Adds a hinge loss to the training procedure.
tf.compat.v1.losses.hinge_loss(
labels, logits, weights=1.0, scope=None, loss_collection=tf.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.
|
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.
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