mnist_blackbox.py 文件源码

def substitute_model(img_rows=28, img_cols=28, nb_classes=10):
    """
    Defines the model architecture to be used by the substitute
    :param img_rows: number of rows in input
    :param img_cols: number of columns in input
    :param nb_classes: number of classes in output
    :return: keras model
    """
    model = Sequential()

    # Find out the input shape ordering
    if keras.backend.image_dim_ordering() == 'th':
        input_shape = (1, img_rows, img_cols)
    else:
        input_shape = (img_rows, img_cols, 1)

    # Define a fully connected model (it's different than the black-box)
    layers = [Flatten(input_shape=input_shape),
              Dense(200),
              Activation('relu'),
              Dropout(0.5),
              Dense(200),
              Activation('relu'),
              Dropout(0.5),
              Dense(nb_classes),
              Activation('softmax')]

    for layer in layers:
        model.add(layer)

    return model