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mindspore.ops.logsumexp

mindspore.ops.logsumexp(x, axis, keep_dims=False)

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

$$logsumexp(x)=\log (\sum (e^{(}x-x_{max})))+x_{max}$$

Note

The dimension of input Tensor on Ascend should be less than or equal to 8, and the dimension of input Tensor on CPU should be less than 8.

Parameters

• x (Tensor) – The input tensor. With float16 or float32 data type.

• axis (Union[int, tuple(int), list(int)]) – The dimensions to reduce. Default: (), reduce all dimensions. 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 x.

• 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 $(x_1,x_3,...,x_R)$.

• If axis is tuple(int), set as (2, 3), and keep_dims is False, the shape of output is $(x_1,x_4,...,x_R)$.

Supported Platforms:

Ascend GPU CPU

Examples

>>> 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)