models.py

from keras.layers import Activation, Convolution2D, Dropout
from keras.layers import AveragePooling2D, BatchNormalization
from keras.layers import GlobalAveragePooling2D
from keras.models import Sequential

def simple_CNN(input_shape, num_classes):

    model = Sequential()
    model.add(Convolution2D(filters=16, kernel_size=(7, 7), padding='same',
                            name='image_array', input_shape=input_shape))
    model.add(BatchNormalization())
    model.add(Convolution2D(filters=16, kernel_size=(7, 7), padding='same'))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(AveragePooling2D(pool_size=(2, 2), padding='same'))
    model.add(Dropout(.5))

    model.add(Convolution2D(filters=32, kernel_size=(5, 5), padding='same'))
    model.add(BatchNormalization())
    model.add(Convolution2D(filters=32, kernel_size=(5, 5), padding='same'))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(AveragePooling2D(pool_size=(2, 2), padding='same'))
    model.add(Dropout(.5))

    model.add(Convolution2D(filters=64, kernel_size=(3, 3), padding='same'))
    model.add(BatchNormalization())
    model.add(Convolution2D(filters=64, kernel_size=(3, 3), padding='same'))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(AveragePooling2D(pool_size=(2, 2), padding='same'))
    model.add(Dropout(.5))

    model.add(Convolution2D(filters=128, kernel_size=(3, 3), padding='same'))
    model.add(BatchNormalization())
    model.add(Convolution2D(filters=128, kernel_size=(3, 3), padding='same'))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(AveragePooling2D(pool_size=(2, 2), padding='same'))
    model.add(Dropout(.5))

    model.add(Convolution2D(filters=256, kernel_size=(3, 3), padding='same'))
    model.add(BatchNormalization())
    model.add(Convolution2D(filters=num_classes, kernel_size=(3, 3), padding='same'))
    model.add(GlobalAveragePooling2D())
    model.add(Activation('softmax',name='predictions'))
    return model