4. Classify FashionMNIST, example solution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CNN for Classification\n",
    "\n",
    "In this notebook, we define and train a CNN to classify images from the [Fashion-MNIST database](https://github.com/zalandoresearch/fashion-mnist). \n",
    "\n",
    "We will not give this model any weights as we did in the last notebook. Instead of pre-defining filters and weights for a convolutional neural network, we are just going to define the structure of a CNN model then train it on the provided training images until the model **learns and generates filter weights on its own**!\n",
    "\n",
    "About the data:\n",
    "* 28x28 grayscales images\n",
    "* Each comes with a label 0-9, indicating the clothing class\n",
    "* 60,000 training images and 10,000 testing images\n",
    "\n",
    "<img src='images/training_data.png' height=40% width=40% />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the [data](http://pytorch.org/docs/master/torchvision/datasets.html)\n",
    "\n",
    "In this cell, we load in both **training and test** datasets from the FashionMNIST class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data, number of images:  60000\n",
      "Test data, number of images:  10000\n"
     ]
    }
   ],
   "source": [
    "# our basic libraries\n",
    "import torch\n",
    "import torchvision\n",
    "\n",
    "# data loading and transforming\n",
    "from torchvision.datasets import FashionMNIST\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision import transforms\n",
    "\n",
    "# The output of torchvision datasets are PILImage images of range [0, 1]. \n",
    "# We transform them to Tensors for input into a CNN\n",
    "\n",
    "## Define a transform to read the data in as a tensor\n",
    "data_transform = transforms.ToTensor()\n",
    "\n",
    "# choose the training and test datasets\n",
    "train_data = FashionMNIST(root='./data', train=True,\n",
    "                                   download=True, transform=data_transform)\n",
    "\n",
    "test_data = FashionMNIST(root='./data', train=False,\n",
    "                                  download=True, transform=data_transform)\n",
    "\n",
    "\n",
    "# Print out some stats about the training and test data\n",
    "print('Train data, number of images: ', len(train_data))\n",
    "print('Test data, number of images: ', len(test_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# prepare data loaders, set the batch_size\n",
    "## TODO: you can try changing the batch_size to be larger or smaller\n",
    "## when you get to training your network, see how batch_size affects the loss\n",
    "batch_size = 20\n",
    "\n",
    "train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True)\n",
    "test_loader = DataLoader(test_data, batch_size=batch_size, shuffle=True)\n",
    "\n",
    "# specify the image classes\n",
    "classes = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', \n",
    "           'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize some training data\n",
    "\n",
    "This cell iterates over the training dataset, loading a random batch of image/label data, using `dataiter.next()`. It then plots the batch of images and labels in a `2 x batch_size/2` grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x288 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "    \n",
    "# obtain one batch of training images\n",
    "dataiter = iter(train_loader)\n",
    "images, labels = dataiter.next()\n",
    "images = images.numpy()\n",
    "\n",
    "# plot the images in the batch, along with the corresponding labels\n",
    "fig = plt.figure(figsize=(25, 4))\n",
    "for idx in np.arange(batch_size):\n",
    "    ax = fig.add_subplot(2, batch_size/2, idx+1, xticks=[], yticks=[])\n",
    "    ax.imshow(np.squeeze(images[idx]), cmap='gray')\n",
    "    ax.set_title(classes[labels[idx]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Define the network architecture\n",
    "\n",
    "The various layers that make up any neural network are documented, [here](http://pytorch.org/docs/master/nn.html). For a convolutional neural network, we'll use a simple series of layers:\n",
    "* Convolutional layers\n",
    "* Maxpooling layers\n",
