Thank you very much. It is right but seems difficult to achieve....... 在 2016年12月21日星期三 UTC+8上午5:57:08，Pascal Lamblin写道： > > Hi, > > Not all implementations of convolutions used in Theano can be modified. > For instance, cuDNN ones are defined externally, and we do not have the > source code. > > In your case, you could probably copy / edit the CorrMM version, and add > the normalization of patches before the call to im2col. You may actually > have to reverse the role of image and kernel for that. > Also, you would have to redefine the gradients as well. > > On Tue, Dec 20, 2016, Eric Huang wrote: > > > > > > I wanna to normalize (substract mean, divide standard deviation) input > in > > each patch during convolution. > > > > > > For example, > > Input: (1,224, 224) > > kernel: (64,5,5) > > stride: 1 > > During the calculation of the first feature map, I'd like to do the > > following operations for each position: > > feaMap[0, 0, 0] = conv( (Input[0, 0:5, 0:5] - mean)/std, kernel[0, :, :] > ) > > feaMap[0, 0, 1] = conv( (Input[0, 0:5, 1:6] - mean)/std, kernel[0, :, :] > ) > > feaMap[0, 0, 2] = conv( (Input[0, 0:5, 2:7] - mean)/std, kernel[0, :, :] > ) > > > > ....... > > where mean is a fix matrix of shape (1, 5, 5), std is a fix matrix of > shape > > (1, 5, 5). > > > > It is great cost if I write my own code to extract each patch, do > > normalization then conduct convolution. So I just want to edit the conv > > function to add "substract mean", "divide standard deviation" operation. > > But it seems difficult to edit the conv function to achieve my goal. > > > > How can I achieve it ? Any idea? > > > > -- > > > > --- > > You received this message because you are subscribed to the Google > Groups "theano-users" group. > > To unsubscribe from this group and stop receiving emails from it, send > an email to [email protected] <javascript:>. > > For more options, visit https://groups.google.com/d/optout. > > > -- > Pascal >
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