logaddexp

paddle. logaddexp ( x, y, name=None ) [source]

Elementwise LogAddExp Operator. Add of exponentiations of the inputs The equation is:

\[Out=log(X.exp()+Y.exp())\]

$X$ the tensor of any dimension. $Y$ the tensor whose dimensions must be less than or equal to the dimensions of $X$.

There are two cases for this operator:

  1. The shape of $Y$ is the same with $X$.

  2. The shape of $Y$ is a continuous subsequence of $X$.

For case 2:

  1. Broadcast $Y$ to match the shape of $X$, where axis is the start dimension index for broadcasting $Y$ onto $X$.

  2. If $axis$ is -1 (default), $axis$=rank($X$)-rank($Y$).

  3. The trailing dimensions of size 1 for $Y$ will be ignored for the consideration of subsequence, such as shape($Y$) = (2, 1) => (2).

    For example:

    shape(X) = (2, 3, 4, 5), shape(Y) = (,)
shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5), with axis=-1(default) or axis=2
shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
shape(X) = (2, 3, 4, 5), shape(Y) = (2, 1), with axis=0
Parameters

  • x (Tensor) – Tensor or LoDTensor of any dimensions. Its dtype should be int32, int64, float32, float64, float16.

  • y (Tensor) – Tensor or LoDTensor of any dimensions. Its dtype should be int32, int64, float32, float64, float16.

  • name (string, optional) – For details, please refer to Name. Generally, no setting is required. Default: None.

Returns

N-D Tensor. A location into which the result is stored. It’s dimension equals with x.

Examples

>>> import paddle

>>> x = paddle.to_tensor([-1, -2, -3], 'float64')
>>> y = paddle.to_tensor([-1], 'float64')
>>> z = paddle.logaddexp(x, y)
>>> print(z)
Tensor(shape=[3], dtype=float64, place=Place(cpu), stop_gradient=True,
[-0.30685282, -0.68673831, -0.87307199])