exercises/1_5_CNN_Layers/4_2. Classify FashionMNIST, solution 1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CNN for Classification\n",
    "---\n",
    "In this and the next notebook, we define **and train** a 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 first solution will be a simple CNN with two convolutional layers. \n",
    "\n",
    "Please note that 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 0x10e3bcd68>"
      ]
     },
     "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=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",
    "        # 10 output channels (for the 10 classes)\n",
    "        self.fc1 = nn.Linear(20*5*5, 10)\n",
    "        \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",
    "        # flatten the inputs into a vector\n",
    "        x = x.view(x.size(0), -1)\n",
    "        \n",
    "        # one linear layer\n",
    "        x = F.relu(self.fc1(x))\n",
    "        # a softmax layer to convert the 10 outputs into a distribution of class scores\n",
    "        x = F.log_softmax(x, dim=1)\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()`; cross entropy loss combines `softmax` and `NLL loss` so, alternatively (as in this example), you may see NLL Loss being used when the output of our Net is a distribution of class scores. \n",
    "\n",
    "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",
    "# cross entropy loss combines softmax and nn.NLLLoss() in one single class.\n",
    "criterion = nn.NLLLoss()\n",
    "\n",
    "## specify optimizer \n",
    "# stochastic gradient descent with a small learning rate\n",
    "optimizer = optim.SGD(net.parameters(), lr=0.001)"
   ]
  },
  {
   "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:  3.32\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",
    "\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": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1, Batch: 1000, Avg. Loss: 2.293711015701294\n",
      "Epoch: 1, Batch: 2000, Avg. Loss: 2.2614983594417573\n",
      "Epoch: 1, Batch: 3000, Avg. Loss: 2.1278420504331588\n",
      "Epoch: 2, Batch: 1000, Avg. Loss: 1.7922429409027099\n",
      "Epoch: 2, Batch: 2000, Avg. Loss: 1.567815233528614\n",
      "Epoch: 2, Batch: 3000, Avg. Loss: 1.45271438485384\n",
      "Epoch: 3, Batch: 1000, Avg. Loss: 1.3911882250905037\n",
      "Epoch: 3, Batch: 2000, Avg. Loss: 1.3407422359585761\n",
      "Epoch: 3, Batch: 3000, Avg. Loss: 1.307439180970192\n",
      "Epoch: 4, Batch: 1000, Avg. Loss: 1.2853169726729392\n",
      "Epoch: 4, Batch: 2000, Avg. Loss: 1.2773892575800418\n",
      "Epoch: 4, Batch: 3000, Avg. Loss: 1.2523164848685264\n",
      "Epoch: 5, Batch: 1000, Avg. Loss: 1.247465244114399\n",
      "Epoch: 5, Batch: 2000, Avg. Loss: 1.2230736655294896\n",
      "Epoch: 5, Batch: 3000, Avg. Loss: 1.2230081340670587\n",
      "Epoch: 6, Batch: 1000, Avg. Loss: 1.2086039925813674\n",
      "Epoch: 6, Batch: 2000, Avg. Loss: 1.201804144859314\n",
      "Epoch: 6, Batch: 3000, Avg. Loss: 1.1991582120358943\n",
      "Epoch: 7, Batch: 1000, Avg. Loss: 1.178821917563677\n",
      "Epoch: 7, Batch: 2000, Avg. Loss: 1.1927010530233384\n",
