Function compute_weighted_loss

tensorflow/python/keras/utils/losses_utils.py:70–112

Computes the weighted loss. Args: losses: `Tensor` of shape `[batch_size, d1, ... dN]`. sample_weight: Optional `Tensor` whose rank is either 0, or the same rank as `losses`, or be broadcastable to `losses`. reduction: (Optional) Type of `tf.keras.losses.Reduction` to apply to l

(losses,
                          sample_weight=None,
                          reduction=ReductionV2.SUM_OVER_BATCH_SIZE,
                          name=None)

def compute_weighted_loss(losses,
                          sample_weight=None,
                          reduction=ReductionV2.SUM_OVER_BATCH_SIZE,
                          name=None):
  """Computes the weighted loss.

  Args:
    losses: `Tensor` of shape `[batch_size, d1, ... dN]`.
    sample_weight: Optional `Tensor` whose rank is either 0, or the same rank as
      `losses`, or be broadcastable to `losses`.
    reduction: (Optional) Type of `tf.keras.losses.Reduction` to apply to loss.
      Default value is `SUM_OVER_BATCH_SIZE`.
    name: Optional name for the op.

  Raises:
    ValueError: If the shape of `sample_weight` is not compatible with `losses`.

  Returns:
    Weighted loss `Tensor` of the same type as `losses`. If `reduction` is
    `NONE`, this has the same shape as `losses`; otherwise, it is scalar.
  """
  ReductionV2.validate(reduction)

  # If this function is called directly, then we just default 'AUTO' to
  # 'SUM_OVER_BATCH_SIZE'. Eg. Canned estimator use cases.
  if reduction == ReductionV2.AUTO:
    reduction = ReductionV2.SUM_OVER_BATCH_SIZE
  if sample_weight is None:
    sample_weight = 1.0
  with K.name_scope(name or 'weighted_loss'):
    # Save the `reduction` argument for loss normalization when distributing
    # to multiple replicas. Used only for estimator + v1 optimizer flow.
    ops.get_default_graph()._last_loss_reduction = reduction  # pylint: disable=protected-access

    losses = ops.convert_to_tensor(losses)
    input_dtype = losses.dtype
    weighted_losses = tf_losses_utils.scale_losses_by_sample_weight(
        losses, sample_weight)
    # Apply reduction function to the individual weighted losses.
    loss = reduce_weighted_loss(weighted_losses, reduction)
    # Convert the result back to the input type.
    loss = math_ops.cast(loss, input_dtype)
    return loss


def scale_loss_for_distribution(loss_value):