tf.losses.hinge_loss
tf.losses.hinge_loss
tf.losses.hinge_loss
hinge_loss( labels, logits, 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
.
Adds a hinge loss to the training procedure.
Args:
-
labels
: The ground truth output tensor. Its shape should match the shape of logits. The values of the tensor are expected to be 0.0 or 1.0. -
logits
: The logits, a float tensor. -
weights
: OptionalTensor
whose rank is either 0, or the same rank aslabels
, and must be broadcastable tolabels
(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
: collection to which the loss will be added. -
reduction
: Type of reduction to apply to loss.
Returns:
Weighted loss float Tensor
. If reduction
is NONE
, this has the same shape as labels
; otherwise, it is scalar.
Raises:
-
ValueError
: If the shapes oflogits
andlabels
don't match.
© 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/hinge_loss