cavis/cavis-dnn/cavis-dnn-api/src/main/java/org/nd4j/linalg/learning/NesterovsUpdater.java

/*
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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
 *  * License for the specific language governing permissions and limitations
 *  * under the License.
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 *  * SPDX-License-Identifier: Apache-2.0
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package org.nd4j.linalg.learning;

import lombok.Data;
import lombok.NonNull;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.shape.Shape;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.learning.config.Nesterovs;

import java.util.Collections;
import java.util.Map;

@Data
public class NesterovsUpdater implements GradientUpdater<Nesterovs> {
    public static final String V_STATE = "V";

    private final Nesterovs config;

    private INDArray v;
    private char gradientReshapeOrder;

    public NesterovsUpdater(Nesterovs config) {
        this.config = config;
    }

    @Override
    public void setState(@NonNull Map<String, INDArray> stateMap, boolean initialize) {
        if(!stateMap.containsKey(V_STATE) || stateMap.size() != 1){
            throw new IllegalStateException("State map should contain only key [" + V_STATE + "] but has keys " + stateMap.keySet());
        }
        this.v = stateMap.get(V_STATE);
    }

    @Override
    public Map<String, INDArray> getState() {
        return Collections.singletonMap(V_STATE, this.v);
    }

    @Override
    public void setStateViewArray(INDArray viewArray, long[] gradientShape, char gradientOrder, boolean initialize) {
        if (!viewArray.isRowVectorOrScalar())
            throw new IllegalArgumentException("Invalid input: expect row vector input");
        if (initialize)
            viewArray.assign(0);

        this.v = viewArray;

        //Reshape to match the expected shape of the input gradient arrays
        this.v = Shape.newShapeNoCopy(this.v, gradientShape, gradientOrder == 'f');
        if (v == null)
            throw new IllegalStateException("Could not correctly reshape gradient view array");
        this.gradientReshapeOrder = gradientOrder;
    }

    /**
     * Get the nesterov update
     *
     * @param gradient  the gradient to get the update for
     * @param iteration
     */
    @Override
    public void applyUpdater(INDArray gradient, int iteration, int epoch) {
        if (v == null)
            throw new IllegalStateException("Updater has not been initialized with view state");

        double momentum = config.currentMomentum(iteration, epoch);
        double learningRate = config.getLearningRate(iteration, epoch);

        //reference https://cs231n.github.io/neural-networks-3/#sgd 2nd equation
        //DL4J default is negative step function thus we flipped the signs:
        // x += mu * v_prev + (-1 - mu) * v
        //i.e., we do params -= updatedGradient, not params += updatedGradient
        //v = mu * v - lr * gradient

        Nd4j.exec(new org.nd4j.linalg.api.ops.impl.updaters.NesterovsUpdater(gradient, v, learningRate, momentum));
    }
}