LSTM Long Term Dependencies Keras
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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?
neural-network recurrent-neural-net
asked 1 min ago
Mike TauberMike Tauber
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$begingroup$
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?
neural-network recurrent-neural-net
asked 1 min ago
Mike TauberMike Tauber
1
New contributor
Mike Tauber is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
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?
neural-network recurrent-neural-net
asked 1 min ago
Mike TauberMike Tauber
1
New contributor
Mike Tauber is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
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?
neural-network recurrent-neural-net
neural-network recurrent-neural-net
asked 1 min ago
Mike TauberMike Tauber
1
New contributor
Mike Tauber is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 1 min ago
Mike TauberMike Tauber
1
New contributor
Mike Tauber is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 1 min ago
Mike TauberMike Tauber
1
New contributor
Mike Tauber is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 1 min ago
Mike TauberMike Tauber
1
asked 1 min ago
Mike TauberMike Tauber
1
1
New contributor
Mike Tauber is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Mike Tauber is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Mike Tauber is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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