exercises/1_5_CNN_Layers/4_3. Classify FashionMNIST, solution 2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CNN for Classification\n",
    "---\n",
    "In this notebook, we define **and train** an CNN to classify images from the [Fashion-MNIST database](https://github.com/zalandoresearch/fashion-mnist). \n",
    "\n",
    "We are providing two solutions to show you how different network structures and training strategies can affect the performance and accuracy of a CNN. This second solution will be a CNN with two convolutional layers **and** additional fully-connected and dropout layers to avoid overfitting the data and gradient descent with momentum to avoid reaching a local minimum. The batch size and number of epochs to train are the same as in the first example solution so that you can see how the structure of the network and loss hyperparameters have affected the accuracy of the model!\n",
    "\n",
    "Again, this is just one possible solution out of many."
   ]
  },
  {
   "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": {},
   "outputs": [],
   "source": [
    "# prepare data loaders, set the batch_size\n",
    "## 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": [
       "<matplotlib.figure.Figure at 0x10cba0d68>"
      ]
     },
     "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": [
    "### 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",
    "You are also encouraged to look at adding [dropout layers](http://pytorch.org/docs/stable/nn.html#dropout) to avoid overfitting this data.\n",
    "\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)`.\n",
    "\n",
    "### Define the rest of the layers\n",
    "\n",
    "It will be up to you to define the other layers in this network; we have some recommendations, but you may change the architecture and parameters as you see fit.\n",
    "\n",
    "Recommendations/tips:\n",
    "* Use at least two convolutional layers\n",
    "* Your output must be a linear layer with 10 outputs (for the 10 classes of clothing)\n",
    "* Use a dropout layer to avoid overfitting\n",
    "\n",
    "### A note on output size\n",
    "\n",
    "For any convolutional layer, the output feature maps will have the specified depth (a depth of 10 for 10 filters in a convolutional layer) and the dimensions of the produced feature maps (width/height) can be computed as the _input image_ width/height, W, minus the filter size, F, divided by the stride, S, all + 1. The equation looks like: `output_dim = (W-F)/S + 1`, for an assumed padding size of 0. You can find a derivation of this formula, [here](http://cs231n.github.io/convolutional-networks/#conv).\n",
    "\n",
    "For a pool layer with a size 2 and stride 2, the output dimension will be reduced by a factor of 2. Read the comments in the code below to see the output size for each layer."
   ]
  },
  {
   "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",
      "  (pool): 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",
      "  (fc1): Linear(in_features=500, out_features=50, bias=True)\n",
      "  (fc1_drop): Dropout(p=0.4)\n",
      "  (fc2): Linear(in_features=50, 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",
    "        # 1 input image channel (grayscale), 10 output channels/feature maps\n",
    "        # 3x3 square convolution kernel\n",
    "        ## output size = (W-F)/S +1 = (28-3)/1 +1 = 26\n",
    "        # the output Tensor for one image, will have the dimensions: (10, 26, 26)\n",
    "        # after one pool layer, this becomes (10, 13, 13)\n",
    "        self.conv1 = nn.Conv2d(1, 10, 3)\n",
    "        \n",
    "        # maxpool layer\n",
    "        # pool with kernel_size=2, stride=2\n",
    "        self.pool = nn.MaxPool2d(2, 2)\n",
    "        \n",
    "        # second conv layer: 10 inputs, 20 outputs, 3x3 conv\n",
    "        ## output size = (W-F)/S +1 = (13-3)/1 +1 = 11\n",
    "        # the output tensor will have dimensions: (20, 11, 11)\n",
    "        # after another pool layer this becomes (20, 5, 5); 5.5 is rounded down\n",
    "        self.conv2 = nn.Conv2d(10, 20, 3)\n",
