963710e3d38c9ad1d8b8cc1419a3bd1b3dddde1f,opennmt/utils/optim.py,,optimize,#Any#Any#Any#,90

Before Change

  learning_rate = float(params["learning_rate"])
  clip_gradients = params.get("clip_gradients")
  if clip_gradients is not None:
    clip_gradients = float(clip_gradients)

  optimizer_class = get_optimizer_class(params["optimizer"])
  optimizer_params = params.get("optimizer_params", {})

  if optimizer_class.__name__ == "AdafactorOptimizer":

After Change

          start_decay_steps=params.get("start_decay_steps", 0),
          minimum_learning_rate=params.get("minimum_learning_rate", 0))
      learning_rate = decay_fn(learning_rate, global_step)
    tf.summary.scalar("learning_rate", learning_rate)

    // Optimizer.
    optimizer_class = get_optimizer_class(params["optimizer"])
    optimizer_params = params.get("optimizer_params", {})

Instances