Let's say I have a tensor (None, 2, 56, 56, 256). Now I want to have my tensor with shape (None, 2, 55, 55, 256) by dropping last col and last row. How can I acheive this using Keras/Tensorflow?
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donto
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Possible duplicate of [Slicing a tensor by using indices in Tensorflow](https://stackoverflow.com/questions/41715511/slicing-a-tensor-by-using-indices-in-tensorflow) – Sharky Jun 09 '19 at 13:17
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In tensorflow we can slice tensors using python slice notation. SO, given a tensor X with shape (20,2,56,56,256) say (as you have described but with a batch size of 20), we can easily slice it taking all but the last 'row' in the 2nd and 3rd dimension as follows:
X[:,:,:-1,:-1,:]
Note the use of :-1 to denote "everything before the last 'row'".
Given this know-how about slicing the tensor in tensorflow we just need to adapt it for keras. We could, of course, write a full blown custom layer implementing this (or possibly even find one out there someone else has written - I've not looked but slicing is pretty common so suspect someone has written something somewhere!).
However, for something as simple as this, I'd advocate just using a Lambda layer which we can define as follows:
my_slicing_layer = Lambda(lambda x: x[:,:,:-1,:-1,:], name='slice')
And can use in our keras models as normal:
my_model = Sequential([
Activation('relu', input_shape=(2,56,56,256)),
my_slicing_layer
])
Stewart_R
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Ah, I didn't know that we can treat a tensor in Tensorflow like usual array. Thanks ! – donto Jun 09 '19 at 14:33