Can somebody explain me the the following parameters of Keras LSTM layer The Next CEO of Stack Overflow2019 Community Moderator ElectionClarification on the Keras Recurrent Unit CellReformatting data for future time series prediction LSTM(Keras)TypeError: unsupported operand type(s) for %: 'int' and 'NoneType'(Stateful LSTM Keras)LSTM not learning with extra nontemporal data added after LSTM layer - KerasBatch Size of Stateful LSTM in kerasLSTM with teacher forcing - NN fails to predict the sequenceWhat does GlobalMaxPooling1D() do to output of LSTM unit in Keras?Can't understand Output shape of a Dense layer - kerasHow to implement keras LSTM time seriesWhat is the difference between “Adding more LSTM layers” or “Adding more units on existence layers”?
How can I get through very long and very dry, but also very useful technical documents when learning a new tool?
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Can somebody explain me the the following parameters of Keras LSTM layer
The Next CEO of Stack Overflow2019 Community Moderator ElectionClarification on the Keras Recurrent Unit CellReformatting data for future time series prediction LSTM(Keras)TypeError: unsupported operand type(s) for %: 'int' and 'NoneType'(Stateful LSTM Keras)LSTM not learning with extra nontemporal data added after LSTM layer - KerasBatch Size of Stateful LSTM in kerasLSTM with teacher forcing - NN fails to predict the sequenceWhat does GlobalMaxPooling1D() do to output of LSTM unit in Keras?Can't understand Output shape of a Dense layer - kerasHow to implement keras LSTM time seriesWhat is the difference between “Adding more LSTM layers” or “Adding more units on existence layers”?
$begingroup$
keras.layers.LSTM(units,stateful=False,unroll=False)
What units,stateful and unroll represents here??
deep-learning keras lstm
asked Mar 22 at 19:02
surajsuraj
91
New contributor
suraj 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 |
$begingroup$
keras.layers.LSTM(units,stateful=False,unroll=False)
What units,stateful and unroll represents here??
deep-learning keras lstm
asked Mar 22 at 19:02
surajsuraj
91
New contributor
suraj is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
1
$begingroup$
You may want to see here.
$endgroup$
– Media
Mar 22 at 19:30
$begingroup$
These are explained in Keras documentation, is that documentation ambiguous ?
$endgroup$
– Shamit Verma
Mar 23 at 8:56
$begingroup$
No docs are not ambiguous but i am getting it from there thats y i asked it.
$endgroup$
– suraj
Mar 23 at 15:41
add a comment |
$begingroup$
keras.layers.LSTM(units,stateful=False,unroll=False)
What units,stateful and unroll represents here??
deep-learning keras lstm
asked Mar 22 at 19:02
surajsuraj
91
New contributor
suraj is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
keras.layers.LSTM(units,stateful=False,unroll=False)
What units,stateful and unroll represents here??
deep-learning keras lstm
deep-learning keras lstm
asked Mar 22 at 19:02
surajsuraj
91
New contributor
suraj is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked Mar 22 at 19:02
surajsuraj
91
New contributor
suraj is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked Mar 22 at 19:02
surajsuraj
91
New contributor
suraj is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked Mar 22 at 19:02
surajsuraj
91
asked Mar 22 at 19:02
surajsuraj
91
91
New contributor
suraj is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
suraj is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
suraj is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
1
$begingroup$
You may want to see here.
$endgroup$
– Media
Mar 22 at 19:30
$begingroup$
These are explained in Keras documentation, is that documentation ambiguous ?
$endgroup$
– Shamit Verma
Mar 23 at 8:56
$begingroup$
No docs are not ambiguous but i am getting it from there thats y i asked it.
$endgroup$
– suraj
Mar 23 at 15:41
add a comment |
1
$begingroup$
You may want to see here.
$endgroup$
– Media
Mar 22 at 19:30
$begingroup$
These are explained in Keras documentation, is that documentation ambiguous ?
$endgroup$
– Shamit Verma
Mar 23 at 8:56
$begingroup$
No docs are not ambiguous but i am getting it from there thats y i asked it.
$endgroup$
– suraj
Mar 23 at 15:41
1
1
$begingroup$
You may want to see here.
$endgroup$
– Media
Mar 22 at 19:30
$begingroup$
You may want to see here.
$endgroup$
– Media
Mar 22 at 19:30
$begingroup$
These are explained in Keras documentation, is that documentation ambiguous ?
$endgroup$
– Shamit Verma
Mar 23 at 8:56
$begingroup$
These are explained in Keras documentation, is that documentation ambiguous ?
$endgroup$
– Shamit Verma
Mar 23 at 8:56
$begingroup$
No docs are not ambiguous but i am getting it from there thats y i asked it.
