beta.py 文件源码

def tf_parameterize(self, x):
        # Softplus to ensure alpha and beta >= 1
        # epsilon < 1.0, hence negative
        log_eps = log(util.epsilon)

        alpha = self.alpha.apply(x=x)
        alpha = tf.clip_by_value(t=alpha, clip_value_min=log_eps, clip_value_max=-log_eps)
        alpha = tf.log(x=(tf.exp(x=alpha) + 1.0)) + 1.0

        beta = self.beta.apply(x=x)
        beta = tf.clip_by_value(t=beta, clip_value_min=log_eps, clip_value_max=-log_eps)
        beta = tf.log(x=(tf.exp(x=beta) + 1.0)) + 1.0

        shape = (-1,) + self.shape
        alpha = tf.reshape(tensor=alpha, shape=shape)
        beta = tf.reshape(tensor=beta, shape=shape)

        alpha_beta = tf.maximum(x=(alpha + beta), y=util.epsilon)
        log_norm = tf.lgamma(x=alpha) + tf.lgamma(x=beta) - tf.lgamma(x=alpha_beta)

        return alpha, beta, alpha_beta, log_norm