# tf.contrib.kernel_methods.sparse_multiclass_hinge_loss

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


Adds Ops for computing the multiclass hinge loss.

The implementation is based on the following paper: On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines by Crammer and Singer. link: http://jmlr.csail.mit.edu/papers/volume2/crammer01a/crammer01a.pdf

This is a generalization of standard (binary) hinge loss. For a given instance with correct label c, the loss is given by:

$$loss = max_{c != c} logitsc - logits{c} + 1.$$</div> or equivalently <div>$$loss = max_c { logitsc - logits{c} + I{c != c*} }$$
where (I{c != c} = 1\ ext{if}\ c != c) and 0 otherwise.

#### Args:

• labels: Tensor of shape [batch_size] or [batch_size, 1]. Corresponds to the ground truth. Each entry must be an index in [0, num_classes).
• logits: Tensor of shape [batch_size, num_classes] corresponding to the unscaled logits. Its dtype should be either float32 or float64.
• weights: Optional (python) scalar or Tensor. If a non-scalar Tensor, its rank should be either 1 ([batch_size]) or 2 ([batch_size, 1]).
• 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 a scalar.

#### Raises:

• ValueError: If logits, labels or weights have invalid or inconsistent shapes.
• ValueError: If labels tensor has invalid dtype.