栈自编码器结合LSTMpytorch案例代码预测

这里是一个使用栈自编码器结合LSTM来预测时间序列的PyTorch案例代码。

首先，我们需要导入必要的库和定义一些超参数：

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler

# 超参数
input_size = 1
hidden_size = 64
num_layers = 2
stack_size = 3
output_size = 1
batch_size = 64
num_epochs = 100
learning_rate = 0.001

然后，我们需要加载数据并对其进行归一化：

# 加载数据
data = pd.read_csv('data.csv', index_col=0)
data = data.values.astype('float32')

# 归一化
scaler = MinMaxScaler(feature_range=(-1, 1))
data = scaler.fit_transform(data)

接下来，我们需要定义模型。我们将使用一个栈自编码器和一个LSTM来构建模型。在栈自编码器中，我们将把输入数据压缩成一个更小的向量，然后再解压缩回原始形式。这个过程可以帮助我们提取更有用的特征。然后，我们将使用LSTM来学习时间序列的模式。

class StackedAutoEncoder(nn.Module):
    def __init__(self, input_size, hidden_size, stack_size):
        super(StackedAutoEncoder, self).__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.stack_size = stack_size
        self.encoder = nn.ModuleList([
            nn.Linear(input_size, hidden_size) for i in range(stack_size)])
        self.decoder = nn.ModuleList([
            nn.Linear(hidden_size, input_size) for i in range(stack_size)])

    def forward(self, x):
        for i in range(self.stack_size):
            x = self.encoder[i](x)
            x = torch.relu(x)
        for i in range(self.stack_size - 1, -1, -1):
            x = self.decoder[i](x)
            x = torch.relu(x)
        return x


class LSTM(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, output_size):
        super(LSTM, self).__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.output_size = output_size
        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
        out, _ = self.lstm(x, (h0, c0))
        out = self.fc(out[:, -1, :])
        return out

然后，我们需要定义训练函数：

# 初始化模型和优化器
sae = StackedAutoEncoder(input_size, hidden_size, stack_size).to(device)
lstm = LSTM(hidden_size, hidden_size, num_layers, output_size).to(device)
params = list(sae.parameters()) + list(lstm.parameters())
optimizer = optim.Adam(params, lr=learning_rate)

# 定义损失函数
criterion = nn.MSELoss()

def train(data):
    # 将数据转换成序列
    data = torch.from_numpy(data).float()
    dataset = []
    for i in range(len(data) - stack_size):
        dataset.append((data[i:i+stack_size], data[i+stack_size]))
    dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True)
    # 训练模型
    for epoch in range(num_epochs):
        for seq, label in dataloader:
            seq = seq.to(device)
            label = label.to(device)
            # 前向传播
            encoded_seq = sae(seq.view(-1, input_size))
            encoded_seq = encoded_seq.view(batch_size, stack_size, hidden_size)
            output = lstm(encoded_seq)
            loss = criterion(output, label)
            # 反向传播和优化
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
        # 打印损失
        if (epoch+1) % 10 == 0:
            print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))

    return sae, lstm

最后，我们可以使用训练好的模型来进行预测：

# 加载模型并预测
sae, lstm = train(data)
sae.eval()
lstm.eval()
test_data = np.array([0.5, 0.6, 0.7, 0.8, 0.9])
test_data = scaler.transform(test_data.reshape(-1, 1)).reshape(1, -1)
test_data = torch.from_numpy(test_data).float().to(device)
with torch.no_grad():
    encoded_seq = sae(test_data.view(-1, input_size))
    encoded_seq = encoded_seq.view(1, stack_size, hidden_size)
    output = lstm(encoded_seq)
    output = scaler.inverse_transform(output.cpu().numpy())
    print(output)

这就是使用栈自编码器结合LSTM来预测时间序列的PyTorch案例代码。