tf.losses.compute_weighted_loss
tf.losses.compute_weighted_loss
tf.losses.compute_weighted_loss
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.
© 2017 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/losses/compute_weighted_loss