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]. For more options, visit https://groups.google.com/d/optout.
