187a5921a39ba2791176c1573c40850277d4a2ef,opennmt/training.py,Trainer,_steps,#Trainer#Any#Any#Any#,150

Before Change

    Returns:
      A generator that yields a loss value to report for this step.
    
    raise NotImplementedError()

  def _run_model(self, source, target):
    Computes the loss of the given source and target pair.

After Change

    def _step():
      return self._step()

    for i in itertools.count():
      try:
        loss = _forward()
      except tf.errors.OutOfRangeError:  // Dataset iterator exhausted.
        break
      if tf.math.is_nan(loss):
        raise RuntimeError("Model diverged with loss = NaN.")

      if i == 0 or (i + 1) % accum_steps == 0:
        _step()
        if i == 0:
          self._broadcast_variables()
        yield loss

  def _run_model(self, source, target):
    Computes the loss of the given source and target pair.

    Args:

Instances