mindspore.ops.softmin
- mindspore.ops.softmin(x, axis=- 1, *, dtype=None)[source]
Applies the Softmin operation to the input tensor on the specified axis. Suppose a slice in the given axis \(x\), then for each element \(x_i\), the Softmin function is shown as follows:
\[\text{output}(x_i) = \frac{\exp(-x_i)}{\sum_{j = 0}^{N-1}\exp(-x_j)},\]where \(N\) is the length of the tensor.
- Parameters
- Keyword Arguments
dtype (
mindspore.dtype
, optional) – When set, x 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 x.
- Raises
TypeError – If axis is not an int or a tuple.
TypeError – If dtype of x is neither float16 nor float32.
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 [-len(logits.shape), len(logits.shape)).
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import mindspore >>> import numpy as np >>> from mindspore import Tensor, ops >>> x = Tensor(np.array([-1, -2, 0, 2, 1]), mindspore.float16) >>> output = ops.softmin(x) >>> print(output) [0.2341 0.636 0.0862 0.01165 0.03168 ]