mindspore.ops.softmax

mindspore.ops.softmax(x, axis=- 1)[source]

Applies the Softmax operation to the input tensor on the specified axis. Suppose a slice in the given axis \(x\), then for each element \(x_i\), the Softmax 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

  • axis (Union[int, tuple[int]], optional) – The axis to perform the Softmax operation. Default: -1.

  • x (Tensor) – Tensor of shape \((N, *)\), where \(*\) means, any number of additional dimensions, with float16 or float32 data type.

Returns

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

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

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