pytorch如何搭建卷积神经网络

在PyTorch中搭建卷积神经网络通常涉及以下步骤：

1. 导入必要的库和模块：

import torch
import torch.nn as nn
import torch.nn.functional as F

2. 定义卷积神经网络模型类：

class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=16, kernel_size=3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, stride=1, padding=1)
        self.fc1 = nn.Linear(32*7*7, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.max_pool2d(x, kernel_size=2, stride=2)
        x = F.relu(self.conv2(x))
        x = F.max_pool2d(x, kernel_size=2, stride=2)
        x = x.view(-1, 32*7*7)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x

3. 实例化模型类并定义损失函数和优化器：

model = CNN()
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.001)

4. 训练模型：

for epoch in range(num_epochs):
    for images, labels in train_loader:
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

5. 测试模型：

correct = 0
total = 0
with torch.no_grad():
    for images, labels in test_loader:
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

accuracy = correct / total
print('Accuracy: {:.2f}%'.format(100 * accuracy))

以上是一个简单的卷积神经网络的搭建过程，你可以根据具体的任务和数据集自行调整网络结构和超参数。