tf.losses.hinge_loss

Adds a hinge loss to the training procedure.

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Compat aliases for migration

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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
)

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.