I am familiar with the LSTM unit (memory cell, forget gate, output gate etc) however I am struggling to see how this links to the LSTM implementation in Keras.
In Keras the input data structure for X is: (nb_samples, timesteps, input_dim).
Suppose that the shape of X is: (1000, timesteps = 10, 40).
1) Does this mean that the LSTM cells will only consider ‘batches’ of 10 previous time steps ?
2) Or is the output from LSTM cells passed between these sets of 10 timesteps I.e could you capture long term dependencies 50 timesteps out?