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Is it always better to using stacked LSTM than single LSTM?
2019 Community Moderator ElectionHyperparameter search for LSTM-RNN using Keras (Python)Is finetuning from a pretrained model always better than training from scratch?Why mini batch size is better than one single “batch” with all training data?Convnet training error does not decreaseLSTM Validation MSE always lower than Train MSEThe model of LSTM with more than one unitLSTM Implementation using tensorflow (anaconda)Using RNN (LSTM) for Gesture Recognition SystemUsing t-SNE to track progress of a word vector embedding model. Pitfalls?Why do recurrent layers work better than simple feed-forward networks?
$begingroup$
I am currently studying LSTM and RNNs.
I came across several concepts like Multidimensional LSTM and Stacked LSTM.
I have used Stacked LSTM and it gives me a better performance than single LSTM. As per my understanding, if I increase the depth of LSTM, the number of hidden units also increases. It means overfitting, right? Then why am I getting better results?
[Note: I have used BatchNorm and Dropout after every stack of LSTM ]
machine-learning deep-learning lstm rnn machine-learning-model
asked Mar 25 at 18:42
DukeLoverDukeLover
717
$endgroup$
add a comment |
$begingroup$
I am currently studying LSTM and RNNs.
I came across several concepts like Multidimensional LSTM and Stacked LSTM.
I have used Stacked LSTM and it gives me a better performance than single LSTM. As per my understanding, if I increase the depth of LSTM, the number of hidden units also increases. It means overfitting, right? Then why am I getting better results?
[Note: I have used BatchNorm and Dropout after every stack of LSTM ]
machine-learning deep-learning lstm rnn machine-learning-model
asked Mar 25 at 18:42
DukeLoverDukeLover
717
$endgroup$
add a comment |
$begingroup$
I am currently studying LSTM and RNNs.
I came across several concepts like Multidimensional LSTM and Stacked LSTM.
I have used Stacked LSTM and it gives me a better performance than single LSTM. As per my understanding, if I increase the depth of LSTM, the number of hidden units also increases. It means overfitting, right? Then why am I getting better results?
[Note: I have used BatchNorm and Dropout after every stack of LSTM ]
machine-learning deep-learning lstm rnn machine-learning-model
asked Mar 25 at 18:42
DukeLoverDukeLover
717
$endgroup$
I am currently studying LSTM and RNNs.
I came across several concepts like Multidimensional LSTM and Stacked LSTM.
I have used Stacked LSTM and it gives me a better performance than single LSTM. As per my understanding, if I increase the depth of LSTM, the number of hidden units also increases. It means overfitting, right? Then why am I getting better results?
[Note: I have used BatchNorm and Dropout after every stack of LSTM ]
machine-learning deep-learning lstm rnn machine-learning-model
machine-learning deep-learning lstm rnn machine-learning-model
asked Mar 25 at 18:42
DukeLoverDukeLover
717
asked Mar 25 at 18:42
DukeLoverDukeLover
717
asked Mar 25 at 18:42
DukeLoverDukeLover
717
asked Mar 25 at 18:42
DukeLoverDukeLover
717
asked Mar 25 at 18:42
DukeLoverDukeLover
717
717
add a comment |
add a comment |
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