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。