1e339c1a0a186da155c9dc2ab8798075d248dc3e,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 = gamma * IF - rho * JF + beta * JtF
                    op7 = tf.group(*[tf.assign_add(gsw, (jg * self._beta)) if jg is not None else tf.no_op() for gsw, jg in zip(gswap, Jgrads)])
                    with tf.get_default_graph().control_dependencies([op7]):
                        flin_grads_and_vars = zip(gswap, var_list)
                        // step 1
                        op8 = self.optimizer.apply_gradients(list(flin_grads_and_vars).copy(), global_step=global_step, name=name)

After Change

                with tf.get_default_graph().control_dependencies([op5]):
                    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 = list(zip(flin, var_list))
                    op6 = self.optimizer.apply_gradients(step3.copy(), global_step=global_step, name=name)
                    with tf.get_default_graph().control_dependencies([op6]):
                        return tf.no_op()

Instances