Class EagerParamBase

python/paddle/base/framework.py:7796–8044

EagerParamBase is derived from Tensor( Which is the concept in Eager-Dygraph Mode). A EagerParamBase is a persistable Tensor, and will be updated by optimizers after each iteration. The training of a neural network is essentially the updating of its EagerParamBase. Relative

class EagerParamBase(core.eager.Tensor):
    """
    EagerParamBase is derived from Tensor( Which is the concept in Eager-Dygraph Mode).
    A EagerParamBase is a persistable Tensor, and will be updated by optimizers
    after each iteration.
    The training of a neural network is essentially the updating of
    its EagerParamBase.

    Relative to a general Tensor, a EagerParamBase has several its own
    member variables:

    Args:
        trainable(bool): True if the EagerParamBase need to be updated after
            iterations.
        optimize_attr(map): EagerParamBase attributes related with optimizing.
            Currently, it only contains 'learning_rate'.
            Default: {'learning_rate': 1.0}
        regularizer(WeightDecayRegularizer): The Regularizer which will
            be applied on the EagerParamBase. Default: None
        do_model_average(bool): True if the model average strategy will
            be applied on this EagerParamBase.
        need_clip (bool): Whether the parameter gradient need to be clipped
            in optimizer. Default is True.
    """

    @dygraph_only
    def __init__(self, *args, **kwargs):
        if (len(args) > 0 and isinstance(args[0], list)) or 'shape' in kwargs:
            self.__init_by_shape__(*args, **kwargs)
        else:
            self.__init_by_tensor__(*args, **kwargs)

    def __init_by_shape__(self, shape, dtype, **kwargs):
        if shape is None:
            raise ValueError("The shape of Parameter should not be None")
        if dtype is None:
            raise ValueError("The dtype of Parameter should not be None")

        for each in shape:
            if each < 0:
                raise ValueError(
                    f"Each dimension of shape for Parameter must be greater than 0, but received {list(shape)}"
                )

        if dtype is not None:
            dtype = convert_to_proto_type(dtype)
        else:
            dtype = core.VarDesc.VarType.FP32

        name = kwargs.get("name", unique_name.generate("_eager_param_base"))

        if isinstance(shape, core.eager.Tensor):
            shape = shape.numpy()

        super().__init__(
            dtype,
            list(shape) if shape else [],
            name,