjhZMgeY2dJguglhVGS6/X2VSVoLHeL91GGX7qQke0RyaYi5HNsJzTCYTU3uh/YMsyJjsTsCGdC/lU3c/hZVZNHtN9uI9KKA9aFNMZtzDNJ1XlNuOTeInuN6cuZymU2IvpvelHuf/7ZzB1oDCLhhuD/E7PqdnpzwYPyBOYoq8oFk0sbAW9sYQrSAOqWSBZTxFY5X/dl2stMbQUn4VyqFrURSmKCeKIqMZpABYIAUv8jmYk2wmRIzg6TtP89att3j23WexsLCxaQWtGdH18N+bv/Qm4W5I+7zNZ9/+LK7n8s2b36QbdDlID/hi9kVcz+WNF99g2BmCxez/w8sOrIBP3PoE//DkP/Djzo+5m9zlq6OvAvMiHoCCApoQ1AKz045AcjOXjdt12OWVEAwdKpZR9iuqElLLMpFSF6n7RSRc1c6bmBFte03NRWFxODnkD/7HH9Bv9MnJeeW/v0LmZHQbXXJysKDf7s9ObsOwMyRxE7DhODimsAruufcI7ZA0SBl1RjNdXfKjkyJhFIwYdoYUdsFxcMxoOCLI5lVZaZaS/l5Kcivh6OSIj/7wo8a8iobUky60fnlMr/LXVsKU/DQg3IWYEJ0qL8NsqJal5PUc25kNi2M5/P7bv88fvv2H2IXN9nibnfEOz737HE7m8Nw7z9HpdiCH5lmTZq8J+cys1Is69aLOjfjGTLjSQ1pWiw17g/agDTnYUxtycDJn9r1+m2AYsBPuYBUWB9MDWlbLXKdlWRSNgvxWDg5EexGpP++FKU92uTBZHwc+BO0D7xc6LwFzZ0u3CegyPJi3A5jClySm/+U+2UGG98Bj/1v7NJMmT02ewkKxpVj88f/8Y3qNHpvjTQqr4Ac3f8BffuIvcTyHxE1oWk3slo1t2fz6g18nbIbcsG/MzonFL/7HL3IanZINM2hALa/htl023U3aO22+OvoqeZbTslqkdkpqpXNfaVRg3bEoPlLQ7DVp0DAaUTrbNCEozdNVVWCX4bEQDO18loXjsnS+ePNpmpIFGdlBBs6M1MprOXZSvaLswmZnvDPzLwqL5x88z6tPv8rEm+AVHvXabO8Ky7b41s1vcRwccyu7xZcHXzbNyp7rMQ2mZvLk/8bGxixtb82rxXXdh23Z1P5XDXfT5Zlbz2Btz8PRq/a80LiqhPKxEAwNEYTpdGpK92C+UspklynOCS2cew7Z4UxjuImL4y1fWQUFiZ3gFi53O3cJa+GFmo3IieY+hnOPyInMVkmyyqWETxxdrf71xGkewnVcvNjDc+f7ecmiuA7fc532gZUQjGW27rqElgyykD5S3xmG4UJvhhT/6g1S5HtZmuF/zcfesGk7bYJWwNgb81bjLZ6aPGXOpc3Kn/3qn4EDQ2cI+WLPBkAtq3EjusFx7ZjD/JCO1yElNZMvGdxyYk9Ws/45z3OSaULRLGC8aCb0Ris6PJVx0dekUweXaZiVEIz3C/EXykzgsnS8DIowokvjewv+9D/9KZvpJvvJPq/8v1cImEUI7269y9Af4rgORbrY0yoVXAAv33+ZeCPmVnALy5lrLf2kA63WtWOo7yHLM37wwg8YvzzGPXKp/3V94X7K0MVFZQ4jz/OFFokqPBaCIdACoJ0t+S8FOHoXHLNyLEj+a0J2mJE8SGh8rzE7ng3RRsRd7y5/8uSfsDPYIQxC+k4frMV8hBQZm/S36/Cdg+9wHBxzmB7ye+nvLbCvcLGHVdeh6tcJk1km14b0IMVpzzeGEV9JGFdd9lfeHQi4IIRVeCzCVZ1UKlPjmk4vaxFdF1k0CrLDufMpCSs5hm3bYMNp55SoGaEsSqXJs2170cdw7xFaobmWKt9CU9blc3tTj3q3Dhk4Rw61rGaOJfcgzKhGOTUgx/vQFAO/H2hTIitHV4jDPKmmd+3TQuLGLu6RS3qQ4t53Z3mSh9paBrOcY1hWZCsaoJbV2Iv2Zj5GdsiGs0E6nfMOurywSij073EUc/jdQ86Tc4pxQWO/Yc4vmqFccV4WAJ1+v4rkWgnBeL9tA+WwVBxPQRAExHG8sEWB+CJmIzUsOt/okPopfurPqre86qysOLR607RyR5q+L6HMYd6YlCSJKa7RzqHQ97q+U7ZgisKIfJKbfc518ZHWQkVRmK67cmuDOOf1ev1SgmslTEm52/tRG5xlgAHTDyIFv7CYTCuvcNn727ZtirzAmlhQzJ03raJlhesWxXKxsVy37/tETkS31jXh6iAdmOhINJouZtaFwuXrltaH8n5fAm0qq3pUZGzks1fOybVH/6eMRxUGjavIGpm88oBIWZwkogoKisasQlu+p5uQys6azmzqzdSGwyFJklDLauyGu1iFxc3pTUbH80dXSOq9nChclteYTqdkeUYapOTFTAD0BrEibHosqzKq10m5w4qYkveLhbDu4QBpCryclhf1a1nW/OEwjs3wK0OmB1P8+z7+3/pGU+hHX8Gcr9BCIY+pmqZT7vbvslfssd/a55fe/CXy7Zz6pM6/d/+d/ek+e409RqMRrVbrwpaL8rNe/XmeM02n9L7UI7mZ0Dht8NT3n6J/3jcaqNFoLLRkVjG/4phfhyFdGcF4lFqBZdAFO1rNawiHoMvcXNfFbtmmzjPZT6ABpPPv6NbGKs1RFAWu5/JPn/onztvn/JAfsjvZpcgLTlunuLlL+szsgAfTA375/JcrVb8urtF/S9yE5OYsYzvZmRDa4YWQVkxlldbRuM5Yr4wpkQnV/8uNNsvMjRaEcr5EhKP8s7yKI1fLawTHAeTg3/exo/m5dKU5sEBdSz9HEARM/SnnG+emzuKkccJZ62zWsGSn5u/3vfsUjaLyXnRDsuYprNAy19c8a+Il8/IB2dylvBBEm1Q1OV9ltldGY1yFsv2sSjHrGF6vHk1yiTcvpsTwCa7H4XcOCe0Qa2JhexdbBgXiW2hSyrIs7Mxme7LNWfMMgN3JLjATEK/wmDLjRg7TQ3bru8RRfMGE6NUu58jznCRO2P2bXdxNl47XwarN70k73vLAnLLvon2Lq3wyWBHBKFdfweIuNFVETFW/SVWJvC7lE2hTIivedV081yOLslmau55fKKQpn1+bFznvS++8xMSekAUZO/nOzHndSjnwDxj5I5zEmZXq+bMQVpxbOYYWFN3InBc5eT3HiRwsb3Fraxk/vYtweXz1eFwHKyEYsCgcl0mzlnotHGXGU/dXSMTgOI7Z5U5exSzIZq5xEnPy6yck+wn1kzrP/OMzlTZZC6VwF/K31554jZPGCTvhDhQzjeEXPqk1E5DfHf8urrM49BKmyuOtYK4dk2nCvZfvEd+IqXfrbP3frQVNoR3NMqn3XjrdYYUEQ6AnWtvHZbG3DKZ8p5xA0/tJ6BUl7wstLXtrhk