How to apply LSTM-autoencoder to variant-length time-series data?

Question

I read LSTM-autoencoder in this tutorial: https://blog.keras.io/building-autoencoders-in-keras.html, and paste the corresponding keras implementation below:

Answer 1

You can use shape=(None, input_dim)

But the RepeatVector will need some hacking taking dimensions directly from the input tensor. (The code works with tensorflow, not sure about theano)

import keras.backend as K

def repeat(x):

    stepMatrix = K.ones_like(x[0][:,:,:1]) #matrix with ones, shaped as (batch, steps, 1)
    latentMatrix = K.expand_dims(x[1],axis=1) #latent vars, shaped as (batch, 1, latent_dim)

    return K.batch_dot(stepMatrix,latentMatrix)


decoded = Lambda(repeat)([inputs,encoded])
decoded = LSTM(input_dim, return_sequences=True)(decoded)