mnist_mlp.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 to data\\MNIST\\raw\\train-images-idx3-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100.1%"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting data\\MNIST\\raw\\train-images-idx3-ubyte.gz to data\\MNIST\\raw\n",
      "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to data\\MNIST\\raw\\train-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "113.5%"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting data\\MNIST\\raw\\train-labels-idx1-ubyte.gz to data\\MNIST\\raw\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to data\\MNIST\\raw\\t10k-images-idx3-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100.4%"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting data\\MNIST\\raw\\t10k-images-idx3-ubyte.gz to data\\MNIST\\raw\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to data\\MNIST\\raw\\t10k-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "180.4%"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting data\\MNIST\\raw\\t10k-labels-idx1-ubyte.gz to data\\MNIST\\raw\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": [
       "<Figure size 1800x288 with 20 Axes>"
      ]
     },
     "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": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "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": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Net(\n",
      "  (fc1): Linear(in_features=784, out_features=256, bias=True)\n",
      "  (fc2): Linear(in_features=256, out_features=128, bias=True)\n",
      "  (fc3): Linear(in_features=128, out_features=10, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "## TODO: Define the NN architecture\n",
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        # linear layer (784 -> 1 hidden node)\n",
    "        self.fc1 = nn.Linear(28 * 28, 256)\n",
    "        self.fc2 = nn.Linear(256, 128)\n",
    "        self.fc3 = nn.Linear(128, 10)\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",
    "        x = F.relu(self.fc2(x))\n",
    "        x = self.fc3(x) # when using cross-entropyloss, do not need log_softmax as it is done by the loss calculation\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": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "## TODO: Specify loss and optimization functions\n",
    "from torch import optim\n",
    "\n",
    "# specify loss function\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "# specify optimizer\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.005)"
   ]
  },
  {
   "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": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 \tTraining Loss: 0.236925\n",
      "Epoch: 2 \tTraining Loss: 0.143207\n",
      "Epoch: 3 \tTraining Loss: 0.124108\n",
      "Epoch: 4 \tTraining Loss: 0.107215\n",
      "Epoch: 5 \tTraining Loss: 0.100418\n",
      "Epoch: 6 \tTraining Loss: 0.089584\n",
      "Epoch: 7 \tTraining Loss: 0.085278\n",
      "Epoch: 8 \tTraining Loss: 0.078443\n",
      "Epoch: 9 \tTraining Loss: 0.078970\n",
      "Epoch: 10 \tTraining Loss: 0.073981\n",
      "Epoch: 11 \tTraining Loss: 0.077452\n",
      "Epoch: 12 \tTraining Loss: 0.065044\n",
      "Epoch: 13 \tTraining Loss: 0.069665\n",
      "Epoch: 14 \tTraining Loss: 0.067156\n",
      "Epoch: 15 \tTraining Loss: 0.067396\n",
      "Epoch: 16 \tTraining Loss: 0.062811\n",
      "Epoch: 17 \tTraining Loss: 0.062382\n",
      "Epoch: 18 \tTraining Loss: 0.063364\n",
      "Epoch: 19 \tTraining Loss: 0.055496\n",
      "Epoch: 20 \tTraining Loss: 0.070100\n"
     ]
    }
   ],
   "source": [
    "# number of epochs to train the model\n",
    "n_epochs = 20  # 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.sampler)\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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Loss: 0.326492\n",
      "\n",
      "Test Accuracy of     0: 98% (969/980)\n",
      "Test Accuracy of     1: 98% (1119/1135)\n",
      "Test Accuracy of     2: 95% (986/1032)\n",
      "Test Accuracy of     3: 97% (983/1010)\n",
      "Test Accuracy of     4: 97% (959/982)\n",
      "Test Accuracy of     5: 94% (846/892)\n",
      "Test Accuracy of     6: 97% (931/958)\n",
      "Test Accuracy of     7: 97% (1001/1028)\n",
      "Test Accuracy of     8: 97% (949/974)\n",
      "Test Accuracy of     9: 94% (956/1009)\n",
      "\n",
      "Test Accuracy (Overall): 96% (9699/10000)\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(len(target)):\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.sampler)\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": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "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",
    "\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\"))"
   ]
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}