    "* Fully-connected (linear) layers\n",
    "\n",
    "<img src='images/2_layer_labeled_model.png' height=70% width=70% />\n",
    "\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",
    "You must place any layers with trainable weights, such as convolutional layers, in the `__init__` function 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, may appear *only* in the `forward` function. In practice, you'll often see conv/pool layers defined in `__init__` and activations defined in `forward`.\n",
    "\n",
    "#### Convolutional layer\n",
    "The first convolution layer has been defined for you, it takes in a 1 channel (grayscale) image and outputs 10 feature maps as output, after convolving the image with 3x3 filters.\n",
    "\n",
    "#### Flattening\n",
    "\n",
    "Recall that to move from the output of a convolutional/pooling layer to a linear layer, you must first flatten your extracted features into a vector. If you've used the deep learning library, Keras, you may have seen this done by `Flatten()`, and in PyTorch you can flatten an input `x` with `x = x.view(x.size(0), -1)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Net(\n",
      "  (conv1): Conv2d(1, 10, kernel_size=(3, 3), stride=(1, 1))\n",
      "  (pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (conv2): Conv2d(10, 20, kernel_size=(3, 3), stride=(1, 1))\n",
      "  (pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (fc1): Linear(in_features=500, out_features=10, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "class Net(nn.Module):\n",
    "\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        \n",
    "        ## Define a convolutional layer\n",
    "        # 1 input image channel (grayscale), 10 output channels/feature maps\n",
    "        # 3x3 square convolution kernel\n",
    "        filters_1 = 10\n",
    "        self.conv1 = nn.Conv2d(1, filters_1, 3)\n",
    "        \n",
    "        # a pooling layer that reduces the size of the image by 2\n",
    "        self.pool1 = nn.MaxPool2d(2,2)\n",
    "        \n",
    "        # a second convolutional layer that increases the number of filtered outputs\n",
    "        filters_2 = 20\n",
    "        self.conv2 = nn.Conv2d(filters_1, filters_2, 3)\n",
    "\n",
    "        # another pooling layer\n",
    "        self.pool2 = nn.MaxPool2d(2,2)\n",
    "        \n",
    "        # finally, a full-connected linear layer\n",
    "        # Why is this filers_2*5*5? \n",
    "        # check the image above to see how the shape of the input changes as it moved through this model\n",
    "        n_classes = 10\n",
    "        self.fc1 = nn.Linear(filters_2*5*5, n_classes)\n",
    "        \n",
    "\n",
    "    ## Define the forward behavior\n",
    "    def forward(self, x):\n",
    "        # both conv/pool layers\n",
    "        x = F.relu(self.conv1(x))\n",
    "        x = self.pool1(x)\n",
    "        x = F.relu(self.conv2(x))\n",
    "        x = self.pool2(x)\n",
    "        \n",
    "        # flatten the output of the final pooling layer\n",
    "        # pass it as input to the fully-connected layer\n",
    "        x = x.view(x.size(0), -1)\n",
    "        x = self.fc1(x)\n",
    "        \n",
    "        # final output\n",
    "        return x\n",
    "\n",
    "# create and print your Net\n",
    "net = Net()\n",
    "print(net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Specify the loss function and optimizer\n",
    "\n",
    "Learn more about [loss functions](http://pytorch.org/docs/stable/nn.html#loss-functions) and [optimizers](http://pytorch.org/docs/stable/optim.html) in the online documentation.\n",
    "\n",
    "Note that for a classification problem like this, one typically uses cross entropy loss, which can be defined in code like: `loss_function = nn.CrossEntropyLoss()`. PyTorch also includes some standard stochastic optimizers like stochastic gradient descent and Adam. You're encouraged to try different optimizers and see how your model responds to these choices as it trains.\n",
    "\n",
    "#### Learning Rate\n",
    "\n",
