org.nd4j.linalg.api.ops.impl.loss.SparseSoftmaxCrossEntropyLossWithLogits 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;
import lombok.NoArgsConstructor;
import lombok.val;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.imports.NoOpNameFoundException;
import org.nd4j.imports.descriptors.properties.PropertyMapping;
import org.nd4j.imports.graphmapper.tf.TFGraphMapper;
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.HashMap;
import java.util.LinkedHashMap;
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 SparseSoftmaxCrossEntropyLossWithLogits extends DynamicCustomOp {
public SparseSoftmaxCrossEntropyLossWithLogits(SameDiff sameDiff, SDVariable logits, SDVariable labels) {
super(null, sameDiff, new SDVariable[]{logits, labels}, false);
}
public void addArgs() {
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
TFGraphMapper.getInstance().initFunctionFromProperties(nodeDef.getOp(), this, attributesForNode, nodeDef, graph);
//Switch order: TF uses [logits, labels]; libnd4j expects [labels, logits]
String[] inputs = initWith.getInputsForFunction(this);
String temp = inputs[0];
inputs[0] = inputs[1];
inputs[1] = temp;
}
@Override
public String opName() {
return "sparse_softmax_cross_entropy_loss_with_logits";
}
@Override
public String onnxName() {
throw new NoOpNameFoundException("No onnx op opName found for " + opName());
}
@Override
public String tensorflowName() {
return "SparseSoftmaxCrossEntropyWithLogits";
}
@Override
public Op.Type opType() {
return Op.Type.CUSTOM;
}
}