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nbro
nbro
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LTSM: clearing up some question Do I need LSTM units everywhere in the network?

I have recently begun researching LSTM networks, as I have finished my GA and am looking to progress to something more difficult. I I believe I am using the classic LSTM (if that makes any sense) and have a few questions:.

Do I need LSTM cellsunits everywhere in all cell locationthe network? Or wouldFor example, can I only use them inLSTM units for the inputfirst and output cellslast layer and use simple feedforward for the hidden neuronsunits everywhere else?

How do I go about implementing Biasbias values into aan LSTM?

Assuming I create a Networknetwork that predicts the next few words of a sentence, does that mean my outputs should be every possible word that the network could conceivably use?

Any help would be appreciated. Thanks!

LTSM: clearing up some question

I have recently begun researching LSTM networks as I have finished my GA and am looking to progress to something more difficult. I believe I am using the classic LSTM (if that makes any sense) and have a few questions:

Do I need LSTM cells in all cell location? Or would I only use them in the input and output cells and use simple feedforward for the hidden neurons?

How do I go about implementing Bias values into a LSTM?

Assuming I create a Network that predicts the next few words of a sentence, does that mean my outputs should be every possible word that the network could conceivably use?

Any help would be appreciated. Thanks!

Do I need LSTM units everywhere in the network?

I have recently begun researching LSTM networks, as I have finished my GA and am looking to progress to something more difficult. I believe I am using the classic LSTM (if that makes any sense) and have a few questions.

Do I need LSTM units everywhere in the network? For example, can I only use LSTM units for the first and last layer and use feedforward units everywhere else?

How do I go about implementing bias values into an LSTM?

Assuming I create a network that predicts the next few words of a sentence, does that mean my outputs should be every possible word that the network could conceivably use?

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edit approved Jun 5, 2019 at 19:34
xrfxlp
edit approved Jun 5, 2019 at 19:34
xrfxlp
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thank for taking the time to read my questions. II have recently begun researching LSTM networks as I have finished my GA and am looking to progress to something more difficult. I believe I am using the classic LSTM (if that makes any sense) and have a few questions:

Do I need LSTM cells in all cell location? Or would I only use them in the input and output cells and use simple feedforward for the hidden neurons?

How do I go about implementing Bias values into a LSTM?

Assuming I create a Network that predicts the next few words of a sentence, does that mean my outputs should be every possible word that the network could conceivably use?

Any and all information is helpfulhelp would be appreciated. Thanks!

thank for taking the time to read my questions. I have recently begun researching LSTM networks as I have finished my GA and am looking to progress to something more difficult. I believe I am using the classic LSTM (if that makes any sense) and have a few questions:

Do I need LSTM cells in all cell location? Or would I only use them in the input and output cells and use simple feedforward for the hidden neurons?

How do I go about implementing Bias values into a LSTM?

Assuming I create a Network that predicts the next few words of a sentence, does that mean my outputs should be every possible word that the network could conceivably use?

Any and all information is helpful!

I have recently begun researching LSTM networks as I have finished my GA and am looking to progress to something more difficult. I believe I am using the classic LSTM (if that makes any sense) and have a few questions:

Do I need LSTM cells in all cell location? Or would I only use them in the input and output cells and use simple feedforward for the hidden neurons?

How do I go about implementing Bias values into a LSTM?

Assuming I create a Network that predicts the next few words of a sentence, does that mean my outputs should be every possible word that the network could conceivably use?

Any help would be appreciated. Thanks!

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user11376058
user11376058
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LTSM: clearing up some question

thank for taking the time to read my questions. I have recently begun researching LSTM networks as I have finished my GA and am looking to progress to something more difficult. I believe I am using the classic LSTM (if that makes any sense) and have a few questions:

Do I need LSTM cells in all cell location? Or would I only use them in the input and output cells and use simple feedforward for the hidden neurons?

How do I go about implementing Bias values into a LSTM?

Assuming I create a Network that predicts the next few words of a sentence, does that mean my outputs should be every possible word that the network could conceivably use?

Any and all information is helpful!