vbutils.py 文件源码

def __init__(self, name, shape, initial_stdev = 2.0, initial_prec = 5.0, a0 = 1.0, b0 = 1.0):
        mean_std = 1.0 / np.sqrt(shape[-1])
        with tf.variable_scope(name) as scope:
            self.mean   = tf.Variable(tf.random_uniform(shape, minval=-mean_std, maxval=mean_std))
            self.logvar = tf.Variable(np.log(initial_stdev**2.0) * np.ones(shape), name = "logvar", dtype = tf.float32)
            self.prec   = np.repeat(initial_prec, shape[-1])
            self.prec_ph= tf.placeholder(shape=shape[-1], name="prec", dtype = tf.float32)
            self.var    = tf.exp(self.logvar, name = "var")
            self.a0     = a0
            self.b0     = b0
            self.shape  = shape

#    def prec_div(self):
#        return - tf.reduce_sum(gammaPrior(self.prec_a, self.prec_b, self.a0, self.b0))

    ## outputs E_q[ log N( x | 0, prec^-1) ] + Entropy(q(x))
    ## where x is the normally distributed variable