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

以下是一个简单的Pytorch CNN网络，可以用于读取40个大小为42x42的矩阵，并将它们与相应的标签进行训练和测试：

import torch.nn as nn
import torch.optim as optim
import torch.utils.data as data
import torch.nn.functional as F
import numpy as np

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 9 * 9, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 2)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 9 * 9)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

net = Net()

# Define loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

# Load data
data = np.load('data.npy')
labels = np.load('labels.npy')

# Split data into train and test sets
train_data = data[:30]
train_labels = labels[:30]
test_data = data[30:]
test_labels = labels[30:]

# Create PyTorch dataset and dataloader
class MyDataset(data.Dataset):
    def __init__(self, data, labels):
        super(MyDataset, self).__init__()
        self.data = data
        self.labels = labels

    def __getitem__(self, index):
        return self.data[index], self.labels[index]

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

train_dataset = MyDataset(train_data, train_labels)
train_dataloader = data.DataLoader(train_dataset, batch_size=10, shuffle=True)

# Train the network
for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(train_dataloader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = net(inputs.float())
        loss = criterion(outputs, labels.long())
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        if i % 3 == 2:    
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 3))
            running_loss = 0.0

print('Finished Training')

# Test the network
correct = 0
total = 0
with torch.no_grad():
    for data in test_data:
        images, labels = data
        outputs = net(images.float())
        _, 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))

其中，`data.npy`是一个包含40个大小为42x42的矩阵的numpy数组，`labels.npy`是一个包含相应标签的numpy数组。这个网络使用了2个卷积层和3个全连接层，可以通过调整网络结构和训练参数来优化性能。