栈自编码器(Stacked Autoencoder,SAE)对过程参数进行特征提取,并通过基于Attention机制的长短期记忆模型(Long Short Term Memory,LSTM)对参数进行初步预测 pytorch

下面是一个使用PyTorch实现栈自编码器和基于Attention机制的LSTM对过程参数进行特征提取和预测的示例代码：

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np

# 栈自编码器模型
class SAE(nn.Module):
    def __init__(self, input_size, hidden_sizes):
        super(SAE, self).__init__()
        self.encoder = nn.Sequential(
            nn.Linear(input_size, hidden_sizes[0]),
            nn.ReLU(),
            nn.Linear(hidden_sizes[0], hidden_sizes[1]),
            nn.ReLU(),
            nn.Linear(hidden_sizes[1], hidden_sizes[2])
        )
        self.decoder = nn.Sequential(
            nn.Linear(hidden_sizes[-1], hidden_sizes[-2]),
            nn.ReLU(),
            nn.Linear(hidden_sizes[-2], hidden_sizes[-3]),
            nn.ReLU(),
            nn.Linear(hidden_sizes[-3], input_size)
        )

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

# 基于Attention机制的LSTM模型
class AttentionLSTM(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, attention_size):
        super(AttentionLSTM, self).__init__()
        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
        self.attention = nn.Sequential(
            nn.Linear(hidden_size, attention_size),
            nn.Tanh(),
            nn.Linear(attention_size, 1),
            nn.Softmax(dim=1)
        )

    def forward(self, x):
        output, _ = self.lstm(x)
        attention_weights = self.attention(output)
        context = torch.bmm(attention_weights.transpose(1, 2), output)
        return context
        

# 准备数据
input_data = np.random.rand(100, 10)  # 假设有100个样本，每个样本有10个特征
input_data = torch.tensor(input_data, dtype=torch.float32)

# 定义模型和优化器
sae = SAE(input_size=10, hidden_sizes=[32, 16, 8])
lstm = AttentionLSTM(input_size=8, hidden_size=16, num_layers=2, attention_size=8)
optimizer = optim.Adam(list(sae.parameters()) + list(lstm.parameters()), lr=0.01)

# 训练模型
for epoch in range(10):
    encoded_data = sae.encoder(input_data)
    context = lstm(encoded_data.unsqueeze(1))
    output = sae.decoder(context.squeeze(1))
    loss = nn.MSELoss()(output, input_data)
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    print('Epoch: {}, Loss: {:.4f}'.format(epoch+1, loss.item()))

# 使用模型进行预测
test_data = np.random.rand(1, 10)  # 假设有1个测试样本，每个样本有10个特征
test_data = torch.tensor(test_data, dtype=torch.float32)
encoded_test_data = sae.encoder(test_data)
context = lstm(encoded_test_data.unsqueeze(1))
predicted_output = sae.decoder(context.squeeze(1))
print('Test Data:', test_data)
print('Predicted Output:', predicted_output)

这个示例代码中，首先定义了一个栈自编码器模型和一个基于Attention机制的LSTM模型，并将它们的参数同时进行优化。然后，使用输入数据训练模型，计算模型在每个epoch的损失，并输出。最后，使用训练好的模型对一个测试样本进行预测，并输出预测结果。