tf.compat.v1.losses.hinge_loss
Adds a hinge loss to the training procedure.
tf.compat.v1.losses.hinge_loss(
labels,
logits,
weights=1.0,
scope=None,
loss_collection=ops.GraphKeys.LOSSES,
reduction=Reduction.SUM_BY_NONZERO_WEIGHTS
)
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. Internally
the {0,1} labels are converted to {-1,1} when calculating the hinge loss.
|
logits
|
The logits, a float tensor. Note that logits are assumed to be
unbounded and 0-centered. A value > 0 (resp. < 0) is considered a positive
(resp. negative) binary prediction.
|
weights
|
Optional Tensor whose rank is either 0, or the same rank as
labels , and must be broadcastable to labels (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
|
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 of logits and labels don't match or
if labels or logits is None.
|
The loss_collection
argument is ignored when executing eagerly. Consider
holding on to the return value or collecting losses via a tf.keras.Model
.
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Last updated 2023-10-06 UTC.
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