tf.losses.hinge_loss

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Adds a hinge loss to the training procedure.

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.losses.hinge_loss

 
tf.losses.hinge_loss(
    labels, logits, weights=1.0, scope=None, loss_collection=tf.GraphKeys.LOSSES,
    reduction=Reduction.SUM_BY_NONZERO_WEIGHTS
)

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.

Weighted loss float Tensor. If reduction is NONE, this has the same shape as labels; otherwise, it is scalar.

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.