logsumexp

paddle. logsumexp ( x: Tensor, axis: int | Sequence[int] | None = None, keepdim: bool = False, name: str | None = None, *, out: Tensor | None = None ) → Tensor [source]

Calculates the log of the sum of exponentials of x along axis .

\[logsumexp(x) = \log\sum exp(x)\]

Parameters

x (Tensor) – The input Tensor with data type bfloat16, float16, float32, float64, uint8, int8, int16, int32, int64, which have no more than 4 dimensions.
axis (int|list|tuple|None, optional) – The axis along which to perform logsumexp calculations. axis should be int, list(int) or tuple(int). If axis is a list/tuple of dimension(s), logsumexp is calculated along all element(s) of axis . axis or element(s) of axis should be in range [-D, D), where D is the dimensions of x . If axis or element(s) of axis is less than 0, it works the same way as \(axis + D\) . If axis is None, logsumexp is calculated along all elements of x. Default is None.
keepdim (bool, optional) – Whether to reserve the reduced dimension(s) in the output Tensor. If keep_dim is True, the dimensions of the output Tensor is the same as x except in the reduced dimensions(it is of size 1 in this case). Otherwise, the shape of the output Tensor is squeezed in axis . Default is False.
name (str|None, optional) – Name for the operation (optional, default is None). For more information, please refer to api_guide_Name.

Keyword Arguments

out (Tensor|optional) – The output tensor.

Returns

Tensor, results of logsumexp along axis of x, with the same data type as x (integer types are autocasted into float32).

Examples:

           >>> import paddle

>>> x = paddle.to_tensor([[-1.5, 0., 2.], [3., 1.2, -2.4]])
>>> out1 = paddle.logsumexp(x)
>>> out1
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
3.46912265)
>>> out2 = paddle.logsumexp(x, 1)
>>> out2
Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True,
[2.15317822, 3.15684605])

          