e170a8bad93da7676dadfdc8be32072a7bb46f75,official/transformer/v2/transformer_main.py,TransformerTask,train,#TransformerTask#,201

Before Change

    cased_score, uncased_score = None, None
    cased_score_history, uncased_score_history = [], []
    for i in range(1, iterations + 1):
      print("Start train iteration:{}/{}".format(i, iterations))
      history = None
      if params["use_ctl"]:
        if not self.use_tpu:
          raise NotImplementedError(
              "Custom training loop on GPUs is not implemented.")
        train_steps_per_eval = tf.convert_to_tensor(
            flags_obj.steps_between_evals, dtype=tf.int32)

        // Runs training steps.
        train_steps(train_ds_iterator, train_steps_per_eval)
        train_loss = train_loss_metric.result().numpy().astype(float)
        logging.info("Train Step: %d/%d / loss = %s",
                     i * flags_obj.steps_between_evals, flags_obj.train_steps,
                     train_loss)

        checkpoint_name = checkpoint.save(
            os.path.join(
                flags_obj.model_dir,
                "ctl_step_{}.ckpt".format(i * flags_obj.steps_between_evals)))
        logging.info("Saved checkpoint to %s", checkpoint_name)
      else:
        if self.use_tpu:
          raise NotImplementedError(
              "Keras model.fit on TPUs is not implemented.")
        history = model.fit(
            train_ds,
            initial_epoch=i - 1,
            epochs=i,
            steps_per_epoch=flags_obj.steps_between_evals,
            callbacks=callbacks,
            // If TimeHistory is enabled, progress bar would be messy. Increase
            // the verbose level to get rid of it.
            verbose=(2 if flags_obj.enable_time_history else 1))
        logging.info("Train history: {}".format(history.history))

      print("End train iteration:{}/{} global step:{}".format(
          i,
          iterations,
          i*flags_obj.steps_between_evals))

      if (flags_obj.bleu_source and flags_obj.bleu_ref):
        uncased_score, cased_score = self.eval()
        cased_score_history.append([i, cased_score])
        uncased_score_history.append([i, uncased_score])

    stats = ({
        "loss": train_loss
    } if history is None else misc.build_stats(history, callbacks))
    if uncased_score and cased_score:

After Change

        // Runs training steps.
        train_steps(train_ds_iterator,
                    tf.convert_to_tensor(train_steps_per_eval, dtype=tf.int32))
        current_step += train_steps_per_eval
        train_loss = train_loss_metric.result().numpy().astype(float)
        logging.info("Train Step: %d/%d / loss = %s",
                     current_step, flags_obj.train_steps, train_loss)

        checkpoint_name = checkpoint.save(
            os.path.join(
                flags_obj.model_dir,
                "ctl_step_{}.ckpt".format(current_step)))
        logging.info("Saved checkpoint to %s", checkpoint_name)
      else:
        if self.use_tpu:
          raise NotImplementedError(
              "Keras model.fit on TPUs is not implemented.")
        history = model.fit(
            train_ds,
            initial_epoch=current_iteration,
            epochs=current_iteration + 1,
            steps_per_epoch=train_steps_per_eval,
            callbacks=callbacks,
            // If TimeHistory is enabled, progress bar would be messy. Increase
            // the verbose level to get rid of it.
            verbose=(2 if flags_obj.enable_time_history else 1))
        current_step += train_steps_per_eval
        logging.info("Train history: {}".format(history.history))

      print("End train iteration at global step:{}".format(current_step))

      if (flags_obj.bleu_source and flags_obj.bleu_ref):
        uncased_score, cased_score = self.eval()

Instances