pytorch卷积神经网络如何搭建

要搭建一个基本的PyTorch卷积神经网络，你需要做以下几个步骤：

1. 导入PyTorch库

import torch
import torch.nn as nn
import torch.optim as optim

1. 定义一个继承自nn.Module的卷积神经网络类

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.relu = nn.ReLU()
        self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc = nn.Linear(16 * 14 * 14, 10)
    
    def forward(self, x):
        x = self.conv1(x)
        x = self.relu(x)
        x = self.maxpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

1. 实例化神经网络模型并定义损失函数和优化器

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

1. 训练神经网络模型

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

这样就可以搭建一个简单的PyTorch卷积神经网络模型了。你可以根据自己的需求调整模型的结构和参数。