`scipy.special._logsumexp:log_softmax`

Signature

def log_softmax(x, axis = None)

Compute the logarithm of the softmax function.

In principle

log_softmax(x) = log(softmax(x))

but using a more accurate implementation.

x : array_like: Input array.
axis : int or tuple of ints, optional: Axis to compute values along. Default is None and softmax will be computed over the entire array x.

s : ndarray or scalar: An array with the same shape as x. Exponential of the result will sum to 1 along the specified axis. If x is a scalar, a scalar is returned.

log_softmax is more accurate than np.log(softmax(x)) with inputs that make softmax saturate (see examples below).

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

✓

x = np.array([1000.0, 1.0])

✓

y = log_softmax(x)
y

✓

array([   0., -999.])

with np.errstate(divide='ignore'):
  y = np.log(softmax(x))
y

✓

array([  0., -inf])