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scipy.special.

# softmax

scipy.special.softmax(x, axis=None)

  Compute the softmax function.

  The softmax function transforms each element of a collection by computing the exponential of each element divided by the sum of the exponentials of all the elements. That is, if x is a one-dimensional numpy array:

      softmax(x) = np.exp(x)/sum(np.exp(x))

  Parameters:
    xarray_like

      Input array.

    axisint or tuple of ints, optional

      Axis to compute values along. Default is None and softmax will be computed over the entire array x.

  Returns:
    sndarray

      An array the same shape as x. The result will sum to 1 along the specified axis.

  Notes

  The formula for the softmax function \(\sigma(x)\) for a vector \(x = \{x_0, x_1, ..., x_{n-1}\}\) is

  \[\sigma(x)_j = \frac{e^{x_j}}{\sum_k e^{x_k}}\]

  The `softmax` function is the gradient of `logsumexp`.

  The implementation uses shifting to avoid overflow. See [1] for more details.

  Added in version 1.2.0.

  `softmax` has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable `SCIPY_ARRAY_API=1` and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

  Library | CPU | GPU
  --- | --- | ---
  NumPy | ✅ | n/a
  CuPy | n/a | ✅
  PyTorch | ✅ | ✅
  JAX | ✅ | ✅
  Dask | ✅ | n/a

  See Support for the array API standard for more information.

  References

  [1]

  P. Blanchard, D.J. Higham, N.J. Higham, “Accurately computing the log-sum-exp and softmax functions”, IMA Journal of Numerical Analysis, Vol.41(4), DOI:10.1093/imanum/draa038.

  Examples

      >>> import numpy as np
      >>> from scipy.special import softmax
      >>> np.set_printoptions(precision=5)

      >>> x = np.array([[1, 0.5, 0.2, 3],
      ...               [1,  -1,   7, 3],
      ...               [2,  12,  13, 3]])
      ...

  Compute the softmax transformation over the entire array.

      >>> m = softmax(x)
      >>> m
      array([[  4.48309e-06,   2.71913e-06,   2.01438e-06,   3.31258e-05],
             [  4.48309e-06,   6.06720e-07,   1.80861e-03,   3.31258e-05],
             [  1.21863e-05,   2.68421e-01,   7.29644e-01,   3.31258e-05]])

      >>> m.sum()
      1.0

  Compute the softmax transformation along the first axis (i.e., the columns).

      >>> m = softmax(x, axis=0)

      >>> m
      array([[  2.11942e-01,   1.01300e-05,   2.75394e-06,   3.33333e-01],
             [  2.11942e-01,   2.26030e-06,   2.47262e-03,   3.33333e-01],
             [  5.76117e-01,   9.99988e-01,   9.97525e-01,   3.33333e-01]])

      >>> m.sum(axis=0)
      array([ 1.,  1.,  1.,  1.])

  Compute the softmax transformation along the second axis (i.e., the rows).

      >>> m = softmax(x, axis=1)
      >>> m
      array([[  1.05877e-01,   6.42177e-02,   4.75736e-02,   7.82332e-01],
             [  2.42746e-03,   3.28521e-04,   9.79307e-01,   1.79366e-02],
             [  1.22094e-05,   2.68929e-01,   7.31025e-01,   3.31885e-05]])

      >>> m.sum(axis=1)
      array([ 1.,  1.,  1.])