TensorFlow 1.x Deep Learning Cookbook

ConvNets summary

CNNs are basically several layers of convolutions with nonlinear activation functions and pooling layers applied to the results. Each layer applies different filters (hundreds or thousands). The key observation to understand is that the filters are not pre-assigned, but instead, they are learned during the training phase in such a way that a suitable loss function is minimized. It has been observed that lower layers will learn to detect basic features, while the higher layers detect progressively more sophisticated features such as shapes or faces. Note that, thanks to pooling, individual neurons in later layers see more of the original image, hence they are able to compose basic features learned in the earlier layers.