pytorch卷积神经网络把一组图分为两类代码

在PyTorch中创建一个卷积神经网络（Convolutional Neural Network, CNN）用于图像分类的基本步骤包括以下几个部分：

1. 导入必要的库：

import torch
import torchvision
import torchvision.transforms as transforms

2. 数据预处理：

transform = transforms.Compose([
    transforms.Resize((64, 64)),  # 图像缩放大小
    transforms.RandomHorizontalFlip(),  # 随机水平翻转增强数据
    transforms.ToTensor(),  # 转换成张量
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))  # 归一化像素值
])

train_dataset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
test_dataset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)

3. 创建数据加载器：

batch_size = 128
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

4. 构建CNN模型：

model = torchvision.models.resnet18(pretrained=True)  # 使用预训练的ResNet18
num_classes = 2  # 分类的类别数，这里是二分类任务
model.fc = torch.nn.Linear(model.fc.in_features, num_classes)  # 修改最后一层全连接层
model = model.to(device)  # 将模型移到GPU上，如果可用

5. 定义损失函数和优化器：

criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

6. 训练过程：

num_epochs = 10
for epoch in range(num_epochs):
    for images, labels in train_loader:
        images, labels = images.to(device), labels.to(device)

        # 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()

    print(f"Epoch [{epoch + 1}/{num_epochs}], Loss: {loss.item()}")

7. 测试模型：

model.eval()  # 设置为评估模式
correct = 0
total = 0
with torch.no_grad():
    for images, labels in test_loader:
        images, labels = images.to(device), labels.to(device)
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print(f"Test Accuracy of the Model: {100 * correct / total}%")