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/*-
 *
 *  * Copyright 2017 Skymind,Inc.
 *  *
 *  *    Licensed under the Apache License, Version 2.0 (the "License");
 *  *    you may not use this file except in compliance with the License.
 *  *    You may obtain a copy of the License at
 *  *
 *  *        http://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.
 *
 *
 */

package org.nd4j.linalg.learning;

import lombok.Data;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.shape.Shape;
import org.nd4j.linalg.learning.config.RmsProp;
import org.nd4j.linalg.ops.transforms.Transforms;

/**
 * RMS Prop updates:
 * 

* http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf * http://cs231n.github.io/neural-networks-3/#ada * * @author Adam Gibson */ @Data public class RmsPropUpdater implements GradientUpdater { private final RmsProp config; private INDArray lastGradient; private char gradientReshapeOrder; public RmsPropUpdater(RmsProp config) { this.config = config; } @Override public void setStateViewArray(INDArray viewArray, int[] gradientShape, char gradientOrder, boolean initialize) { if (!viewArray.isRowVector()) throw new IllegalArgumentException("Invalid input: expect row vector input"); if (initialize) viewArray.assign(config.getEpsilon()); this.lastGradient = viewArray; //Reshape to match the expected shape of the input gradient arrays this.lastGradient = Shape.newShapeNoCopy(this.lastGradient, gradientShape, gradientOrder == 'f'); if (lastGradient == null) throw new IllegalStateException("Could not correctly reshape gradient view array"); gradientReshapeOrder = gradientOrder; } @Override public void applyUpdater(INDArray gradient, int iteration, int epoch) { if (lastGradient == null) throw new IllegalStateException("Updater has not been initialized with view state"); double learningRate = config.getLearningRate(iteration, epoch); double rmsDecay = config.getRmsDecay(); double epsilon = config.getEpsilon(); lastGradient.muli(rmsDecay).addi(gradient.mul(gradient).muli(1 - rmsDecay)); // lr * gradient / (sqrt(cache) + 1e-8) gradient.muli(learningRate).divi(Transforms.sqrt(lastGradient.dup(gradientReshapeOrder), false).addi(epsilon)); } }





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