python tcn代码

TCN（Temporal Convolutional Network）是一种时间卷积网络，用于序列建模和预测。Python中有很多开源库可以实现TCN模型，下面是一个简单的Python TCN代码示例：

import torch
from torch import nn

class TemporalBlock(nn.Module):
    def __init__(self, input_channels, output_channels, kernel_size, stride, dilation, padding):
        super(TemporalBlock, self).__init__()
        self.conv1 = nn.Conv1d(input_channels, output_channels, kernel_size,
                               stride=stride, padding=padding, dilation=dilation)
        self.relu1 = nn.ReLU()
        self.dropout1 = nn.Dropout()
        self.conv2 = nn.Conv1d(output_channels, output_channels, kernel_size,
                               stride=stride, padding=padding, dilation=dilation)
        self.relu2 = nn.ReLU()
        self.dropout2 = nn.Dropout()

        self.downsample = nn.Conv1d(input_channels, output_channels, 1) if input_channels != output_channels else None
        self.relu = nn.ReLU()
        self.init_weights()

    def init_weights(self):
        """初始化权重"""
        self.conv1.weight.data.normal_(0, 0.01)
        self.conv2.weight.data.normal_(0, 0.01)
        if self.downsample is not None:
            self.downsample.weight.data.normal_(0, 0.01)

    def forward(self, x):
        out = self.conv1(x)
        out = self.relu1(out)
        out = self.dropout1(out)

        out = self.conv2(out)
        out = self.relu2(out)
        out = self.dropout2(out)

        res = x if self.downsample is None else self.downsample(x)
        out = out + res
        out = self.relu(out)
        return out

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.append(TemporalBlock(in_channels, out_channels, kernel_size, stride=1, dilation=dilation_size,
                                        padding=(kernel_size - 1) * dilation_size))
        self.network = nn.Sequential(*layers)
        self.fc = nn.Linear(num_channels[-1], 1)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        out = self.network(x)
        out = out.permute(0, 2, 1)  # 调整维度
        out = self.fc(out[:, -1, :])  # 只取最后一个时间步的输出
        out = self.dropout(out)
        return out

# 使用TCN进行预测
num_inputs = 1  # 输入特征维度
num_channels = [64, 64, 64]  # TCN的通道数列表
kernel_size = 2  # 卷积核大小
dropout = 0.2  # Dropout概率

tcn = TemporalConvNet(num_inputs, num_channels, kernel_size, dropout)
input_data = torch.randn(10, 1, 50)  # 输入数据，维度为(batch_size, num_inputs, sequence_length)
output = tcn(input_data)

print(output.shape)  # 输出形状为(batch_size, 1)

以上代码就是一个用Python实现的简单的TCN模型，该模型包括TemporalBlock和TemporalConvNet两个类。TemporalBlock实现了卷积块，TemporalConvNet则通过堆叠多个TemporalBlock来构建TCN模型。代码示例中只是简单地使用TCN进行了预测，输入数据的形状为(batch_size, num_inputs, sequence_length)，输出形状为(batch_size, 1)。你可以根据自己的数据和任务需求对代码进行适当调整和修改。