灰度图像数据集用cnn五分类训练代码

好的，以下是一个简单的灰度图像数据集的CNN五分类训练代码的示例：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from torch.utils.data import DataLoader

# 定义CNN模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 16, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, padding=1)
        self.fc1 = nn.Linear(32 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 5)

    def forward(self, x):
        x = nn.functional.relu(self.conv1(x))
        x = nn.functional.max_pool2d(nn.functional.relu(self.conv2(x)), 2)
        x = x.view(-1, 32 * 5 * 5)
        x = nn.functional.relu(self.fc1(x))
        x = nn.functional.relu(self.fc2(x))
        x = self.fc3(x)
        return x

# 加载数据集
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])
trainset = datasets.ImageFolder(root='./train', transform=transform)
testset = datasets.ImageFolder(root='./test', transform=transform)
trainloader = DataLoader(trainset, batch_size=4, shuffle=True, num_workers=2)
testloader = DataLoader(testset, batch_size=4, shuffle=False, num_workers=2)

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

# 训练模型
for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        if i % 2000 == 1999:
            print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 2000))
            running_loss = 0.0

# 测试模型
correct = 0
total = 0
with torch.no_grad():
    for data in testloader:
        images, labels = data
        outputs = net(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print('Accuracy of the network on the test images: %d %%' % (100 * correct / total))

在这个示例中，我们使用了一个简单的CNN模型来对灰度图像数据集进行五分类训练。我们使用了PyTorch内置的transforms来将图像转换为张量，并进行了归一化处理。我们还使用了ImageFolder来加载数据集，并使用DataLoader来生成批次数据。在训练过程中，我们使用了交叉熵损失函数和随机梯度下降优化器。在测试过程中，我们使用了预测精度来评估模型的性能。