Pytorch搭建CNN网络读取40个42×42的矩阵，和40和0或1的标签

以下是基于PyTorch的CNN网络读取40个42x42的矩阵和相应的标签的示例代码：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader

# 定义数据集
class MatrixDataset(Dataset):
    def __init__(self, matrices, labels):
        self.matrices = matrices
        self.labels = labels

    def __len__(self):
        return len(self.matrices)

    def __getitem__(self, idx):
        matrix = self.matrices[idx]
        label = self.labels[idx]
        return matrix, label

# 定义CNN网络
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 16, kernel_size=3)
        self.conv2 = nn.Conv2d(16, 32, kernel_size=3)
        self.fc1 = nn.Linear(32 * 18 * 18, 128)
        self.fc2 = nn.Linear(128, 2)

    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 * 18 * 18)
        x = nn.functional.relu(self.fc1(x))
        x = self.fc2(x)
        return nn.functional.log_softmax(x, dim=1)

# 训练函数
def train(model, device, train_loader, optimizer, epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = nn.functional.nll_loss(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % 10 == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.item()))

# 测试函数
def test(model, device, test_loader):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            test_loss += nn.functional.nll_loss(output, target, reduction='sum').item()
            pred = output.argmax(dim=1, keepdim=True)
            correct += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(test_loader.dataset)

    print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
        test_loss, correct, len(test_loader.dataset),
        100. * correct / len(test_loader.dataset)))

# 主程序
if __name__ == '__main__':
    # 读取数据
    matrices = []
    labels = []
    for i in range(40):
        matrix = torch.randn(1, 42, 42)  # 随机生成42x42的矩阵
        matrices.append(matrix)
        label = torch.randint(0, 2, (1,))  # 随机生成0或1的标签
        labels.append(label)
    dataset = MatrixDataset(matrices, labels)
    train_loader = DataLoader(dataset, batch_size=4, shuffle=True)

    # 初始化模型和优化器
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    model = Net().to(device)
    optimizer = optim.Adam(model.parameters(), lr=0.001)

    # 训练和测试
    for epoch in range(1, 11):
        train(model, device, train_loader, optimizer, epoch)
        test(model, device, train_loader)

在上面的代码中，我们首先定义了一个 `MatrixDataset` 类来读取矩阵和标签。然后，我们定义了一个简单的CNN网络 `Net`，其中包含两个卷积层和两个全连接层。我们使用 `nn.functional` 中的激活函数和损失函数，以及 PyTorch 自带的优化器 Adam。接着，我们定义了训练函数 `train` 和测试函数 `test`，并在主程序中循环训练和测试。在训练过程中，我们使用随机梯度下降法优化模型参数，并使用交叉熵损失函数衡量模型性能。在测试过程中，我们计算模型在测试集上的准确率和损失函数值。