Hi Jesse,
Thank you for the reply. I guess what you are then saying is when I am
defining the convolutional layer to have the two consecutive filters to use
the same weights (as in the code below), right? Will theano be able to
properly update this (I mean it would need to average the gradient
calculated at the two layers)?
# convolve 2d with separable 1d filters
conv_outx = conv2d(
input=input,
filters=self.W,
filter_shape=filter_shape,
image_shape=image_shape
)
conv_outxy = conv2d(
input=conv_outx,
filters=np.transpose(self.W,(0,1,3,2)),
filter_shape=filter_shape,
image_shape=image_shape
)
terça-feira, 12 de Julho de 2016 às 17:56:53 UTC+1, Jesse Livezey escreveu:
>
> This should be as simple as creating just one shared variable to train and
> then using it in different places in the network.
>
> On Tuesday, July 12, 2016 at 9:48:28 AM UTC-7, André Ribeiro wrote:
>>
>> Hi,
>>
>> I am trying to create a CNN that share weights between 2 consecutive
>> layers. This is particularly interesting if we are looking into separable
>> filters.
>> For example:
>> If we want to approximate a 2d gaussian filter using a CNN, we could
>> potentially just have to learn a 1 layer 1d convolutional network (with k
>> elements) and apply it first in the x direction and then in the y
>> direction, instead of a 1 layer 2d convolutional network (with k^2
>> elements).
>>
>> Any help here?
>>
>
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