variational_inference.py 文件源码

def build_elbo(self, n_samples, training=False):
    cfg = self.config
    reuse = False
    if training:
      reuse = True
    z = self.variational.sample(self.data, n_samples=n_samples, reuse=reuse)
    log_q_z = self.variational.log_prob(z, reuse=reuse)
    self.log_q_z = log_q_z
    log_p_x_z = self.model.log_prob(self.data, z, reuse=reuse)
    if cfg['optim/deterministic_annealing'] and training:
      self.build_magnitude()
      tf.summary.scalar('c/magnitude', self.magnitude)
      magnitude = tf.maximum(1., self.magnitude)
      elbo = log_p_x_z - magnitude * log_q_z
    else:
      elbo = log_p_x_z - log_q_z
    if training:
      self.elbo_loss = elbo
      _, variance = tf.nn.moments(elbo, [0])
      self.elbo_variance = tf.reduce_mean(variance)
      self.log_q_z_loss = log_q_z
      self.variational.build_entropy(z)
      self.q_z_sample = z
      slim.summarize_collection('variational')
      slim.summarize_collection('model')
      slim.summarize_activations('variational')
      slim.summarize_activations('model')
    else:
      self.elbo = elbo
      self.log_q_z = log_q_z
      self.log_p_x_hat = (tf.reduce_logsumexp(elbo, [0], keep_dims=True) -
                          tf.log(float(cfg['q/n_samples_stats'])))
      tf.summary.scalar('o/log_p_x_hat', tf.reduce_mean(self.log_p_x_hat))

      def sum_mean(x): return tf.reduce_sum(tf.reduce_mean(x, 0))
      self.elbo_sum = sum_mean(elbo)
      self.q_entropy = -sum_mean(log_q_z)
      self.E_log_lik = sum_mean(log_p_x_z)
      tf.summary.scalar('o/elbo_sum', sum_mean(elbo))
      tf.summary.scalar('o/elbo_mean', sum_mean(elbo) / cfg['batch_size'])
      tf.summary.scalar('o/E_log_q_z', sum_mean(log_q_z))
      tf.summary.scalar('o/E_log_p_x_z', self.E_log_lik)