$endgroup$
– suraj
Mar 23 at 15:41
$begingroup$
No docs are not ambiguous but i am getting it from there thats y i asked it.
$endgroup$
– suraj
Mar 23 at 15:41
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
units : According to the official docs, it defines the output dimensionality. In simple words, the number of LSTM units which will be used.
units: Positive integer, dimensionality of the output space.
Units are nothing but the LSTM cells which will be used to process the inputs.
stateful :
According to the docs :
stateful: Boolean (default False). If True, the last state for each sample at index i in a batch will be used as initial state for the sample of index i in the following batch.
If this argument is set to False then, the states or the memory of the LSTM cell will get reset at every sample which is passed through it. It follows this manner:
- Take a sample from the batch.
- The sample will pass through the LSTM and produce a state vector ( conditional memory ).
- The output is given
- If
stateful=Truethen, the state vector will be used as the initial state for the 2nd sample. - If
stateful=Falsethen, a new state vector will be used for the 2nd sample.
Hence, the memory should migrate from one sample to another is decided by the stateful argument.
unroll :
According to the docs,
unroll: Boolean (default False). If True, the network will be unrolled, else a symbolic loop will be used. Unrolling can speed-up a RNN, although it tends to be more memory-intensive. Unrolling is only suitable for short sequences.
It can thought as :
RNNs, once unfolded in time, can be seen as very deep feedforward networks in which all the layers share the same weights. ( Deep Learning, Nature, 2015 )
You can read an excellent article here.
The unrolling will the make the LSTM, a deep feed forward network which has shared weights. You can see and understand through this figure.
answered Mar 23 at 8:27
Shubham PanchalShubham Panchal
36118
$endgroup$
$begingroup$
Thank you so much Subham ✌✌👍
$endgroup$
– suraj
Mar 23 at 15:41
add a comment |
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1 Answer
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1 Answer
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$begingroup$
units : According to the official docs, it defines the output dimensionality. In simple words, the number of LSTM units which will be used.
units: Positive integer, dimensionality of the output space.
Units are nothing but the LSTM cells which will be used to process the inputs.
stateful :
According to the docs :
stateful: Boolean (default False). If True, the last state for each sample at index i in a batch will be used as initial state for the sample of index i in the following batch.
If this argument is set to False then, the states or the memory of the LSTM cell will get reset at every sample which is passed through it. It follows this manner:
- Take a sample from the batch.
- The sample will pass through the LSTM and produce a state vector ( conditional memory ).
- The output is given
- If
stateful=Truethen, the state vector will be used as the initial state for the 2nd sample. - If
stateful=Falsethen, a new state vector will be used for the 2nd sample.
Hence, the memory should migrate from one sample to another is decided by the stateful argument.
unroll :
According to the docs,
unroll: Boolean (default False). If True, the network will be unrolled, else a symbolic loop will be used. Unrolling can speed-up a RNN, although it tends to be more memory-intensive. Unrolling is only suitable for short sequences.
It can thought as :
RNNs, once unfolded in time, can be seen as very deep feedforward networks in which all the layers share the same weights. ( Deep Learning, Nature, 2015 )
You can read an excellent article here.
The unrolling will the make the LSTM, a deep feed forward network which has shared weights. You can see and understand through this figure.
answered Mar 23 at 8:27
Shubham PanchalShubham Panchal
36118
$endgroup$
$begingroup$
Thank you so much Subham ✌✌👍
$endgroup$
– suraj
Mar 23 at 15:41
add a comment |
$begingroup$
units : According to the official docs, it defines the output dimensionality. In simple words, the number of LSTM units which will be used.
units: Positive integer, dimensionality of the output space.
Units are nothing but the LSTM cells which will be used to process the inputs.
stateful :
According to the docs :
stateful: Boolean (default False). If True, the last state for each sample at index i in a batch will be used as initial state for the sample of index i in the following batch.
If this argument is set to False then, the states or the memory of the LSTM cell will get reset at every sample which is passed through it. It follows this manner:
- Take a sample from the batch.
- The sample will pass through the LSTM and produce a state vector ( conditional memory ).
- The output is given
- If
stateful=Truethen, the state vector will be used as the initial state for the 2nd sample. - If
stateful=Falsethen, a new state vector will be used for the 2nd sample.
Hence, the memory should migrate from one sample to another is decided by the stateful argument.
unroll :
According to the docs,
unroll: Boolean (default False). If True, the network will be unrolled, else a symbolic loop will be used. Unrolling can speed-up a RNN, although it tends to be more memory-intensive. Unrolling is only suitable for short sequences.
It can thought as :
RNNs, once unfolded in time, can be seen as very deep feedforward networks in which all the layers share the same weights. ( Deep Learning, Nature, 2015 )
You can read an excellent article here.
