mxnet.npx.softmax¶
-
softmax
(data, length=None, axis=-1, temperature=None, use_length=False, dtype=None)¶ Applies the softmax function.
The resulting array contains elements in the range (0,1) and the elements along the given axis sum up to 1.
\[softmax(\mathbf{z/t})_j = \frac{e^{z_j/t}}{\sum_{k=1}^K e^{z_k/t}}\]for \(j = 1, ..., K\)
t is the temperature parameter in softmax function. By default, t equals 1.0
- Parameters
data (NDArray) – The input array.
axis (int, optional, default='-1') – The axis along which to compute softmax.
length (NDArray) – The length array.
temperature (double or None, optional, default=None) – Temperature parameter in softmax
dtype ({None, 'float16', 'float32', 'float64'},optional, default='None') – DType of the output in case this can’t be inferred. Defaults to the same as input’s dtype if not defined (dtype=None).
use_length (boolean or None, optional, default=0) – Whether to use the length input as a mask over the data input.
- Returns
out – The output of this function.
- Return type
NDArray or list of NDArrays
Example
>>> data = np.ones((2, 3)) >>> npx.softmax(data, axis=0) array([[0.5, 0.5, 0.5], [0.5, 0.5, 0.5]]) >>> npx.softmax(data, axis=1) array([[0.33333334, 0.33333334, 0.33333334], [0.33333334, 0.33333334, 0.33333334]])