org.nd4j.linalg.learning.RmsPropUpdater 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.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) {
if (lastGradient == null)
throw new IllegalStateException("Updater has not been initialized with view state");
double learningRate = config.getLearningRate();
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));
}
}