# tensorplay.functional.logsumexp

Source: https://www.tensorplay.cn/docs/generated/tensorplay.functional.logsumexp.html

```python
tensorplay.functional.logsumexp(input, dim, keepdim=False, *, out=None)
```

Returns the log of summed exponentials of each row of the input
tensor in the given dimension dim. The computation is numerically
stabilized.

For summation index $j$ given by dim and other indices $i$, the result is

$$\text{logsumexp}(x)_{i} = \log \sum_j \exp(x_{ij})$$

If keepdim is True, the output tensor is of the same size
as input except in the dimension(s) dim where it is of size 1.
Otherwise, dim is squeezed (see tensorplay.squeeze()), resulting in the
output tensor having 1 (or len(dim)) fewer dimension(s).

Parameters:

- input ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor)) – the input tensor.

- dim ([int](https://docs.python.org/3/builtins/functions.html#int) or [tuple](https://docs.python.org/3/builtins/stdtypes.html#tuple) of ints) – the dimension or dimensions to reduce. keepdim (bool, optional): whether the output tensor has dim retained or not. Default: False.

Keyword Arguments:

out ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor), optional) – the output tensor.

Example:

```
>>> a = tensorplay.randn(3, 3)
>>> tensorplay.logsumexp(a, 1)
tensor([1.4907, 1.0593, 1.5696])
>>> tensorplay.dist(tensorplay.logsumexp(a, 1), tensorplay.log(tensorplay.sum(tensorplay.exp(a), 1)))
tensor(1.6859e-07)
```