      "Epoch: 7, Batch: 3000, Avg. Loss: 0.9883719678223133\n",
      "Epoch: 8, Batch: 1000, Avg. Loss: 0.9610524806082249\n",
      "Epoch: 8, Batch: 2000, Avg. Loss: 0.9545967155843973\n",
      "Epoch: 8, Batch: 3000, Avg. Loss: 0.9444614236354828\n",
      "Epoch: 9, Batch: 1000, Avg. Loss: 0.848079521715641\n",
      "Epoch: 9, Batch: 2000, Avg. Loss: 0.7992876795530319\n",
      "Epoch: 9, Batch: 3000, Avg. Loss: 0.7709207954108716\n",
      "Epoch: 10, Batch: 1000, Avg. Loss: 0.7776889860704541\n",
      "Epoch: 10, Batch: 2000, Avg. Loss: 0.7634907150417566\n",
      "Epoch: 10, Batch: 3000, Avg. Loss: 0.7673224983215332\n",
      "Epoch: 11, Batch: 1000, Avg. Loss: 0.7628517677783966\n",
      "Epoch: 11, Batch: 2000, Avg. Loss: 0.7484397180825472\n",
      "Epoch: 11, Batch: 3000, Avg. Loss: 0.7444565385580063\n",
      "Epoch: 12, Batch: 1000, Avg. Loss: 0.755182758808136\n",
      "Epoch: 12, Batch: 2000, Avg. Loss: 0.7403829108923674\n",
      "Epoch: 12, Batch: 3000, Avg. Loss: 0.7183869410604239\n",
      "Epoch: 13, Batch: 1000, Avg. Loss: 0.7331990741193295\n",
      "Epoch: 13, Batch: 2000, Avg. Loss: 0.7244378483593464\n",
      "Epoch: 13, Batch: 3000, Avg. Loss: 0.7160020467936993\n",
      "Epoch: 14, Batch: 1000, Avg. Loss: 0.7133379264250398\n",
      "Epoch: 14, Batch: 2000, Avg. Loss: 0.7269235459640622\n",
      "Epoch: 14, Batch: 3000, Avg. Loss: 0.7018592798113823\n",
      "Epoch: 15, Batch: 1000, Avg. Loss: 0.7194379655569791\n",
      "Epoch: 15, Batch: 2000, Avg. Loss: 0.6989473836570979\n",
      "Epoch: 15, Batch: 3000, Avg. Loss: 0.6228373557701707\n",
      "Epoch: 16, Batch: 1000, Avg. Loss: 0.5411538640186191\n",
      "Epoch: 16, Batch: 2000, Avg. Loss: 0.5233812501430511\n",
      "Epoch: 16, Batch: 3000, Avg. Loss: 0.5075103376805782\n",
      "Epoch: 17, Batch: 1000, Avg. Loss: 0.5107238769084215\n",
      "Epoch: 17, Batch: 2000, Avg. Loss: 0.5087694025710225\n",
      "Epoch: 17, Batch: 3000, Avg. Loss: 0.4944440108910203\n",
      "Epoch: 18, Batch: 1000, Avg. Loss: 0.502114748314023\n",
      "Epoch: 18, Batch: 2000, Avg. Loss: 0.48034292860329153\n",
      "Epoch: 18, Batch: 3000, Avg. Loss: 0.4951906952485442\n",
      "Epoch: 19, Batch: 1000, Avg. Loss: 0.4867162222266197\n",
      "Epoch: 19, Batch: 2000, Avg. Loss: 0.47913133818656206\n",
      "Epoch: 19, Batch: 3000, Avg. Loss: 0.4777128367498517\n",
      "Epoch: 20, Batch: 1000, Avg. Loss: 0.4784162274450064\n",
      "Epoch: 20, Batch: 2000, Avg. Loss: 0.47024892734736207\n",
      "Epoch: 20, Batch: 3000, Avg. Loss: 0.4680948257446289\n",
      "Epoch: 21, Batch: 1000, Avg. Loss: 0.4639972406327724\n",
      "Epoch: 21, Batch: 2000, Avg. Loss: 0.4679495306611061\n",
      "Epoch: 21, Batch: 3000, Avg. Loss: 0.4580956963971257\n",
      "Epoch: 22, Batch: 1000, Avg. Loss: 0.46124175368994474\n",
      "Epoch: 22, Batch: 2000, Avg. Loss: 0.45181121753901243\n",
      "Epoch: 22, Batch: 3000, Avg. Loss: 0.45743834134936334\n",
      "Epoch: 23, Batch: 1000, Avg. Loss: 0.4553787228167057\n",
      "Epoch: 23, Batch: 2000, Avg. Loss: 0.4531098504140973\n",
      "Epoch: 23, Batch: 3000, Avg. Loss: 0.44192337393760683\n",
      "Epoch: 24, Batch: 1000, Avg. Loss: 0.4524531869329512\n",
