tensorflow_probability/python/optimizer/linesearch/hager_zhang.py
def _fix_step_size(value_and_gradients_function,
                   val_c_input,
                   active,
                   step_size_shrink_param):
  """Shrinks the input step size until the value and grad become finite."""
  # The maximum iterations permitted are determined as the number of halvings
  # it takes to reduce 1 to 0 in the given dtype.
  iter_max = np.ceil(-np.log2(_machine_eps(val_c_input.x.dtype)))

  def _cond(i, val_c, to_fix):
    del val_c  # Unused.
    return (i < iter_max) & tf.reduce_any(input_tensor=to_fix)

  def _body(i, val_c, to_fix):
    next_c = tf.where(to_fix, val_c.x * step_size_shrink_param, val_c.x)
    next_val_c = value_and_gradients_function(next_c)
    still_to_fix = to_fix & ~hzl.is_finite(next_val_c)
    return (i + 1, next_val_c, still_to_fix)

  to_fix = active & ~hzl.is_finite(val_c_input)
  return tf.while_loop(
      cond=_cond, body=_body, loop_vars=(0, val_c_input, to_fix))
def _fix_step_size(value_and_gradients_function,
                   val_c_input,
                   active,
                   step_size_shrink_param):
  # The maximum iterations permitted are determined as the number of halvings
  # it takes to reduce 1 to 0 in the given dtype.
  iter_max = np.ceil(-np.log2(_machine_eps(val_c_input.x.dtype)))

  def _cond(i, val_c, to_fix):
    del val_c  # Unused.
    return (i < iter_max) & tf.reduce_any(input_tensor=to_fix)

  def _body(i, val_c, to_fix):
    next_c = tf.where(to_fix, val_c.x * step_size_shrink_param, val_c.x)
    next_val_c = value_and_gradients_function(next_c)
    still_to_fix = to_fix & ~hzl.is_finite(next_val_c)
    return (i + 1, next_val_c, still_to_fix)

  to_fix = active & ~hzl.is_finite(val_c_input)
  return tf.while_loop(
      cond=_cond, body=_body, loop_vars=(0, val_c_input, to_fix))
def _fix_step_size ( value_and_gradients_function , val_c_input , active , step_size_shrink_param ) : # The maximum iterations permitted are determined as the number of halvings # it takes to reduce 1 to 0 in the given dtype. iter_max = np . ceil ( - np . log2 ( _machine_eps ( val_c_input . x . dtype ) ) ) def _cond ( i , val_c , to_fix ) : del val_c # Unused. return ( i < iter_max ) & tf . reduce_any ( input_tensor = to_fix ) def _body ( i , val_c , to_fix ) : next_c = tf . where ( to_fix , val_c . x * step_size_shrink_param , val_c . x ) next_val_c = value_and_gradients_function ( next_c ) still_to_fix = to_fix & ~ hzl . is_finite ( next_val_c ) return ( i + 1 , next_val_c , still_to_fix ) to_fix = active & ~ hzl . is_finite ( val_c_input ) return tf . while_loop ( cond = _cond , body = _body , loop_vars = ( 0 , val_c_input , to_fix ) )
Shrinks the input step size until the value and grad become finite.
Shrinks the input step size until the value and grad become finite .