3.cnn.for.digits.classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deep Learning with PyTorch\n",
    "\n",
    "\n",
    "Author: [Anand Saha](http://teleported.in/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Computer Vision: CNN for digits recognition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.cuda as cuda\n",
    "import torch.nn as nn\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from torch.autograd import Variable\n",
    "\n",
    "# Torchvision module contains various utilities, classes, models and datasets \n",
    "# used towards computer vision usecases\n",
    "from torchvision import datasets\n",
    "from torchvision import transforms\n",
    "\n",
    "# Functional module contains helper functions\n",
    "import torch.nn.functional as F"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create the DataLoaders**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mean and standard deviation of all the pixels in the MNIST dataset\n",
    "mean_gray = 0.1307\n",
    "stddev_gray = 0.3081\n",
    "\n",
    "transform=transforms.Compose([transforms.ToTensor(),\n",
    "                              transforms.Normalize((mean_gray,), (stddev_gray,))])\n",
    "\n",
    "mnist_train = datasets.MNIST('./data', train=True, download=True, transform=transform)\n",
    "mnist_valid = datasets.MNIST('./data', train=False, download=True, transform=transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = mnist_train[12][0].numpy() * stddev_gray + mean_gray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fea0c2476a0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dSD+c4QfExBl+QFCEHwiK8ANBEX4gKMIPBEX4gaAIPxAU4QeC+n8ZCToN0NMbAwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fea179b3b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img.reshape(28, 28), cmap='gray')\n",
    "# Note that each image is 28 x 28 pixel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label = mnist_train[12][1]\n",
    "label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 1024 # Reduce this if you get out-of-memory error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist_train_loader = torch.utils.data.DataLoader(mnist_train, batch_size=batch_size, shuffle=True, num_workers=1)\n",
    "mnist_valid_loader = torch.utils.data.DataLoader(mnist_valid, batch_size=batch_size, shuffle=True, num_workers=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create the model**"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Dropout**:\n",
    "\n",
    "* A regularization technique: i.e. a technique to prevent overfitting, where the model becomes really good on the training dataset, but poor on the validation/testing dataset (unseen data)\n",
    "* Randomly selected neurons are \"switched off\" or prevented from firing during each pass\n",
    "* Thus, at every iteration, the model works with n% of it's neurons missing, where n is specified in the dropout layer (typically 0.5 or 50%)\n",
    "* The result of this is that when random neurons are dropped, the alive neurons have to step in to generate the correct representation to decrease the loss function\n",
    "* Thus the network becomes less sensitive to the weights of a selective few neurons, and become better at generalizing the features without overfitting the training data\n",
    "\n",
    "Paper: [http://jmlr.org/papers/v15/srivastava14a.html](http://jmlr.org/papers/v15/srivastava14a.html)\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MNISTNet(nn.Module):\n",
    "    \n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "               \n",
