mnist_mlp_exercise.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multi-Layer Perceptron, MNIST\n",
    "---\n",
    "In this notebook, we will train an MLP to classify images from the [MNIST database](http://yann.lecun.com/exdb/mnist/) hand-written digit database.\n",
    "\n",
    "The process will be broken down into the following steps:\n",
    ">1. Load and visualize the data\n",
    "2. Define a neural network\n",
    "3. Train the model\n",
    "4. Evaluate the performance of our trained model on a test dataset!\n",
    "\n",
    "Before we begin, we have to import the necessary libraries for working with data and PyTorch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import torch\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Load and Visualize the [Data](http://pytorch.org/docs/stable/torchvision/datasets.html)\n",
    "\n",
    "Downloading may take a few moments, and you should see your progress as the data is loading. You may also choose to change the `batch_size` if you want to load more data at a time.\n",
    "\n",
    "This cell will create DataLoaders for each of our datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
      "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
      "Processing...\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "from torchvision import datasets\n",
    "import torchvision.transforms as transforms\n",
    "\n",
    "# number of subprocesses to use for data loading\n",
    "num_workers = 0\n",
    "# how many samples per batch to load\n",
    "batch_size = 20\n",
    "\n",
    "# convert data to torch.FloatTensor\n",
    "transform = transforms.ToTensor()\n",
    "\n",
    "# choose the training and test datasets\n",
    "train_data = datasets.MNIST(root='data', train=True,\n",
    "                                   download=True, transform=transform)\n",
    "test_data = datasets.MNIST(root='data', train=False,\n",
    "                                  download=True, transform=transform)\n",
    "\n",
    "# prepare data loaders\n",
    "train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size,\n",
    "    num_workers=num_workers)\n",
    "test_loader = torch.utils.data.DataLoader(test_data, batch_size=batch_size, \n",
    "    num_workers=num_workers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize a Batch of Training Data\n",
    "\n",
    "The first step in a classification task is to take a look at the data, make sure it is loaded in correctly, then make any initial observations about patterns in that data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4948fa2cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\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(20):\n",
    "    ax = fig.add_subplot(2, 20/2, idx+1, xticks=[], yticks=[])\n",
    "    ax.imshow(np.squeeze(images[idx]), cmap='gray')\n",
    "    # print out the correct label for each image\n",
    "    # .item() gets the value contained in a Tensor\n",
    "    ax.set_title(str(labels[idx].item()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### View an Image in More Detail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f48ed9b0780>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = np.squeeze(images[1])\n",
    "\n",
    "fig = plt.figure(figsize = (12,12)) \n",
    "ax = fig.add_subplot(111)\n",
    "ax.imshow(img, cmap='gray')\n",
    "width, height = img.shape\n",
    "thresh = img.max()/2.5\n",
    "for x in range(width):\n",
    "    for y in range(height):\n",
    "        val = round(img[x][y],2) if img[x][y] !=0 else 0\n",
    "        ax.annotate(str(val), xy=(y,x),\n",
    "                    horizontalalignment='center',\n",
    "                    verticalalignment='center',\n",
    "                    color='white' if img[x][y]<thresh else 'black')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Define the Network [Architecture](http://pytorch.org/docs/stable/nn.html)\n",
    "\n",
    "The architecture will be responsible for seeing as input a 784-dim Tensor of pixel values for each image, and producing a Tensor of length 10 (our number of classes) that indicates the class scores for an input image. This particular example uses two hidden layers and dropout to avoid overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Net(\n",
      "  (fc1): Linear(in_features=784, out_features=512, bias=True)\n",
      "  (fc2): Linear(in_features=512, out_features=512, bias=True)\n",
      "  (fc3): Linear(in_features=512, out_features=10, bias=True)\n",
      "  (dropout): Dropout(p=0.2)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "## Define the NN architecture\n",
