[keras] LSTM-Autoencoder

import tensorflow as tf

from tensorflow.keras.layers import Dense, LSTM, Masking, TimeDistributed, RepeatVector
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.models import Sequential

import numpy as np

#(samples，timesteps,features) samples=4，features=3, timesteps=variable length
train_X = np.array([
[[0, 1, 2], [9, 8, 7],[3, 6, 8]],
[[3, 4, 5]],
[[6, 7, 8], [6, 5, 4],[1, 7, 4]],
[[9, 0, 1], [3, 7, 4]]
])

train_Y = np.array([0, 1, 1, 0])

n_in = 3
n_feat = 3
n_out = 1

# padding
'''
train_X = np.array([
[[0, 1, 2], [9, 8, 7],[3, 6, 8]],
[[3, 4, 5], [0, 0, 0],[0, 0, 0]],
[[6, 7, 8], [6, 5, 4],[1, 7, 4]],
[[9, 0, 1], [3, 7, 4],[0, 0, 0]]
])
'''
train_X = pad_sequences(train_X, padding='post')

model = Sequential()
inputs = tf.keras.Input(shape=(n_in, n_feat))

# Masking
masked_input = Masking(mask_value=0)(inputs)

# encoder
encoder = LSTM(100, activation='relu')(masked_input)

# decoder 1: reconstruct input sequence
decoder1 = RepeatVector(n_in)(encoder)
decoder1 = LSTM(100, activation='relu', return_sequences=True)(decoder1)
decoder1 = TimeDistributed(Dense(3))(decoder1)

# decoder 2: reconstruct output sequence
decoder2 = RepeatVector(n_out)(encoder)
decoder2 = LSTM(100, activation='relu', return_sequences=True)(decoder2)
decoder2 = TimeDistributed(Dense(1))(decoder2)

model = tf.keras.Model(inputs=inputs, outputs=[decoder1, decoder2])
model.compile(optimizer='rmsprop', loss='mse')
# print(model.summary())

model.fit(train_X, [train_X, train_Y], epochs=500, verbose=1)
yhat = model.predict(train_X, verbose=0)
print(yhat)
print('==========================================')