cnn2.py

import torch
import torchvision.datasets as dsets
import torchvision.transforms as transforms
import torch.nn.init

device = 'cuda' if torch.cuda.is_available() else 'cpu'

torch.manual_seed(777)

if device == 'cuda':
    torch.cuda.manual_seed(777)

learning_rate = 0.001
training_epoch = 3
batch_size = 100

mnist_train = dsets.MNIST(root='MNIST_DATA/', train=True, transform=transforms.ToTensor(),
                          download=True)
mnist_test = dsets.MNIST(root='MNIST_DATA/', train=False, transform=transforms.ToTensor(),
                         download=True)

data_loader = torch.utils.data.DataLoader(dataset=mnist_train, batch_size=batch_size,
                                          shuffle=True, drop_last=True)

class CNN(torch.nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.layer1 = torch.nn.Sequential(
            torch.nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1),
            torch.nn.ReLU(),
            torch.nn.MaxPool2d(kernel_size=2, stride=2))
        self.layer2 = torch.nn.Sequential(
            torch.nn.Conv2d(32, 128, kernel_size=3, stride=1, padding=1),
            torch.nn.ReLU(),
            torch.nn.MaxPool2d(kernel_size=2, stride=2))
        self.fc = torch.nn.Linear(7 * 7 * 128, 10, bias=True)
        torch.nn.init.xavier_uniform_(self.fc.weight)

    def forward(self, x):
        out = self.layer1(x)
        out = self.layer2(out)
        out = out.view(out.size(0), -1)
        out = self.fc(out)

        return out


model = CNN().to(device)

criterion = torch.nn.CrossEntropyLoss().to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

total_batch = len(data_loader)

for epoch in range(training_epoch):
    avg_cost = 0

    for X, Y in data_loader:
        X = X.to(device)
        Y = Y.to(device)

        optimizer.zero_grad()
        hypothesis = model(X)
        cost = criterion(hypothesis, Y)
        cost.backward()
        optimizer.step()

        avg_cost += cost / total_batch

    print('[Epoch: {:>4}] cost = {:>.9}'.format(epoch + 1, avg_cost))

with torch.no_grad():
    X_test = mnist_test.test_data.view(len(mnist_test), 1, 28, 28).float().to(device)
    Y_test = mnist_test.test_labels.to(device)

    prediction = model(X_test)
    correct_prediction = torch.argmax(prediction, 1) == Y_test
    accuracy = correct_prediction.float().mean()
    print('Accuracy:', accuracy.item())