View source on GitHub |
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:
or equivalently
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.
|