dgp.py 文件源码

def build_likelihood(self):
        Fmean, Fvar = self.build_predict(self.X, full_cov=False, S=self.num_samples)

        S, N, D = shape_as_list(Fmean)
        Y = tile_over_samples(self.Y, self.num_samples)

        f = lambda a: self.likelihood.variational_expectations(a[0], a[1], a[2])
        var_exp = tf.map_fn(f, (Fmean, Fvar, Y), dtype=float_type)
        var_exp = tf.stack(var_exp) #SN

        var_exp = tf.reduce_mean(var_exp, 0) # S,N -> N. Average over samples
        L = tf.reduce_sum(var_exp) # N -> scalar. Sum over data (minibatch)

        KL = 0.
        for layer in self.layers:
            KL += layer.KL()

        scale = tf.cast(self.num_data, float_type)
        scale /= tf.cast(tf.shape(self.X)[0], float_type)  # minibatch size
        return L * scale - KL