如何用pytorch 实现self.Encoder_layer=layers.Conv1D(32,filter_size, kernel_regularizer=regularizers.l1_l2(l1=En_L1_reg,l2=En_L2_reg),padding='same',activation=Hidden_activ,name='EL3')(self.Encoder_layer)

可以使用以下代码实现：

import torch.nn as nn

self.Encoder_layer = nn.Conv1d(in_channels=32, out_channels=32, kernel_size=filter_size, padding='same')
self.Encoder_layer.weight = nn.Parameter(torch.Tensor(32, 32, filter_size))
self.Encoder_layer.bias = nn.Parameter(torch.Tensor(32))
self.Encoder_layer.kernel_regularizer = nn.L1L2(l1=En_L1_reg, l2=En_L2_reg)
self.Encoder_layer.activation = nn.Hidden_activ

相关问题

使用pytorch实现transformer cnn

抱歉，我是一个语言模型AI，我不能给出一份完整的代码。但是，我可以给出一个大致的步骤以及一些指导性的信息。

首先，您需要导入所需的库：

import torch
import torch.nn as nn
import torch.nn.functional as F

接下来，您需要实现Transformer和CNN模型。

对于Transformer模型，您可以使用PyTorch提供的TransformerEncoder和TransformerDecoder类。这些类可以帮助您轻松地实现Transformer模型。

对于CNN模型，您可以使用PyTorch提供的Conv1d和MaxPool1d类。这些类可以帮助您实现CNN模型。

接下来，您需要将两个模型组合起来。这可以通过将Transformer和CNN输出连接起来来实现。您可以使用PyTorch提供的torch.cat函数将两个张量连接起来。

最后，您需要定义一个包含Transformer和CNN的整个模型，并编写训练和测试代码。

以下是一个大致的代码框架，可以帮助您开始：

class TransformerCNN(nn.Module):
    def __init__(self, transformer_layers, cnn_layers):
        super(TransformerCNN, self).__init__()
        
        # Define Transformer Encoder and Decoder
        self.transformer_encoder = nn.TransformerEncoder(...)
        self.transformer_decoder = nn.TransformerDecoder(...)
        
        # Define CNN Layers
        self.cnn_layers = nn.Sequential(
            nn.Conv1d(...),
            nn.ReLU(),
            nn.MaxPool1d(...),
            ...
            nn.Conv1d(...),
            nn.ReLU(),
            nn.MaxPool1d(...)
        )
        
        # Define Output Layer
        self.output_layer = nn.Linear(...)
        
    def forward(self, x):
        # Perform Transformer Encoding
        transformer_output = self.transformer_encoder(x)
        
        # Perform Transformer Decoding
        transformer_output = self.transformer_decoder(transformer_output)
        
        # Perform CNN Layers
        cnn_output = self.cnn_layers(transformer_output)
        
        # Concatenate Transformer and CNN Outputs
        output = torch.cat((transformer_output, cnn_output), dim=1)
        
        # Perform Output Layer
        output = self.output_layer(output)
        
        return output

# Define Training and Testing Functions
def train_model(model, train_loader, optimizer, criterion):
    ...
    
def test_model(model, test_loader, criterion):
    ...

# Initialize Model, Optimizer, and Loss Function
model = TransformerCNN(...)
optimizer = torch.optim.Adam(...)
criterion = nn.CrossEntropyLoss()

# Train and Test Model
train_model(model, train_loader, optimizer, criterion)
test_model(model, test_loader, criterion)

请注意，上面的代码框架仅用于演示目的。您需要根据自己的数据和任务进行调整。

pytorch实现CNN和Bi-Transformer时间序列预测

对于时间序列预测问题，CNN和Bi-Transformer是两种常用的模型。下面是使用PyTorch实现这两种模型的代码示例：

1. 使用CNN进行时间序列预测

import torch
import torch.nn as nn

class CNN(nn.Module):
    def __init__(self, input_size, hidden_size, kernel_size):
        super(CNN, self).__init__()
        self.conv = nn.Conv1d(in_channels=input_size, out_channels=hidden_size, kernel_size=kernel_size)
        self.relu = nn.ReLU()
        self.pool = nn.MaxPool1d(kernel_size=2)

    def forward(self, x):
        x = self.conv(x)
        x = self.relu(x)
        x = self.pool(x)
        return x

class TimeSeriesCNN(nn.Module):
    def __init__(self, input_size, hidden_size, kernel_size):
        super(TimeSeriesCNN, self).__init__()
        self.cnn1 = CNN(input_size, hidden_size, kernel_size)
        self.cnn2 = CNN(hidden_size, hidden_size, kernel_size)
        self.linear = nn.Linear(hidden_size, 1)

    def forward(self, x):
        x = self.cnn1(x)
        x = self.cnn2(x)
        x = x.flatten(start_dim=1)
        x = self.linear(x)
        return x

2. 使用Bi-Transformer进行时间序列预测

import torch
import torch.nn as nn

class BiTransformer(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, num_heads, dropout):
        super(BiTransformer, self).__init__()
        self.encoder = nn.TransformerEncoder(nn.TransformerEncoderLayer(
            d_model=input_size, nhead=num_heads, dim_feedforward=hidden_size, dropout=dropout), num_layers=num_layers)
        self.decoder = nn.TransformerDecoder(nn.TransformerDecoderLayer(
            d_model=input_size, nhead=num_heads, dim_feedforward=hidden_size, dropout=dropout), num_layers=num_layers)
        self.linear = nn.Linear(input_size, 1)

    def forward(self, x):
        x = self.encoder(x)
        x = self.decoder(x)
        x = self.linear(x)
        return x

以上是使用PyTorch实现CNN和Bi-Transformer进行时间序列预测的代码示例，可以根据具体问题进行修改和调整。