How do I create a variable-length input LSTM in Keras?

Question

I am trying to do some vanilla pattern recognition with an LSTM using Keras to predict the next element in a sequence.

My data look like this:

where

Answer 1

I am not clear about the embedding procedure. But still here is a way to implement a variable-length input LSTM. Just do not specify the timespan dimension when building LSTM.

import keras.backend as K
from keras.layers import LSTM, Input

I = Input(shape=(None, 200)) # unknown timespan, fixed feature size
lstm = LSTM(20)
f = K.function(inputs=[I], outputs=[lstm(I)])

import numpy as np
data1 = np.random.random(size=(1, 100, 200)) # batch_size = 1, timespan = 100
print f([data1])[0].shape
# (1, 20)

data2 = np.random.random(size=(1, 314, 200)) # batch_size = 1, timespan = 314
print f([data2])[0].shape
# (1, 20)