spatial features and channel-wise featureswhich is more efficient than learning them jointly more-complex versions of an inception module are also possibletypically involving pooling operationsdifferent spatial convolution sizes (for example instead of on some branches)and branches without spatial convolution (only convolutionan example of such moduletaken from inception is shown in figure output concatenate conv strides= conv strides= conv strides= conv conv conv avgpool strides= conv figure module an inception input the purpose of convolut ions you already know that convolutions extract spatial patches around every tile in an input tensor and apply the same transformation to each patch an edge case is when the patches extracted consist of single tile the convolution operation then becomes equivalent to running each tile vector through dense layerit will compute features that mix together information from the channels of the input tensorbut it won' mix information across space (because it' looking at one tile at timesuch convolutions (also called pointwise convolutionsare featured in inception moduleswhere they contribute to factoring out channel-wise feature learning and spacewise feature learning-- reasonable thing to do if you assume that each channel is highly autocorrelated across spacebut different channels may not be highly correlated with each other here' how you' implement the module featured in figure using the functional api this example assumes the existence of input tensor xlicensed to