Unflattening Layer in Keras
I would like to create a simple Keras neural network that accepts an input matrix of dimension (rows, columns) = (n, m), flattens the matrix to a dimension (n*m, 1), sends the flattened matrix through a number of arbitrary layers, and in the final layer, once more unflattens the matrix to a dimension of (n, m) before releasing this final matrix as an output.
The issue I'm having is that I haven't found any documentation for an Unflatten layer at the keras.io page, and I'm wondering whether there is a reason that such a seemingly standard common use layer doesn't exist. Is there a much more natural and easy way to do what I'm proposing?
python keras neural-network reshape flatten
edited Nov 16 '18 at 7:59
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I would like to create a simple Keras neural network that accepts an input matrix of dimension (rows, columns) = (n, m), flattens the matrix to a dimension (n*m, 1), sends the flattened matrix through a number of arbitrary layers, and in the final layer, once more unflattens the matrix to a dimension of (n, m) before releasing this final matrix as an output.
The issue I'm having is that I haven't found any documentation for an Unflatten layer at the keras.io page, and I'm wondering whether there is a reason that such a seemingly standard common use layer doesn't exist. Is there a much more natural and easy way to do what I'm proposing?
python keras neural-network reshape flatten
edited Nov 16 '18 at 7:59
today
11.3k22239
asked Nov 16 '18 at 0:15
TQMTQM
194
1
did you consider keras.layers.reshape?
– fr_andres
Nov 16 '18 at 0:22
If the answer resolved your issue, kindly accept it by clicking on the checkmark next to the answer to mark it as "answered" - see What should I do when someone answers my question?
– today
Nov 26 '18 at 15:55
add a comment |
I would like to create a simple Keras neural network that accepts an input matrix of dimension (rows, columns) = (n, m), flattens the matrix to a dimension (n*m, 1), sends the flattened matrix through a number of arbitrary layers, and in the final layer, once more unflattens the matrix to a dimension of (n, m) before releasing this final matrix as an output.
The issue I'm having is that I haven't found any documentation for an Unflatten layer at the keras.io page, and I'm wondering whether there is a reason that such a seemingly standard common use layer doesn't exist. Is there a much more natural and easy way to do what I'm proposing?
python keras neural-network reshape flatten
edited Nov 16 '18 at 7:59
today
11.3k22239
asked Nov 16 '18 at 0:15
TQMTQM
194
I would like to create a simple Keras neural network that accepts an input matrix of dimension (rows, columns) = (n, m), flattens the matrix to a dimension (n*m, 1), sends the flattened matrix through a number of arbitrary layers, and in the final layer, once more unflattens the matrix to a dimension of (n, m) before releasing this final matrix as an output.
The issue I'm having is that I haven't found any documentation for an Unflatten layer at the keras.io page, and I'm wondering whether there is a reason that such a seemingly standard common use layer doesn't exist. Is there a much more natural and easy way to do what I'm proposing?
python keras neural-network reshape flatten
python keras neural-network reshape flatten
edited Nov 16 '18 at 7:59
today
11.3k22239
asked Nov 16 '18 at 0:15
TQMTQM
194
edited Nov 16 '18 at 7:59
today
11.3k22239
asked Nov 16 '18 at 0:15
TQMTQM
194
edited Nov 16 '18 at 7:59
today
11.3k22239
edited Nov 16 '18 at 7:59
today
11.3k22239
edited Nov 16 '18 at 7:59
today
11.3k22239
11.3k22239
asked Nov 16 '18 at 0:15
TQMTQM
194
asked Nov 16 '18 at 0:15
TQMTQM
194
asked Nov 16 '18 at 0:15
TQMTQM
194
194
1
did you consider keras.layers.reshape?
– fr_andres
Nov 16 '18 at 0:22
If the answer resolved your issue, kindly accept it by clicking on the checkmark next to the answer to mark it as "answered" - see What should I do when someone answers my question?
– today
Nov 26 '18 at 15:55
add a comment |
1
did you consider keras.layers.reshape?
– fr_andres
Nov 16 '18 at 0:22
If the answer resolved your issue, kindly accept it by clicking on the checkmark next to the answer to mark it as "answered" - see What should I do when someone answers my question?
– today
Nov 26 '18 at 15:55
1
1
did you consider keras.layers.reshape?
– fr_andres
Nov 16 '18 at 0:22
did you consider keras.layers.reshape?
– fr_andres
Nov 16 '18 at 0:22
If the answer resolved your issue, kindly accept it by clicking on the checkmark next to the answer to mark it as "answered" - see What should I do when someone answers my question?
– today
Nov 26 '18 at 15:55
If the answer resolved your issue, kindly accept it by clicking on the checkmark next to the answer to mark it as "answered" - see What should I do when someone answers my question?
– today
Nov 26 '18 at 15:55
add a comment |
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You can use the Reshape layer for this purpose. It accepts the desired output shape as its argument and would reshape the input tensor to that shape. For example:
from keras.layers import Reshape
rsh_inp = Reshape((n*m, 1))(inp) # if don't want the last axis with dimension 1, you can also use Flatten layer
# rsh_inp goes through a number of arbitrary layers ...
# reshape back the output
out = Reshape((n,m))(out_rsh_inp)
answered Nov 16 '18 at 7:57
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You can use the Reshape layer for this purpose. It accepts the desired output shape as its argument and would reshape the input tensor to that shape. For example:
from keras.layers import Reshape
rsh_inp = Reshape((n*m, 1))(inp) # if don't want the last axis with dimension 1, you can also use Flatten layer
# rsh_inp goes through a number of arbitrary layers ...
# reshape back the output
out = Reshape((n,m))(out_rsh_inp)
answered Nov 16 '18 at 7:57
todaytoday
11.3k22239
add a comment |
You can use the Reshape layer for this purpose. It accepts the desired output shape as its argument and would reshape the input tensor to that shape. For example:
from keras.layers import Reshape
rsh_inp = Reshape((n*m, 1))(inp) # if don't want the last axis with dimension 1, you can also use Flatten layer
# rsh_inp goes through a number of arbitrary layers ...
# reshape back the output
out = Reshape((n,m))(out_rsh_inp)
answered Nov 16 '18 at 7:57
todaytoday
11.3k22239
add a comment |
You can use the Reshape layer for this purpose. It accepts the desired output shape as its argument and would reshape the input tensor to that shape. For example:
from keras.layers import Reshape
rsh_inp = Reshape((n*m, 1))(inp) # if don't want the last axis with dimension 1, you can also use Flatten layer
# rsh_inp goes through a number of arbitrary layers ...
# reshape back the output
out = Reshape((n,m))(out_rsh_inp)
answered Nov 16 '18 at 7:57
todaytoday
11.3k22239
You can use the Reshape layer for this purpose. It accepts the desired output shape as its argument and would reshape the input tensor to that shape. For example:
from keras.layers import Reshape
rsh_inp = Reshape((n*m, 1))(inp) # if don't want the last axis with dimension 1, you can also use Flatten layer
# rsh_inp goes through a number of arbitrary layers ...
# reshape back the output
out = Reshape((n,m))(out_rsh_inp)
answered Nov 16 '18 at 7:57
todaytoday
11.3k22239
answered Nov 16 '18 at 7:57
todaytoday
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answered Nov 16 '18 at 7:57
todaytoday
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answered Nov 16 '18 at 7:57
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1
did you consider keras.layers.reshape?
– fr_andres
Nov 16 '18 at 0:22
If the answer resolved your issue, kindly accept it by clicking on the checkmark next to the answer to mark it as "answered" - see What should I do when someone answers my question?
– today
Nov 26 '18 at 15:55