scipy.special.log_softmax#
- scipy.special.log_softmax(x, axis=None)[source]#
Compute the logarithm of the softmax function.
In principle:
log_softmax(x) = log(softmax(x))
but using a more accurate implementation.
- 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 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.
Notes
log_softmax
is more accurate thannp.log(softmax(x))
with inputs that makesoftmax
saturate (see examples below).New in version 1.5.0.
Examples
>>> 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])