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mindspore.nn.Softmax

class mindspore.nn.Softmax(axis=- 1)[source]

Softmax activation function, which is a two-category function mindspore.nn.Sigmoid in the promotion of multi-classification, the purpose is to show the results of multi-classification in the form of probability.

Calculate the value of the exponential function for the elements of the input Tensor on the axis, and then normalized to lie in range [0, 1] and sum up to 1.

Softmax is defined as:

softmax (i n p u t_{i}) = \frac{\exp (i n p u t_{i})}{\sum_{j = 0}^{n - 1} \exp (i n p u t_{j})},

where $i n p u t_{i}$ is the $i$ -th slice in the given dimension of the input Tensor.

Parameters: axis (int, optional) – The axis to apply Softmax operation, if the dimension of input is input.ndim, the range of axis is [-input.ndim, input.ndim), -1 means the last dimension. Default: -1 .

Inputs:

input (Tensor) - The input of Softmax.

Outputs:

Tensor, which has the same type and shape as input with values in the range[0, 1].

Raises

TypeError – If axis is neither an int nor a tuple.
ValueError – If axis is a tuple whose length is less than 1.
ValueError – If axis is a tuple whose elements are not all in range [-input.ndim, input.ndim).

Supported Platforms:: Ascend GPU CPU

Examples

>>> import mindspore
>>> from mindspore import Tensor, nn
>>> import numpy as np
>>> # axis = -1(default), and the sum of return value is 1.0.
>>> input = Tensor(np.array([-1, -2, 0, 2, 1]), mindspore.float16)
>>> softmax = nn.Softmax()
>>> output = softmax(input)
>>> print(output)
[0.03168 0.01166 0.0861  0.636   0.2341 ]