# tf.losses.compute_weighted_loss(losses, weights=1.0, scope=None, loss_collection=tf.GraphKeys.LOSSES)

### tf.losses.compute_weighted_loss(losses, weights=1.0, scope=None, loss_collection=tf.GraphKeys.LOSSES)

Computes the weighted loss.

#### Args:

• 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.

#### Returns:

A scalar Tensor that returns the weighted loss.

#### Raises:

• 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.