44cz5tCHdDinphNIfUgcp5y3Y6TmL6ft90m53UTmZvWJCQgAV3nbucuWfs5rvm/HLfcr96kSRJQi/pEd+IZ9e0F85C6nhuGqRIZ9lYCsScaBO1DCslGOWaiapkzzJ+v7xaytS3JpM0NyErVLRGLayZZqN6t46buPCQS5LPVdUzpFnKq0++yvnGOVZqgQv10xmnHu6EWJlF4RS4hctfNP6Cw+yQrwy+gsX8OSpVhUQAjGcOcbI/C1Xd+OK0ibMqxxBfSo9Lmc7/0JX2lWsSL0v2wHw/TlnR5U4tQ3uX8gSyY45su+x5Hr7n0/lGB5qwHWzjb833zZRjai2WZRnxNOad4p1ZRGJD4RVz9vQhFW4VFgWFiU7uOncZ5kNatBbMnnYahb7un/fZ+Zsd3LZLy26R+IsFvqIFBNdJMVxFKK5EuFrFKj4qlrGHVcyf0Myy/ZL8l9S567gwhtiNDQsqzma5khwb/uqZv+K1Z1+bnUDmxIbx1pjR1qwiPPfy+Wg/lPNvbXyLvJg/A7bqms0+5nmBE81K+8ooh+Ryf1exwZeO56Xv/gygeYzrQtPB5chE12XozKt21nSxbL1ep9FscP6lc27/zm3eeOGNGVW+5Hq6RZfUmXMUt753Cyt5mG/JbFM5birC7z8UWgvu+/fpZ31zrHJ4nGUZYRQysScUzDOxy/ytKrZzGa5aiCtpSnTI9iifF0ikoR1OHVLqJyjLeaTHtdPp0Dns8NbhW7Meka0haZBiJ4vho6jvzekmbuaSOin21KZhNWamxILcyfn0Dz6Njc2Gu8Gd8A7JIOHt33574Xr1ROqe0+FoyN9//O8ZfGaA/8Bn89XNhShD0/Qamv2UvpkqzXkZVkJjlDXEoybUtE0ux/8awlBqWltexayEYUjLbs2KYnJonDTwEm9hAvRxG7UGv/OT3+Gzb3yW5qjJm7/2JnZqz8yFBW88+wbpIOW1T73Gf/zaf3DyKydsjbaggP1kn2bRvHDvohX60z6DzmBG09+c0fRljbHMDOuQV65ZO/VXCcbKaYzrRCBV39HCpR9OoyGrTQ9uvV439jhNU0ajEZ7n8cw/PcPJ5IQgC8j2Muq1xU5z7cwGXsC2s206zHL3ITlmw2R7QrFTmPdGWyN+885vshPvUMtqFMwLf3V1VZZlDB8MCToB8Y2Y4DjAiR3cxuI2lBo6KilPviT8giAwpnTlo5Jl9u66giGTJBGI4zimP1UKamC+anSJf7lJWJ5kZNs2Ha9DnMem0lo7nRLuySQ1iga74WxTlM3RJtNsynhzTKvXYivbYnO4Sb/dx81d/vojf83h9JDf6v2WScAJhyHXnyQJvbMenb+aRUiNokHhFxe2dBBzKddjkoJKY5QzzVp4lmElTMkHBR3Da1NSzifAYve3zqWkacpgMDCbttu2zTSdEjohlj0vptGD6jgOFha/evtX+eKbX5xt4NaZbcFk2bN8jO3YFBRM7SlYcOQdzfIyismV1zAM6Z50Zzv4FQXBNCDwA8NN6OeUaJ4GWIi4xLHW96jH6bKFtxIa44OArGLJO+jyt8tQ9m9832c8Hhu1m2Yptz93m/hGzF60x+fvfv7CMZMkmWmlYlbG12v1TLHwsDOEHeg1e/PO9gLTyyrMrRa2wXDA9574HoPnZ07nR/7uIxfUvq73lFeofmZKVQ7pKqysYDxKuKoTbqIdpJRfl/gJdMyvB8+yZlsjtlqt2c59cUwapIaOPqmfkPopbuYuTKT8nGUZfuqzG+7SrXeBWYZ1hx32k33u+/fZn+7zhfALtGhhedYFWz+ZTDgeHTN4eu505vUcq7i4c6EeJwnDJfSWa5IoRxN08vtlWCnBeFT+QqB7K2QApGxPb5Yi9lwSa/q7ArHDxi8JM9O62B60sSKLsBbSpDlnNS31GIm84KV3XyJyZhvDblgbBLWAL51/icRNaLttHHu+F3kcxwsTPZlMGHfHBMeBSZp5iUfhLj5WXEJSHZrqrrNyRrW8d7oh6JZgpQSjjOuqPq0qxXeQx2SXB0SSanoDWIkKNEcgggWw8ZcbFI2Cj+19jO++9F16rR434ht8ofsFbOZ7kerUeT2rm2P4vj9L0tn1haSZRA4izGEYcnx8zHnvnBt/ewOrZc1CZXeRu9BaQq65qrNM/11rCX2MZVg5waiqzbgK5bheNEWr1TLPCwMWBELOI9sgyiSW2UPxVQBOJ6ezIhxrtiHKhAlNu3nhOmRnnmXhoKh8mGd/J5MJD44f8M7ZOyRhgmM71PIaTjAXCm0Oygkxub88z02RsPa7JPrSJvcyrJxgaFxXSKo+J1pDHgUFiwUsZWZUE0saonKzLMPLPRqnDSY7E3bCHRo0Fj5bDv82NxD9AAAKOklEQVREteseWRE6bTajKKI/6POPT/8jgxcG1I5rPPG9J0zoKYKsyxdhMaMsGkuEQ2sVnTfR93wZVkowyllC+dt1oVeTPKKy2WyaB8zpuk/dAVZFqcvxdKFPq9XiU//nU6R+ys2Nmww3hzRbTRI3wU/9hdwLzBNvmgOR67MsiziJGRUjet0eR4MjBs/PHM7oRoSz4eCmM66lXGuh60nKY1T2O+RvemyuIrdgRQRDr4D3Ykqq1Ktt22bX3PPzc/OQPB33l+st5aF4evM2fY1FMdvm0Q4fPjXAtnn1469yUj/hRnyDl++9bIQgzVIiJ6Ke1y8Ii2VZ5EXONzrf4L5/n3atzVP/8BTNsybj7TGtsxZ+6i9dFOUwXJNemsjS0Gakqqjnwjmu/MTPAI8qHFWflZW2tbVFv99f8DX0oMnqEUJMl9bleU4QBNTrdePI6QRdL+lxUptVax0Hx0ROhJ/4FBS8+rFXOWmcsBvu8hsPfmOhqwxgWAy57892/R20ByRuwpOvPknRKPCSmY9kOcufCA1z7aAXlCTMpDKtXAkHiyTfMqycYLwXMmYZk+e6Lu12myeeeIJ+v0+/3zc+g5iJVqu10L6o2VPLstjb26NWq3F+fo7rurRarXniLZ2ajeF3wlnuw3ZtxtmYk8bD8r76CZEdEdjBQlo9SAPDbbQHbWpZjZgYa2KRsVhxpcdCBFNYWnkVaE0ozrWuDRXy76p6jZUQjDJp86ioInz0MTc2Ntje3iaO4wsRigiAOG9aDTuOw+7uLq7rmmKeLMsWTM0LP3iBqT+lntdJ2onpUZHN5PfCPWp5zWgMUeOO4/DlwWzX3/69PsPW0FSNl30V3Tmmt0+QDjidb5Fj27a90NMr3Id8Rj/mogorIRhQLRyPkl297HtBELC/v0+v1zMNz2YLR2WfdcOwmBd5xnq/3zcTJVscZVk22w8rn3EF0tNhYfHSnZcIrZCm1cTx5yypji4CJ2C32MXb8XDs2U480pUv+5rrZ665rkuj0TDZYwmL5ZlvokGazaZ5fol2tGH51g1lrIxgwKPR4Jd9TyfFZNJv3LiBbdscHx/z5ptvGgo7SRIsa7afle/7NJtN01Pa6XR48sknKYrCaBvZRVjvyiN+Sjlf0bbbF66z3IsC0Ol0aDabhGHIaDQiyzL6/T5vvfUWvu9zeHjI/v4+4/HYbF8QBAG3b9821yR+hYToRVGYpwtIPkYWn9z7ZVp6pQTj/UILSFWksru7S6vVIk1Tut2ueSa6bdvU63U++tGP0ul0jNbY3t5mc3OTyWRCu93GdV1+8pOfEIahefqifkid3iQNFivVqyIFnccQzkJXcMVxTL1e5/DwkJ/7uZ9jNBoBGDPh+z7d7uwpBkdHRxe2j9KaQTaev6o6XPBYCMYyTaP9DUG9XufWrVtsb28TRRHHx8c4jkO9XufGjRsLk9tut82mK77vs729zenpqXlCwbLSOoGEwcKXlN9b6E19KDS1Ws1oAJlI13XZ2toyky1bNzSbTW7evMnJyQnvvvuuMY2yz5iYFumEFx9DbzG1DI+VYFSpRk1xi/rc3t4GZrZZIpbJZMLR0REf//jH2draMtnZIAjY3NxkPB7TaDT45Cc/yeuvv04cxwu+SLmQt+y3lCHMrA43JbdzenpKt9ul1Wqxvb1tnM1GY7ZhnDRHiYk4ODigKApeffVV7ty5s0DQlcNd13XpdrvmkeDL8FgIxmXQAyPbLIqNjaKIO3fu8Pbbb9Pr9Xj66acXGnXEURMHNYoiOp2O0TZhGBpzUtVsLQNfpbrL5Yi6QVq2ihKyrbyPp3xPtEGapnz2s5+l2Wzyta99jTiOGY/Hxixp4RMfSbaXXoaVFoxlJkImQbOJVTWO8ndR+a7rMhgMuHv3Lu+++y4PHjyg2+0yHo+p1WrcunULwLCkMulxHBv+w/d9Njc3za7DEv5KKKpLAMrNQ/JeuR5VHOXpdMp4PDbVY3meG59HV2CJo6tzKPV6nc9//vP827/9G6+//vpCWl0XAssxpAJsGVZaMKpUnWb4dJmdTIasdM/zzN5V9+7d46233uL09JTRaMR4PDYDL77Cz//8z7Ozs3OhjgHmm7ENBgMcx+ETn/gE3W6XoihMN5vuaRVNo7VIuWo7z3OzFYOYBIlKhsOhiV46nY4xMQIp7O10OobQevDgAZ/73Od45ZVXeO211/B9nzAMF9LxVc75Mqy0YFRBF+7CfHMQXZw7HA7p9Xrcvn2bo6Mjut2uifPLYVqSJCYslWNov0BnK6VmQthQ4RHkmJorgLmW0NGA5jE0siwz6XIp9ZN2B10SIOG1CLQel9PTU5555hk2NjbMc2Yl7a7H7jq0wIdOMLSKFvYuTVPu3bvH0dERd+7c4c6dOwwGgwXySrOOQoHLwD377LMcHBzgeZ7Ji+jPaedQhGRra8s4epqhlPdFCERDyLl0pCQCEEWR8Svu37/PeDwmyzL29vbY3NwkCALjK8g1tlot4ydkWcbOzg55nrO3t8fTTz/NP//zP5uQV5chXqevFVZcMKpMiahGcdJ6vR737t3jRz/6Ed1ul8lkshAhRFE0z3iWtnMUoel0OsY01ev1BXusV6WEruPxmP39fbrd7kLBjezUI9sSlH0cgQiaaL44jkmSxPSp6vPJ50XjiPbRuw0KryFFyc8995yJnOS85ap5Oe4yrIRgaJpWVLa2zXogAc7Pz7l37x5vvvkmJycnpt5Cq0xNOonq1cklvcmq5EIajQZhGF6oAdUklkzg7u4ujUbD+ANaM+koBVg4vxZaXbxbFoqiKMyzTbRfoguKJNzVmiOOYz7zmc/w9a9/fUE7lE3oh8LH0L2gcPHxCnmeE4YhDx484F//9V85OjoiDEPj4ElIWUWNXwbREEIAic+gH/NdZix1A9MzzzzD66+/Dsw1hth0SWBpP0NHK/JZobTDMJzt/vuw6951XTY2Ni4wlfV6na2tLXZ3d2m328b5FUECePHFF3nxxRf55je/SbPZvFCxJaTbZVgJwdAla3KT4gDmeW60ww9/+EPOz88XQlHdsHudyiSBCF6tVjMDqivKNRtZrvyybZskSdjY2DBOqGzoCvPdhrVfoSdDftc1mmJOJHurd8nRmd9Go8H29rYh6bQgNptNo+Gef/55vvOd75isrm6DlGu4DCshGLLyy0zd8fExd+/e5fvf/z4nJyeMRiOjIeTpgFUk0XXT+FKPIcXDmhCS48mxtRNaFAXj8ZhOp0OjMa/71ButaTpbzgWLxJaEzOIbaP9HNIDkcZrNpuE1tre3aTQaptZCHpvVbDbNNT733HPs7u5ydnZWKZBX9ZVY7zWjucbjjceqd3WNDw5rwVijEmvBWKMSa8FYoxJrwVijEmvBWKMSa8FYoxJrwVijEmvBWKMSa8FYoxJrwVijEmvBWKMSa8FYoxJrwVijEmvBWKMSa8FYoxJrwVijEmvBWKMSa8FYoxJrwVijEmvBWKMSa8FYoxJrwVijEv8f9k1D1Pnuu+UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "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": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "## TODO: Define the loss and optimization\n",
    "import torch.optim as optim\n",
    "\n",
    "criterion = nn.MSELoss()\n",
    "\n",
    "optimizer = optim.Adam(net.parameters(), lr=0.001, betas=(0.9, 0.999), eps=1e-08)\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": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import time\n",
    "import pylab as pl\n",
    "from IPython import display\n",
    "\n",
    "train_loss = []\n",
    "\n",
    "\n",
    "def train_net(n_epochs):\n",
    "    train_loss = []\n",
    "    running_loss = 0.0\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",
    "            \n",
    "            # Without CUDA\n",
