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
 * License for the specific language governing permissions and limitations
 * under the License.
 *
 * SPDX-License-Identifier: Apache-2.0
 ******************************************************************************/

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.autodiff.samediff.internal.SameDiffOp;
import org.nd4j.base.Preconditions;
import org.nd4j.imports.NoOpNameFoundException;
import org.nd4j.imports.descriptors.properties.PropertyMapping;
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.*;


/**
 * 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[]{labels, logits}, 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]
        SameDiffOp op = initWith.getOps().get(this.getOwnName());
        List list = op.getInputsToOp();
        List newList = Arrays.asList(list.get(1), list.get(0));
        op.setInputsToOp(newList);
    }

    @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;
    }

    @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 List doDiff(List grad){
        //args: label, logits
        SDVariable[] ret = f().lossSparseSoftmaxCrossEntropyBp(arg(1), arg(0));
        return Arrays.asList(f().zerosLike(arg(0)), ret[0]);
    }
}