无监督异常检测之LSTM组成的AE

我本来就是处理时间序列异常检测的，之前用了全连接层以及CNN层组成的AE去拟合原始时间序列，发现效果不佳。当利用LSTM组成AE去拟合时间序列时发现，拟合的效果很好。但是，利用重构误差去做异常检测这条路依旧不通，因为发现异常曲线的拟合效果也很好……算了，这次先不打算做时间序列异常检测了。在这里把“基于LSTM的auto-encoder”的代码分享出来。

代码参考了Jason Brownlee大佬修改的：具体链接我找不到了，当他的博客我还能找到，感兴趣自己翻一翻，记得在LSTM网络那一章

https://machinelearningmastery.com/multivariate-time-series-forecasting-lstms-keras/

from keras.layers import Input, Dense, LSTM

from keras.models import Model

from keras import backend as K

import numpy as np

from pandas import read_csv

from matplotlib import pyplot

import numpy

from numpy import array

from keras.models import Sequential

from keras.layers import RepeatVector

from keras.layers import TimeDistributed

from keras.utils import plot_model

#导入数据，前8000个正常样本，剩下的样本包括正常和异常时间序列，每个样本是1行48列

dataset = read_csv('randperm_zerone_Dataset.csv')

values = dataset.values

XY= values

n_train_hours1 =7000

n_train_hours3 =8000

trainX=XY[:n_train_hours1,:]

validX =XY[n_train_hours1:n_train_hours3, :]

testX =XY[n_train_hours3:, :]

train3DX = trainX.reshape((trainX.shape[0], trainX.shape[1],1))

valid3DX =validX.reshape((validX.shape[0], validX.shape[1],1))

test3DX = testX.reshape((testX.shape[0],testX.shape[1],1))

# 编码器

sequence = train3DX

# reshape input into [samples, timesteps, features]

n_in = 48

# define model

model = Sequential()

model.add(LSTM(100, activation='relu', input_shape=(n_in,1)))

model.add(RepeatVector(n_in))

model.add(LSTM(100, activation='relu', return_sequences=True))

model.add(TimeDistributed(Dense(1)))

model.compile(optimizer='adam', loss='mse')

model.summary()

# fit model

history=model.fit(train3DX, train3DX, shuffle=True,epochs=300,validation_data=(valid3DX, valid3DX))

pyplot.plot(history.history['loss'], label='train')

pyplot.plot(history.history['val_loss'], label='valid')

pyplot.legend()

pyplot.show()

# demonstrate recreation

yhat = model.predict(sequence)

ReconstructedData=yhat.reshape((yhat.shape[0], -1))

numpy.savetxt("ReconstructedData.csv", ReconstructedData, delimiter=',')