    "The learning rate, `lr`, in our optimizer defines how much this model will update the weights when it detects an error. A larger learning rate will make bigger changes (and fix errors more quickly) but a smaller learning rate should find the best weights within a smaller range. This is often the first parameter I try to set when I start training. It is usually a velua between 0.001 and 0.01.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import torch.optim as optim\n",
    "\n",
    "## Specify loss function (try categorical cross-entropy)\n",
    "loss_function = nn.CrossEntropyLoss()\n",
    "\n",
    "## Specify optimizer (I'm using stochastic gradient descent)\n",
    "optimizer = optim.SGD(net.parameters(), lr=0.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A note on accuracy\n",
    "\n",
    "It's interesting to look at the accuracy of your network **before and after** training. This way you can really see that your network has learned something. In the next cell, let's see what the accuracy of an untrained network is (we expect it to be around 10% which is the same accuracy as just guessing for all 10 classes)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy before training:  8\n"
     ]
    }
   ],
   "source": [
    "# Calculate accuracy before training\n",
    "correct = 0\n",
    "total = 0\n",
    "\n",
    "# Iterate through test dataset\n",
    "for images, labels in test_loader:\n",
    "\n",
    "    # forward pass to get outputs\n",
    "    # the outputs are a series of class scores\n",
    "    outputs = net(images)\n",
    "\n",
    "    # get the predicted class from the maximum value in the output-list of class scores\n",
    "    _, predicted = torch.max(outputs.data, 1)\n",
    "\n",
    "    # count up total number of correct labels\n",
    "    # for which the predicted and true labels are equal\n",
    "    total += labels.size(0)\n",
    "    correct += (predicted == labels).sum()\n",
    "\n",
    "# calculate the accuracy\n",
    "accuracy = 100 * correct / total\n",
    "\n",
    "# print it out!\n",
    "print('Accuracy before training: ', accuracy.item())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the Network\n",
    "\n",
    "Below, we've defined a `train` function that takes in a number of epochs to train for. The number of epochs is how many times a network will cycle through the training dataset. \n",
    "\n",
    "Here are the steps that this training function performs as it iterates over the training dataset:\n",
    "\n",
    "1. Zero's the gradients to prepare for a forward pass\n",
    "2. Passes the input through the network (forward pass)\n",
    "3. Computes the loss (how far is the predicted classes are from the correct labels)\n",
    "4. Propagates gradients back into the network’s parameters (backward pass)\n",
    "5. Updates the weights (parameter update)\n",
    "6. Prints out the calculated loss\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def train(n_epochs):\n",
    "    \n",
    "    for epoch in range(n_epochs):  # loop over the dataset multiple times\n",
    "\n",
    "        running_loss = 0.0\n",
    "        for batch_i, data in enumerate(train_loader):\n",
    "            # get the input images and their corresponding labels\n",
    "            inputs, labels = data       \n",
    "\n",
    "            # zero the parameter (weight) gradients\n",
    "            optimizer.zero_grad()\n",
    "\n",
    "            # forward pass to get outputs\n",
    "            outputs = net(inputs)\n",
    "\n",
    "            # calculate the loss\n",
    "            loss = loss_function(outputs, labels)\n",
    "\n",
    "            # backward pass to calculate the parameter gradients\n",
    "            loss.backward()\n",
    "\n",
    "            # update the parameters\n",
    "            optimizer.step()\n",
    "\n",
    "            # print loss statistics\n",
    "            # to convert loss into a scalar and add it to running_loss, we use .item()\n",
    "            running_loss += loss.item()\n",
    "            if batch_i % 1000 == 999:    # print every 1000 mini-batches\n",
    "                print('Epoch: {}, Batch: {}, Avg. Loss: {}'.format(epoch + 1, batch_i+1, running_loss/1000))\n",
    "                running_loss = 0.0\n",
    "\n",
    "    print('Finished Training')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1, Batch: 1000, Avg. Loss: 1.2288419450223447\n",