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        self.fc1 = nn.Linear(28 * 28, 512)\n",
    "        # linear layer (n_hidden -> hidden_2)\n",
    "        self.fc2 = nn.Linear(512, 512)\n",
    "        # linear layer (n_hidden -> 10)\n",
    "        self.fc3 = nn.Linear(512, 10)\n",
    "        # dropout layer (p=0.2)\n",
    "        # dropout prevents overfitting of data\n",
    "        self.dropout = nn.Dropout(0.2)\n",
    "\n",
    "    def forward(self, x):\n",
    "        # flatten image input\n",
    "        x = x.view(-1, 28 * 28)\n",
    "        # add hidden layer, with relu activation function\n",
    "        x = F.relu(self.fc1(x))\n",
    "        return x\n",
    "\n",
    "# initialize the NN\n",
    "model = Net()\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Specify [Loss Function](http://pytorch.org/docs/stable/nn.html#loss-functions) and [Optimizer](http://pytorch.org/docs/stable/optim.html)\n",
    "\n",
    "It's recommended that you use cross-entropy loss for classification. If you look at the documentation (linked above), you can see that PyTorch's cross entropy function applies a softmax funtion to the output layer *and* then calculates the log loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Specify loss and optimization functions\n",
    "\n",
    "# specify loss function\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "# specify optimizer\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=0.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Train the Network\n",
    "\n",
    "The steps for training/learning from a batch of data are described in the comments below:\n",
    "1. Clear the gradients of all optimized variables\n",
    "2. Forward pass: compute predicted outputs by passing inputs to the model\n",
    "3. Calculate the loss\n",
    "4. Backward pass: compute gradient of the loss with respect to model parameters\n",
    "5. Perform a single optimization step (parameter update)\n",
    "6. Update average training loss\n",
    "\n",
    "The following loop trains for 30 epochs; feel free to change this number. For now, we suggest somewhere between 20-50 epochs. As you train, take a look at how the values for the training loss decrease over time. We want it to decrease while also avoiding overfitting the training data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 \tTraining Loss: 0.791893\n",
      "Epoch: 2 \tTraining Loss: 0.412267\n",
      "Epoch: 3 \tTraining Loss: 0.370351\n",
      "Epoch: 4 \tTraining Loss: 0.349161\n",
      "Epoch: 5 \tTraining Loss: 0.335674\n",
      "Epoch: 6 \tTraining Loss: 0.326078\n",
      "Epoch: 7 \tTraining Loss: 0.318780\n",
      "Epoch: 8 \tTraining Loss: 0.312977\n",
      "Epoch: 9 \tTraining Loss: 0.308213\n",
      "Epoch: 10 \tTraining Loss: 0.304205\n",
      "Epoch: 11 \tTraining Loss: 0.300768\n",
      "Epoch: 12 \tTraining Loss: 0.297775\n",
      "Epoch: 13 \tTraining Loss: 0.295137\n",
      "Epoch: 14 \tTraining Loss: 0.292785\n",
      "Epoch: 15 \tTraining Loss: 0.290670\n",
      "Epoch: 16 \tTraining Loss: 0.288754\n",
      "Epoch: 17 \tTraining Loss: 0.287005\n",
      "Epoch: 18 \tTraining Loss: 0.285400\n",
      "Epoch: 19 \tTraining Loss: 0.283920\n",
      "Epoch: 20 \tTraining Loss: 0.282548\n",
      "Epoch: 21 \tTraining Loss: 0.281271\n",
      "Epoch: 22 \tTraining Loss: 0.280078\n",
      "Epoch: 23 \tTraining Loss: 0.278960\n",
      "Epoch: 24 \tTraining Loss: 0.277910\n",
      "Epoch: 25 \tTraining Loss: 0.276919\n",
      "Epoch: 26 \tTraining Loss: 0.275983\n",
      "Epoch: 27 \tTraining Loss: 0.275096\n",
      "Epoch: 28 \tTraining Loss: 0.274255\n",
      "Epoch: 29 \tTraining Loss: 0.273454\n",
      "Epoch: 30 \tTraining Loss: 0.272691\n"
     ]
    }
   ],
   "source": [
    "# number of epochs to train the model\n",
    "n_epochs = 30  # suggest training between 20-50 epochs\n",
    "\n",
    "model.train() # prep model for training\n",
    "\n",
    "for epoch in range(n_epochs):\n",
    "    # monitor training loss\n",
    "    train_loss = 0.0\n",
    "    \n",
    "    ###################\n",
    "    # train the model #\n",
    "    ###################\n",
    "    for data, target in train_loader:\n",
    "        # clear the gradients of all optimized variables\n",
    "        optimizer.zero_grad()\n",
    "        # forward pass: compute predicted outputs by passing inputs to the model\n",
    "        output = model(data)\n",
    "        # calculate the loss\n",
    "        loss = criterion(output, target)\n",
