lab-07_2_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"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe1f2a58>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "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()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3.0
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}