    "        \n",
    "        # 20 outputs * the 5*5 filtered/pooled map size\n",
    "        self.fc1 = nn.Linear(20*5*5, 50)\n",
    "        \n",
    "        # dropout with p=0.4\n",
    "        self.fc1_drop = nn.Dropout(p=0.4)\n",
    "        \n",
    "        # finally, create 10 output channels (for the 10 classes)\n",
    "        self.fc2 = nn.Linear(50, 10)\n",
    "\n",
    "    # define the feedforward behavior\n",
    "    def forward(self, x):\n",
    "        # two conv/relu + pool layers\n",
    "        x = self.pool(F.relu(self.conv1(x)))\n",
    "        x = self.pool(F.relu(self.conv2(x)))\n",
    "\n",
    "        # prep for linear layer\n",
    "        # this line of code is the equivalent of Flatten in Keras\n",
    "        x = x.view(x.size(0), -1)\n",
    "        \n",
    "        # two linear layers with dropout in between\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = self.fc1_drop(x)\n",
    "        x = self.fc2(x)\n",
    "        \n",
    "        # final output\n",
    "        return x\n",
    "\n",
    "# instantiate 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/master/nn.html#loss-functions) and [optimizers](http://pytorch.org/docs/master/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: `criterion = 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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.optim as optim\n",
    "\n",
    "## specify loss function\n",
    "# using cross entropy whcih combines softmax and NLL loss\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "## specify optimizer \n",
    "# stochastic gradient descent with a small learning rate AND some momentum\n",
    "optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)"
   ]
  },
  {
   "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:  8.55\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",
    "# to convert `correct` from a Tensor into a scalar, use .item()\n",
    "accuracy = 100.0 * correct.item() / total\n",
    "\n",
    "# print it out!\n",
    "print('Accuracy before training: ', accuracy)"
   ]
  },
  {
   "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. \n",
    "* The number of epochs is how many times a network will cycle through the entire training dataset. \n",
    "* Inside the epoch loop, we loop over the training dataset in batches; recording the loss every 1000 batches.\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(n_epochs):\n",
    "    \n",
    "    loss_over_time = [] # to track the loss as the network trains\n",
    "    \n",
    "    for epoch in range(n_epochs):  # loop over the dataset multiple times\n",
    "        \n",
    "        running_loss = 0.0\n",
    "        \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 = criterion(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",
    "            \n",
    "            if batch_i % 1000 == 999:    # print every 1000 batches\n",
    "                avg_loss = running_loss/1000\n",
    "                # record and print the avg loss over the 1000 batches\n",
    "                loss_over_time.append(avg_loss)\n",
    "                print('Epoch: {}, Batch: {}, Avg. Loss: {}'.format(epoch + 1, batch_i+1, avg_loss))\n",
    "                running_loss = 0.0\n",
    "\n",
    "    print('Finished Training')\n",
    "    return loss_over_time\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1, Batch: 1000, Avg. Loss: 0.3147320107854903\n",
      "Epoch: 1, Batch: 2000, Avg. Loss: 0.3105658622719347\n",
      "Epoch: 1, Batch: 3000, Avg. Loss: 0.323147666445002\n",
      "Epoch: 2, Batch: 1000, Avg. Loss: 0.313219755101949\n",
      "Epoch: 2, Batch: 2000, Avg. Loss: 0.3203617183715105\n",
      "Epoch: 2, Batch: 3000, Avg. Loss: 0.30810909819975496\n",
      "Epoch: 3, Batch: 1000, Avg. Loss: 0.3166225519236177\n",
      "Epoch: 3, Batch: 2000, Avg. Loss: 0.3042223061528057\n",
      "Epoch: 3, Batch: 3000, Avg. Loss: 0.3094628683775663\n",
      "Epoch: 4, Batch: 1000, Avg. Loss: 0.3076044572973624\n",
      "Epoch: 4, Batch: 2000, Avg. Loss: 0.30473846194148063\n",
