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:

from keras.layers import Input, LSTM, RepeatVector
from keras.models import Model

inputs = Input(shape=(timesteps, input_dim))
encoded = LSTM(latent_dim)(inputs)

decoded = RepeatVector(timesteps)(encoded)
decoded = LSTM(input_dim, return_sequences=True)(decoded)

sequence_autoencoder = Model(inputs, decoded)
encoder = Model(inputs, encoded)

In this implementation, they fixed the input to be of shape (timesteps, input_dim), which means length of time-series data is fixed to be timesteps. If I remember correctly RNN/LSTM can handle time-series data of variable lengths and I am wondering if it is possible to modify the code above somehow to accept data of any length?

Thanks!

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)