e3fff8776cd7544e418a2aa8bef8574da99bafac,hypergan/trainers/fitness_trainer.py,FitnessTrainer,_step,#FitnessTrainer#Any#,352

Before Change

                // l2 = ||(pg(z0) - g(z0))||2
                prev_l2_loss = sess.run(self.prev_l2_loss, prev_feed_dict)
                // pg(z0) = g(z)
                self.prev_g = sess.run(self.update_prev_sample, feed_dict)
                // z0 = z
                self.prev_zs = zs
                // optimize(l2, gl, dl)

                feed_dict[self.prev_l2_loss] = prev_l2_loss

                _, *metric_values = sess.run([self.optimizer] + self.output_variables(metrics), feed_dict)
                if ((self.current_step % (self.config.constraint_every or 100)) == 0):
                    if self.config.weight_constraint:
                        sess.run(self.update_weight_constraints, feed_dict)
                sess.run(self.assign_ema)
                sess.run(self.assign_past_weights)
            else:
                self.hist[1]+=1
                fitness_decay = config.fitness_decay or 0.99
                self.min_fitness = self.min_fitness + (1.00-fitness_decay)*(fitness-self.min_fitness)
                if(config.train_d_on_fitness_failure):
                    metric_values = sess.run([self.d_optimizer]+self.output_variables(metrics), feed_dict)[1:]
                else:
                    metric_values = sess.run(self.output_variables(metrics), feed_dict)
                self.current_step-=1
        else:
            if ((self.current_step % (self.config.constraint_every or 100)) == 0):
                if self.config.weight_constraint:
                    print("Updating constraints")
                    sess.run(self.update_weight_constraints, feed_dict)
            //standard
            gl, dl, *metric_values = sess.run([self.g_loss, self.d_loss, self.optimizer] + self.output_variables(metrics), feed_dict)[1:]
            sess.run(self.assign_ema)
            sess.run(self.assign_past_weights)
            if(gl == 0 or dl == 0):
                self.steps_since_fit=0
                self.mix_threshold_reached = True
                print("Zero, lne?")
                return
            self.steps_since_fit=0

        if config.g_ema_decay is not None:
            feed_dict = {}
            for p,pvalue in zip(self.pg_vars, prev):
                feed_dict[p]=pvalue
            _ = sess.run(self.g_ema, feed_dict)

        if ((self.current_step % 10) == 0 and self.steps_since_fit == 0):
            hist_output = "  " + "".join(["G"+str(i)+":"+str(v)+" "for i, v in enumerate(self.hist)])

After Change

    def required(self):
        return "trainer learn_rate".split()

    def _step(self, feed_dict):
        gan = self.gan
        sess = gan.session
        config = self.config
        loss = self.loss or gan.loss
        metrics = loss.metrics

        if self.current_step == 0 and self.steps_since_fit == 0:
                sess.run(self.assign_past_weights)
                sess.run(self.update_prev_sample)
        
        if config.fitness_test is not None:
            self.steps_since_fit+=1
            if config.fitness_failure_threshold and self.steps_since_fit > (config.fitness_failure_threshold or 1000):
                print("Fitness achieved.", self.hist[0], self.min_fitness)

Instances