828381fe30ae454cda23b971991861346afd1b97,hypergan/optimizers/curl_optimizer.py,CurlOptimizer,apply_gradients,#CurlOptimizer#Any#Any#Any#,44

Before Change

                    op5 = tf.group(*[ tf.assign(w,v) for w,v in zip(restored_vars, tmp_vars)])
                    with tf.get_default_graph().control_dependencies([op5]):
                        flin = gswap
                        flin = []
                        for grad, jg in zip(gswap, Jgrads):
                            if jg is None:
                                print("JG NONE", grad)
                                flin += [grad]
                            else:
                                flin += [grad + jg * self._beta]
                            
                        step3 = zip(flin, var_list)
                        op6 = self.optimizer.apply_gradients(step3, global_step=global_step, name=name)
                        with tf.get_default_graph().control_dependencies([op6]):
                            return tf.no_op()

After Change

                            consensus_reg = 0.5 * sum(
                                    tf.reduce_sum(tf.square(g)) for g in all_grads[:len(d_vars)] if g is not None
                            )
                            Jgrads = tf.gradients(consensus_reg, d_vars) + [tf.zeros_like(g) for g in g_vars]
                            op7 = [tf.assign_sub(v, (jg * self._beta)) if jg is not None else tf.assign_sub(v,grad) for v,grad, jg in zip(var_list, all_grads, Jgrads)]
                            with tf.get_default_graph().control_dependencies(op7):
                                return tf.no_op()

  

Instances