loss_ops.py 文件源码

def compute_weighted_loss(losses, weight=1.0):
  """Computes the weighted loss.

  Args:
    losses: A tensor of size [batch_size, d1, ... dN].
    weight: A tensor of size [1] or [batch_size, d1, ... dK] where K < N.

  Returns:
    A scalar `Tensor` that returns the weighted loss.

  Raises:
    ValueError: If the weight is None or the shape is not compatible with the
      losses shape or if the number of dimensions (rank) of either losses or
      weight is missing.
  """
  if weight is None:
    raise ValueError("`weight` cannot be None")
  input_dtype = losses.dtype
  losses = math_ops.to_float(losses)
  weight = math_ops.to_float(ops.convert_to_tensor(weight))

  if losses.get_shape().ndims is None:
    raise ValueError("losses.get_shape().ndims cannot be None")
  if weight.get_shape().ndims is None:
    raise ValueError("weight.get_shape().ndims cannot be None")

  total_loss = _scale_losses(losses, weight)
  num_present = _num_present(losses, weight)
  mean_loss = _safe_mean(total_loss, num_present)
  # convert the result back to the input type
  mean_loss = math_ops.cast(mean_loss, input_dtype)
  add_loss(mean_loss)
  return mean_loss