def ProbMaxPool(images, rnd, targets, numChannels, subsX, startX, strideX, outputsX):
 numImages = images.shape[0]
 assert (targets.shape == (numImages, ((numChannels * outputsX) * outputsX)))
 assert (rnd.shape == images.shape)
 _ConvNet.ProbMaxPool(images.p_mat, rnd.p_mat, targets.p_mat, numChannels, subsX, startX, strideX, outputsX)

def ProbMaxPool ( images , rnd , targets , numChannels , subsX , startX , strideX , outputsX ) : numImages = images . shape [ 0 ] assert ( targets . shape == ( numImages , ( ( numChannels * outputsX ) * outputsX ) ) ) assert ( rnd . shape == images . shape ) _ConvNet . ProbMaxPool ( images . p_mat , rnd . p_mat , targets . p_mat , numChannels , subsX , startX , strideX , outputsX )
images - rnd - numchannels - number of filter/color channels subsx - width of pooling area startx - pixel where pooling starts stridex - stride outputsx - number of pooling sites .