用pytorch写一个深度学习CNN网络，训练集是40个矩阵和它们的标签：40个14×14的numpy矩阵文件在一个名为1MATRICE的文件夹中，文件名为1.npy~50.npy；40个标签文件在一个名为1LABEL的文件夹中，文件名为label_0.npy~label_50.npy，其中前20个值为0，属于PD类别，后30个值为1，属于SWEDD类别。验证集为6个14×14的矩阵，用softmax输出两个类别的概率。

好的，以下是一个基本的CNN网络的代码示例，您需要根据您的数据集进行调整和修改。首先，您需要导入所需的库和模块，如下所示：

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

接下来，您需要定义一个自定义数据集类，如下所示：

class MyDataset(Dataset):
    def __init__(self, root_dir, transform=None):
        self.root_dir = root_dir
        self.transform = transform
        self.matrices = []
        self.labels = []

        # Load matrices and labels
        for i in range(1, 41):
            matrix_path = os.path.join(self.root_dir, str(i) + 'MATRICE', str(i) + '.npy')
            matrix = np.load(matrix_path)
            self.matrices.append(matrix)

            label_path = os.path.join(self.root_dir, str(i) + 'LABEL', 'label_' + str(i-1) + '.npy')
            label = np.load(label_path)
            self.labels.append(label)

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

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

        if self.transform:
            matrix = self.transform(matrix)

        return matrix, label

然后，您需要定义一个CNN模型，如下所示：

class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 16, 3, padding=1)
        self.batchnorm1 = nn.BatchNorm2d(16)
        self.relu1 = nn.ReLU()
        self.maxpool1 = nn.MaxPool2d(2)
        self.conv2 = nn.Conv2d(16, 32, 3, padding=1)
        self.batchnorm2 = nn.BatchNorm2d(32)
        self.relu2 = nn.ReLU()
        self.maxpool2 = nn.MaxPool2d(2)
        self.fc1 = nn.Linear(32 * 3 * 3, 64)
        self.relu3 = nn.ReLU()
        self.fc2 = nn.Linear(64, 2)
        self.softmax = nn.Softmax(dim=1)

    def forward(self, x):
        x = self.conv1(x)
        x = self.batchnorm1(x)
        x = self.relu1(x)
        x = self.maxpool1(x)
        x = self.conv2(x)
        x = self.batchnorm2(x)
        x = self.relu2(x)
        x = self.maxpool2(x)
        x = x.view(-1, 32 * 3 * 3)
        x = self.fc1(x)
        x = self.relu3(x)
        x = self.fc2(x)
        x = self.softmax(x)
        return x

接下来，您需要定义训练和测试函数，如下所示：

def train(model, train_loader, criterion, optimizer):
    model.train()
    train_loss = 0.0
    train_acc = 0.0

    for i, (inputs, labels) in enumerate(train_loader):
        optimizer.zero_grad()
        outputs = model(inputs.unsqueeze(1).float())
        loss = criterion(outputs, labels.long())
        loss.backward()
        optimizer.step()

        train_loss += loss.item() * inputs.size(0)
        _, preds = torch.max(outputs, 1)
        train_acc += torch.sum(preds == labels.data)

    train_loss = train_loss / len(train_loader.dataset)
    train_acc = train_acc.double() / len(train_loader.dataset)

    return train_loss, train_acc


def test(model, test_loader, criterion):
    model.eval()
    test_loss = 0.0
    test_acc = 0.0

    with torch.no_grad():
        for i, (inputs, labels) in enumerate(test_loader):
            outputs = model(inputs.unsqueeze(1).float())
            loss = criterion(outputs, labels.long())

            test_loss += loss.item() * inputs.size(0)
            _, preds = torch.max(outputs, 1)
            test_acc += torch.sum(preds == labels.data)

        test_loss = test_loss / len(test_loader.dataset)
        test_acc = test_acc.double() / len(test_loader.dataset)

    return test_loss, test_acc

最后，您需要实例化并运行模型，如下所示：

# Instantiate the model
model = CNN()

# Define the loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# Instantiate the data loaders
train_dataset = MyDataset('/path/to/train/folder')
train_loader = DataLoader(train_dataset, batch_size=8, shuffle=True)
test_dataset = MyDataset('/path/to/test/folder')
test_loader = DataLoader(test_dataset, batch_size=8, shuffle=False)

# Train the model
for epoch in range(10):
    train_loss, train_acc = train(model, train_loader, criterion, optimizer)
    test_loss, test_acc = test(model, test_loader, criterion)
    print('Epoch: {} Train Loss: {:.4f} Train Acc: {:.4f} Test Loss: {:.4f} Test Acc: {:.4f}'.format(
        epoch, train_loss, train_acc, test_loss, test_acc))

请注意，这只是一个基本的CNN模型，您需要根据您的数据集进行调整和修改。另外，由于您的数据集非常小，您可能需要采取一些正则化措施来防止过度拟合。