cnn.py

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


# Hyper Parameters
num_epochs = 5
batch_size = 100
learning_rate = 0.001

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

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

# Data Loader (Input Pipeline)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
                                           batch_size=batch_size,
                                           num_workers=10,
                                           pin_memory=True,
                                           shuffle=True)

test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
                                          batch_size=batch_size,
                                          num_workers=10,
                                          pin_memory=True,
                                          shuffle=False)

# CNN Model (2 conv layer)
class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.layer1 = nn.Sequential(
            nn.Conv2d(1, 16, kernel_size=5, padding=2),
            nn.BatchNorm2d(16),
            nn.ReLU(),
            nn.MaxPool2d(2))
        self.layer2 = nn.Sequential(
            nn.Conv2d(16, 32, kernel_size=5, padding=2),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.MaxPool2d(2))
        self.fc = nn.Linear(7*7*32, 10)

    def forward(self, x):
        out = self.layer1(x)
        out = self.fc(out)
        return out

if __name__ == '__main__':

    cnn = CNN()
    cnn.cuda()

    # Loss and Optimizer
    criterion = nn.CrossEntropyLoss()
    optimizer = torch.optim.Adam(cnn.parameters(), lr=learning_rate)

    # Train the Model
    for epoch in range(num_epochs):
        for i, (images, labels) in enumerate(train_loader):
            images = Variable(images).cuda()
            labels = Variable(labels).cuda()

            # Forward + Backward + Optimize
            optimizer.zero_grad()
            outputs = cnn(images)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()

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

    # Test the Model
    cnn.eval()    # Change model to 'eval' mode (BN uses moving mean/var).
    correct = 0
    total = 0
    for images, labels in test_loader:
        images = Variable(images).cuda()
        outputs = cnn(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted.cpu() == labels).sum()

    print('Test Accuracy of the model on the 10000 test images: %d %%' % (100 * correct / total))

    # Save the Trained Model
    torch.save(cnn.state_dict(), 'cnn.pkl')