tf.losses.hinge_loss( labels, logits, weights=1.0, scope=None, loss_collection=tf.GraphKeys.LOSSES, reduction=Reduction.SUM_BY_NONZERO_WEIGHTS )
Adds a hinge loss to the training procedure.
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.
Tensorwhose 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
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
NONE, this has the same
labels; otherwise, it is scalar.
ValueError: If the shapes of
labelsdon't match or if