simple_model.py

from keras.models import Sequential, Model
from keras.layers import Activation, Convolution2D, MaxPooling2D, BatchNormalization, Flatten, Dense, Dropout, Conv2D,MaxPool2D, ZeroPadding2D

def model(input):
    model = Sequential()
    model.add(Conv2D(filters=16, kernel_size=3, activation='relu', input_shape=input))
    model.add(MaxPooling2D(pool_size=2))

    model.add(Conv2D(filters=32, kernel_size=3, activation='relu'))
    model.add(MaxPooling2D(pool_size=2))

    model.add(Conv2D(filters=64, kernel_size=3, activation='relu'))
    model.add(MaxPooling2D(pool_size=2))

    model.add(Conv2D(filters=128, kernel_size=3, activation='relu'))
    model.add(MaxPooling2D(pool_size=2))

    model.add(Conv2D(filters=256, kernel_size=3, activation='relu'))
    model.add(MaxPooling2D(pool_size=2))

    model.add(Conv2D(filters=512, kernel_size=3, activation='relu'))
    model.add(MaxPooling2D(pool_size=2))

    model.add(Flatten())
    model.add(Dense(1024, activation='relu'))
    model.add(Dropout(0.2))
    model.add(Dense(512, activation='relu'))
    model.add(Dropout(0.2))

    model.add(Dense(42))
    model.summary()
    return model
#"""
if __name__=="__main__":
    input_shape = (256,256, 3)
    model = model(input_shape)

#"""