      "Epoch: 1, Batch: 2000, Avg. Loss: 0.6937933005094529\n",
      "Epoch: 1, Batch: 3000, Avg. Loss: 0.5920797569081188\n",
      "Epoch: 2, Batch: 1000, Avg. Loss: 0.5322626407891512\n",
      "Epoch: 2, Batch: 2000, Avg. Loss: 0.5146656086966396\n",
      "Epoch: 2, Batch: 3000, Avg. Loss: 0.4933703503347933\n",
      "Epoch: 3, Batch: 1000, Avg. Loss: 0.47358307205140593\n",
      "Epoch: 3, Batch: 2000, Avg. Loss: 0.44969217107445003\n",
      "Epoch: 3, Batch: 3000, Avg. Loss: 0.4456645141467452\n",
      "Epoch: 4, Batch: 1000, Avg. Loss: 0.42868348659574984\n",
      "Epoch: 4, Batch: 2000, Avg. Loss: 0.41917451394349337\n",
      "Epoch: 4, Batch: 3000, Avg. Loss: 0.42407298716157676\n",
      "Epoch: 5, Batch: 1000, Avg. Loss: 0.4059670155346394\n",
      "Epoch: 5, Batch: 2000, Avg. Loss: 0.40310325153172016\n",
      "Epoch: 5, Batch: 3000, Avg. Loss: 0.40133672138676046\n",
      "Finished Training\n"
     ]
    }
   ],
   "source": [
    "# define the number of epochs to train for\n",
    "n_epochs = 5 # start small to see if your model works, initially\n",
    "\n",
    "# call train\n",
    "train(n_epochs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test the Trained Network\n",
    "\n",
    "Once you are satisfied with how the loss of your model has decreased, there is one last step: test!\n",
    "\n",
    "You must test your trained model on a previously unseen dataset to see if it generalizes well and can accurately classify this new dataset. For FashionMNIST, which contains many pre-processed training images, a good model should reach **greater than 85% accuracy** on this test dataset. If you are not reaching this value, try training for a larger number of epochs, tweaking your hyperparameters, or adding/subtracting layers from your CNN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Loss: 0.434078\n",
      "\n",
      "Test Accuracy of T-shirt/top: 66% (667/1000)\n",
      "Test Accuracy of Trouser: 96% (968/1000)\n",
      "Test Accuracy of Pullover: 83% (833/1000)\n",
      "Test Accuracy of Dress: 87% (874/1000)\n",
      "Test Accuracy of  Coat: 62% (626/1000)\n",
      "Test Accuracy of Sandal: 93% (935/1000)\n",
      "Test Accuracy of Shirt: 69% (692/1000)\n",
      "Test Accuracy of Sneaker: 96% (969/1000)\n",
      "Test Accuracy of   Bag: 93% (939/1000)\n",
      "Test Accuracy of Ankle boot: 92% (926/1000)\n",
      "\n",
      "Test Accuracy (Overall): 84% (8429/10000)\n"
     ]
    }
   ],
   "source": [
    "# initialize tensor and lists to monitor test loss and accuracy\n",
    "test_loss = torch.zeros(1)\n",
    "class_correct = list(0. for i in range(10))\n",
    "class_total = list(0. for i in range(10))\n",
    "\n",
    "# set the module to evaluation mode\n",
    "net.eval()\n",
    "\n",
    "for batch_i, data in enumerate(test_loader):\n",
    "    \n",
    "    # get the input images and their corresponding labels\n",
    "    inputs, labels = data\n",
    "    \n",
    "    # forward pass to get outputs\n",
    "    outputs = net(inputs)\n",
    "\n",
    "    # calculate the loss\n",
    "    loss = loss_function(outputs, labels)\n",
    "            \n",
    "    # update average test loss \n",
    "    test_loss = test_loss + ((torch.ones(1) / (batch_i + 1)) * (loss.data - test_loss))\n",
    "    \n",
    "    # get the predicted class from the maximum value in the output-list of class scores\n",
    "    _, predicted = torch.max(outputs.data, 1)\n",
    "    \n",
    "    # compare predictions to true label\n",
    "    correct = np.squeeze(predicted.eq(labels.data.view_as(predicted)))\n",
    "    \n",
    "    # calculate test accuracy for *each* object class\n",
    "    # we get the scalar value of correct items for a class, by calling `correct[i].item()`\n",
    "    for i in range(batch_size):\n",
    "        label = labels.data[i]\n",
    "        class_correct[label] += correct[i].item()\n",
    "        class_total[label] += 1\n",
    "\n",
    "print('Test Loss: {:.6f}\\n'.format(test_loss.numpy()[0]))\n",
    "\n",
    "for i in range(10):\n",
    "    if class_total[i] > 0:\n",
    "        print('Test Accuracy of %5s: %2d%% (%2d/%2d)' % (\n",
    "            classes[i], 100 * class_correct[i] / class_total[i],\n",
    "            np.sum(class_correct[i]), np.sum(class_total[i])))\n",
