Method create_variable

python/paddle/nn/layer/layers.py:1103–1154

Create Tensor for this layer. Parameters: name(str, optional): name of the tensor. Please refer to :ref:`api_guide_Name` . Default: None persistable(bool, optional): if set this tensor persistable. Default: False dtype(str, optional): data typ

(
        self,
        name: str | None = None,
        persistable: bool | None = None,
        dtype: DTypeLike | None = None,
    )

        reason="New api in create_tensor, easier to use.",
    )
    def create_variable(
        self,
        name: str | None = None,
        persistable: bool | None = None,
        dtype: DTypeLike | None = None,
    ) -> Tensor:
        """

        Create Tensor for this layer.

        Parameters:
            name(str, optional): name of the tensor. Please refer to :ref:`api_guide_Name` . Default: None

            persistable(bool, optional): if set this tensor persistable. Default: False

            dtype(str, optional): data type of this parameter. If set str, it can be "bool", "float16", "float32", "float64","int8", "int16", "int32", "int64", "uint8" or "uint16". If set None, it will be "float32". Default: None

        Returns:
            Tensor, created Tensor.

        Examples:
            .. code-block:: pycon

                >>> import paddle

                >>> class MyLinear(paddle.nn.Layer):
                ...     def __init__(self, in_features, out_features):
                ...         super().__init__()
                ...         self.linear = paddle.nn.Linear(10, 10)
                ...
                ...         self.back_var = self.create_variable(name="linear_tmp_0", dtype=self._dtype)
                ...
                ...     def forward(self, input):
                ...         out = self.linear(input)
                ...         paddle.assign(out, self.back_var)
                ...
                ...         return out

        """
        if name is not None:
            var_name = ".".join([self._full_name, name])
        else:
            var_name = unique_name.generate(
                ".".join([self._full_name, "_generated_var"])
            )

        return self._helper.main_program.current_block().create_var(
            name=var_name,
            persistable=persistable,
            dtype=dtype,
            type=core.VarDesc.VarType.DENSE_TENSOR,
        )

    # TODO: Add more parameter list when we need them
    def create_tensor(