tensorflow_probability/python/optimizer/lbfgs.py
def _get_initial_state(value_and_gradients_function,
                       initial_position,
                       num_correction_pairs,
                       tolerance):
  """Create LBfgsOptimizerResults with initial state of search procedure."""
  init_args = bfgs_utils.get_initial_state_args(
      value_and_gradients_function,
      initial_position,
      tolerance)
  empty_queue = _make_empty_queue_for(num_correction_pairs, initial_position)
  init_args.update(position_deltas=empty_queue, gradient_deltas=empty_queue)
  return LBfgsOptimizerResults(**init_args)
def _get_initial_state(value_and_gradients_function,
                       initial_position,
                       num_correction_pairs,
                       tolerance):
  init_args = bfgs_utils.get_initial_state_args(
      value_and_gradients_function,
      initial_position,
      tolerance)
  empty_queue = _make_empty_queue_for(num_correction_pairs, initial_position)
  init_args.update(position_deltas=empty_queue, gradient_deltas=empty_queue)
  return LBfgsOptimizerResults(**init_args)
def _get_initial_state ( value_and_gradients_function , initial_position , num_correction_pairs , tolerance ) : init_args = bfgs_utils . get_initial_state_args ( value_and_gradients_function , initial_position , tolerance ) empty_queue = _make_empty_queue_for ( num_correction_pairs , initial_position ) init_args . update ( position_deltas = empty_queue , gradient_deltas = empty_queue ) return LBfgsOptimizerResults ( * * init_args )
Create LBfgsOptimizerResults with initial state of search procedure.
Create LBfgsOptimizerResults with initial state of search procedure .