main.py

#!/usr/bin/env python

from __future__ import print_function
import argparse
import random

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable

from torchvision import datasets, transforms

def get_data_loaders(path):
    transform = transforms.Compose([
       transforms.ToTensor(),
       transforms.Normalize((0.1307,), (0.3081,))
    ])

    train_dataset = datasets.MNIST(path, train=True, transform=transform)
    train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True)

    test_dataset = datasets.MNIST(path, train=False, transform=transform)
    test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=args.batch_size, shuffle=True)

    return train_loader, test_loader


class LogisticRegression(nn.Module):
    def __init__(self):
        super(LogisticRegression, self).__init__()
        self.output_proj = nn.Linear(784, 10)

    def forward(self, x):
        x = self.output_proj(x)
        return F.log_softmax(x)


def train(epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        data = data.view(-1, 784)
        data, target = Variable(data), Variable(target)
        optimizer.zero_grad()
        output = model(data)
        loss = F.nll_loss(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % args.log_interval == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.data[0]))


def test(epoch):
    model.eval()
    test_loss = 0
    correct = 0
    for data, target in test_loader:
        data = data.view(-1, 784)
        data, target = Variable(data, volatile=True), Variable(target)
        output = model(data)
        test_loss += F.nll_loss(output, target).data[0]
        pred = output.data.max(1)[1] # get the index of the max log-probability
        correct += pred.eq(target.data).sum()

    test_loss = test_loss
    test_loss /= len(test_loader) # loss function already averages over batch size
    print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
        test_loss, correct, len(test_loader.dataset),
        100. * correct / len(test_loader.dataset)))


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
    parser.add_argument('--batch-size', type=int, default=64, metavar='N',
                        help='input batch size for training (default: 64)')
    parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
                        help='input batch size for testing (default: 1000)')
    parser.add_argument('--epochs', type=int, default=10, metavar='N',
                        help='number of epochs to train (default: 10)')
    parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
                        help='learning rate (default: 0.01)')
    parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
                        help='SGD momentum (default: 0.5)')
    parser.add_argument('--seed', type=int, default=1, metavar='S',
                        help='random seed (default: 1)')
    parser.add_argument('--log-interval', type=int, default=10, metavar='N',
                        help='how many batches to wait before logging training status')
    parser.add_argument('--data', default='mnist-data',
                        help='folder with downloaded data')
    args = parser.parse_args()

    torch.manual_seed(args.seed)

    train_loader, test_loader = get_data_loaders(args.data)
    model = LogisticRegression()
    optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)

    for epoch in range(1, args.epochs + 1):
        train(epoch)
        test(epoch)