57c07ec7b0ed64ee9562b1b3eb74c181dbf15ceb,hypergan/trainers/consensus_trainer.py,ConsensusTrainer,_create,#ConsensusTrainer#,28

Before Change

        control_dependency = []

        if config.g_exponential_moving_average_decay:
            self.var_ema = tf.train.ExponentialMovingAverage(config.g_exponential_moving_average_decay)
            ema = self.var_ema
            ema_op = ema.apply(g_vars)
            control_dependency = [ema_op]

        with tf.control_dependencies(control_dependency):
            optimizer = tr.apply_gradients(apply_vec, global_step=self.global_step)
            d_optimizer = tr.apply_gradients(apply_vec_d, global_step=self.global_step)
            g_optimizer = tr.apply_gradients(apply_vec_g, global_step=self.global_step)

After Change

            with tf.control_dependencies([tr.apply_gradients(apply_vec, global_step=self.global_step)]):
                optimizer = ema_op
            with tf.control_dependencies([tr.apply_gradients(apply_vec_g, global_step=self.global_step)]):
                g_optimizer = tr.apply_gradients(apply_vec_g, global_step=self.global_step)
            d_optimizer = tr.apply_gradients(apply_vec_d, global_step=self.global_step)

        else:
            optimizer = tr.apply_gradients(apply_vec, global_step=self.global_step)

Instances