    "        # NOTE: All Conv2d layers have a default padding of 0 and stride of 1,\n",
    "        # which is what we are using.\n",
    "        \n",
    "        # Convolution Layer 1                             # 28 x 28 x 1  (input)\n",
    "        self.conv1 = nn.Conv2d(1, 20, kernel_size=5)      # 24 x 24 x 20  (after 1st convolution)\n",
    "        self.relu1 = nn.ReLU()                            # Same as above\n",
    "        \n",
    "        # Convolution Layer 2\n",
    "        self.conv2 = nn.Conv2d(20, 30, kernel_size=5)     # 20 x 20 x 30  (after 2nd convolution)\n",
    "        self.conv2_drop = nn.Dropout2d(p=0.5)             # Same as above\n",
    "        self.maxpool2 = nn.MaxPool2d(2)                   # 10 x 10 x 30  (after pooling)\n",
    "        self.relu2 = nn.ReLU()                            # Same as above \n",
    "        \n",
    "        # Fully connected layers\n",
    "        self.fc1 = nn.Linear(3000, 500)\n",
    "        self.fc2 = nn.Linear(500, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        \n",
    "        # Convolution Layer 1                    \n",
    "        x = self.conv1(x)                        \n",
    "        x = self.relu1(x)                        \n",
    "        \n",
    "        # Convolution Layer 2\n",
    "        x = self.conv2(x)               \n",
    "        x = self.conv2_drop(x)\n",
    "        x = self.maxpool2(x)\n",
    "        x = self.relu2(x)\n",
    "        \n",
    "        # Switch from activation maps to vectors\n",
    "        x = x.view(-1, 3000)\n",
    "        \n",
    "        # Fully connected layer 1\n",
    "        x = self.fc1(x)\n",
    "        x = F.relu(x)\n",
    "        x = F.dropout(x, training=True)\n",
    "        \n",
    "        # Fully connected layer 2\n",
    "        x = self.fc2(x)\n",
    "        \n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create the objects**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The model\n",
    "net = MNISTNet()\n",
    "\n",
    "if cuda.is_available():\n",
    "    net = net.cuda()\n",
    "\n",
    "# Our loss function\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "# Our optimizer\n",
    "learning_rate = 0.01\n",
    "optimizer = torch.optim.SGD(net.parameters(), lr=learning_rate, momentum=0.9) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Training loop**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100, Tr Loss: 1.0912, Tr Acc: 64.8150, Val Loss: 0.3349, Val Acc: 90.3600\n",
      "Epoch 2/100, Tr Loss: 0.3399, Tr Acc: 89.9783, Val Loss: 0.2105, Val Acc: 93.6800\n",
      "Epoch 3/100, Tr Loss: 0.2483, Tr Acc: 92.6450, Val Loss: 0.1625, Val Acc: 94.9800\n",
      "Epoch 4/100, Tr Loss: 0.2048, Tr Acc: 93.8767, Val Loss: 0.1343, Val Acc: 95.9100\n",
      "Epoch 5/100, Tr Loss: 0.1717, Tr Acc: 94.8750, Val Loss: 0.1185, Val Acc: 96.4300\n",
      "Epoch 6/100, Tr Loss: 0.1549, Tr Acc: 95.4083, Val Loss: 0.1045, Val Acc: 96.8500\n",
      "Epoch 7/100, Tr Loss: 0.1366, Tr Acc: 95.8583, Val Loss: 0.0953, Val Acc: 97.0400\n",
      "Epoch 8/100, Tr Loss: 0.1248, Tr Acc: 96.2850, Val Loss: 0.0874, Val Acc: 97.1500\n",
      "Epoch 9/100, Tr Loss: 0.1158, Tr Acc: 96.4650, Val Loss: 0.0808, Val Acc: 97.5200\n",
      "Epoch 10/100, Tr Loss: 0.1077, Tr Acc: 96.7767, Val Loss: 0.0803, Val Acc: 97.5400\n",
      "Epoch 11/100, Tr Loss: 0.0994, Tr Acc: 96.9867, Val Loss: 0.0744, Val Acc: 97.7100\n",
      "Epoch 12/100, Tr Loss: 0.0952, Tr Acc: 97.2017, Val Loss: 0.0670, Val Acc: 97.8600\n",
      "Epoch 13/100, Tr Loss: 0.0894, Tr Acc: 97.2550, Val Loss: 0.0640, Val Acc: 97.9800\n",
