org.nd4j.linalg.api.ops.impl.loss.bp.SparseSoftmaxCrossEntropyLossWithLogitsBp Maven / Gradle / Ivy
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* Copyright (c) 2015-2018 Skymind, Inc.
*
* 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.
*
* 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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* under the License.
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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.autodiff.samediff.internal.SameDiffOp;
import org.nd4j.base.Preconditions;
import org.nd4j.imports.NoOpNameFoundException;
import org.nd4j.imports.graphmapper.tf.TFGraphMapper;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.nd4j.linalg.api.ops.Op;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;
import java.util.Map;
/**
* Sparse softmax cross entropy loss with logits.
* Applies softmax to the input, then calculates cross entropy loss. Labels should be in integer-index format,
* not one-hot format
*
* @author Alex Black
*/
@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;
}
}