tf.contrib.kernel_methods.sparse_multiclass_hinge_loss

Adds Ops for computing the 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
)

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:

$$loss = max_{c != c*} logits_c - logits_{c*} + 1.$$

or equivalently

$$loss = max_c { logits_c - logits_{c*} + I_{c != c*} }$$

where $I_{c != c*} = 1\ \text{if}\ c != c*$ 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.

© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/contrib/kernel_methods/sparse_multiclass_hinge_loss