Keras allows building the deep neural networks by adding new layers one by one. Note, that all layers should be familiar to you to this moment.
from keras.models import Sequential from keras.layers import Activation, Dropout, Flatten, Dense, BatchNormalization, Conv2D, MaxPool2D model = Sequential() model.add(Conv2D(16, (3, 3), padding='same', activation='relu', input_shape=(height, width, depth))) model.add(Conv2D(16, (3, 3), padding='same')) model.add(BatchNormalization()) model.add(Activation('relu')) model.add(MaxPool2D((2,2))) model.add(Conv2D(32, (3, 3), padding='same', activation='relu')) model.add(Conv2D(32, (3, 3), padding='same')) model.add(BatchNormalization()) model.add(Activation('relu')) model.add(MaxPool2D((2,2))) ...