      "Epoch: 24, Batch: 2000, Avg. Loss: 0.44925868370383976\n",
      "Epoch: 24, Batch: 3000, Avg. Loss: 0.43288518612086774\n",
      "Epoch: 25, Batch: 1000, Avg. Loss: 0.42988146520406006\n",
      "Epoch: 25, Batch: 2000, Avg. Loss: 0.43842076568305494\n",
      "Epoch: 25, Batch: 3000, Avg. Loss: 0.44615162058547136\n",
      "Epoch: 26, Batch: 1000, Avg. Loss: 0.4286475028693676\n",
      "Epoch: 26, Batch: 2000, Avg. Loss: 0.43873732385039327\n",
      "Epoch: 26, Batch: 3000, Avg. Loss: 0.436057441405952\n",
      "Epoch: 27, Batch: 1000, Avg. Loss: 0.43456322553008797\n",
      "Epoch: 27, Batch: 2000, Avg. Loss: 0.4266567186899483\n",
      "Epoch: 27, Batch: 3000, Avg. Loss: 0.42651354531943797\n",
      "Epoch: 28, Batch: 1000, Avg. Loss: 0.4224758724123239\n",
      "Epoch: 28, Batch: 2000, Avg. Loss: 0.43466416219249365\n",
      "Epoch: 28, Batch: 3000, Avg. Loss: 0.41815647181123494\n",
      "Epoch: 29, Batch: 1000, Avg. Loss: 0.42059914069622756\n",
      "Epoch: 29, Batch: 2000, Avg. Loss: 0.4180913874581456\n",
      "Epoch: 29, Batch: 3000, Avg. Loss: 0.4248819703683257\n",
      "Epoch: 30, Batch: 1000, Avg. Loss: 0.41685047436505557\n",
      "Epoch: 30, Batch: 2000, Avg. Loss: 0.41523356790095567\n",
      "Epoch: 30, Batch: 3000, Avg. Loss: 0.4227082119993866\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 and record the loss over time\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 can see that it takes a little bit for a big initial loss decrease, and the loss is flattening out over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x120889eb8>"
      ]
     },
     "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": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Loss: 0.444342\n",
      "\n",
      "Test Accuracy of T-shirt/top: 73% (731/1000)\n",
      "Test Accuracy of Trouser: 95% (958/1000)\n",
      "Test Accuracy of Pullover: 70% (700/1000)\n",
      "Test Accuracy of Dress: 85% (855/1000)\n",
      "Test Accuracy of  Coat: 79% (795/1000)\n",
      "Test Accuracy of Sandal: 92% (924/1000)\n",
      "Test Accuracy of Shirt: 63% (637/1000)\n",
      "Test Accuracy of Sneaker: 95% (956/1000)\n",
      "Test Accuracy of   Bag: 94% (942/1000)\n",
      "Test Accuracy of Ankle boot: 94% (941/1000)\n",
      "\n",
      "Test Accuracy (Overall): 84% (8439/10000)\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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bc07d30>"
      ]
     },
     "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**: This model performs well on everything but shirts and pullovers (0% accuracy); it looks like this incorrectly classifies most of those as a coat which has a similar overall shape. Because it performs well on everything but hese two classes, I suspect this model is overfitting ceratin classes at the cost of generalization. I suspect that this accuracy could be improved by adding some dropout layers to aoid overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving the model\n",
    "model_dir = 'saved_models/'\n",
    "model_name = 'fashion_net_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)"
   ]
  }
 ],
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