deep learning for computer vision different channels in that depth axis no longer stand for specific colors as in rgb inputratherthey stand for filters filters encode specific aspects of the input dataat high levela single filter could encode the concept "presence of face in the input,for instance in the mnist examplethe first convolution layer takes feature map of size ( and outputs feature map of size ( )it computes filters over its input each of these output channels contains grid of valueswhich is response map of the filter over the inputindicating the response of that filter pattern at different locations in the input (see figure that is what the term feature map meansevery dimension in the depth axis is feature (or filter)and the tensor output[::nis the spatial map of the response of this filter over the input response mapquantifying the presence of the filter' pattern at different locations original input single filter figure the concept of response mapa map of he presence of patt ern at different locations in an input convolutions are defined by two key parameterssize of the patches extracted from the inputs--these are typically or in the examplethey were which is common choice depth of the output feature map--the number of filters computed by the convolution the example started with depth of and ended with depth of in keras conv layersthese parameters are the first arguments passed to the layerconv (output_depth(window_heightwindow_width) convolution works by sliding these windows of size or over the input feature mapstopping at every possible locationand extracting the patch of surrounding features (shape (window_heightwindow_widthinput_depth)each such patch is then transformed (via tensor product with the same learned weight matrixcalled the convolution kernel into vector of shape (output_depth,all of these vectors are then spatially reassembled into output map of shape (heightwidthoutput_depthevery spatial location in the output feature map corresponds to the same location in the input feature map (for examplethe lower-right corner of the output contains information about the lower-right corner of the inputfor instancewith windowsthe vector output[ij:comes from the patch input[ - : + - : + :the full process is detailed in figure licensed to