softmax#
- scipy.special.softmax(x, axis=None)[source]#
- 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 - softmaxfunction is the gradient of- logsumexp.- The implementation uses shifting to avoid overflow. See [1] for more details. - Added in version 1.2.0. - 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.])