      "Epoch 14/100, Tr Loss: 0.0858, Tr Acc: 97.4483, Val Loss: 0.0609, Val Acc: 98.2300\n",
      "Epoch 15/100, Tr Loss: 0.0816, Tr Acc: 97.5500, Val Loss: 0.0541, Val Acc: 98.3000\n",
      "Epoch 16/100, Tr Loss: 0.0766, Tr Acc: 97.7083, Val Loss: 0.0565, Val Acc: 98.2500\n",
      "Epoch 17/100, Tr Loss: 0.0724, Tr Acc: 97.7867, Val Loss: 0.0551, Val Acc: 98.2300\n",
      "Epoch 18/100, Tr Loss: 0.0704, Tr Acc: 97.8333, Val Loss: 0.0512, Val Acc: 98.3800\n",
      "Epoch 19/100, Tr Loss: 0.0666, Tr Acc: 98.0083, Val Loss: 0.0502, Val Acc: 98.4000\n",
      "Epoch 20/100, Tr Loss: 0.0660, Tr Acc: 98.0417, Val Loss: 0.0482, Val Acc: 98.4600\n",
      "Epoch 21/100, Tr Loss: 0.0635, Tr Acc: 98.0433, Val Loss: 0.0483, Val Acc: 98.4900\n",
      "Epoch 22/100, Tr Loss: 0.0597, Tr Acc: 98.2233, Val Loss: 0.0452, Val Acc: 98.5700\n",
      "Epoch 23/100, Tr Loss: 0.0586, Tr Acc: 98.1617, Val Loss: 0.0457, Val Acc: 98.5800\n",
      "Epoch 24/100, Tr Loss: 0.0561, Tr Acc: 98.2650, Val Loss: 0.0457, Val Acc: 98.5400\n",
      "Epoch 25/100, Tr Loss: 0.0532, Tr Acc: 98.3517, Val Loss: 0.0440, Val Acc: 98.4900\n",
      "Epoch 26/100, Tr Loss: 0.0543, Tr Acc: 98.2567, Val Loss: 0.0444, Val Acc: 98.5700\n",
      "Epoch 27/100, Tr Loss: 0.0519, Tr Acc: 98.3300, Val Loss: 0.0424, Val Acc: 98.5900\n",
      "Epoch 28/100, Tr Loss: 0.0512, Tr Acc: 98.4550, Val Loss: 0.0439, Val Acc: 98.6600\n",
      "Epoch 29/100, Tr Loss: 0.0478, Tr Acc: 98.5017, Val Loss: 0.0441, Val Acc: 98.7100\n",
      "Epoch 30/100, Tr Loss: 0.0472, Tr Acc: 98.5350, Val Loss: 0.0422, Val Acc: 98.6100\n",
      "Epoch 31/100, Tr Loss: 0.0464, Tr Acc: 98.5483, Val Loss: 0.0368, Val Acc: 98.8600\n",
      "Epoch 32/100, Tr Loss: 0.0452, Tr Acc: 98.5800, Val Loss: 0.0392, Val Acc: 98.7600\n",
      "Epoch 33/100, Tr Loss: 0.0460, Tr Acc: 98.5433, Val Loss: 0.0392, Val Acc: 98.6500\n",
      "Epoch 34/100, Tr Loss: 0.0431, Tr Acc: 98.6583, Val Loss: 0.0371, Val Acc: 98.9200\n",
      "Epoch 35/100, Tr Loss: 0.0439, Tr Acc: 98.6683, Val Loss: 0.0389, Val Acc: 98.7200\n",
      "Epoch 36/100, Tr Loss: 0.0412, Tr Acc: 98.6800, Val Loss: 0.0380, Val Acc: 98.7800\n",
      "Epoch 37/100, Tr Loss: 0.0414, Tr Acc: 98.7167, Val Loss: 0.0393, Val Acc: 98.8300\n",
      "Epoch 38/100, Tr Loss: 0.0404, Tr Acc: 98.7700, Val Loss: 0.0363, Val Acc: 98.8600\n",
      "Epoch 39/100, Tr Loss: 0.0398, Tr Acc: 98.7483, Val Loss: 0.0392, Val Acc: 98.7900\n",
      "Epoch 40/100, Tr Loss: 0.0390, Tr Acc: 98.8000, Val Loss: 0.0354, Val Acc: 98.8900\n",
      "Epoch 41/100, Tr Loss: 0.0388, Tr Acc: 98.8317, Val Loss: 0.0375, Val Acc: 98.8400\n",
      "Epoch 42/100, Tr Loss: 0.0366, Tr Acc: 98.8350, Val Loss: 0.0380, Val Acc: 98.7000\n",
      "Epoch 43/100, Tr Loss: 0.0363, Tr Acc: 98.8833, Val Loss: 0.0353, Val Acc: 98.8900\n",
      "Epoch 44/100, Tr Loss: 0.0345, Tr Acc: 98.9133, Val Loss: 0.0339, Val Acc: 98.8600\n",
      "Epoch 45/100, Tr Loss: 0.0335, Tr Acc: 98.9600, Val Loss: 0.0348, Val Acc: 98.8100\n",
      "Epoch 46/100, Tr Loss: 0.0340, Tr Acc: 98.9183, Val Loss: 0.0359, Val Acc: 98.8700\n",
      "Epoch 47/100, Tr Loss: 0.0335, Tr Acc: 98.9200, Val Loss: 0.0366, Val Acc: 98.8800\n",
      "Epoch 48/100, Tr Loss: 0.0318, Tr Acc: 98.9667, Val Loss: 0.0326, Val Acc: 98.9200\n",
      "Epoch 49/100, Tr Loss: 0.0325, Tr Acc: 98.9350, Val Loss: 0.0361, Val Acc: 98.8300\n",
      "Epoch 50/100, Tr Loss: 0.0323, Tr Acc: 98.9850, Val Loss: 0.0319, Val Acc: 99.0200\n",
      "Epoch 51/100, Tr Loss: 0.0318, Tr Acc: 98.9583, Val Loss: 0.0331, Val Acc: 98.9700\n",
