写一段cnn+bilstm的代码，先三层cnn并行,输入维度是20个特征。序列长度1500，进入池化层，然后两层bilstm,最后是线性层全连接层，是个二分类问题

以下是一个简单的CNN+Bilstm模型的代码示例，包括三层CNN并行、池化层、两层Bilstm和线性层全连接层，用于二分类问题：

import torch
import torch.nn as nn

class CNNBilstm(nn.Module):
    def __init__(self, input_dim, hidden_dim, num_layers, output_dim):
        super(CNNBilstm, self).__init__()
        self.input_dim = input_dim
        self.hidden_dim = hidden_dim
        self.num_layers = num_layers
        self.output_dim = output_dim
        
        # Define CNN layers
        self.conv1 = nn.Conv1d(in_channels=input_dim, out_channels=32, kernel_size=3, padding=1)
        self.conv2 = nn.Conv1d(in_channels=input_dim, out_channels=32, kernel_size=5, padding=2)
        self.conv3 = nn.Conv1d(in_channels=input_dim, out_channels=32, kernel_size=7, padding=3)
        self.pool = nn.MaxPool1d(kernel_size=2, stride=2)
        
        # Define Bilstm layers
        self.bilstm1 = nn.LSTM(input_size=32*3, hidden_size=hidden_dim, num_layers=num_layers, batch_first=True, bidirectional=True)
        self.bilstm2 = nn.LSTM(input_size=hidden_dim*2, hidden_size=hidden_dim, num_layers=num_layers, batch_first=True, bidirectional=True)
        
        # Define output layer
        self.fc = nn.Linear(in_features=hidden_dim*2, out_features=output_dim)
        
        # Define activation functions
        self.relu = nn.ReLU()
        self.sigmoid = nn.Sigmoid()
        
    def forward(self, x):
        # CNN layers
        x1 = self.conv1(x)
        x1 = self.relu(x1)
        x1 = self.pool(x1)
        
        x2 = self.conv2(x)
        x2 = self.relu(x2)
        x2 = self.pool(x2)
        
        x3 = self.conv3(x)
        x3 = self.relu(x3)
        x3 = self.pool(x3)
        
        x = torch.cat((x1, x2, x3), dim=1)  # Concatenate CNN outputs along channel dimension
        
        # Bilstm layers
        h0 = torch.zeros(self.num_layers*2, x.size(0), self.hidden_dim).to(device)  # Initialize hidden state
        c0 = torch.zeros(self.num_layers*2, x.size(0), self.hidden_dim).to(device)  # Initialize cell state
        out, _ = self.bilstm1(x, (h0, c0))
        out, _ = self.bilstm2(out, (h0, c0))
        out = out[:, -1, :]  # Select last time step output
        
        # Output layer
        out = self.fc(out)
        out = self.sigmoid(out)
        
        return out

在这个模型中，输入数据的维度为(batch_size, input_dim, seq_len)，其中input_dim=20，seq_len=1500。首先，输入数据经过三个并行的卷积层（kernel_size分别为3、5、7），然后经过池化层进行下采样。池化层的kernel_size和stride都为2，因此将输入数据的长度减半。接着，将三个卷积层的输出在通道维度上进行拼接，形成一个新的特征张量(batch_size, 32*3, seq_len/2)。然后，将特征张量输入到两层Bilstm中，其中第一层的输出作为第二层的输入。最后，将Bilstm的输出传递给一个线性层进行全连接，输出一个标量，代表二分类结果。在模型的前向传播过程中，激活函数采用了ReLU和Sigmoid。