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

以下是使用Pytorch实现的TCN端到端分类代码，适用于您的数据集：

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=2, dropout=0.2):
        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=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, num_channels, kernel_size=kernel_size, dilation=32),
            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):
        """Inputs have to have dimension (N, C_in, L_in)"""
        output = self.tcn(inputs)
        return output

class CustomDataset(Dataset):
    def __init__(self, x, y):
        self.x = x
        self.y = y

    def __getitem__(self, index):
        return self.x[index], self.y[index]

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

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

def evaluate(model, criterion, val_loader, device):
    model.eval()
    val_loss = 0
    with torch.no_grad():
        for x, y in val_loader:
            x, y = x.to(device), y.to(device)
            output = model(x)
            loss = criterion(output, y)
            val_loss += loss.item()
    return val_loss / len(val_loader)

input_size = 1 # 单特征一维
output_size = 7 # 七分类
num_channels = 64 # 每层卷积核的数量
kernel_size = 2 # 卷积核大小
dropout = 0.2 # dropout概率
lr = 0.01 # 学习率
batch_size = 10 # 每个batch的样本数
num_epochs = 10 # 迭代次数

# 准备数据
x = torch.randn(100, input_size, 2000)
y = torch.randint(0, 7, (100, output_size))
dataset = CustomDataset(x, y)
train_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)

# 初始化模型和优化器
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=lr)
criterion = nn.CrossEntropyLoss()

# 训练和验证
for epoch in range(num_epochs):
    train_loss = train(model, optimizer, criterion, train_loader, device)
    print(f"Epoch {epoch+1}, Training Loss: {train_loss:.4f}")

其中，TCN类定义了TCN模型的结构，包含多层1D卷积和批标准化层。CustomDataset类定义了自定义数据集，用于加载数据。train函数和evaluate函数分别对模型进行训练和评估。最后，在主函数中，我们准备数据，初始化模型和优化器，然后进行训练和验证。

这个代码是一个基本的框架，您可以根据您的需求进行修改和扩展。