    "            #key_pts = key_pts.type(torch.FloatTensor)\n",
    "            #images = images.type(torch.FloatTensor)\n",
    "            \n",
    "            key_pts = key_pts.type(torch.cuda.FloatTensor)\n",
    "            images = images.type(torch.cuda.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",
    "        train_loss.append(running_loss)\n",
    "#             if batch_i % 10 == 9:    # print every 10 batches\n",
    "        pl.plot(train_loss)\n",
    "        display.clear_output(wait=True)\n",
    "        display.display(pl.gcf())\n",
    "        display.display('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": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Epoch: 50, Batch: 56, Avg. Loss: 0.43521017804741857'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished Training\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# train your network\n",
    "n_epochs = 50 # 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",
    "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": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([62, 1, 224, 224])\n",
      "torch.Size([62, 68, 2])\n",
      "torch.Size([62, 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": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6GozI9DJUoyopJ0XJKlFL1VhJrzBXmGN+fp5cLsfCwkJiZNPv9zk6OuLBgwfm/EejEYSQHWRJe2+myyPwuz6ddIdgEJByUrhtFzttMwpPi4JkAOOpfBlU7VskiZieXC5HuVwmn8+TSqVMrkVMyWAwoNVqmTWw02QmgAHJ4NAmYhpw4rb4LOLGJN9Cm1948AtsdDd4I/0GlmVxrXeNvtUnHaU5ODzgWz/5Ley0TctrUSqXyOVyE45ju92m0WiMtUg/wG259NI9PvB7H6C0W6K6VuUbH/8GH/7Ch5nfm6d+oc63n/s2P/ivftB8//WPf92YMe37iPkUUMQjM5H4Z0LCSURl2zb9ft84tbJCr9PpAKc9OpJkJoChE0Dx0FWHeVqSciaayBKRWW6Yw8ji777xd7nSvYKFxfX+dQAcHDJRBguLpYMl3vtb7+Vy7zLbxW3+7bP/lgsXL7CxsUGhUGBubg4Yh4WXLlxi7VfXSL2eIrwRYj+0sUKL+Yfz/GDlB1nYW8AKLYo7RRbaC5R2S9ihTflhmdwgRy/TYzQcURgWaLvtiS49nU7HmDuJKOSahMEVx3k4HJLL5bhx4waLi4sm+ScEW7PZ5OjoiP39fdMHLCnRJzITwDjLYZSLm8YBJB1DM4PxY2cHWTa6G1hYRETc9+4zskdc712nZ/XwI58H/gMu9y7j4HCxfpGtl7ao1qp0Oh0WFhZYW1ujWCzS6/XoPuxy6c8uYYUW9us2wbUA757H4PsG2FdsBt83wH3dpX+9T+lWidZGi/xmnsbFBuFcCAF89Pc/ysLDBQ6XD/mDv/gHE35U3KTo+6IztbZtk8/nzbkBE4uw2+02zWbTFAm/KwiupJyGSBwc+vPRaGQSSfHvNEEGp9FNw25wL3uPq52r3PPu8auXfnUcZXRcjgfHLLgLeBWPn7vzc2w0N3jDf4Pj0THBcUA2mzVJKFMQkx7Sutwi/yBP52qHO//HHZy2Q+lyiWF/yPYvbRMcBvT8HqVUieqnqjRaDU7CE9K1NFbdYmFvATuyqexXSHfTExxN0r2SeyJgCYLxuS0tLVEulw0z2mg0qFarhqmVxJrv++Tz+XeHxpAYW6tL7YlrcMi28Hg9go7p4157GIZERPzaxV/D7bg07AYpUgwHQ+qjOvm5PHbWZn5xnufnnieqRjzqPSJrZU2aXVLvuij3O3/9OwT7AXu9PWpfq4075X0Nut0ucOr3CK2eyWTI5XIA+Gmfo+UjFh8tcrRyRDfdJZPJUCqVTJGvNpHii0ieRop5VldXuXz5Mrlcjk6nQ71e5/j42KTYJaKSfTudjvE1kmRmgBF/rc2Hjkp0rC+DHdcWScfXYVsYhXSdrlHDw+GQ0WDEgr2AW3CxsPA6HsepY5yUM+HNC0chazXS6TTBIKBm19g/2KdarVKr1Tg5OsHv+XS9LvPOPF2/i5MaO4DZbJa5uTnK5TKLi4v8lx//L+QGOTpeB3pMhMyaRDNLIiOL3DBHL9Uzk2llZYX5+Xls26bRaFCr1eh2xyCTjjzCs+j60mkyU8CIJ9CmmRjN+Mn7s3yPxEglhMKoQBAG2KHNr1R/hWuH19g72COMQtZr62zmN/ns8mfx+h51q84oPQZPN9s1IJPahzAMcWyHYlhklB3xyd4nudK+QtAL8EYe28Vtnv+B53m4+xC34/Jw9yFhOO6UMzc3Ry/Tg3CycNjzPLrd7gS7aWPzj6v/mBvBDe54d/h05dOUy2Vu3LhBLpcz0ZKsqHNdl0ajQavVmigAFo5jmswEMHQlU5wJ1KJNinwvF6pp5yQNpKvArcjiH+z/A270b/BG+g3s0Ob68DoWFmsna+PjYLPR3ODvD/8+G90N7mfvE0URV7tXeTj/kFff8yqFuQKtVmvMfAYDfvKbP8ladY2HhYestddwcPBHPhYWF+sXGR2O+EeH/4i16hpbhS3+hfUvODk5oVgsUiqVzBoTMZfdbpd2uz3RDqEYFrkZ3MTB4WZwk6sLV/FXfYrFIu12m5OTE8IwJJfLkclkqFarVKtV46jqJRNnyUwAQ6fM4+l0Ea0VNAk0MZtiFeSaA9D+ydxojhv9Gzg4JlyVKGWYGhIR4Q09hs6Qje4GDg5