      "Epoch 52/100, Tr Loss: 0.0301, Tr Acc: 99.0167, Val Loss: 0.0369, Val Acc: 98.8400\n",
      "Epoch 53/100, Tr Loss: 0.0303, Tr Acc: 99.0383, Val Loss: 0.0352, Val Acc: 98.8700\n",
      "Epoch 54/100, Tr Loss: 0.0303, Tr Acc: 99.0817, Val Loss: 0.0326, Val Acc: 99.0100\n",
      "Epoch 55/100, Tr Loss: 0.0284, Tr Acc: 99.1350, Val Loss: 0.0341, Val Acc: 98.9100\n",
      "Epoch 56/100, Tr Loss: 0.0280, Tr Acc: 99.1300, Val Loss: 0.0350, Val Acc: 98.8700\n",
      "Epoch 57/100, Tr Loss: 0.0273, Tr Acc: 99.1067, Val Loss: 0.0306, Val Acc: 99.0000\n",
      "Epoch 58/100, Tr Loss: 0.0278, Tr Acc: 99.1117, Val Loss: 0.0313, Val Acc: 99.0600\n",
      "Epoch 59/100, Tr Loss: 0.0264, Tr Acc: 99.1767, Val Loss: 0.0334, Val Acc: 99.0700\n",
      "Epoch 60/100, Tr Loss: 0.0260, Tr Acc: 99.1783, Val Loss: 0.0319, Val Acc: 98.9400\n",
      "Epoch 61/100, Tr Loss: 0.0255, Tr Acc: 99.1950, Val Loss: 0.0342, Val Acc: 98.9100\n",
      "Epoch 62/100, Tr Loss: 0.0266, Tr Acc: 99.1283, Val Loss: 0.0325, Val Acc: 98.9800\n",
      "Epoch 63/100, Tr Loss: 0.0257, Tr Acc: 99.1783, Val Loss: 0.0349, Val Acc: 98.9600\n",
      "Epoch 64/100, Tr Loss: 0.0244, Tr Acc: 99.2367, Val Loss: 0.0359, Val Acc: 98.9100\n",
      "Epoch 65/100, Tr Loss: 0.0254, Tr Acc: 99.1667, Val Loss: 0.0311, Val Acc: 99.0100\n",
      "Epoch 66/100, Tr Loss: 0.0241, Tr Acc: 99.2000, Val Loss: 0.0316, Val Acc: 98.9400\n",
      "Epoch 67/100, Tr Loss: 0.0246, Tr Acc: 99.2583, Val Loss: 0.0304, Val Acc: 98.9700\n",
      "Epoch 68/100, Tr Loss: 0.0245, Tr Acc: 99.1767, Val Loss: 0.0298, Val Acc: 99.0600\n",
      "Epoch 69/100, Tr Loss: 0.0234, Tr Acc: 99.2400, Val Loss: 0.0318, Val Acc: 99.0600\n",
      "Epoch 70/100, Tr Loss: 0.0228, Tr Acc: 99.2683, Val Loss: 0.0271, Val Acc: 99.1100\n",
      "Epoch 71/100, Tr Loss: 0.0236, Tr Acc: 99.2183, Val Loss: 0.0308, Val Acc: 99.1000\n",
      "Epoch 72/100, Tr Loss: 0.0232, Tr Acc: 99.2250, Val Loss: 0.0298, Val Acc: 99.1400\n",
      "Epoch 73/100, Tr Loss: 0.0229, Tr Acc: 99.2733, Val Loss: 0.0335, Val Acc: 99.0100\n",
      "Epoch 74/100, Tr Loss: 0.0219, Tr Acc: 99.2967, Val Loss: 0.0282, Val Acc: 99.1300\n",
      "Epoch 75/100, Tr Loss: 0.0215, Tr Acc: 99.3367, Val Loss: 0.0338, Val Acc: 98.9300\n",
      "Epoch 76/100, Tr Loss: 0.0205, Tr Acc: 99.3050, Val Loss: 0.0296, Val Acc: 99.0000\n",
      "Epoch 77/100, Tr Loss: 0.0208, Tr Acc: 99.3167, Val Loss: 0.0317, Val Acc: 99.0800\n",
      "Epoch 78/100, Tr Loss: 0.0199, Tr Acc: 99.3217, Val Loss: 0.0326, Val Acc: 99.0700\n",
      "Epoch 79/100, Tr Loss: 0.0210, Tr Acc: 99.3200, Val Loss: 0.0330, Val Acc: 99.0400\n",
      "Epoch 80/100, Tr Loss: 0.0206, Tr Acc: 99.3483, Val Loss: 0.0334, Val Acc: 99.0600\n",
      "Epoch 81/100, Tr Loss: 0.0194, Tr Acc: 99.3583, Val Loss: 0.0313, Val Acc: 98.9800\n",
      "Epoch 82/100, Tr Loss: 0.0192, Tr Acc: 99.3517, Val Loss: 0.0308, Val Acc: 98.9800\n",
      "Epoch 83/100, Tr Loss: 0.0188, Tr Acc: 99.3450, Val Loss: 0.0276, Val Acc: 99.2400\n",
      "Epoch 84/100, Tr Loss: 0.0201, Tr Acc: 99.3417, Val Loss: 0.0279, Val Acc: 99.1900\n",
      "Epoch 85/100, Tr Loss: 0.0183, Tr Acc: 99.3883, Val Loss: 0.0302, Val Acc: 99.1000\n",
      "Epoch 86/100, Tr Loss: 0.0195, Tr Acc: 99.3617, Val Loss: 0.0299, Val Acc: 99.0600\n",
      "Epoch 87/100, Tr Loss: 0.0184, Tr Acc: 99.3800, Val Loss: 0.0298, Val Acc: 99.0900\n",
      "Epoch 88/100, Tr Loss: 0.0198, Tr Acc: 99.3900, Val Loss: 0.0312, Val Acc: 99.0300\n",
      "Epoch 89/100, Tr Loss: 0.0190, Tr Acc: 99.3950, Val Loss: 0.0316, Val Acc: 99.1200\n",
