mindspore.ops.softmax

mindspore.ops.softmax(input, axis=-1, *, dtype=None)[source]

Applies the Softmax operation to the input tensor on the specified axis.

Warning

After version 2.9.0, the parameter axis will be renamed to dim, and the default value will change from -1 to None.

Consider a slice along the given axis \(axis\), then for each element \(input_i\), the Softmax function is shown as follows:

\[\text{output}(input_i) = \frac{\exp(input_i)}{\sum_{j = 0}^{N-1}\exp(input_j)},\]

where \(N\) is the length of the tensor.

Parameters:

input (Tensor) – Tensor of shape \((N, *)\), where \(*\) means any number of additional dimensions, with float16 or float32 data type.
axis (int, optional) – The axis to perform the Softmax operation. Default: -1 .

Keyword Arguments:

dtype (mindspore.dtype, optional) – When set, input will be converted to the specified type, dtype, before execution, and dtype of returned Tensor will also be dtype. Default: None .

Returns:

Tensor, with the same type and shape as the input.

Raises:

TypeError – If axis is not an int.

Supported Platforms:: Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input = Tensor(np.array([1, 2, 3, 4, 5]), mindspore.float32)
>>> output = ops.softmax(input)
>>> print(output)
[0.01165623 0.03168492 0.08612854 0.23412167 0.6364086 ]