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mindspore.ops.logsumexp

mindspore.ops.logsumexp(input, axis, keep_dims=False)[source]

Reduces a dimension of a tensor by calculating exponential for all elements in the dimension, then calculate logarithm of the sum.

l o g s u m e x p (i n p u t) = \log (\sum (e^{i n p u t - i n p u t_{m a x}})) + i n p u t_{m a x}

Parameters

input (Tensor) – The input tensor. With float16 or float32 data type.
axis (Union[int, tuple(int), list(int)]) – The dimensions to reduce. Only constant value is allowed.
keep_dims (bool) – If True, keep these reduced dimensions and the length is 1. If False , don't keep these dimensions. Default : False .

Returns

Tensor, has the same dtype as the input.

If axis is (), and keep_dims is False, the output is a 0-D tensor representing the sum of all elements in the input tensor.
If axis is int, set as 2, and keep_dims is False, the shape of output is $(i n p u t_{1}, i n p u t_{3}, . . ., i n p u t_{R})$ .
If axis is tuple(int), set as (2, 3), and keep_dims is False, the shape of output is $(i n p u t_{1}, i n p u t_{4}, . . ., i n p u t_{R})$ .

Supported Platforms:: Ascend GPU CPU

Examples

>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
>>> output = ops.logsumexp(x, 1, keep_dims=True)
>>> print(output.shape)
(3, 1, 5, 6)