      "Epoch 90/100, Tr Loss: 0.0183, Tr Acc: 99.4200, Val Loss: 0.0306, Val Acc: 99.1000\n",
      "Epoch 91/100, Tr Loss: 0.0175, Tr Acc: 99.4133, Val Loss: 0.0324, Val Acc: 99.0600\n",
      "Epoch 92/100, Tr Loss: 0.0180, Tr Acc: 99.4317, Val Loss: 0.0276, Val Acc: 99.1900\n",
      "Epoch 93/100, Tr Loss: 0.0177, Tr Acc: 99.3983, Val Loss: 0.0322, Val Acc: 98.9700\n",
      "Epoch 94/100, Tr Loss: 0.0178, Tr Acc: 99.4150, Val Loss: 0.0319, Val Acc: 99.0700\n",
      "Epoch 95/100, Tr Loss: 0.0164, Tr Acc: 99.4300, Val Loss: 0.0345, Val Acc: 99.1000\n",
      "Epoch 96/100, Tr Loss: 0.0165, Tr Acc: 99.4283, Val Loss: 0.0340, Val Acc: 98.9800\n",
      "Epoch 97/100, Tr Loss: 0.0168, Tr Acc: 99.4350, Val Loss: 0.0275, Val Acc: 99.1100\n",
      "Epoch 98/100, Tr Loss: 0.0161, Tr Acc: 99.4767, Val Loss: 0.0282, Val Acc: 99.1600\n",
      "Epoch 99/100, Tr Loss: 0.0168, Tr Acc: 99.4833, Val Loss: 0.0318, Val Acc: 99.0600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 100/100, Tr Loss: 0.0165, Tr Acc: 99.4183, Val Loss: 0.0291, Val Acc: 99.0400\n"
     ]
    }
   ],
   "source": [
    "num_epochs = 100\n",
    "\n",
    "train_loss = []\n",
    "valid_loss = []\n",
    "train_accuracy = []\n",
    "valid_accuracy = []\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    \n",
    "    ############################\n",
    "    # Train\n",
    "    ############################\n",
    "    \n",
    "    iter_loss = 0.0\n",
    "    correct = 0\n",
    "    iterations = 0\n",
    "    \n",
    "    net.train()                   # Put the network into training mode\n",
    "    \n",
    "    for i, (items, classes) in enumerate(mnist_train_loader):\n",
    "        \n",
    "        # Convert torch tensor to Variable\n",
    "        items = Variable(items)\n",
    "        classes = Variable(classes)\n",
    "        \n",
    "        # If we have GPU, shift the data to GPU\n",
    "        if cuda.is_available():\n",
    "            items = items.cuda()\n",
    "            classes = classes.cuda()\n",
    "        \n",
    "        optimizer.zero_grad()     # Clear off the gradients from any past operation\n",
    "        outputs = net(items)      # Do the forward pass\n",
    "        loss = criterion(outputs, classes) # Calculate the loss\n",
    "        iter_loss += loss.data[0] # Accumulate the loss\n",
    "        loss.backward()           # Calculate the gradients with help of back propagation\n",
    "        optimizer.step()          # Ask the optimizer to adjust the parameters based on the gradients\n",
    "        \n",
    "        # Record the correct predictions for training data \n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "        correct += (predicted == classes.data).sum()\n",
    "        iterations += 1\n",
    "    \n",
    "    # Record the training loss\n",
    "    train_loss.append(iter_loss/iterations)\n",
    "    # Record the training accuracy\n",
    "    train_accuracy.append((100 * correct / len(mnist_train_loader.dataset)))\n",
    "   \n",
    "\n",
    "    ############################\n",
    "    # Validate - How did we do on the unseen dataset?\n",
    "    ############################\n",
    "    \n",
    "    loss = 0.0\n",
    "    correct = 0\n",
    "    iterations = 0\n",
    "\n",
    "    net.eval()                    # Put the network into evaluate mode\n",
    "    \n",
    "    for i, (items, classes) in enumerate(mnist_valid_loader):\n",
    "        \n",
    "        # Convert torch tensor to Variable\n",
    "        items = Variable(items)\n",
    "        classes = Variable(classes)\n",
    "        \n",
