TCN端到端多分类代码

以下是一个简单的使用TCN进行端到端多分类的代码示例，使用PyTorch实现：

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

# 定义TCN模型
class TemporalBlock(nn.Module):
    def __init__(self, n_inputs, n_outputs, kernel_size, stride, dilation):
        super(TemporalBlock, self).__init__()
        padding = (kernel_size - 1) * dilation
        self.conv1 = nn.Conv1d(n_inputs, n_outputs, kernel_size, stride=stride, padding=padding, dilation=dilation)
        self.relu1 = nn.ReLU()
        self.dropout1 = nn.Dropout(0.2)
        self.conv2 = nn.Conv1d(n_outputs, n_outputs, kernel_size, stride=stride, padding=padding, dilation=dilation)
        self.relu2 = nn.ReLU()
        self.dropout2 = nn.Dropout(0.2)
        self.net = nn.Sequential(self.conv1, self.relu1, self.dropout1, self.conv2, self.relu2, self.dropout2)
        self.downsample = nn.Conv1d(n_inputs, n_outputs, 1) if n_inputs != n_outputs else None
        self.relu = nn.ReLU()

    def forward(self, x):
        out = self.net(x)
        res = x if self.downsample is None else self.downsample(x)
        return self.relu(out + res)

class TemporalConvNet(nn.Module):
    def __init__(self, num_inputs, num_channels, kernel_size=2, dropout=0.2):
        super(TemporalConvNet, self).__init__()
        layers = []
        num_levels = len(num_channels)
        for i in range(num_levels):
            dilation_size = 2 ** i
            in_channels = num_inputs if i == 0 else num_channels[i-1]
            out_channels = num_channels[i]
            layers += [TemporalBlock(in_channels, out_channels, kernel_size, stride=1, dilation=dilation_size)]
        self.network = nn.Sequential(*layers)
        self.fc = nn.Linear(num_channels[-1], num_classes)

    def forward(self, x):
        out = self.network(x)
        out = out[:, :, -1]
        out = self.fc(out)
        return out

# 加载数据
X_train, y_train = load_data('train_data.csv') # 加载训练数据
X_val, y_val = load_data('val_data.csv') # 加载验证数据
X_test, y_test = load_data('test_data.csv') # 加载测试数据

# 转换数据为Tensor格式
X_train = torch.tensor(X_train, dtype=torch.float32)
y_train = torch.tensor(y_train, dtype=torch.long)
X_val = torch.tensor(X_val, dtype=torch.float32)
y_val = torch.tensor(y_val, dtype=torch.long)
X_test = torch.tensor(X_test, dtype=torch.float32)
y_test = torch.tensor(y_test, dtype=torch.long)

# 将数据放入DataLoader中
train_dataset = TensorDataset(X_train, y_train)
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
val_dataset = TensorDataset(X_val, y_val)
val_loader = DataLoader(val_dataset, batch_size=64, shuffle=False)
test_dataset = TensorDataset(X_test, y_test)
test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)

# 定义超参数
num_inputs = X_train.shape[1]
num_channels = [64, 64, 64, 64]
kernel_size = 3
dropout = 0.2
num_classes = 10
lr = 0.001
num_epochs = 100

# 定义模型、损失函数和优化器
model = TemporalConvNet(num_inputs, num_channels, kernel_size, dropout)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=lr)

# 训练模型
for epoch in range(num_epochs):
    train_loss = 0.0
    train_acc = 0.0
    val_loss = 0.0
    val_acc = 0.0
    model.train() # 训练模式
    for X, y in train_loader:
        optimizer.zero_grad()
        y_pred = model(X.transpose(1,2))
        loss = criterion(y_pred, y)
        loss.backward()
        optimizer.step()
        train_loss += loss.item() * X.size(0)
        train_acc += (y_pred.argmax(dim=1) == y).sum().item()
    model.eval() # 验证模式
    with torch.no_grad():
        for X, y in val_loader:
            y_pred = model(X.transpose(1,2))
            loss = criterion(y_pred, y)
            val_loss += loss.item() * X.size(0)
            val_acc += (y_pred.argmax(dim=1) == y).sum().item()
    print('Epoch [{}/{}], Train Loss: {:.4f}, Train Acc: {:.4f}, Val Loss: {:.4f}, Val Acc: {:.4f}'.format(
        epoch+1, num_epochs, train_loss/len(train_dataset), train_acc/len(train_dataset),
        val_loss/len(val_dataset), val_acc/len(val_dataset)
    ))

# 测试模型
test_acc = 0.0
model.eval()
with torch.no_grad():
    for X, y in test_loader:
        y_pred = model(X.transpose(1,2))
        test_acc += (y_pred.argmax(dim=1) == y).sum().item()
print('Test Acc: {:.4f}'.format(test_acc/len(test_dataset)))

这个代码实现了一个使用TCN进行端到端多分类的例子，包括加载数据、定义模型、训练模型、测试模型等步骤。需要注意的是，在TCN中使用的是一维卷积，所以需要将输入数据从二维张量转换为三维张量，其中第一维是batch_size，第二维是时间步，第三维是特征维度。同时需要将输出结果从三维张量取出最后一个时间步的结果，作为全连接层的输入。