以下是一个使用 PyTorch 进行二维 CNN 数据进行二分类的代码示例： ```python 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=3, stride=1, padding=1) self.relu1 = nn.ReLU() self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2) self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1) self.relu2 = nn.ReLU() self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2) self.fc1 = nn.Linear(32 * 8 * 8, 128) self.relu3 = nn.ReLU() self.fc2 = nn.Linear(128, 2) 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 # 加载数据集 train_dataset = datasets.CIFAR10(root='./data', train=True, transform=transforms.ToTensor(), download=True) test_dataset = datasets.CIFAR10(root='./data', train=False, transform=transforms.ToTensor()) # 定义训练参数 batch_size = 64 learning_rate = 0.001 num_epochs = 10 # 定义数据加载器 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) # 定义模型、损失函数和优化器 model = CNN() criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=learning_rate) # 训练模型 for epoch in range(num_epochs): for i, (images, labels) in enumerate(train_loader): # 前向传播 outputs = model(images) loss = criterion(outputs, labels) # 反向传播和优化 optimizer.zero_grad() loss.backward() optimizer.step() # 打印训练信息 if (i+1) % 100 == 0: print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, i+1, len(train_loader), loss.item())) # 测试模型 model.eval() with torch.no_grad(): correct = 0 total = 0 for images, labels in test_loader: outputs = model(images) _, predicted = torch.max(outputs.data, 1) total += labels.size(0) correct += (predicted == labels).sum().item() print('Accuracy of the model on the test images: {} %'.format(100 * correct / total)) ``` 这个代码示例使用 CIFAR-10 数据集进行二分类，其中包含了一个简单的 CNN 模型，以及训练和测试的代码。