pytorch 卷积神经网络

PyTorch 是一个广泛使用的深度学习框架，支持建立卷积神经网络（Convolutional Neural Network，CNN）模型。以下是一个简单的 PyTorch CNN 模型的示例代码：

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(3, 16, kernel_size=5, padding=2)
        self.relu1 = nn.ReLU()
        self.pool1 = nn.MaxPool2d(kernel_size=2)
        self.conv2 = nn.Conv2d(16, 32, kernel_size=5, padding=2)
        self.relu2 = nn.ReLU()
        self.pool2 = nn.MaxPool2d(kernel_size=2)
        self.fc1 = nn.Linear(32 * 8 * 8, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = self.relu1(x)
        x = self.pool1(x)
        x = self.conv2(x)
        x = self.relu2(x)
        x = self.pool2(x)
        x = x.view(-1, 32 * 8 * 8)
        x = self.fc1(x)
        return x

# 加载 CIFAR10 数据集
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
trainset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=32, shuffle=True)

# 训练模型
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
net = CNN().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data[0].to(device), data[1].to(device)
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        if i % 100 == 99:
            print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 100))
            running_loss = 0.0
print('Finished Training')

在这个示例代码中，定义了一个简单的 CNN 模型，包含两个卷积层和两个池化层，最后连接一个全连接层。模型的训练部分则使用了 CIFAR10 数据集，使用交叉熵损失函数和随机梯度下降（SGD）优化器进行训练。