Code for one-dimensional convolutional wavelet neural network

Here is an example code for a one-dimensional convolutional wavelet neural network using PyTorch:

import torch
import torch.nn as nn
import pywt

class ConvWaveletNet(nn.Module):
    def __init__(self, num_classes):
        super(ConvWaveletNet, self).__init__()
        self.conv1 = nn.Conv1d(1, 16, kernel_size=3, stride=1, padding=1)
        self.relu1 = nn.ReLU()
        self.pool1 = nn.MaxPool1d(kernel_size=2, stride=2)
        self.conv2 = nn.Conv1d(16, 32, kernel_size=3, stride=1, padding=1)
        self.relu2 = nn.ReLU()
        self.pool2 = nn.MaxPool1d(kernel_size=2, stride=2)
        self.conv3 = nn.Conv1d(32, 64, kernel_size=3, stride=1, padding=1)
        self.relu3 = nn.ReLU()
        self.pool3 = nn.MaxPool1d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(64 * 4, 128)
        self.relu4 = nn.ReLU()
        self.fc2 = nn.Linear(128, num_classes)
    
    def forward(self, x):
        # Apply wavelet transform to the input signal
        cA, cD = pywt.dwt(x, 'db1')
        x = cA + cD
        x = torch.tensor(x).unsqueeze(0).unsqueeze(0).float()  # add batch and channel dimensions
        
        # Convolutional layers
        x = self.conv1(x)
        x = self.relu1(x)
        x = self.pool1(x)
        x = self.conv2(x)
        x = self.relu2(x)
        x = self.pool2(x)
        x = self.conv3(x)
        x = self.relu3(x)
        x = self.pool3(x)
        
        # Fully connected layers
        x = x.view(-1, 64 * 4)
        x = self.fc1(x)
        x = self.relu4(x)
        x = self.fc2(x)
        return x

This network consists of three convolutional layers followed by two fully connected layers. The input signal is first transformed using the discrete wavelet transform, and then passed through the convolutional layers. The output of the last convolutional layer is flattened and passed through the fully connected layers to produce the final classification result. Note that this implementation uses the 'db1' wavelet for the wavelet transform, but other wavelets can also be used.