deep learning for computer vision note the depth of the feature maps progressively increases in the network (from to )whereas the size of the feature maps decreases (from to this is pattern you'll see in almost all convnets because you're attacking binary-classification problemyou'll end the network with single unit ( dense layer of size and sigmoid activation this unit will encode the probability that the network is looking at one class or the other listing inst ant iat ing small convnet for dogs vs cats classificat ion from keras import layers from keras import models model models sequential(model add(layers conv ( ( )activation='relu'input_shape=( ))model add(layers maxpooling (( ))model add(layers conv ( ( )activation='relu')model add(layers maxpooling (( ))model add(layers conv ( ( )activation='relu')model add(layers maxpooling (( ))model add(layers conv ( ( )activation='relu')model add(layers maxpooling (( ))model add(layers flatten()model add(layers dense( activation='relu')model add(layers dense( activation='sigmoid')let' look at how the dimensions of the feature maps change with every successive layermodel summary(layer (typeoutput shape param ===============================================================conv d_ (conv (none maxpooling d_ (maxpooling (none conv d_ (conv (none maxpooling d_ (maxpooling (none conv d_ (conv (none maxpooling d_ (maxpooling (none conv d_ (conv (none maxpooling d_ (maxpooling (none flatten_ (flatten(none dense_ (dense(none licensed to