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

def build_keras_model():
  # this is the same configuration that Francois Chollet used in his cats vs dogs problem
    model = Sequential()
  # CNN layers
    model.add(Conv2D(filters=64, activation='relu', kernel_size=ksize,
                     strides=strides, padding='same',
                     input_shape=(image_height, image_width, num_channels)))
    model.add(MaxPooling2D(pool_size=psize))
    model.add(Conv2D(filters=128, activation='relu', kernel_size=ksize,
                     strides=strides, padding='same'))
    model.add(MaxPooling2D(pool_size=psize))
    model.add(Conv2D(filters=128, activation='relu', kernel_size=ksize,
                     strides=strides, padding='same'))
    model.add(MaxPooling2D(pool_size=psize))
  # Flatten
    model.add(Flatten())
  # Dense (fully connected) layers
    model.add(Dense(512, activation='relu'))
  # output layer with softmax
    model.add(Dense(10, activation='softmax'))
  # compile with categorical_crossentropy loss function & adam optimizer
    #adam = Adam(lr=0.001)
    model.compile(optimizer='rmsprop', loss='categorical_crossentropy',
                  metrics=['accuracy'])
    return model

# create the model & show structure
kr_base_model = build_keras_model()
print(kr_base_model.summary())