models.py 文件源码

def model1(weights_path=None):
    '''
    Basic ResNet-FT for baseline comparisions.
    Creates a model by for all aesthetic attributes along
    with overall aesthetic score, by finetuning resnet50
    :param weights_path: path of the weight file
    :return: Keras model instance
    '''
    _input = Input(shape=(299, 299, 3))
    resnet = ResNet50(include_top=False, weights='imagenet', input_tensor=_input)

    last_layer_output = GlobalAveragePooling2D()(resnet.get_layer('activation_49').output)

    # output of model
    outputs = []
    attrs = ['BalacingElements', 'ColorHarmony', 'Content', 'DoF',
             'Light', 'MotionBlur', 'Object', 'RuleOfThirds', 'VividColor']
    for attribute in attrs:
        outputs.append(Dense(1, init='glorot_uniform', activation='tanh', name=attribute)(last_layer_output))

    non_negative_attrs = ['Repetition', 'Symmetry', 'score']
    for attribute in non_negative_attrs:
        outputs.append(Dense(1, init='glorot_uniform', activation='sigmoid', name=attribute)(last_layer_output))

    model = Model(input=_input, output=outputs)
    if weights_path:
        model.load_weights(weights_path)
    return model