自编码器对数据降维，最后采用LSTM对数据进行拟合预测

最新推荐文章于 2024-07-27 11:05:40 发布

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最新推荐文章于 2024-07-27 11:05:40 发布 · 7.8k 阅读

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#自编码器 #LSTM

采用自编码器对高维特征表达成低维特征，最后采用LSTM对数据进行拟合，预测准确率很高。

import torch
import torch.nn as nn
from sklearn.datasets import load_iris
import matplotlib.pyplot as plt
import numpy as np
from sklearn.model_selection import train_test_split
# 超参数
EPOCH = 200
LR = 0.005



data = load_iris()
y = data.target
x = data.data


class AutoEncoder(nn.Module):
    def __init__(self):
        super(AutoEncoder, self).__init__()
        self.encoder = nn.Sequential(
            nn.Linear(4, 128),
            nn.ReLU(),
            nn.Linear(128, 64),
            nn.ReLU(),
            nn.Linear(64, 12),
            nn.ReLU(),
            nn.Linear(12, 2),
        )

        self.decoder = nn.Sequential(
            nn.Linear(2, 12),
            nn.ReLU(),
            nn.Linear(12, 64),
            nn.ReLU(),
            nn.Linear(64, 128),
            nn.ReLU(),
            nn.Linear(128, 4),
        )

    def forward(self, x):
        encoded = self.encoder(x)
        decoded = sel