第4关：使用pytorch搭建卷积神经网络识别手写数字要 代码

好的，下面是使用 PyTorch 搭建卷积神经网络（CNN）识别手写数字的代码：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.datasets as datasets
import torchvision.transforms as transforms

# 定义 CNN 模型
class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1)
        self.relu1 = nn.ReLU()
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
        self.relu2 = nn.ReLU()
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(7*7*64, 128)
        self.relu3 = nn.ReLU()
        self.fc2 = nn.Linear(128, 10)
    
    def forward(self, x):
        out = self.conv1(x)
        out = self.relu1(out)
        out = self.pool1(out)
        out = self.conv2(out)
        out = self.relu2(out)
        out = self.pool2(out)
        out = out.view(out.size(0), -1)
        out = self.fc1(out)
        out = self.relu3(out)
        out = self.fc2(out)
        return out

# 加载 MNIST 数据集
train_dataset = datasets.MNIST(root='./data', train=True, transform=transforms.ToTensor(), download=True)
test_dataset = datasets.MNIST(root='./data', train=False, transform=transforms.ToTensor())

# 定义超参数
batch_size = 100
learning_rate = 0.001
num_epochs = 5

# 加载数据集
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)

# 实例化 CNN 模型、损失函数和优化器
cnn = CNN()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(cnn.parameters(), lr=learning_rate)

# 训练 CNN 模型
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        images = images.cuda()
        labels = labels.cuda()
        optimizer.zero_grad()
        outputs = cnn(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        if (i+1) % 100 == 0:
            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, i+1, len(train_loader), loss.item()))

# 测试 CNN 模型
cnn.eval()
with torch.no_grad():
    correct = 0
    total = 0
    for images, labels in test_loader:
        images = images.cuda()
        labels = labels.cuda()
        outputs = cnn(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()
    print('Test Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total))

在上面的代码中，我们使用了 PyTorch 自带的 MNIST 数据集，并实例化了一个包含两个卷积层和两个全连接层的 CNN 模型。我们使用交叉熵损失函数和 Adam 优化器进行训练，并在测试集上计算了模型的准确率。