pytorch 深度学习入门代码 （五）多层卷积神经网络实现 MNIST 手写数字分类

cnn.py

import torch.nn as nn

class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.layer1 = nn.Sequential(
            nn.Conv2d(1, 16, kernel_size=3),
            nn.BatchNorm2d(16),
            nn.ReLU(inplace=True))

        self.layer2 = nn.Sequential(
            nn.Conv2d(16, 32, kernel_size=3),
            nn.BatchNorm2d(32),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )

        self.layer3 = nn.Sequential(
            nn.Conv2d(32, 64, kernel_size=3),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True))

        self.layer4 = nn.Sequential(
            nn.Conv2d(64, 128, kernel_size=3),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )

        self.fc = nn.Sequential(
            nn.Linear(128*4*4, 1024),
            nn.ReLU(inplace=True),
            nn.Linear(1024, 128),
            nn.ReLU(inplace=True),
            nn.Linear(128, 10)
        )
    def forward(self, x):
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

mnist.py

import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
import torch.optim as optim
import cnn

# (Hyper parameters)
batch_size = 64
learning_rate = 1e-2
num_epochs = 5

if __name__ == '__main__':
    data_tf = transforms.Compose(
        [transforms.ToTensor(),
         transforms.Normalize([0.5], [0.5])])
    train_dataset = datasets.MNIST(
        root='./data', train=True, transform=data_tf, download=True)
    test_dataset = datasets.MNIST(root='./data', train=False, transform=data_tf)

    train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
    test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

    model = cnn.CNN()
    if torch.cuda.is_available():
        model = model.cuda()

    criterion = nn.CrossEntropyLoss()
    optimizer = optim.SGD(model.parameters(), lr=learning_rate)

    epoch = 0
    for epoch in range(num_epochs):
        for data in train_loader:
            img, label = data
            # img = img.view(img.size(0), -1)
            if torch.cuda.is_available():
                img = img.cuda()
                label = label.cuda()
            out = model(img)
            loss = criterion(out, label)
            print_loss = loss.data.item()

            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

        epoch+=1

        model.eval()
        eval_loss = 0
        eval_acc = 0
        for data in test_loader:
            img, label = data
            if torch.cuda.is_available():
                img = img.cuda()
                label = label.cuda()

            out = model(img)
            loss = criterion(out, label)
            eval_loss+=loss.data.item()*label.size(0)
            _, pred = torch.max(out, 1)
            num_correct = (pred == label).sum()
            eval_acc += num_correct.item()
        print('Test Loss: {:.6f}, Acc: {:.6f}'.format(
            eval_loss / (len(test_dataset)),
            eval_acc / (len(test_dataset))
        ))