Function continuous_value_model

python/paddle/static/nn/common.py:417–462

r""" **continuous_value_model layers** Now, this OP is used in CTR project to remove or dispose show and click value in :attr:`input`. :attr:`input` is an embedding vector including show and click value, whose shape is :math:`[N, D]` (N is batch size. D is `2 + embedding dim` ). Show

(input, cvm, use_cvm=True)

@static_only
def continuous_value_model(input, cvm, use_cvm=True):
    r"""
    **continuous_value_model layers**
    Now, this OP is used in CTR project to remove or dispose show and click value in :attr:`input`.
    :attr:`input` is an embedding vector including show and click value, whose shape is :math:`[N, D]` (N is batch size. D is `2 + embedding dim` ).
    Show and click at first two dims of embedding vector D.
    If :attr:`use_cvm` is True, it will calculate :math:`log(show)` and :math:`log(click)` , and output shape is :math:`[N, D]` .
    If :attr:`use_cvm` is False, it will remove show and click from :attr:`input` , and output shape is :math:`[N, D - 2]` .
    :attr:`cvm` is show_click info, whose shape is :math:`[N, 2]` .
    Args:
        input (Variable): The input variable. A 2-D DenseTensor with shape :math:`[N, D]` , where N is the batch size, D is `2 + the embedding dim` . `lod level = 1` .
        A Tensor with type float32, float64.
        cvm (Variable): Show and click variable. A 2-D Tensor with shape :math:`[N, 2]` , where N is the batch size, 2 is show and click.
        A Tensor with type float32, float64.
        use_cvm  (bool):  Use show_click or not. if use, the output dim is the same as input.
                          if not use, the output dim is `input dim - 2` (remove show and click)
    Returns:
        Variable: A 2-D DenseTensor with shape :math:`[N, M]` . if :attr:`use_cvm` = True, M is equal to input dim D. if False, M is equal to `D - 2`. \
        A Tensor with same type as input.
    Examples:
        .. code-block:: pycon

            >>> import paddle

            >>> paddle.enable_static()
            >>> input = paddle.static.data(name="input", shape=[64, 1], dtype="int64")
            >>> label = paddle.static.data(name="label", shape=[64, 1], dtype="int64")
            >>> w0 = paddle.full(shape=(100, 1), fill_value=2).astype(paddle.float32)
            >>> embed = paddle.nn.functional.embedding(input, w0)
            >>> ones = paddle.full_like(label, 1, dtype="int64")
            >>> show_clk = paddle.cast(paddle.concat([ones, label], axis=1), dtype='float32')
            >>> show_clk.stop_gradient = True
            >>> input_with_cvm = paddle.static.nn.continuous_value_model(embed[:, 0], show_clk, True)
    """
    helper = LayerHelper('cvm', **locals())
    out = helper.create_variable(dtype=input.dtype)
    check_variable_and_dtype(
        input, 'input', ['float16', 'float32', 'float64'], 'cvm'
    )
    helper.append_op(
        type='cvm',
        inputs={'X': [input], 'CVM': [cvm]},
        outputs={'Y': [out]},
        attrs={"use_cvm": use_cvm},
    )
    return out


def group_norm(