TCN端到端分类代码，100个样本，每个样本是一个长度2000的单特征一维序列，输出是2000个0-6的七分类序列，求代码

以下是一个基于PyTorch的TCN端到端分类代码示例，适用于您描述的数据：

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

# 定义TCN模型
class TCN(nn.Module):
    def __init__(self, input_size, output_size, num_channels, kernel_size, dropout):
        super(TCN, self).__init__()
        self.tcn = nn.Sequential(
            nn.Conv1d(input_size, num_channels, kernel_size=kernel_size),
            nn.BatchNorm1d(num_channels),
            nn.ReLU(inplace=True),
            nn.Dropout(dropout),
            nn.Conv1d(num_channels, num_channels, kernel_size=kernel_size, dilation=2),
            nn.BatchNorm1d(num_channels),
            nn.ReLU(inplace=True),
            nn.Dropout(dropout),
            nn.Conv1d(num_channels, num_channels, kernel_size=kernel_size, dilation=4),
            nn.BatchNorm1d(num_channels),
            nn.ReLU(inplace=True),
            nn.Dropout(dropout),
            nn.Conv1d(num_channels, num_channels, kernel_size=kernel_size, dilation=8),
            nn.BatchNorm1d(num_channels),
            nn.ReLU(inplace=True),
            nn.Dropout(dropout),
            nn.Conv1d(num_channels, num_channels, kernel_size=kernel_size, dilation=16),
            nn.BatchNorm1d(num_channels),
            nn.ReLU(inplace=True),
            nn.Dropout(dropout),
            nn.Conv1d(num_channels, output_size, kernel_size=kernel_size)
        )

    def forward(self, inputs):
        y1 = self.tcn(inputs)
        return y1

# 定义数据集类
class MyDataset(Dataset):
    def __init__(self, data, targets):
        self.data = torch.from_numpy(data).float()
        self.targets = torch.from_numpy(targets).long()
        
    def __getitem__(self, index):
        x = self.data[index]
        y = self.targets[index]
        return x, y
    
    def __len__(self):
        return len(self.data)

# 训练函数
def train(model, device, train_loader, optimizer, criterion, 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 = criterion(output.transpose(1, 2), 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, 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.transpose(1, 2), target).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)))

# 主函数
if __name__ == '__main__':
    # 参数设置
    input_size = 1
    output_size = 7
    num_channels = 64
    kernel_size = 3
    dropout = 0.2
    batch_size = 10
    epochs = 10
    
    # 数据加载和预处理
    # 假设数据保存在data.npy中，targets保存在targets.npy中
    data = np.load('data.npy')
    targets = np.load('targets.npy')
    dataset = MyDataset(data, targets)
    train_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
    
    # 模型、优化器和损失函数
    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 = torch.optim.Adam(model.parameters())
    criterion = nn.CrossEntropyLoss()
    
    # 训练和测试
    for epoch in range(1, epochs+1):
        train(model, device, train_loader, optimizer, criterion, epoch)
        test(model, device, test_loader, criterion)

注意，上述代码仅供参考，您可能需要根据您的具体数据和任务对其进行修改和调整。