XulcgGoe1a9U1HvKQTD5Dq9Ua108ejVirruFEDmuNNQIrwI98QkLsN//9zft/k7XWGDAbzQ02ihschUcTGVWh0GUhsg7Xh8MhNavGnfQdbvRvsFvexVvyGA6HvPzyy5TLZRYWFgwdPhgM2NraYmdnx2gnwKTzdS/Sx8bkv8XA/v+VaWCI+xda4qDQtZHaT9F/4mfUrTq3vdvcDG5yx7tDykpxrX8NC4vU6M1yfizc0OVg4YDKSYWH5YeEo5ALjQvsL+6TWc2YXEQURRwODtkqbHGpeYmd3A4XWheweBOghDg4rLfXeTj3kLXmGo8WHkEZLmQuUKlUzPnpQmVN3IlZyGQy/M7132E9v45bccn1czSbTeNkplIpstksjuPQ6XRIp9MsLCzQbrfpdDo4jkO5XGY4HBrnOElmAhg6pBTRcbu+WbK9mBEd78v32tEUIkfT2ZZl8enKp5mL5uimuziWwye3P8n14XWqa1Ucx2FuZ47DpUO+/OyXSffSNO0m6U4a13NZe3qNZ1afMeeXyWQozBX44ke+iNfxqIZV/saf/A0uty7zIP+AlJPiQuMCu6VdPnfrc/h9H2fBIZsep+1zuZwJfyVdLz2yZFnB4uKiyXHk83myhbEDG4YhxWLRhKxSZXZwcMC9e/fY3t4291OWQrZaLbrd7uxnV+NkVHzmJ9HlGhzxgh3JlWhw6e8syyKyIppOk5SVIrIj/kn5n/CjH/xRVp9cJZfNMTwe0rAbLLNMFEb8pc//JRYfLXKydsLDjz3E9336/b4p5snlcmPnzh2SG+b4/Ic/TzEqEhXHNZhu26Wb7pLr5bCL9kSSKwgCo31ES0jzt2w2a9hWqTKXFXHlctns53meCYdlJbxoBMkGW5ZlHOW3KqGcCWDEq6Qlexln/uAURPFyNxFdqyBet66HtCwLK7IoU6ZBw5T6p7wUvUyPlJsi5aVIX0iTt8ZdAcPjkMX9RezIZmFvgWF6aEgj6aUpjUl0pCLLDwByizlWc6smRGw0GhOrw+S6pfpKZrauTu90OhzsHxCehNSCGgsLC6Z32NzcnCnju3fvHpubmxweHppjipOsczNnyUwCAyZD1WnUra6h1NVZeh8hh4zjim0ikjvpO3xm6TMApmJL/BWh1R3HoZftUb9Qp7hTpHu1C6WxA9dut7Esy5Be8ludTodWq2V+UxYyWZY10UlQIhDJnoq20GtepczQsizCUchPfeunWD1ZZXtum69e+iphOG53MD8/byKbg4MDWq3WxAo0y7KoVCoAVKvViW6FSTIzwNAzGh6ng+PaI85l6IuM5000c1iiZCKSG/0bFClSj+qGtJJ9dAo8jEK+/dPfphgVmb86T9EpmvpPaWgi3XgtyzJPJtB+jWQ4xW+Yn583r4VfkD/LsgwrmU6nTb2p3bBZO1nDjmwuNi7i93yTIymXywBsb2+zublpnlMifIiYLKlYjycs4zITwIiLToLB48xoHCBxUky/jx+nStWEe7e92xwEB1i2RblcJp1Om5yCzHDHcbAii3lrHnfZJe2P2xpJRbi+uUI5w/ixFTrrK81k4z6EZVmG3dRLEKR1AWAiDkpwtDKmz09WT3ArrgFXGIbs7+/zyiuvsLm5ied5JnQVihzGmnFubo5UKkWz2Zw6BjMJDDi7EZuIruGUfeIZyDiIAD5d+TSZfoZOukMYhNjRuMBFBkwKWDzPgxBufe4Whc0Cnasdqp+qGuDKbNSRj3wuyT3AgELOQ4qJZZ94SZ/MagFEPp83jVNe/JkXSXfT9PweGes0ZN7f32dzc5P9/X3m5+cpFouEYcijR4+Iosg0rO90OpycnJgczDSZCWC8VQo9DpB4lJL0vfyf5H+MwhF1p45rjQdPYn1ZB6Ibqbhdl/xmHiu0yN7L0uq0SBVSJqIQz1/WpOjuOJpYkwov0QzihMp7vdQAmNAWkttIp9Nkc1mcOYdUOB466TO+u7vLo0ePzDH1YqMoimg2mxPno813kswEMKbJW4VUWkNoP0L2E78jSfs4lkOmm6EbdU2NheQaRM13u126UZfaWo3Sbone1R6D3ICoH02st81kMqRSKdrtNmEY4nmeef5YHPS6Eb5eFScaQq8VEVMmib7hYAhVsOdt/My4mEdo8Lt379JsNrEsy1Dgy8vL+L7PyckJ9Xp9orWjXmCdJDMDjLja1zc0KdqQfTR4NDjix9LhrhVZ/PLxL5tE1O/lfs+UwslMsm17bFKisfOJBZZtjd+Hp43cxH/wPM/01hTNIbZfBkAvdpIQVUAhx9R5IIlWstksWT/L07/9NMWdIs1LTe783B16/R71ep1arWb8iCAITIularXK/v4++Xye1dVVwnDcA6zb7eK6Lvl8fup4zAwwRLR6i5sAEZlJom516Z+EnPHqaK1ZynZ5IhG1ml012/u+b2b+aDQi388zvzuPjY1/xyeshvSzfYLDgE6qQ1SLCEoBlm2ZfMZwODQtEuQ4cj36//iKdr06LIpO+4A6jkMmnaG4U8QObea25hgcDdhp7FCv100Pr0qlYsyU8CjLy8sAptGbFAzLMolpMjPAmBZtxFXxNG5DNI4GyDRV2XSaJjLZLm7jLZ02XhUHtNVqYWHxsT/82ESCbfPeJk/9u6co75QZuANSwxR783v8x7/wH1laXjK+x/7+vmm8qpu26nWuAgzhQYwP9GYY3Ol0SKVS43WoVo2bSzepHFSorlXZamxRq9U4ODig2WxSqVTI5XI8evSIw8NDVlZWWFlZ4eTkxJzL0tL4/KTZy8z7GPGwM+l9kr8hqjapqEcfW2sLy7IIo5BPVz7NanaV5e9bZsFbIIoiw0lI5VQxLFLcKZpkWGqQ4od+44ew3vznDTwsLFZPVglPQqKlyPTQkn4ZQsAJK6oXNcNkwbP8afOSSqUMq/n6973Od/e/y257l151DKx8Po/jOGxubtLr9SiXy9y8eZNms8nrr79OPp/n4sWL9Pt99vb26Pf75hmsM18lrgdvGoOpGVFTVxHTEkn5FpF4JbmdshnlR5QoEYWnj7ESc9Dv92ln2zQuNihuFU+Pg01EREhIkApwRy5783tQmmykJucgDqXuICy+hYieufFV61H0Zt9vP2tKDYNBYI4hrRY2NjZwXZd+v0+1WgVgfn7eaC8xk4VCYaJ90zSZCWBMA4WIHnj5Pymppo+h99U9JSxr/GjMYqHIJx58grUX1zhYOpiozras8Wr6lJviW899i2w3i92yufb716gcVdgubPPl932ZcC5k2V/GWXC4kLsw0TtDcifxBmg68tA8jIBWN2Qrl8tcvnyZlJPi6v91lZWjFW4s3eCrf+WruJ7L5uamCWOlUGdnZ4fRaMTNmzdZWFjgzp07Y+1XLJpWkpKDeVc84SgOBh16arWrJb7eZBo44vt4nsdSesnQy0sHS6SaKchPduoR7fH0//00pd0SB4sH/OuP/Wv20/sUwgJRLsJdcCnkC+QGOUbeiOHodL2GJqn0WlXdHEX4DOE+9GJpYFxkcxTy0aOPmnNNd9P0rT4rKytks1kePXrEq6++iud5XLx4kVwuZ3yLUqnE4uIi7Xab/f19hsPhhO8zTWYGGHGtoDvlxD/Xy/i1eYg7q9pX0S2SOp0O29Y293P3udy+zG5pl7bXJmgGdDod01hk7+Ee/df7lHZK2JHN8sEyz/3hcwxSA9yRy6PFR3zlf/8K7/t372Px0SLHq8d89a98lYhT51JHJFEUGc4DTkEqql38EMlldLtd9vf36Xa6PMg/YKM5LjH8+itfJ+Weto2WJi1hGJrkXbPZpFarmbbRe3t71Go14PTx4u8KH2OagwmTFV5JjGfcVsrnutGJDIzQzdValV+Kfgk3cjmpnlD4gwLr6+tks1lWV1ZZ89f42e/8LJXjCn27jxd5pkTPG46dzpXDFUq1EouPTlPyjc2G0XJ9v2+qzOHUvzC5D06fPBB3RkejEblcbgywoM/nbn2OTJDheHRsnsGazWYplUqmbUMURQZMGxsbPP300/zpn/4pr7+3weOyAAAQh0lEQVT+OouLi1y5csXQ+FLhPk1mAhjxzCo8TnLFuQuYfLZZXARoEgrqfhCWNU6aPfHEE1y5coXBYMDR0RGvvfYajVqDn3/957navjreFgs/HNduThyfCAuLZ/7rMxxUDlg6XOLRwiN+7Fs/xsrhCgC787t86aNfIuWe3mY5Bz0RhCHVa2uCIKBcLptZ7fkeI3+E23Zx+o7ps3F8fMxoNCKbzTI/P2/2uX//PrZts76+zsbGBvv7++zu7gIYRnTmfQzdGkmX6snN09SwRCQ6lNULkOQzOE1eCT/R7XbpdDq8733v44Mf/KBx0BzLYXg8ZOd/3SE4CLjy2SsmRI1404ThEBExYsROZocL3Qs4OCwfLfMPr/xDem4PN+Xy2c3Pmn1XT1bJ9DMMveFjzwfp9/uGx5B8hlynhM7Xr15n/8/22Qw2JyaJ67omGgHMqrMgCFhYWDDPXpXnmEgZn+u65HI5FhcXmZubm32NoUPMOK0dRaeLeYV+1l593DFNqsUYDAacnJxQKBR45pln+MhHPjLOVL74ItWTKj/xX3+CleMVRosjfv+jv896aZ2LtYsA1BfqFI9Pw9XqYpWLxxeJ3vw3SA1Ye2qNOeYIMgH7nX1WD1cBOFw6xJ63ccLJB/fKtWg/SrepDsMQ27K59Tu3eOZPn+F+7j6//fRv47iO8SuiKDLtk+Sae70e7XbbVI5JYm40GlGpVEyCTzTUWdnrmQDGNGJKZlF8QXN87Uh8Hy3CTzz11FOUSiUsy+Jzn/scBwcHjEYjLhUu8Xeqf2c8+w+XefDdB/z6xq9TvFjkb23+LdaP1w3D2VpvjenxyDamxAs9fuaFn6F8UKZxscFLP/cSt6u3OTk+oeW1sC0byz69Pkm+aU0opYDpdJpCoQDAamaV1GspiOBy6zK5QY6W1TKJMNEq4uTKMY+Ojmi1WqarjnQdbDabNBoN45ckJfi0zAQwJJaXqEJMhSal9GzS5IypsgpPO+PpfloSiUhroSiKuHjxIpcuXRoDMozYenmLjeYGdzN3edR7xLA9ZBANWGusYWPjDlxCQqqNKrVijQv1C7xpYRg6Q8qPytjYFDYL/Pvf/fdEpYgbN27ghM5EiCoJOl38LA+2gbFJqNfrzM/P8+QzT2LtWgy/O+Suf5eu3yUajR9tJbKwsGAetitsrYS/UrpXr9dNiaDUbrTbbbrd7uw/4SgelegMZDz+l230kgJhBwUIAiQp1dOchPye7DsYDPiNy7+B1/U4CU8gfLN7rttis7DJ5dZlrMjCweFC8wIP8w/HPgs2FhbOwGE7v82F1gVue7epRlW8jsedO3dMsmpubs6AWofUwl7KKnrf92k2mxSLRW59/y2s/8Xi13/513nj6A1KYemxqC2JCBRtqsN8WYCtH9MpqfxpMnPASMqT6PUPsjpL3yRdS6nZR61NdB8sfUOllK4VtSCCclSm6lZJuSl+68nf4omVJ/jRb/4oS4dLHC0csX60bmjxESM2C5v8y2v/kswgQ9NpUrJLRFHE7u6uyXFIM7T4Knz5P5Uar7hPt9MUVgtcu3aNy5cvs729Tctt4Wf8idBct4hOEs2d6BICmWDy2zNvSnRpnP4TcyDRiCzdSypk1doCTllFmT26eklEblwUjdezfnLvk1zvXecN/w1+7eKvMQyHtNNt/tP/9p8oRkWsksWHvvAhFh4ucLB0wB/+0B/yqP+IdDVNp9ch42XMsVdXV8156rUg3W7XRF9yfYNgwF/7o7/GenWd4RNDyj9fJpVKsbe3R7fbNRGV9C8Xh1yvVk8i9kTDSs4GMACV7abJTAAjjly5adKBV+hb0QpnVXZpraBvgKbLNaEkjGEuzHG9d328nrV3nWyQNSV1vu/Tz/dZnFvkuz/7XeyGTdNpko2y5PZzHB0fmaSXDLp+hqr+XM5BTORwOCTTzbBWHdPz3m2PTC+DbdvmiYd6Te40Mk/fuyRCUO6BBsvM+xhxgkqILPEbxFYngWIa+6lF0+b6M/ktx3HoWl3Taedu5i5VqqR7afP4KD1DR/4IJ3KwQsucl3T30WpcfJvBYGCWAejvJLLw5jxOVk9Y3F/EfcYlyAcU7AK9Xm/i2SdaksyAThHI/3p7DQxg9n0MOCW3dHtm3/dJp9Nm9iUtRRQR/0QDRPME2meRnITmSKTTTn6U52R4QmFY4LB2SKfS4caNGzSbTb7zne+Qz+cnGEsdWUh7AclfCBhljWk+nx83nA8GZIMsQTZgdXWVH/mRH+Har1yj4lbo+l0qlQrNZpOvfOUrwKSWExDC5HNX4NScagdXPw1B13wkmWMtMwEMMRNiOnSYqftrw+P1GXD2o6g1ULTtFdWqybUwCmlYDT6590mz7uRL2S/x/ve/H8dx+MIXvsDx8bFplxjvfCcLkcUPkicpC/fQ7XaxLZu/+v/+VdZr6xwuHbLzf+7w5JNPUp4vM2RI2h4/g21nZ8fQ3VpT6jT9OxF932bex5Ae3ZJQ0uoQzi7AEUmKOKY5WdN8jzAMyfazZqXazeAmZcoMBgOWlpb4xV/8RV577TVefvllQ5BZ1njZoRTkep7HyckJrVaLQqHA0tKSWVAchiGDowHrtXWcaEyoXb111dRSDAYDLly4wPHxMV/72tcee9x3nN+Bx9tlT7s/8l284m2aTF9x8j9QZEaJ1pjW8DV+0TqCib+P/8Ul6Tvbtmm7be6k7zBixG3vNoPcAL/rQzQmlJ555pkxefWmzyHmSApw1tfXuXnzJsuVZZa9ZfK5vGmYVq1WoTSmyiM7gvdA8UrRXJekw/f29rh79+6ZPbLiZvTtaBHtc7zVtjOhMXR9gp4hZ5mIJM0gEs+6xivD4gud4dTjH4Uj/un8P2UhtUAtrPHPvvrPWP/SOuFTIc3fbFKeL3Pr1i0ePnzI/fv3jWMpae+VlRX8tM9P//FPs9HcYPd4ly9/9MsMBgMymQzf/8z30/upHt6Sx9zGnGnAJutdgyDg/v37bG9vJxYiTZvpb7VORGsYHeVMk5kABkxmSZMijLfypJMqypOApT9LSuGL81a36uSHeVZPVsfM52sOUTWialVZWFjgJ/7yT/BHf/BH/Iev/wdS7vgJzKVSiZWVFYL9gI3WuEXTem2d8CRkdXWV97znPXzkIx9hbm7O9Np0XXdiEdLW1hZ/8id/ws7OzmPmNC5J6QB9ffFrnVbXkiQzYUqS2iDEZZpJkO90jkXnXOLf6d+cdoPEH6hRY6e4Y9R+4dK4q3+n3SH8hZA/96t/jr/96t9mGAzNUxV93+d4dMz97P1xir64w/zVeT7wgQ/woQ99iOXl5YlnmOma0F6vxyuvvMK9e/eMYys+QdK90OGpSDz0P+u+nSUzozHgbC4iSZJ8i/jgx30I2T5O/Ojv4c3wl4jfvPabfPzPf5z3f+z9ZlWYVbNovtLEGo278f3A9R/ghXsvsLe3RxAEbG1t8fesv8fa8hrv++j7+LFnf4z19XWKxaJpqwRMDH4URTQaDW7fvs3+/j6DwWCi9E47yfFreasUuuwXX2LxrjAlbwcU8VArTgNrriIplS+iNZTeJ37zoiii2+9St+s0mg0y2QyVSoXKzQrDDwzpfqdL6laKH//rP876d9d54YUXzJOFnn32WZaXl7l16xaXLl1iNBqZtkyyeFp+T1ojbW1tcf/+fVNnITyJnJ/2kd6pvKvC1TjX8Ha3jYt2Kt/OzdPUu87kyncwzi1I+yOpf0in07znP7+HzsMO/+b/+Tf82Zf/jJOTE46OjnBd10QVsjzwqaeeYnFxcaJ2QjrdiF/V7/c5Pj6mWq1OmBcNjHiuJ36v3onJmCYzAQx4XL0nSZKtTfo+HtU8ZiKmpKv1jdd1INKySApr0uk0O7s7fOMb3+Cf/8Y/N91v5MEx9XrdZDQfPHjAgwcPeO655/jgBz9o/AL5LanwDoKA4+Nj6vW6SZMnaUV9rXFn863u7/fCY8wEMOJcxNsJU6eBQl7HI46kNa9xLkDyF2LzHccx0Ya0MwrDkBdeeIHnn3+eF1980Sz1k7oK3/ep1+tmzUe32+Xzn/88v/u7v8uHPvQhPvGJT/DEE09QKpXMOQdBwPb2Ni+//LLpfAOnlHc8rIZTsOulE0nyThnSmYhKdOYvKf0uF66X1unoQwZe/wGPvZ8mMnu0VpB9NjY22NjYIJPJ0O12uXfvHnfv3uXq1at8+MMfNgXHuVzONF5dWVnBtm1arRbtdpvl5WXm5+d58cUX+dSnPsXzzz9PrVbD931yuRzHx8e88sor3LlzhyiKjB+iGVpN50PyUoppIa3+0wzyWaZnJoAhuQt9cUmzBJLZzXcqZ4WrkqORTjmpVIrNzU1effVV9vb2KBQKXLx4kYsXL5rEmvgigGmfKHUYpVKJ9fV18vk8r7zyCn/8x38MjOs2ms0m29vbNJvNiZRAPPyOTxo9QXSPjaQ+G9+rzIQpEYkDIOl1krxTcLwVZyIZ0VKpxHA45Pbt22xtbZknBhQKBW7evInruuapiaJxpC1zr9ej0WiYYz355JOEYcgXv/hFbt++zbPPPsvrr7/OgwcPaLfb5PP5x8xl0sTQA68nloTAAqx3akpmAhg6vNShosjbQfw7mRXxc0jSQJlMhjAc97Q6PDw0DpwU8KbTaZMAlMhGBkU6AFqWZZ5NUiwWjfN57949jo+P2dzc5OTkxJgv3RBWn59cZ5zO199peaeggBkBxtvNify3DMem/YYAM5VKUS6XsSyLb37zm9y/f59KpcLGxoapso6icRFyuVymVCpxfHxsyvJ1L69Lly5RqVTIZDLjwhzP49KlSwwGA7a3t7l7965ZgS7LKvUjw5POVxcEaa2RtKb3Xc98xmVaBHLWtvD2Q7mkbeRGdzodRqMRL7744rhbXjbLc889x9raGgcHB9y5c4d6vc5gMKBcLmPbNi+99BK+7xvWUto1y2IfeTKArApbXR0vTHrhhRdM/YlozSRToD+PO5FJEZZ+nyRn3Z+ZAMZZth4m+YizuI54yPq9kmW6YFYWP1+7do2VlRXW19dZWloim82ysbHBpUuXaLfbfPOb3zRL/V566SVc16XZbJpV7fPz8ywtLRmntFKpUK1WKZfLrKys8Nprr9Hr9SY0RLwK/KxQXr5LKv+L3zPZ/u34HjMHjLdbjfVWx3knosPeKIqYm5vj4x//OO9973sZjUY0Gg22trYmKrdrtZoJTzU5Js3hZaGx+CJzc3M0Gg3W19fN4yOCIDB5Ea0x4mtytcR5mmnbvVNTPBPAgOmAeLu0ry59g3eWT5DZJTYbTp1LqbsYjcZtnU9OTmi326awSHiWTqdjBlfOxfd981htodjlsVSHh4endaex2Rwf7LcDEvGR3oolfiuZGWAkSRztZ9URxBlB+N4jlTiYbNs2DVOlt7c8D0ScxLm5OYIgME8tbDabZoAlbNXLICzLMmbm8PCQWq02sULsrHPXoEn6/qyUu476ku5VXKz/Xp7+uby7ZSaYz3OZPTkHxrkkyjkwziVRzoFxLolyDoxzSZRzYJxLopwD41wS5RwY55Io58A4l0Q5B8a5JMo5MM4lUc6BcS6Jcg6Mc0mUc2CcS6KcA+NcEuUcGOeSKOfAOJdEOQfGuSTKOTDOJVHOgXEuiXIOjHNJlHNgnEuinAPjXBLl/wNRsfR8SAhEdAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "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",
    "visualize_output(test_images, test_outputs, gt_pts=None)"
   ]
  },
  {
   "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": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "## TODO: change the name to something uniqe for each new model\n",
    "model_dir = 'saved_models/'\n",
