org.nd4j.linalg.learning.NesterovsUpdater Maven / Gradle / Ivy
/*-
*
* * 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
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package org.nd4j.linalg.learning;
import lombok.Data;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.impl.transforms.arithmetic.AddOp;
import org.nd4j.linalg.api.shape.Shape;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.learning.config.Nesterovs;
/**
* Nesterov's momentum.
* Keep track of the previous layer's gradient
* and use it as a way of updating the gradient.
*
* @author Adam Gibson
*/
@Data
public class NesterovsUpdater implements GradientUpdater {
private final Nesterovs config;
private INDArray v;
private char gradientReshapeOrder;
public NesterovsUpdater(Nesterovs 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(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
* @return
*/
@Override
public void applyUpdater(INDArray gradient, int iteration) {
if (v == null)
throw new IllegalStateException("Updater has not been initialized with view state");
double momentum = config.getMomentum();
double learningRate = config.getLearningRate();
//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
INDArray vPrev = v.dup(gradientReshapeOrder);
v.muli(momentum).subi(gradient.dup(gradientReshapeOrder).muli(learningRate)); //Modify state array in-place
/*
Next line is equivalent to:
INDArray ret = vPrev.muli(momentum).addi(v.mul(-momentum - 1));
gradient.assign(ret);
*/
Nd4j.getExecutioner().exec(new AddOp(vPrev.muli(momentum), v.mul(-momentum - 1), gradient));
}
}