    "    else:\n",
    "        print('Test Accuracy of %5s: N/A (no training examples)' % (classes[i]))\n",
    "\n",
    "        \n",
    "print('\\nTest Accuracy (Overall): %2d%% (%2d/%2d)' % (\n",
    "    100. * np.sum(class_correct) / np.sum(class_total),\n",
    "    np.sum(class_correct), np.sum(class_total)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize sample test results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x288 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# obtain one batch of test images\n",
    "dataiter = iter(test_loader)\n",
    "images, labels = dataiter.next()\n",
    "# get predictions\n",
    "preds = np.squeeze(net(images).data.max(1, keepdim=True)[1].numpy())\n",
    "images = images.numpy()\n",
    "\n",
    "# plot the images in the batch, along with predicted and true labels\n",
    "fig = plt.figure(figsize=(25, 4))\n",
    "for idx in np.arange(batch_size):\n",
    "    ax = fig.add_subplot(2, batch_size/2, idx+1, xticks=[], yticks=[])\n",
    "    ax.imshow(np.squeeze(images[idx]), cmap='gray')\n",
    "    ax.set_title(\"{} ({})\".format(classes[preds[idx]], classes[labels[idx]]),\n",
    "                 color=(\"green\" if preds[idx]==labels[idx] else \"red\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question: What are some weaknesses of your model? (And how might you improve these in future iterations.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer**: It doesn't classify coats or shirts very well, I suspect this is becase the structure of these two items of clothing is kind of similar (like the sleeves). This model may too simple or may be overfitting other classes at the cost of these two. So, if I had more time to train, I'd probably add one more convolutional layer, train for a longer time, and/or add a dropout layer to avoid overfitting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Save Your Best Model\n",
    "\n",
    "Once you've decided on a network architecture and are satisfied with the test accuracy of your model after training, it's time to save this so that you can refer back to this model, and use it at a later data for comparison of for another classification task!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## TODO: change the model_name to something uniqe for any new model\n",
    "## you wish to save, this will save it in the saved_models directory\n",
    "model_dir = 'saved_models/'\n",
    "model_name = 'model_simple.pt'\n",
    "\n",
    "# after training, save your model parameters in the dir 'saved_models'\n",
    "# when you're ready, un-comment the line below\n",
    "torch.save(net.state_dict(), model_dir+model_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Load a Trained, Saved Model\n",
    "\n",
    "After you've trained a couple models and want to select the best one, you can load it by name (you'll also have to define the architecture in this notebook in `Net`.\n",
    "\n",
    "To instantiate a trained model, you'll first instantiate a new `Net()` and then initialize it with a saved dictionary of parameters, which our the learned weights of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Net(\n",
      "  (conv1): Conv2d(1, 10, kernel_size=(3, 3), stride=(1, 1))\n",
      "  (pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (conv2): Conv2d(10, 20, kernel_size=(3, 3), stride=(1, 1))\n",
      "  (pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (fc1): Linear(in_features=500, out_features=10, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# instantiate your Net\n",
    "# this refers to your Net class defined above\n",
    "net = Net()\n",
    "\n",
    "# load the net parameters by name\n",
    "# uncomment and write the name of a saved model\n",
    "net.load_state_dict(torch.load('saved_models/model_simple.pt'))\n",
    "\n",
    "print(net)\n",
    "\n",
    "# Once you've loaded a specific model in, you can then \n",
    "# us it or further analyze it! \n",
    "# This will be especialy useful for feature visualization "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# 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. These techniques are called feature visualization and they are useful for understanding the inner workings of a CNN.\n",
