softmax(x, axis=None)
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))
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
.
Input array.
Axis to compute values along. Default is None and softmax will be computed over the entire array x
.
Softmax function
>>> 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() 1.0000000000000002
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.])See :
The following pages refer to to this document either explicitly or contain code examples using this.
scipy.special._logsumexp.softmax
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