CNN architecture

A CNN consists of multiple layers of convolution and pooling. A convolution layers uses a set (multiple kernels) to learn from its input. The output is fed into the pooling layers which has no weigh and bias like the convolution layer as it has nothing to learn. Pooling later submarines the output of a convolution layer by dividing the input matrix into a set of 2x2 grid and extract the maximum value from each grid cell. This reduce the spatial extent of the input which feed into the next convolution layers  

The final convolution layer feed into a conventional neural network called the dense layer to give the final output