org.nd4j.linalg.api.ops.impl.loss.bp.SparseSoftmaxCrossEntropyLossWithLogitsBp Maven / Gradle / Ivy
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* * This program and the accompanying materials are made available under the
* * terms of the Apache License, Version 2.0 which is available at
* * https://www.apache.org/licenses/LICENSE-2.0.
* *
* * See the NOTICE file distributed with this work for additional
* * information regarding copyright ownership.
* * Unless required by applicable law or agreed to in writing, software
* * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* * SPDX-License-Identifier: Apache-2.0
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package org.nd4j.linalg.api.ops.impl.loss.bp;
import lombok.NoArgsConstructor;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.common.base.Preconditions;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import java.util.Collections;
import java.util.List;
@NoArgsConstructor
public class SparseSoftmaxCrossEntropyLossWithLogitsBp extends DynamicCustomOp {
public SparseSoftmaxCrossEntropyLossWithLogitsBp(SameDiff sameDiff, SDVariable logits, SDVariable labels) {
super(null, sameDiff, new SDVariable[]{labels, logits}, false);
}
@Override
public String opName() {
return "sparse_softmax_cross_entropy_loss_with_logits_grad";
}
@Override
public List doDiff(List grad){
throw new UnsupportedOperationException("Differentiation of " + getClass().getName() + " not supported");
}
@Override
public List calculateOutputDataTypes(List inputDataTypes){
Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == 2, "Expected 2 input datatypes for %s, got %s", getClass(), inputDataTypes);
return Collections.singletonList(inputDataTypes.get(1)); //Same as predictions (logits)
}
@Override
public int getNumOutputs(){
return 1;
}
}