logistic_regression.py

import torch
import torch.nn as nn
import torchvision.datasets as dsets
import torchvision.transforms as transforms
from torch.autograd import Variable

input_size = 784
num_classes = 10
num_epochs = 5
batch_size = 100
learning_rate = 0.01

train_dataset = dsets.MNIST(root='data/',
                            train=True,
                            transform=transforms.ToTensor(),
                            download=True)

test_dataset = dsets.MNIST(root='data/',
                           train=False,
                           transform=transforms.ToTensor())

train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
                                           batch_size=batch_size,
                                           shuffle=True)

test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
                                          batch_size=batch_size,
                                          shuffle=False)

class Logistic(nn.Module):
    def __init__(self, input_size, num_classes):
        super(Logistic, self).__init__()
        self.linear = nn.Linear(input_size, num_classes)

    def forward(self, x):
        out = self.linear(x)
        return out


model = Logistic(input_size, num_classes)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)

for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        images = Variable(images.view(-1, 28 * 28))
        labels = Variable(labels)

        optimizer.zero_grad()
        ouputs = model(images)
        loss = criterion(ouputs, labels)
        loss.backward()
        optimizer.step()

        if (i + 1) % 100 == 0:
            print "Epoch: [%d/%d], Step: [%d/%d], Loss: %.4f" % (epoch + 1, num_epochs, i + 1, len(train_dataset) // batch_size, loss.data[0])

        correct = 0
        total = 0

for images, labels in test_loader:
    images = Variable(images.view(-1, 28 * 28))
    outputs = model(images)
    _, predicted = torch.max(outputs.data, 1)
    total += labels.size(0)
    correct += (predicted == labels).sum()

print "Accuracy: %d %%" % (100 * correct / total)