The unrolling will the make the LSTM, a deep feed forward network which has shared weights. You can see and understand through this figure.
answered Mar 23 at 8:27
Shubham PanchalShubham Panchal
36118
$endgroup$
$begingroup$
Thank you so much Subham ✌✌👍
$endgroup$
– suraj
Mar 23 at 15:41
add a comment |
$begingroup$
units : According to the official docs, it defines the output dimensionality. In simple words, the number of LSTM units which will be used.
units: Positive integer, dimensionality of the output space.
Units are nothing but the LSTM cells which will be used to process the inputs.
stateful :
According to the docs :
stateful: Boolean (default False). If True, the last state for each sample at index i in a batch will be used as initial state for the sample of index i in the following batch.
If this argument is set to False then, the states or the memory of the LSTM cell will get reset at every sample which is passed through it. It follows this manner:
- Take a sample from the batch.
- The sample will pass through the LSTM and produce a state vector ( conditional memory ).
- The output is given
- If
stateful=Truethen, the state vector will be used as the initial state for the 2nd sample. - If
stateful=Falsethen, a new state vector will be used for the 2nd sample.
Hence, the memory should migrate from one sample to another is decided by the stateful argument.
unroll :
According to the docs,
unroll: Boolean (default False). If True, the network will be unrolled, else a symbolic loop will be used. Unrolling can speed-up a RNN, although it tends to be more memory-intensive. Unrolling is only suitable for short sequences.
It can thought as :
RNNs, once unfolded in time, can be seen as very deep feedforward networks in which all the layers share the same weights. ( Deep Learning, Nature, 2015 )
You can read an excellent article here.
The unrolling will the make the LSTM, a deep feed forward network which has shared weights. You can see and understand through this figure.
answered Mar 23 at 8:27
Shubham PanchalShubham Panchal
36118
$endgroup$
units : According to the official docs, it defines the output dimensionality. In simple words, the number of LSTM units which will be used.
units: Positive integer, dimensionality of the output space.
Units are nothing but the LSTM cells which will be used to process the inputs.
stateful :
According to the docs :
stateful: Boolean (default False). If True, the last state for each sample at index i in a batch will be used as initial state for the sample of index i in the following batch.
If this argument is set to False then, the states or the memory of the LSTM cell will get reset at every sample which is passed through it. It follows this manner:
- Take a sample from the batch.
- The sample will pass through the LSTM and produce a state vector ( conditional memory ).
- The output is given
- If
stateful=Truethen, the state vector will be used as the initial state for the 2nd sample. - If
stateful=Falsethen, a new state vector will be used for the 2nd sample.
Hence, the memory should migrate from one sample to another is decided by the stateful argument.
unroll :
According to the docs,
unroll: Boolean (default False). If True, the network will be unrolled, else a symbolic loop will be used. Unrolling can speed-up a RNN, although it tends to be more memory-intensive. Unrolling is only suitable for short sequences.
It can thought as :
RNNs, once unfolded in time, can be seen as very deep feedforward networks in which all the layers share the same weights. ( Deep Learning, Nature, 2015 )
You can read an excellent article here.
The unrolling will the make the LSTM, a deep feed forward network which has shared weights. You can see and understand through this figure.
answered Mar 23 at 8:27
Shubham PanchalShubham Panchal
36118
answered Mar 23 at 8:27
Shubham PanchalShubham Panchal
36118
answered Mar 23 at 8:27
Shubham PanchalShubham Panchal
36118
answered Mar 23 at 8:27
Shubham PanchalShubham Panchal
36118
36118
$begingroup$
Thank you so much Subham ✌✌👍
$endgroup$
– suraj
Mar 23 at 15:41
add a comment |
$begingroup$
Thank you so much Subham ✌✌👍
$endgroup$
– suraj
Mar 23 at 15:41
$begingroup$
Thank you so much Subham ✌✌👍
$endgroup$
– suraj
Mar 23 at 15:41
$begingroup$
Thank you so much Subham ✌✌👍
$endgroup$
– suraj
Mar 23 at 15:41
add a comment |
suraj is a new contributor. Be nice, and check out our Code of Conduct.
suraj is a new contributor. Be nice, and check out our Code of Conduct.
suraj is a new contributor. Be nice, and check out our Code of Conduct.
suraj is a new contributor. Be nice, and check out our Code of Conduct.
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1
$begingroup$
You may want to see here.
$endgroup$
– Media
Mar 22 at 19:30
$begingroup$
These are explained in Keras documentation, is that documentation ambiguous ?
$endgroup$
– Shamit Verma
Mar 23 at 8:56
$begingroup$
No docs are not ambiguous but i am getting it from there thats y i asked it.
$endgroup$
– suraj
Mar 23 at 15:41