ai.djl.training.loss.TabNetClassificationLoss 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
*
* http://aws.amazon.com/apache2.0/
*
* 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 for tabNet in Classification 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 final class TabNetClassificationLoss extends Loss {
/** Calculates the loss of a TabNet instance for regression tasks. */
public TabNetClassificationLoss() {
this("TabNetClassificationLoss");
}
/**
* Calculates the loss of a TabNet instance for regression tasks.
*
* @param name the name of the loss function
*/
public TabNetClassificationLoss(String name) {
super(name);
}
/** {@inheritDoc} */
@Override
public NDArray evaluate(NDList labels, NDList predictions) {
return Loss.softmaxCrossEntropyLoss()
.evaluate(labels, new NDList(predictions.get(0)))
.add(predictions.get(1).mean());
}
}