Module « scipy.special »

Fonction log_softmax - module scipy.special

Signature de la fonction log_softmax

def log_softmax(x, axis=None) 

Description

log_softmax.__doc__

    Logarithm of softmax function::

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

    Parameters
    ----------
    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`.

    Returns
    -------
    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.

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

    .. versionadded:: 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])