_2d.py 文件源码

def ResNet50(inputs, blocks=None, include_top=True, classes=1000, *args, **kwargs):
    """
    Constructs a `keras.models.Model` according to the ResNet50 specifications.

    :param inputs: input tensor (e.g. an instance of `keras.layers.Input`)

    :param blocks: the network’s residual architecture

    :param include_top: if true, includes classification layers

    :param classes: number of classes to classify (include_top must be true)

    :return model: ResNet model with encoding output (if `include_top=False`) or classification output (if `include_top=True`)

    Usage:

        >>> import keras_resnet.models

        >>> shape, classes = (224, 224, 3), 1000

        >>> x = keras.layers.Input(shape)

        >>> model = keras_resnet.models.ResNet50(x)

        >>> model.compile("adam", "categorical_crossentropy", ["accuracy"])
    """
    if blocks is None:
        blocks = [3, 4, 6, 3]
    numerical_names = [False, False, False, False]

    return ResNet(inputs, blocks, numerical_names=numerical_names, block=keras_resnet.blocks.bottleneck_2d, include_top=include_top, classes=classes, *args, **kwargs)