ai.djl.training.loss.TabNetRegressionLoss Maven / Gradle / Ivy
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*
* Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance
* with the License. A copy of the License is located at
*
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*
* or in the "license" file accompanying this file. This file is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES
* OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions
* and limitations under the License.
*/
package ai.djl.training.loss;
import ai.djl.ndarray.NDArray;
import ai.djl.ndarray.NDList;
/**
* Calculates the loss of tabNet for regression tasks.
*
* Actually, tabNet is not only used for Supervised Learning, it's also widely used in
* unsupervised learning. For unsupervised learning, it should come from the decoder(aka
* attentionTransformer of tabNet)
*/
public class TabNetRegressionLoss extends Loss {
/** Calculates the loss of a TabNet instance for regression tasks. */
public TabNetRegressionLoss() {
this("TabNetRegressionLoss");
}
/**
* Calculates the loss of a TabNet instance for regression tasks.
*
* @param name the name of the loss function
*/
public TabNetRegressionLoss(String name) {
super(name);
}
/** {@inheritDoc} */
@Override
public NDArray evaluate(NDList labels, NDList predictions) {
// sparseLoss is already calculated inside the forward of tabNet
// so here we just need to get it out from prediction
return labels.singletonOrThrow()
.sub(predictions.get(0))
.square()
.mean()
.add(predictions.get(1).mean());
}
}