mnist_keras_model.py

from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout

def build_model():
    model = Sequential()
    # add Convolutional layers
    model.add(Conv2D(filters=32, kernel_size=(3,3), activation='relu', padding='same',
                     input_shape=(image_height, image_width, num_channels)))
    model.add(MaxPooling2D(pool_size=(2,2)))
    model.add(Conv2D(filters=64, kernel_size=(3,3), activation='relu', padding='same'))
    model.add(MaxPooling2D(pool_size=(2,2)))
    model.add(Conv2D(filters=64, kernel_size=(3,3), activation='relu', padding='same'))
    model.add(MaxPooling2D(pool_size=(2,2)))    
    model.add(Flatten())
    # Densely connected layers
    model.add(Dense(128, activation='relu'))
    # output layer
    model.add(Dense(num_classes, activation='softmax'))
    # compile with adam optimizer & categorical_crossentropy loss function
    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
    return model

model = build_model()
print(model.summary())