an input then M would be equal to 3/4 the RG and B channels we apply convolution on a kernel K of shape DK cross DK cross M this will give us an output of shape D G cross DG cross 1 if we apply n such kernels on the input then we get an output volume G of shape DG cross DG cross n the convolution operation takes the sum of products of the input and the kernel to return a scalar this operation is continued by sliding the kernel over the input I've explained this concept in detail on my video on convolution neural networks