    "        # If we have GPU, shift the data to GPU\n",
    "        if cuda.is_available():\n",
    "            items = items.cuda()\n",
    "            classes = classes.cuda()\n",
    "        \n",
    "        outputs = net(items)      # Do the forward pass\n",
    "        loss += criterion(outputs, classes).data[0] # Calculate the loss\n",
    "        \n",
    "        # Record the correct predictions for training data\n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "        correct += (predicted == classes.data).sum()\n",
    "        \n",
    "        iterations += 1\n",
    "\n",
    "    # Record the validation loss\n",
    "    valid_loss.append(loss/iterations)\n",
    "    # Record the validation accuracy\n",
    "    valid_accuracy.append(correct / len(mnist_valid_loader.dataset) * 100.0)\n",
    "\n",
    "    \n",
    "    print ('Epoch %d/%d, Tr Loss: %.4f, Tr Acc: %.4f, Val Loss: %.4f, Val Acc: %.4f'\n",
    "           %(epoch+1, num_epochs, train_loss[-1], train_accuracy[-1], \n",
    "             valid_loss[-1], valid_accuracy[-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(net.state_dict(), \"./3.model.pth\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Visialize the loss and accuracy**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fea0c198e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Loss\n",
    "\n",
    "f = plt.figure(figsize=(10, 8))\n",
    "plt.plot(train_loss, label='training loss')\n",
    "plt.plot(valid_loss, label='validation loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fea0c178128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Accuracy\n",
    "\n",
    "f = plt.figure(figsize=(10, 8))\n",
    "plt.plot(train_accuracy, label='training accuracy')\n",
    "plt.plot(valid_accuracy, label='validation accuracy')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Standalone inference**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_index = 23\n",
    "img = mnist_valid[image_index][0].resize_((1, 1, 28, 28))\n",
    "img = Variable(img)\n",
    "label = mnist_valid[image_index][1]\n",
    "\n",
    "net.eval()\n",
    "\n",
    "if cuda.is_available():\n",
    "    net = net.cuda()\n",
    "    img = img.cuda()\n",
    "else:\n",
    "    net = net.cpu()\n",
    "    img = img.cpu()\n",
    "    \n",
    "output = net(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\n",
       "Columns 0 to 7 \n",
       "-11.0635  -8.2341  -8.3585  14.9212  -6.3458  31.9319   1.8713 -12.6704\n",
       "\n",
       "Columns 8 to 9 \n",
       " -2.6972  12.8850\n",
       "[torch.cuda.FloatTensor of size 1x10 (GPU 0)]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction is:  \n",
      " 5\n",
      "[torch.cuda.LongTensor of size 1 (GPU 0)]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "_, predicted = torch.max(output.data, 1)\n",
    "print(\"Prediction is: \", predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual is is:  5\n"
     ]
    }
   ],
   "source": [
    "print(\"Actual is is: \", label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Homework\n",
    "\n",
    "* Play with the various moving parts in this notebook and try to increase the accuracy\n",
    "* Try a different dataset (http://pytorch.org/docs/master/torchvision/datasets.html)\n",
    "* Try data augmentation (http://pytorch.org/docs/master/torchvision/transforms.html)\n",
    "* Read about contest winning CNN architectures (https://adeshpande3.github.io/The-9-Deep-Learning-Papers-You-Need-To-Know-About.html)"
   ]
  }
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