    "model_name = '50ep_NaimishNet_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**: \n",
    "For the Loss function:\n",
    "We have a regression problem so i used Mean Square Error nn.MSELoss()\n",
    "because it is the most commonly used regression loss function. as MSE is the sum of squared distances between our target variable and predicted values.\n",
    "\n",
    "For the optimizer : \n",
    "I used Adam with learning rate 0.001\n",
    "after a research i found that Adam learn the fastest and is more stable than the other optimizers\n",
    "optimizer = optim.Adam(net.parameters(), lr=0.001, betas=(0.9, 0.999), eps=1e-08)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2: What kind of network architecture did you start with and how did it change as you tried different architectures? Did you decide to add more convolutional layers or any layers to avoid overfitting the data?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer**: I started with a sample Neural Network\n",
    "Just two Conv2 with MaxPooling layers and than Fully connected layer\n",
    "It was just to test the workflow but i didn't got good result, the model couldn't detect complex features of the faces\n",
    "then i used NaimishNet https://arxiv.org/pdf/1710.00977.pdf but the result was ok.\n",
    "I didn't added any extra conv, i just putted dropout layer in between to avoid overfitting"
   ]
  },
  {
   "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**: i started by testing with only one epoch then when i found the right model i moved to more, i stopped at 10 epoch because i got resonable result ~90% accuracy \n",
    "and going more will take a lot of time in training"
   ]
  },
  {
   "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": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.19174452  0.03144374  0.00471197 -0.15108284]\n",
      " [-0.17180946 -0.07951047  0.07735085  0.22548424]\n",
      " [-0.01447063  0.20013109  0.11494781  0.15968311]\n",
      " [-0.22684836 -0.1161227   0.00915653  0.10063936]]\n",
      "(4, 4)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f147decb588>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "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.cpu().detach().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": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##TODO: load in and display any image from the transformed test dataset\n",
    "dataiter = iter(test_loader)\n",
    "sample = dataiter.next()\n",
    "images = sample['image']\n",
    "images = images.cpu().detach().numpy() \n",
    "\n",
    "\n",
    "idx = 3\n",
    "img = np.squeeze(images[idx])\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",
    "import cv2\n",
    "\n",
    "weights = net.conv2.weight.data\n",
    "w = weights.cpu().detach().numpy() \n",
    "\n",
    "c = cv2.filter2D(img, -1, w[0][0])\n",
    "\n",
    "f, ax = plt.subplots(1,3)\n",
    "ax[0].imshow(w[0][0], cmap='gray')\n",
    "ax[1].imshow(img)\n",
    "ax[2].imshow(c, cmap='gray')\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": [
    "**Answer**: The filter do horizental edge detection + a small blur. This filter help to detect the face shape and then we can see the features of the image as beard and the hair and it remove noises."
   ]
  },
  {
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}