103 lines
3.4 KiB
Python
103 lines
3.4 KiB
Python
# -*- coding: utf-8 -*-
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"""
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CNN-BiLSTM-Attention 模型定义，适配药店销售预测系统。
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原始代码来源: python机器学习回归全家桶
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"""
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import torch
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import torch.nn as nn
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# 注意：由于原始代码使用了 TensorFlow/Keras 的层，我们将在这里创建一个 PyTorch 的等效实现。
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# 这是一个更健壮、更符合现有系统架构的做法。
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class Attention(nn.Module):
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"""
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PyTorch 实现的注意力机制。
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"""
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def __init__(self, feature_dim, step_dim, bias=True, **kwargs):
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super(Attention, self).__init__(**kwargs)
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self.supports_masking = True
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self.bias = bias
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self.feature_dim = feature_dim
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self.step_dim = step_dim
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self.features_dim = 0
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weight = torch.zeros(feature_dim, 1)
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nn.init.xavier_uniform_(weight)
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self.weight = nn.Parameter(weight)
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if bias:
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self.b = nn.Parameter(torch.zeros(step_dim))
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def forward(self, x, mask=None):
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feature_dim = self.feature_dim
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step_dim = self.step_dim
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eij = torch.mm(
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x.contiguous().view(-1, feature_dim),
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self.weight
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).view(-1, step_dim)
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if self.bias:
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eij = eij + self.b
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eij = torch.tanh(eij)
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a = torch.exp(eij)
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if mask is not None:
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a = a * mask
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a = a / (torch.sum(a, 1, keepdim=True) + 1e-10)
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weighted_input = x * torch.unsqueeze(a, -1)
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return torch.sum(weighted_input, 1)
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class CnnBiLstmAttention(nn.Module):
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"""
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CNN-BiLSTM-Attention 模型的 PyTorch 实现。
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"""
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def __init__(self, input_dim, output_dim, sequence_length, cnn_filters=64, cnn_kernel_size=1, lstm_units=128):
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super(CnnBiLstmAttention, self).__init__()
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self.sequence_length = sequence_length
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self.cnn_filters = cnn_filters
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self.lstm_units = lstm_units
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# CNN 层
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self.conv1d = nn.Conv1d(in_channels=input_dim, out_channels=cnn_filters, kernel_size=cnn_kernel_size)
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self.relu = nn.ReLU()
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self.maxpool = nn.MaxPool1d(kernel_size=1)
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# BiLSTM 层
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self.bilstm = nn.LSTM(input_size=cnn_filters, hidden_size=lstm_units, num_layers=1, batch_first=True, bidirectional=True)
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# Attention 层
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self.attention = Attention(feature_dim=lstm_units * 2, step_dim=sequence_length)
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# 全连接输出层
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self.dense = nn.Linear(lstm_units * 2, output_dim)
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def forward(self, x):
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# 输入 x 的形状: (batch_size, sequence_length, input_dim)
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# CNN 处理
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x = x.permute(0, 2, 1) # 转换为 (batch_size, input_dim, sequence_length) 以适应 Conv1d
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x = self.conv1d(x)
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x = self.relu(x)
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x = x.permute(0, 2, 1) # 转换回 (batch_size, sequence_length, cnn_filters)
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# BiLSTM 处理
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lstm_out, _ = self.bilstm(x) # lstm_out 形状: (batch_size, sequence_length, lstm_units * 2)
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# Attention 处理
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# 注意：这里的 Attention 实现可能需要根据具体任务微调
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# 一个简化的方法是直接使用 LSTM 的最终隐藏状态或输出
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# 这里我们先用一个简化的逻辑：直接展平 LSTM 输出
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attention_out = self.attention(lstm_out)
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# 全连接层输出
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output = self.dense(attention_out)
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return output
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