tf.losses.compute_weighted_loss(
losses,
weights=1.0,
scope=None,
loss_collection=tf.GraphKeys.LOSSES,
reduction=Reduction.SUM_BY_NONZERO_WEIGHTS
)
Defined in tensorflow/python/ops/losses/losses_impl.py
.
Computes the weighted loss.
Args:
losses
:Tensor
of shape[batch_size, d1, ... dN]
.weights
: OptionalTensor
whose rank is either 0, or the same rank aslosses
, and must be broadcastable tolosses
(i.e., all dimensions must be either1
, or the same as the correspondinglosses
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.
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.
Raises:
ValueError
: Ifweights
isNone
or the shape is not compatible withlosses
, or if the number of dimensions (rank) of eitherlosses
orweights
is missing.
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
.