小波双向长短时记忆神经网络代码

以下是小波双向长短时记忆神经网络的代码：

import torch
import torch.nn as nn
import pywt

class WaveletLSTM(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, wavelet_type='db1'):
        super(WaveletLSTM, self).__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.wavelet_type = wavelet_type
        
        # Wavelet transform layer
        self.wavelet = nn.Conv1d(in_channels=1, out_channels=1, kernel_size=2, stride=2, bias=False)
        self.wavelet.weight.data.fill_(1/2)
        self.wavelet.weight.data[0, 1] = -1/2
        
        # LSTM layers
        self.lstm = nn.LSTM(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, bidirectional=True)
        
    def forward(self, x):
        # Wavelet transform
        cA, cD = pywt.dwt(x, self.wavelet_type)
        cA = torch.from_numpy(cA).float().unsqueeze(0)
        cD = torch.from_numpy(cD).float().unsqueeze(0)
        
        # LSTM input
        lstm_input = torch.cat((cA, cD), dim=2)
        
        # LSTM output
        lstm_output, _ = self.lstm(lstm_input)
        
        return lstm_output.squeeze(0)

# Example usage
model = WaveletLSTM(input_size=10, hidden_size=20, num_layers=2)
x = torch.randn(5, 1, 10) # batch_size=5, sequence_length=1, input_size=10
output = model(x)
print(output.shape) # torch.Size([5, 40])