tf_backend.py 文件源码

def kl_gaussian(mean_, logsigma,
                prior_mean=0., prior_logsigma=0.,
                regularizer_scale=1.):
    ''' KL-divergence between two gaussians.
    Useful for Variational AutoEncoders. Use this as an activation regularizer
    Parameters:
    -----------
    mean, logsigma: parameters of the input distributions
    prior_mean, prior_logsigma: paramaters of the desired distribution (note the
        log on logsigma)
    regularizer_scale: Rescales the regularization cost. Keep this 1 for most cases.

    Note
    ----
    origin implementation from seya:
    https://github.com/Philip-Bachman/ICML-2015/blob/master/LogPDFs.py
    Copyright (c) Philip Bachman
    '''
    gauss_klds = 0.5 * (prior_logsigma - logsigma +
            ((tf.exp(logsigma) + pow((mean_ - prior_mean), 2.0)) / tf.exp(prior_logsigma)) - 1.0)
    return mean(gauss_klds)