crbm.py 文件源码

def __gibbs_sampling(self):
        # Gibbs sampling
        # Sample visible units
        with tf.name_scope('visible') as _:
            signal_back = self.__conv2d(self.hid_state0, self.weights_flipped) + self.cias
            if self.is_continuous:
                # Visible units are continuous
                normal_dist = tf.contrib.distributions.Normal(
                    mu=signal_back, sigma=1.)
                self.vis_1 = tf.reshape(
                    tf.div(normal_dist.sample_n(1), self.weight_size * self.weight_size),
                    self.input_shape, name='vis_1')
            else:
                # Visible units are binary
                vis1_prob = tf.sigmoid(signal_back, name='vis_1')
                self.vis_1 = self.__sample(vis1_prob, 'vis_1')

        # Sample hidden units
        with tf.name_scope('hidden') as _:
            self.hid_prob1 = tf.sigmoid(self.__conv2d(self.vis_1, self.weights) + self.bias, name='hid_prob_1')