CNN/lab-07-4-mnist_introduction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lab 7 Learning rate and Evaluation\n",
    "import torch\n",
    "import torchvision.datasets as dsets\n",
    "import torchvision.transforms as transforms\n",
    "import matplotlib.pyplot as plt\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "\n",
    "# for reproducibility\n",
    "random.seed(777)\n",
    "torch.manual_seed(777)\n",
    "if device == 'cuda':\n",
    "    torch.cuda.manual_seed_all(777)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# parameters\n",
    "training_epochs = 15\n",
    "batch_size = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MNIST dataset\n",
    "mnist_train = dsets.MNIST(root='MNIST_data/',\n",
    "                          train=True,\n",
    "                          transform=transforms.ToTensor(),\n",
    "                          download=True)\n",
    "\n",
    "mnist_test = dsets.MNIST(root='MNIST_data/',\n",
    "                         train=False,\n",
    "                         transform=transforms.ToTensor(),\n",
    "                         download=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataset loader\n",
    "data_loader = torch.utils.data.DataLoader(dataset=mnist_train,\n",
    "                                          batch_size=batch_size,\n",
    "                                          shuffle=True,\n",
    "                                          drop_last=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MNIST data image of shape 28 * 28 = 784\n",
    "linear = torch.nn.Linear(784, 10, bias=True).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define cost/loss & optimizer\n",
    "criterion = torch.nn.CrossEntropyLoss().to(device)    # Softmax is internally computed.\n",
    "optimizer = torch.optim.SGD(linear.parameters(), lr=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0001 cost = 0.535468459\n",
      "Epoch: 0002 cost = 0.359274179\n",
      "Epoch: 0003 cost = 0.331187546\n",
      "Epoch: 0004 cost = 0.316578031\n",
      "Epoch: 0005 cost = 0.307158172\n",
      "Epoch: 0006 cost = 0.300180733\n",
      "Epoch: 0007 cost = 0.295130193\n",
      "Epoch: 0008 cost = 0.290851533\n",
      "Epoch: 0009 cost = 0.287417084\n",
      "Epoch: 0010 cost = 0.284379542\n",
      "Epoch: 0011 cost = 0.281825215\n",
      "Epoch: 0012 cost = 0.279800713\n",
      "Epoch: 0013 cost = 0.277808994\n",
      "Epoch: 0014 cost = 0.276154280\n",
      "Epoch: 0015 cost = 0.274440825\n",
      "Learning finished\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(training_epochs):\n",
    "    avg_cost = 0\n",
    "    total_batch = len(data_loader)\n",
    "\n",
    "    for X, Y in data_loader:\n",
    "        # reshape input image into [batch_size by 784]\n",
    "        # label is not one-hot encoded\n",
    "        X = X.view(-1, 28 * 28).to(device)\n",
    "        Y = Y.to(device)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        hypothesis = linear(X)\n",
    "        cost = criterion(hypothesis, Y)\n",
    "        cost.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        avg_cost += cost / total_batch\n",
    "\n",
    "    print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost))\n",
    "\n",
    "print('Learning finished')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8862999677658081\n",
      "Label:  8\n",
      "Prediction:  3\n"
     ]
    },
    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0xe1f2a58>"
      ]
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    }
   ],
   "source": [
    "# Test the model using test sets\n",
    "with torch.no_grad():\n",
    "    X_test = mnist_test.test_data.view(-1, 28 * 28).float().to(device)\n",
    "    Y_test = mnist_test.test_labels.to(device)\n",
    "\n",
    "    prediction = linear(X_test)\n",
    "    correct_prediction = torch.argmax(prediction, 1) == Y_test\n",
    "    accuracy = correct_prediction.float().mean()\n",
    "    print('Accuracy:', accuracy.item())\n",
    "\n",
    "    # Get one and predict\n",
    "    r = random.randint(0, len(mnist_test) - 1)\n",
    "    X_single_data = mnist_test.test_data[r:r + 1].view(-1, 28 * 28).float().to(device)\n",
    "    Y_single_data = mnist_test.test_labels[r:r + 1].to(device)\n",
    "\n",
    "    print('Label: ', Y_single_data.item())\n",
    "    single_prediction = linear(X_single_data)\n",
    "    print('Prediction: ', torch.argmax(single_prediction, 1).item())\n",
    "\n",
    "    plt.imshow(mnist_test.test_data[r:r + 1].view(28, 28), cmap='Greys', interpolation='nearest')\n",
    "    plt.show()"
   ]
  }
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