What is the difference between “Adding more LSTM layers” or “Adding more units on existence...
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What is the difference between “Adding more LSTM layers” or “Adding more units on existence layers”?
The model of LSTM with more than one unitA practical max-depth of LSTM layers, when trying to overfit?Any difference between adding epochs and duplicating data for neural nets?LSTM Model for predicting the minutely seasonal data of the dayWhy use two LSTM layers one after another?Learning a non-linear mapping using LSTM units: encountering overfittingWhat does GlobalMaxPooling1D() do to output of LSTM unit in Keras?input_dim for Dense Layer after LSTM layers KerasHow to design a LSTM network with different number of input/output units?What is the difference? “Adding more LSTM layers” or “Increasing epochs”?
$begingroup$
What is the difference if we add more LSTM layers or just increase the units of existence layers? Which one is preferred with which situation?
lstm
asked 18 hours ago
user145959user145959
657
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add a comment |
$begingroup$
What is the difference if we add more LSTM layers or just increase the units of existence layers? Which one is preferred with which situation?
lstm
asked 18 hours ago
user145959user145959
657
$endgroup$
add a comment |
$begingroup$
What is the difference if we add more LSTM layers or just increase the units of existence layers? Which one is preferred with which situation?
lstm
asked 18 hours ago
user145959user145959
657
$endgroup$
What is the difference if we add more LSTM layers or just increase the units of existence layers? Which one is preferred with which situation?
lstm
lstm
asked 18 hours ago
user145959user145959
657
asked 18 hours ago
user145959user145959
657
asked 18 hours ago
user145959user145959
657
asked 18 hours ago
user145959user145959
657
asked 18 hours ago
user145959user145959
657
657
add a comment |
add a comment |
1 Answer
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When you add layers you are increasing the depth of the neural network. If you add more units to the existing layers, you are increasing the width.
In terms of hyperparameters selection, the best I can recommend is to try both and see which one gives you the best performance. Take into account considerations like over fitting, which may happen specially when you increase the complexity of a model.
answered 6 hours ago
EscachatorEscachator
248111
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1 Answer
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active
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1 Answer
1
active
oldest
votes
active
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active
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votes
$begingroup$
When you add layers you are increasing the depth of the neural network. If you add more units to the existing layers, you are increasing the width.
In terms of hyperparameters selection, the best I can recommend is to try both and see which one gives you the best performance. Take into account considerations like over fitting, which may happen specially when you increase the complexity of a model.
answered 6 hours ago
EscachatorEscachator
248111
$endgroup$
add a comment |
$begingroup$
When you add layers you are increasing the depth of the neural network. If you add more units to the existing layers, you are increasing the width.
In terms of hyperparameters selection, the best I can recommend is to try both and see which one gives you the best performance. Take into account considerations like over fitting, which may happen specially when you increase the complexity of a model.
answered 6 hours ago
EscachatorEscachator
248111
$endgroup$
add a comment |
$begingroup$
When you add layers you are increasing the depth of the neural network. If you add more units to the existing layers, you are increasing the width.
In terms of hyperparameters selection, the best I can recommend is to try both and see which one gives you the best performance. Take into account considerations like over fitting, which may happen specially when you increase the complexity of a model.
answered 6 hours ago
EscachatorEscachator
248111
$endgroup$
When you add layers you are increasing the depth of the neural network. If you add more units to the existing layers, you are increasing the width.
In terms of hyperparameters selection, the best I can recommend is to try both and see which one gives you the best performance. Take into account considerations like over fitting, which may happen specially when you increase the complexity of a model.
answered 6 hours ago
EscachatorEscachator
248111
answered 6 hours ago
EscachatorEscachator
248111
answered 6 hours ago
EscachatorEscachator
248111
answered 6 hours ago
EscachatorEscachator
248111
248111
add a comment |
add a comment |
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