mindspore.nn.Softmax
- class mindspore.nn.Softmax(axis=- 1)[source]
Softmax activation function. It 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:
\[\text{softmax}(x_{i}) = \frac{\exp(x_i)}{\sum_{j=0}^{n-1}\exp(x_j)},\]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 Softmax 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) - The input of Softmax 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 range [-len(x), len(x)).
- 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) >>> softmax = nn.Softmax() >>> output = softmax(x) >>> print(output) [0.03168 0.01166 0.0861 0.636 0.2341 ] >>> assert(1.0 == output.sum())