      "Epoch: 4, Batch: 3000, Avg. Loss: 0.3134658064302057\n",
      "Epoch: 5, Batch: 1000, Avg. Loss: 0.30673510006815197\n",
      "Epoch: 5, Batch: 2000, Avg. Loss: 0.3064948204047978\n",
      "Epoch: 5, Batch: 3000, Avg. Loss: 0.30184122038912026\n",
      "Epoch: 6, Batch: 1000, Avg. Loss: 0.295202726315707\n",
      "Epoch: 6, Batch: 2000, Avg. Loss: 0.2999333508629352\n",
      "Epoch: 6, Batch: 3000, Avg. Loss: 0.3095918547715992\n",
      "Epoch: 7, Batch: 1000, Avg. Loss: 0.3085897152479738\n",
      "Epoch: 7, Batch: 2000, Avg. Loss: 0.29635640332009644\n",
      "Epoch: 7, Batch: 3000, Avg. Loss: 0.2999342773645185\n",
      "Epoch: 8, Batch: 1000, Avg. Loss: 0.29254270616918804\n",
      "Epoch: 8, Batch: 2000, Avg. Loss: 0.30447358648851514\n",
      "Epoch: 8, Batch: 3000, Avg. Loss: 0.2944874407798052\n",
      "Epoch: 9, Batch: 1000, Avg. Loss: 0.290180125342682\n",
      "Epoch: 9, Batch: 2000, Avg. Loss: 0.2957332210354507\n",
      "Epoch: 9, Batch: 3000, Avg. Loss: 0.29964762318413707\n",
      "Epoch: 10, Batch: 1000, Avg. Loss: 0.29330461398046465\n",
      "Epoch: 10, Batch: 2000, Avg. Loss: 0.2860741341970861\n",
      "Epoch: 10, Batch: 3000, Avg. Loss: 0.29880137866735457\n",
      "Epoch: 11, Batch: 1000, Avg. Loss: 0.28924864789284765\n",
      "Epoch: 11, Batch: 2000, Avg. Loss: 0.29051343988627193\n",
      "Epoch: 11, Batch: 3000, Avg. Loss: 0.29064618530683217\n",
      "Epoch: 12, Batch: 1000, Avg. Loss: 0.2945798238944262\n",
      "Epoch: 12, Batch: 2000, Avg. Loss: 0.2825687867011875\n",
      "Epoch: 12, Batch: 3000, Avg. Loss: 0.28491674675373363\n",
      "Epoch: 13, Batch: 1000, Avg. Loss: 0.29106169317662717\n",
      "Epoch: 13, Batch: 2000, Avg. Loss: 0.28534834506269546\n",
      "Epoch: 13, Batch: 3000, Avg. Loss: 0.28336951331235466\n",
      "Epoch: 14, Batch: 1000, Avg. Loss: 0.28656568147055805\n",
      "Epoch: 14, Batch: 2000, Avg. Loss: 0.2793004598589614\n",
      "Epoch: 14, Batch: 3000, Avg. Loss: 0.2829609395135194\n",
      "Epoch: 15, Batch: 1000, Avg. Loss: 0.2777500115633011\n",
      "Epoch: 15, Batch: 2000, Avg. Loss: 0.27374792783055457\n",
      "Epoch: 15, Batch: 3000, Avg. Loss: 0.28417560454271734\n",
      "Epoch: 16, Batch: 1000, Avg. Loss: 0.27769603265170006\n",
      "Epoch: 16, Batch: 2000, Avg. Loss: 0.280964399327524\n",
      "Epoch: 16, Batch: 3000, Avg. Loss: 0.2768561806473881\n",
      "Epoch: 17, Batch: 1000, Avg. Loss: 0.27566798860020936\n",
      "Epoch: 17, Batch: 2000, Avg. Loss: 0.2793998046750203\n",
      "Epoch: 17, Batch: 3000, Avg. Loss: 0.2789291301602498\n",
      "Epoch: 18, Batch: 1000, Avg. Loss: 0.2761709162294865\n",
      "Epoch: 18, Batch: 2000, Avg. Loss: 0.26587801912333814\n",
      "Epoch: 18, Batch: 3000, Avg. Loss: 0.28315320459380744\n",
      "Epoch: 19, Batch: 1000, Avg. Loss: 0.27602892491966485\n",
      "Epoch: 19, Batch: 2000, Avg. Loss: 0.2780987594760954\n",
      "Epoch: 19, Batch: 3000, Avg. Loss: 0.26858438020013276\n",
      "Epoch: 20, Batch: 1000, Avg. Loss: 0.2718710360862315\n",
      "Epoch: 20, Batch: 2000, Avg. Loss: 0.2730548546090722\n",
      "Epoch: 20, Batch: 3000, Avg. Loss: 0.2713356750756502\n",
      "Epoch: 21, Batch: 1000, Avg. Loss: 0.26560980930179356\n",
      "Epoch: 21, Batch: 2000, Avg. Loss: 0.27399004566296936\n",
      "Epoch: 21, Batch: 3000, Avg. Loss: 0.26428914135880766\n",
      "Epoch: 22, Batch: 1000, Avg. Loss: 0.26555160745047035\n",
      "Epoch: 22, Batch: 2000, Avg. Loss: 0.2745607593934983\n",
      "Epoch: 22, Batch: 3000, Avg. Loss: 0.25798048240318894\n",
      "Epoch: 23, Batch: 1000, Avg. Loss: 0.26372261997824537\n",
      "Epoch: 23, Batch: 2000, Avg. Loss: 0.26404112838581206\n",
      "Epoch: 23, Batch: 3000, Avg. Loss: 0.27338164503593\n",
      "Epoch: 24, Batch: 1000, Avg. Loss: 0.2681110068643466\n",
      "Epoch: 24, Batch: 2000, Avg. Loss: 0.2589369993079454\n",
      "Epoch: 24, Batch: 3000, Avg. Loss: 0.2732691283393651\n",
      "Epoch: 25, Batch: 1000, Avg. Loss: 0.26545006706565616\n",
      "Epoch: 25, Batch: 2000, Avg. Loss: 0.25817053562216463\n",
      "Epoch: 25, Batch: 3000, Avg. Loss: 0.2653294369671494\n",
