3578019be301ae9d8e9b7518daa5fb113d00d2db,open_seq2seq/utils/hooks.py,RunEvaluationHook,after_run,#RunEvaluationHook#Any#Any#,147

Before Change

    inputs_per_batch, outputs_per_batch = [], []
    total_loss = 0.0

    for cnt, feed_dict in enumerate(
      self._model.data_layer.iterate_one_epoch(cross_over=True)
    ):
      loss, inputs, outputs = run_context.session.run(
        self._fetches, feed_dict,
      )
      inputs_per_batch.append(inputs)
      outputs_per_batch.append(outputs)
      total_loss += loss

    total_loss /= (cnt + 1)
    deco_print("Validation loss: {:.4f}".format(total_loss), offset=4)
    dict_to_log = self._model.maybe_evaluate(
      inputs_per_batch,

After Change

      self._model, run_context.session, compute_loss=True,
    )

    if not self._model.on_horovod or self._model._hvd.rank() == 0:
      deco_print("Validation loss: {:.4f}".format(total_loss), offset=4)

      dict_to_log = self._model.maybe_evaluate(
        inputs_per_batch,
        outputs_per_batch,
      )
      dict_to_log["eval_loss"] = total_loss

      // saving the best validation model
      if total_loss < self._best_eval_loss:
        self._best_eval_loss = total_loss
        self._eval_saver.save(
          run_context.session,
          os.path.join(self._model.params["logdir"], "best_models",
                       "val_loss={:.4f}-step".format(total_loss)),
          global_step=step + 1,
        )

      // optionally logging to tensorboard any values
      // returned from maybe_print_logs
      if dict_to_log:
        log_summaries_from_dict(
          dict_to_log,
          self._model.params["logdir"],
          step,
        )

Instances