Class Operator

python/paddle/base/framework.py:3221–4116

In Fluid, all the operation are represented by Operator, and Operator is regarded as a build in an instruction of a Block. Users can use the build in instructions to describe their neural network. Args: block(Block): The block has the current operator. desc(core.OpD

class Operator:
    """
    In Fluid, all the operation are represented by Operator, and Operator
    is regarded as a build in an instruction of a Block. Users can use the
    build in instructions to describe their neural network.

    Args:
        block(Block): The block has the current operator.
        desc(core.OpDesc): The protobuf description of Operator.
        type(str): The type of operator. Default None.
        inputs(dict): The input of this Operator. it is a dictionary, for every
            element, key is the input parameter name, and value is a list of
            variables. Default None.
        outputs(dict): The output of this Operator. it is a dictionary, for
            every element, key is the input parameter name, and value is a list
            of variables. Default None.
        attrs(dict): The attributes of this Operator. it is a dictionary, for
            every element, key is attribute name, and value is the attribute value.
            The attribute type should be as same as the type registered in C++ side.
            Default None.

    Returns:
        Operator: The initialized Operator.

    Raises:
        ValueError: If the passed input, output and attrs doesn't match the
            initializing Operator's that registered in C++ side.

    Notes:
        The constructor of operator should not be invoked directly. Use
        Block.append_op or Block._prepend_op instead.

    Examples:
        .. code-block:: pycon

            >>> import paddle

            >>> paddle.enable_static()
            >>> cur_program = paddle.static.Program()
            >>> cur_block = cur_program.current_block()
            >>> var1 = cur_block.create_var(name="var1", shape=[-1, 23, 48], dtype='float32')
            >>> var2 = cur_block.create_var(name="var2", shape=[-1, 23, 48], dtype='float32')
            >>> var3 = cur_block.create_var(name="var3", shape=[-1, 23, 48], dtype='float32')
            >>> var1 += var2 + var3
            >>> cur_block.append_op(
            ...     type="sum",
            ...     inputs={"X": [var1, var2, var3]},
            ...     outputs={"Out": [var1]},
            ... )
    """

    OP_WITHOUT_KERNEL_SET = {
        "feed",
        "fetch",
        "go",
        "conditional_block",
        "pylayer",
        "while",