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lstm.py
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lstm.py
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import torch
import torch.nn as nn
import torch
import torch.nn as nn
class EnhancedResidualBlock(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride, padding):
super(EnhancedResidualBlock, self).__init__()
self.conv1 = nn.Conv1d(in_channels, out_channels, kernel_size, stride, padding)
self.bn1 = nn.BatchNorm1d(out_channels)
self.conv2 = nn.Conv1d(out_channels, out_channels, kernel_size, stride, padding)
self.bn2 = nn.BatchNorm1d(out_channels)
self.relu = nn.ReLU()
self.shortcut = nn.Sequential()
if in_channels != out_channels:
self.shortcut = nn.Sequential(
nn.Conv1d(in_channels, out_channels, kernel_size=1),
nn.BatchNorm1d(out_channels)
)
def forward(self, x):
residual = self.shortcut(x)
x = self.relu(self.bn1(self.conv1(x)))
x = self.bn2(self.conv2(x))
x += residual
x = self.relu(x)
return x
class LSTMModel(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, num_classes):
super(LSTMModel, self).__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
# 定义残差块
self.resblock1 = EnhancedResidualBlock(input_size, 32, 5, 1, 2)
self.resblock2 = EnhancedResidualBlock(32, 64, 5, 1, 2)
self.resblock3 = EnhancedResidualBlock(64, 128, 5, 1, 2)
# 定义 LSTM 层
self.lstm = nn.LSTM(128, hidden_size, num_layers, batch_first=True)
# 定义输出层
self.fc1 = nn.Linear(hidden_size, 256)
self.dropout = nn.Dropout(0.5)
self.fc2 = nn.Linear(256, 128)
self.fc3 = nn.Linear(128, num_classes)
def forward(self, x):
# 通过残差块
x = self.resblock1(x)
x = self.resblock2(x)
x = self.resblock3(x)
# 重塑数据以适应 LSTM 层
x = x.transpose(1, 2) # 转换为 (batch, seq_len, features)
# 初始化隐藏状态和细胞状态
h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)
c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)
# LSTM 前向传播
x, _ = self.lstm(x, (h0, c0))
# 取最后一个时间步的输出
x = x[:, -1, :]
# 通过全连接层
x = F.relu(self.fc1(x))
x = self.dropout(x)
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x