clj-djl.training.loss — clj-djl 0.1.9

evaluate clj

(evaluate loss label pred)

hinge clj

(hinge)

(hinge name)

(hinge name margin weight)

The hinge loss is used for maximum-margin classification.

hinge-loss clj

l1 clj

(l1)

(l1 name)

(l1 name weight)

Least absolute deviations loss function minimizes the absolute differences between the estimated values and the existing target values.

l1-loss clj

l2 clj

(l2)

(l2 name)

(l2 name weight)

Least square errors loss function minimizes the squared differences between the estimated and existing target values.

l2-loss clj

masked-softmax-cross-entropy clj

(masked-softmax-cross-entropy)

(masked-softmax-cross-entropy name)

(masked-softmax-cross-entropy name weight class-axis sparse-label from-logit)

masked-softmax-cross-entropy-loss clj

sigmoid-binary-cross-entropy clj

(sigmoid-binary-cross-entropy)

(sigmoid-binary-cross-entropy name)

(sigmoid-binary-cross-entropy name weight from-sigmoid)

sigmoid-binary-cross-entropy-loss clj

sotfmax-cross-entropy clj

(sotfmax-cross-entropy)

(sotfmax-cross-entropy name)

(sotfmax-cross-entropy name weight class-axis sparse-label from-logit)

sotfmax-cross-entropy-loss clj