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

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