    "        # backward pass: compute gradient of the loss with respect to model parameters\n",
    "        loss.backward()\n",
    "        # perform a single optimization step (parameter update)\n",
    "        optimizer.step()\n",
    "        # update running training loss\n",
    "        train_loss += loss.item()*data.size(0)\n",
    "        \n",
    "    # print training statistics \n",
    "    # calculate average loss over an epoch\n",
    "    train_loss = train_loss/len(train_loader.dataset)\n",
    "\n",
    "    print('Epoch: {} \\tTraining Loss: {:.6f}'.format(\n",
    "        epoch+1, \n",
    "        train_loss\n",
    "        ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Test the Trained Network\n",
    "\n",
    "Finally, we test our best model on previously unseen **test data** and evaluate it's performance. Testing on unseen data is a good way to check that our model generalizes well. It may also be useful to be granular in this analysis and take a look at how this model performs on each class as well as looking at its overall loss and accuracy.\n",
    "\n",
    "#### `model.eval()`\n",
    "\n",
    "`model.eval(`) will set all the layers in your model to evaluation mode. This affects layers like dropout layers that turn \"off\" nodes during training with some probability, but should allow every node to be \"on\" for evaluation!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Loss: 0.274130\n",
      "\n",
      "Test Accuracy of     0: 98% (962/980)\n",
      "Test Accuracy of     1: 97% (1110/1135)\n",
      "Test Accuracy of     2: 88% (918/1032)\n",
      "Test Accuracy of     3: 90% (913/1010)\n",
      "Test Accuracy of     4: 92% (910/982)\n",
      "Test Accuracy of     5: 87% (780/892)\n",
      "Test Accuracy of     6: 94% (909/958)\n",
      "Test Accuracy of     7: 92% (946/1028)\n",
      "Test Accuracy of     8: 88% (864/974)\n",
      "Test Accuracy of     9: 90% (914/1009)\n",
      "\n",
      "Test Accuracy (Overall): 92% (9226/10000)\n"
     ]
    }
   ],
   "source": [
    "# initialize lists to monitor test loss and accuracy\n",
    "test_loss = 0.0\n",
    "class_correct = list(0. for i in range(10))\n",
    "class_total = list(0. for i in range(10))\n",
    "\n",
    "model.eval() # prep model for *evaluation*\n",
    "\n",
    "for data, target in test_loader:\n",
    "    # forward pass: compute predicted outputs by passing inputs to the model\n",
    "    output = model(data)\n",
    "    # calculate the loss\n",
    "    loss = criterion(output, target)\n",
    "    # update test loss \n",
    "    test_loss += loss.item()*data.size(0)\n",
    "    # convert output probabilities to predicted class\n",
    "    _, pred = torch.max(output, 1)\n",
    "    # compare predictions to true label\n",
    "    correct = np.squeeze(pred.eq(target.data.view_as(pred)))\n",
    "    # calculate test accuracy for each object class\n",
    "    for i in range(batch_size):\n",
    "        label = target.data[i]\n",
    "        class_correct[label] += correct[i].item()\n",
    "        class_total[label] += 1\n",
    "\n",
    "# calculate and print avg test loss\n",
    "test_loss = test_loss/len(test_loader.dataset)\n",
    "print('Test Loss: {:.6f}\\n'.format(test_loss))\n",
    "\n",
    "for i in range(10):\n",
    "    if class_total[i] > 0:\n",
    "        print('Test Accuracy of %5s: %2d%% (%2d/%2d)' % (\n",
    "            str(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",
    "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",
    "This cell displays test images and their labels in this format: `predicted (ground-truth)`. The text will be green for accurately classified examples and red for incorrect predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f48ed8f6898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# obtain one batch of test images\n",
    "dataiter = iter(test_loader)\n",
    "images, labels = dataiter.next()\n",
    "\n",
    "# get sample outputs\n",
    "output = model(images)\n",
    "# convert output probabilities to predicted class\n",
    "_, preds = torch.max(output, 1)\n",
    "# prep images for display\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(20):\n",
    "    ax = fig.add_subplot(2, 20/2, idx+1, xticks=[], yticks=[])\n",
    "    ax.imshow(np.squeeze(images[idx]), cmap='gray')\n",
    "    ax.set_title(\"{} ({})\".format(str(preds[idx].item()), str(labels[idx].item())),\n",
    "                 color=(\"green\" if preds[idx]==labels[idx] else \"red\"))"
   ]
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}