5dc36fed7cb3e31a3f44b99ddd6d870840c885ce,model/dcrnn_supervisor.py,DCRNNSupervisor,_train,#DCRNNSupervisor#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,121

Before Change

            // Learning rate schedule.
            new_lr = max(min_learning_rate, base_lr * (lr_decay_ratio ** np.sum(self._epoch >= np.array(steps))))
            self._train_model.set_lr(sess=sess, lr=new_lr)
            sys.stdout.flush()

            start_time = time.time()
            train_results = self._train_model.run_epoch_generator(sess, self._train_model,
                                                                  self._data["train_loader"].get_iterator(),

After Change

            self._epoch = epoch + 1
        else:
            sess.run(tf.global_variables_initializer())
        self._logger.info("Start training ...")

        while self._epoch <= epochs:
            // Learning rate schedule.
            new_lr = max(min_learning_rate, base_lr * (lr_decay_ratio ** np.sum(self._epoch >= np.array(steps))))

Instances