TCN实现一维单特征时间序列信号端到端分类代码，有100个样本，每个样本数据是一个2000长的数据，每个数据只有一个数，希望输出2000个0-6七分类的标签，每类之间的变化位置不明确需要模型去学习，pytorch实现

以下是一个简单的TCN模型实现，用于端到端分类时间序列信号：

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

class TCN(nn.Module):
    def __init__(self, input_size, output_size, num_channels, kernel_size, dropout):
        super(TCN, self).__init__()
        self.input_size = input_size
        self.output_size = output_size
        self.num_channels = num_channels
        self.kernel_size = kernel_size
        self.dropout = dropout
        self.tcn = nn.Sequential(
            nn.Conv1d(input_size, num_channels, kernel_size, stride=1, padding=(kernel_size-1)),
            nn.BatchNorm1d(num_channels),
            nn.ReLU(inplace=True),
            nn.Dropout(dropout),
            nn.Conv1d(num_channels, num_channels, kernel_size, stride=1, padding=(kernel_size-1)),
            nn.BatchNorm1d(num_channels),
            nn.ReLU(inplace=True),
            nn.Dropout(dropout),
            nn.Conv1d(num_channels, num_channels, kernel_size, stride=1, padding=(kernel_size-1)),
            nn.BatchNorm1d(num_channels),
            nn.ReLU(inplace=True),
            nn.Dropout(dropout),
            nn.Conv1d(num_channels, num_channels, kernel_size, stride=1, padding=(kernel_size-1)),
            nn.BatchNorm1d(num_channels),
            nn.ReLU(inplace=True),
            nn.Dropout(dropout),
            nn.Conv1d(num_channels, num_channels, kernel_size, stride=1, padding=(kernel_size-1)),
            nn.BatchNorm1d(num_channels),
            nn.ReLU(inplace=True),
            nn.Dropout(dropout),
            nn.Conv1d(num_channels, output_size, kernel_size, stride=1, padding=(kernel_size-1))
        )
    
    def forward(self, x):
        y = self.tcn(x)
        return y.permute(0, 2, 1) # reshape output to (batch_size, seq_length, output_size)


class MyDataset(Dataset):
    def __init__(self, data, targets):
        self.data = data
        self.targets = targets
        
    def __len__(self):
        return len(self.data)
    
    def __getitem__(self, index):
        x = self.data[index]
        y = self.targets[index]
        return torch.FloatTensor(x), torch.LongTensor(y)


def train(model, device, train_loader, optimizer, criterion):
    model.train()
    train_loss = 0
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, target)
        train_loss += loss.item()
        loss.backward()
        optimizer.step()
    train_loss /= len(train_loader)
    return train_loss


def test(model, device, test_loader, criterion):
    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 += criterion(output, target).item()
            pred = output.argmax(dim=2, keepdim=True)
            correct += pred.eq(target.view_as(pred)).sum().item()
    test_loss /= len(test_loader)
    accuracy = correct / (len(test_loader.dataset) * target.shape[1])
    return test_loss, accuracy


if __name__ == '__main__':
    # Create some fake data
    data = torch.randn(100, 1, 2000)
    targets = torch.randint(7, size=(100, 2000))
    
    # Split data into train and test sets
    train_data = MyDataset(data[:80], targets[:80])
    test_data = MyDataset(data[80:], targets[80:])
    
    # Create data loaders
    train_loader = DataLoader(train_data, batch_size=16, shuffle=True)
    test_loader = DataLoader(test_data, batch_size=16, shuffle=True)
    
    # Define hyperparameters
    input_size = 1
    output_size = 7
    num_channels = 64
    kernel_size = 3
    dropout = 0.2
    learning_rate = 0.001
    num_epochs = 10
    
    # Define the model and optimizer
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    model = TCN(input_size, output_size, num_channels, kernel_size, dropout).to(device)
    optimizer = optim.Adam(model.parameters(), lr=learning_rate)
    criterion = nn.CrossEntropyLoss()
    
    # Train the model
    for epoch in range(num_epochs):
        train_loss = train(model, device, train_loader, optimizer, criterion)
        test_loss, accuracy = test(model, device, test_loader, criterion)
        print(f'Epoch {epoch}: Train Loss: {train_loss:.4f}, Test Loss: {test_loss:.4f}, Accuracy: {accuracy:.4f}')

在这个例子中，我们创建了一个虚假的数据集，其中包含100个样本，每个样本包含一个具有2000个时间步长的单特征时间序列信号。我们将使用TCN模型来对信号进行端到端分类，将每个时间步长的输出映射到七个可能的类别。我们将使用PyTorch的DataLoader来将数据分批处理，以便模型可以更有效地训练。我们还定义了一个train函数和一个test函数，这些函数分别用于在训练和测试数据上运行模型，并计算损失和准确性。最后，我们将训练模型，每个epoch都输出训练损失、测试损失和准确性。