    "\n",
    "In the cell below, you'll see how to extract and visualize the filter weights for all of the filters in the first convolutional layer.\n",
    "\n",
    "Note the patterns of light and dark pixels and see if you can tell what a particular filter is detecting. For example, the filter pictured in the example below has dark pixels on either side and light pixels in the middle column, and so it may be detecting vertical edges.\n",
    "\n",
    "<img src='images/edge_filter_ex.png' width= 30% height=30%/>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First convolutional layer\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the weights in the first conv layer\n",
    "weights = net.conv1.weight.data\n",
    "w = weights.numpy()\n",
    "\n",
    "# for 10 filters\n",
    "fig=plt.figure(figsize=(20, 8))\n",
    "columns = 5\n",
    "rows = 2\n",
    "for i in range(0, columns*rows):\n",
    "    fig.add_subplot(rows, columns, i+1)\n",
    "    plt.imshow(w[i][0], cmap='gray')\n",
    "    \n",
    "print('First convolutional layer')\n",
    "plt.show()\n",
    "\n",
    "weights = net.conv2.weight.data\n",
    "w = weights.numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filtered Outputs\n",
    "\n",
    "Next, you'll see how to use OpenCV's `filter2D` function to apply these filters to a sample test image and produce a series of **filtered outputs** as a result. We'll do this for the first and second convolutional layers and these outputs should really give you a sense for what features each filter has learned to extract."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# obtain one batch of testing images\n",
    "dataiter = iter(test_loader)\n",
    "images, labels = dataiter.next()\n",
    "images = images.numpy()\n",
    "\n",
    "# select an image by index\n",
    "idx = 10\n",
    "img = np.squeeze(images[idx])\n",
    "\n",
    "# Use OpenCV's filter2D function \n",
    "# apply a specific set of filter weights (like the one's displayed above) to the test image\n",
    "\n",
    "import cv2\n",
    "plt.imshow(img, cmap='gray')\n",
    "\n",
    "weights = net.conv1.weight.data\n",
    "w = weights.numpy()\n",
    "\n",
    "# 1. first conv layer\n",
    "# for 10 filters\n",
    "fig=plt.figure(figsize=(30, 10))\n",
    "columns = 5*2\n",
    "rows = 2\n",
    "for i in range(0, columns*rows):\n",
    "    fig.add_subplot(rows, columns, i+1)\n",
    "    if ((i%2)==0):\n",
    "        plt.imshow(w[int(i/2)][0], cmap='gray')\n",
    "    else:\n",
    "        c = cv2.filter2D(img, -1, w[int((i-1)/2)][0])\n",
    "        plt.imshow(c, cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x720 with 40 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Same process but for the second conv layer (20, 3x3 filters):\n",
    "plt.imshow(img, cmap='gray')\n",
    "\n",
    "# second conv layer, conv2\n",
    "weights = net.conv2.weight.data\n",
    "w = weights.numpy()\n",
    "\n",
    "# 1. first conv layer\n",
    "# for 20 filters\n",
    "fig=plt.figure(figsize=(30, 10))\n",
    "columns = 5*2\n",
    "rows = 2*2\n",
    "for i in range(0, columns*rows):\n",
    "    fig.add_subplot(rows, columns, i+1)\n",
    "    if ((i%2)==0):\n",
    "        plt.imshow(w[int(i/2)][0], cmap='gray')\n",
    "    else:\n",
    "        c = cv2.filter2D(img, -1, w[int((i-1)/2)][0])\n",
    "        plt.imshow(c, cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question: Choose a filter from one of your trained convolutional layers; looking at these activations, what purpose do you think it plays? What kind of feature do you think it detects?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer**: In the first convolutional layer (conv1), the very last filter, pictured as the last filter in the second row of the conv1 filters, appears to detect vertical/slightly-left leaning edges. It has a positively weighted middle and right-most column and seems to detect the vertical edges of sleeves on a shirt."
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:cvnd_1]",
   "language": "python",
   "name": "conda-env-cvnd_1-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  },
  "widgets": {
   "state": {},
   "version": "1.1.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}