      "Epoch: 26, Batch: 1000, Avg. Loss: 0.2589925484880805\n",
      "Epoch: 26, Batch: 2000, Avg. Loss: 0.26132805773802104\n",
      "Epoch: 26, Batch: 3000, Avg. Loss: 0.2748784060180187\n",
      "Epoch: 27, Batch: 1000, Avg. Loss: 0.2595771047268063\n",
      "Epoch: 27, Batch: 2000, Avg. Loss: 0.2596359071836341\n",
      "Epoch: 27, Batch: 3000, Avg. Loss: 0.26171643668413164\n",
      "Epoch: 28, Batch: 1000, Avg. Loss: 0.2569883743273094\n",
      "Epoch: 28, Batch: 2000, Avg. Loss: 0.2555055660624057\n",
      "Epoch: 28, Batch: 3000, Avg. Loss: 0.26430158663354814\n",
      "Epoch: 29, Batch: 1000, Avg. Loss: 0.25391058341041206\n",
      "Epoch: 29, Batch: 2000, Avg. Loss: 0.252470075824298\n",
      "Epoch: 29, Batch: 3000, Avg. Loss: 0.2611294831067789\n",
      "Epoch: 30, Batch: 1000, Avg. Loss: 0.24785954353772105\n",
      "Epoch: 30, Batch: 2000, Avg. Loss: 0.2621684154365212\n",
      "Epoch: 30, Batch: 3000, Avg. Loss: 0.25135755898989737\n",
      "Finished Training\n"
     ]
    }
   ],
   "source": [
    "# define the number of epochs to train for\n",
    "n_epochs = 30 # start small to see if your model works, initially\n",
    "\n",
    "# call train\n",
    "training_loss = train(n_epochs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing the loss\n",
    "\n",
    "A good indication of how much your network is learning as it trains is the loss over time. In this example, we printed and recorded the average loss for each 1000 batches and for each epoch. Let's plot it and see how the loss decreases (or doesn't) over time. \n",
    "\n",
    "In this case, you should see that the loss has an initially large decrease and even looks like it would decrease more (by some small, linear amount) if we let it train for more epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ec495f8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the loss as the network trained\n",
    "plt.plot(training_loss)\n",
    "plt.xlabel('1000\\'s of batches')\n",
    "plt.ylabel('loss')\n",
    "plt.ylim(0, 2.5) # consistent scale\n",
    "plt.show()"
   ]
  },
  {
   "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": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Loss: 0.280025\n",
      "\n",
      "Test Accuracy of T-shirt/top: 84% (840/1000)\n",
      "Test Accuracy of Trouser: 97% (975/1000)\n",
      "Test Accuracy of Pullover: 83% (839/1000)\n",
      "Test Accuracy of Dress: 87% (877/1000)\n",
      "Test Accuracy of  Coat: 87% (870/1000)\n",
      "Test Accuracy of Sandal: 98% (982/1000)\n",
      "Test Accuracy of Shirt: 74% (748/1000)\n",
      "Test Accuracy of Sneaker: 95% (950/1000)\n",
      "Test Accuracy of   Bag: 97% (976/1000)\n",
      "Test Accuracy of Ankle boot: 95% (959/1000)\n",
      "\n",
      "Test Accuracy (Overall): 90% (9016/10000)\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 = criterion(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",
    "    # this creates a `correct` Tensor that holds the number of correctly classified images in a batch\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\n",
    "\n",
    "Format: predicted class (true class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d47ae80>"
      ]
     },
     "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**: Since t-shirts, shirts, and coats have a similar overall shape, my model has trouble distinguishing between those items. In fact, its lowest test class accuracy is: `Test Accuracy of Shirt`, which this model only gets right about 60% of the time . \n",
    "\n",
    "I suspect that this accuracy could be improved by doing some data augmentation with respect to these classes or even adding another convolutional layer to extract even higher level features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving the model\n",
    "model_dir = 'saved_models/'\n",
    "model_name = 'fashion_net_ex.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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.6.6"
  },
  "widgets": {
   "state": {},
   "version": "1.1.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}