theanets.losses.Hinge¶
-
class
theanets.losses.
Hinge
(target, weight=1.0, weighted=False, output_name='out')¶ Hinge loss function for classifiers.
Notes
The hinge loss as implemented here computes the maximum difference between the prediction \(q(x=k)\) for a class \(k\) and the prediction \(q(x=t)\) for the correct class \(t\):
\[\mathcal{L}(x, t) = \max(0, \max_k q(x=k) - q(x=t))\]This loss is zero whenever the prediction for the correct class is the largest over classes, and increases linearly when the prediction for an incorrect class is the largest.
-
__init__
(target, weight=1.0, weighted=False, output_name='out')¶
Methods
__init__
(target[, weight, weighted, output_name])accuracy
(outputs)Build a Theano expression for computing the accuracy of graph output. log
()Log some diagnostic info about this loss. Attributes
variables
A list of Theano variables used in this loss. -