7 ms·

Quick question about your code for the conv net, why do you resize the images down to 32x32? I thought one of the big features of conv nets was the fact that th

by nrmn 13y ago

Quick question about your code for the conv net, why do you resize the images down to 32x32? I thought one of the big features of conv nets was the fact that they input does not have to be the same, it just slides a window around the image. Am I complete wrong with this one? Would you be willing to maybe print out the weights for each layer? I'd be interested to see what features your conv net is capturing.

kkjkok 13y ago
I was (and still am) trying to use an already trained CIFAR10 net in a similar manner to DeCAF/ImageNet. Because CIFAR10 operates on 32x32 color images, I did the same thing for the input of the DeCAF experiment. As far as I know, the inputs to the network need to be identical between train/test sets , though they can be 0-padded/color filled to make the dimensions match, it may affect results - haven't tried anything but scaling personally. I am pretty sure there are 2 sets of scaling happening for my DeCAF experiment: down to 32x32 with convert, then UP to 512x512, then the center 256x256 is pulled out. I think this may affect my results a little :) The plan is to operate on 32x32 data for now, then try scaling up the input images or just scaling to 512x512 to see how input data size/resolution affects the DeCAF/pylearn2 classification result, either positively or negatively. As far as network weights, I haven't tried to print/plot the DeCAF weights yet (though there are images in the DeCAF paper itself). For pure pylearn2 networks, there is a neat utility called show_weights.py in pylearn2/scripts. Another method, which does do "chopping" is http://www.stanford.edu/~acoates/papers/coatesng_nntot2012.pdf http://www.stanford.edu/~acoates/papers/coatesng_nntot2012.p... - which is a little different than what I am currently trying.

7 ms·

Quick question about your code for the conv net, why do you resize the images down to 32x32? I thought one of the big features of conv nets was the fact that th

by nrmn 13y ago

kkjkok 13y ago
I was (and still am) trying to use an already trained CIFAR10 net in a similar manner to DeCAF/ImageNet. Because CIFAR10 operates on 32x32 color images, I did the same thing for the input of the DeCAF experiment. As far as I know, the inputs to the network need to be identical between train/test sets , though they can be 0-padded/color filled to make the dimensions match, it may affect results - haven't tried anything but scaling personally. I am pretty sure there are 2 sets of scaling happening for my DeCAF experiment: down to 32x32 with convert, then UP to 512x512, then the center 256x256 is pulled out. I think this may affect my results a little :) The plan is to operate on 32x32 data for now, then try scaling up the input images or just scaling to 512x512 to see how input data size/resolution affects the DeCAF/pylearn2 classification result, either positively or negatively. As far as network weights, I haven't tried to print/plot the DeCAF weights yet (though there are images in the DeCAF paper itself). For pure pylearn2 networks, there is a neat utility called show_weights.py in pylearn2/scripts. Another method, which does do "chopping" is http://www.stanford.edu/~acoates/papers/coatesng_nntot2012.pdf http://www.stanford.edu/~acoates/papers/coatesng_nntot2012.p... - which is a little different than what I am currently trying.