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From: =?UTF-8?q?=E6=9E=97=E6=9E=97?= <6969846@qq.com>
Date: Thu, 13 Mar 2025 17:56:50 +0800
Subject: [PATCH] =?UTF-8?q?TCN=E6=A1=86=E6=9E=B6=E4=BB=A3=E7=A0=81?=
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---
 周家林/TCN.py | 134 ++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 134 insertions(+)
 create mode 100644 周家林/TCN.py

diff --git a/周家林/TCN.py b/周家林/TCN.py
new file mode 100644
index 0000000..4c0d7e2
--- /dev/null
+++ b/周家林/TCN.py
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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn.utils import weight_norm  # 用于权重归一化的工具
+
+
+# 扩张因果卷积模块
+class DilatedCausalConv1d(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, dilation):
+        """
+        in_channels: 输入通道数
+        out_channels: 输出通道数
+        kernel_size: 卷积核大小
+        dilation: 扩张因子（控制感受野大小）
+        """
+        super().__init__()
+        # 计算因果卷积需要的左侧padding量：(kernel_size-1)*dilation
+        self.padding = (kernel_size - 1) * dilation  # 保证时序因果关系（不泄露未来信息）
+
+        # 创建带权重归一化的1D卷积层
+        self.conv = weight_norm(
+            nn.Conv1d(in_channels,
+                      out_channels,
+                      kernel_size,
+                      padding=self.padding,  # 只在左侧填充
+                      dilation=dilation)  # 设置扩张率
+        )
+
+    def forward(self, x):
+        """
+        输入形状: (batch_size, in_channels, seq_len)
+        输出形状: (batch_size, out_channels, seq_len)
+        """
+        x = self.conv(x)
+        # 裁剪右侧多余的padding，保持输出长度与输入一致
+        return x[:, :, :-self.padding]  # 切片操作去除右侧padding
+
+
+# 残差块模块
+class ResidualBlock(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, dilation, dropout=0.2):
+        super().__init__()
+        # 第一个卷积层（包含所有规范化操作）
+        self.conv1 = DilatedCausalConv1d(in_channels, out_channels, kernel_size, dilation)
+        # 第二个卷积层
+        self.conv2 = DilatedCausalConv1d(out_channels, out_channels, kernel_size, dilation)
+
+        # 公共组件初始化
+        self.dropout = nn.Dropout(dropout)  # 随机失活层
+        self.relu = nn.ReLU()  # 激活函数
+
+        # 当输入输出通道数不同时，使用1x1卷积调整通道数
+        self.downsample = nn.Conv1d(in_channels, out_channels, 1) if in_channels != out_channels else None
+
+    def forward(self, x):
+        residual = x  # 保存原始输入用于残差连接
+
+        # 第一层处理流程
+        x = self.dropout(x)  # 应用Dropout
+        x = self.relu(x)  # 非线性激活
+        x = self.conv1(x)  # 扩张因果卷积
+
+        # 第二层处理流程
+        x = self.dropout(x)  # 再次应用Dropout
+        x = self.relu(x)  # 非线性激活
+        x = self.conv2(x)  # 扩张因果卷积
+
+        # 处理残差连接
+        if self.downsample is not None:
+            residual = self.downsample(residual)  # 通过1x1卷积调整通道数
+        return residual + x  # 残差相加
+
+
+# 完整TCN模型
+class TCN(nn.Module):
+    def __init__(self, input_size, num_channels, kernel_size=3, dropout=0.2):
+        """
+        input_size: 输入特征维度（通道数）
+        num_channels: 每层的输出通道数列表（决定网络深度）
+        kernel_size: 卷积核尺寸
+        """
+        super().__init__()
+        layers = []  # 存储所有残差块
+        num_levels = len(num_channels)  # 网络层数
+
+        # 逐层构建网络
+        for i in range(num_levels):
+            dilation = 2 ** i  # 扩张因子指数增长（2^0, 2^1, 2^2...）
+            in_channels = input_size if i == 0 else num_channels[i - 1]  # 确定输入通道
+            out_channels = num_channels[i]  # 当前层输出通道
+
+            # 添加残差块
+            layers += [
+                ResidualBlock(
+                    in_channels,
+                    out_channels,
+                    kernel_size=kernel_size,
+                    dilation=dilation,
+                    dropout=dropout
+                )
+            ]
+
+        # 将所有残差块组合成序列
+        self.network = nn.Sequential(*layers)
+
+    def forward(self, x):
+        """
+        输入形状: (batch_size, input_size, seq_len)
+        输出形状: (batch_size, num_channels[-1], seq_len)
+        """
+        return self.network(x)
+
+
+# 示例用法
+if __name__ == "__main__":
+    # 配置参数
+    batch_size = 32  # 批大小
+    seq_len = 100  # 序列长度
+    input_size = 64  # 输入特征维度
+    num_channels = [64, 64, 64]  # 各层输出通道配置（这里3层，每层64通道）
+    kernel_size = 3  # 卷积核尺寸
+
+    # 初始化模型
+    model = TCN(input_size, num_channels, kernel_size)
+
+    # 生成测试数据
+    x = torch.randn(batch_size, input_size, seq_len)  # 随机输入数据
+
+    # 前向传播
+    output = model(x)
+
+    # 验证输出形状（应与输入序列长度相同）
+    print(f"Input shape: {x.shape}")  # (32, 64, 100)
+    print(f"Output shape: {output.shape}")  # (32, 64, 100)
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