visualization.py 文件源码

def get_features(image, model):
    '''
    get the feature map of all activation layer for given
    image and given model
    :param image: input image path
    :param model: given model
    :return: all activation layers features
    '''

   # image = load_image(image_src)
    feature_maps = np.zeros((10, 10, 15104))
    activation_layers = ['activation_' + str(i) for i in range(4, 50, 3)]
    start_index = 0

    for i, layer_name in enumerate(activation_layers):
        layer = model.get_layer(layer_name)
        nchannel = layer.output_shape[-1]
        conv_output = layer.output
    # Adujusting pooling size with respect to input layers` size
        if layer.output_shape[-2] == 74:
            conv_output = AveragePooling2D(pool_size=(7, 7))(conv_output)
        if layer.output_shape[-2] == 37:
            conv_output = AveragePooling2D(pool_size=(4, 4), border_mode='same')(conv_output)
        if layer.output_shape[-2] == 19:
            conv_output = AveragePooling2D(pool_size=(2, 2), border_mode='same')(conv_output)

        featuremap_function = K.function([model.input, K.learning_phase()], [conv_output])

        output = featuremap_function([image, 0])
        feature_maps[:, :, start_index:start_index+nchannel] = output[0][0, :, :, :]

        start_index = start_index + nchannel

    return feature_maps