mindspore.nn.Softmin
- class mindspore.nn.Softmin(axis=- 1)[source]
Softmin activation function, which is a two-category function
mindspore.nn.Sigmoid
in the promotion of multi-classification, and 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.
Softmin is defined as:
\[\text{softmin}(x_{i}) = \frac{\exp(-x_i)}{\sum_{j=0}^{n-1}\exp(-x_j)},\]where \(x_{i}\) is the \(i\)-th slice in the given dimension of the input Tensor.
- Parameters
axis (Union[int, tuple[int]]) – The axis to apply Softmin operation, if the dimension of input x is x.ndim, the range of axis is [-x.ndim, x.ndim). -1 means the last dimension. Default: -1.
- Inputs:
x (Tensor) - Tensor for computing Softmin functions with data type of float16 or float32.
- Outputs:
Tensor, which has the same type and shape as x with values in the range [0,1].
- Raises
TypeError – If axis is neither an int nor 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 the range [-x.ndim, x.ndim).
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> # axis = -1(default), and the sum of return value is 1.0. >>> x = Tensor(np.array([-1, -2, 0, 2, 1]), mindspore.float16) >>> softmin = nn.Softmin() >>> output = softmin(x) >>> print(output) [0.2341 0.636 0.0862 0.01165 0.03168 ] >>> assert(1.0 == output.sum())