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Computes the weighted loss.



  • losses: Tensor of shape [batch_size, d1, ... dN].
  • weights: Optional Tensor whose rank is either 0, or the same rank as losses, and must be broadcastable to losses (i.e., all dimensions must be either 1, or the same as the corresponding losses dimension).
  • scope: the scope for the operations performed in computing the loss.
  • loss_collection: the loss will be added to these collections.
  • reduction: Type of reduction to apply to loss.


Weighted loss Tensor of the same type as losses. If reduction is NONE, this has the same shape as losses; otherwise, it is scalar.


  • ValueError: If weights is None or the shape is not compatible with losses, or if the number of dimensions (rank) of either losses or weights is missing.


When calculating the gradient of a weighted loss contributions from both losses and weights are considered. If your weights depend on some model parameters but you do not want this to affect the loss gradient, you need to apply tf.stop_gradient to weights before passing them to compute_weighted_loss.

Eager Compatibility

The loss_collection argument is ignored when executing eagerly. Consider holding on to the return value or collecting losses via a tf.keras.Model.