org.nd4j.linalg.learning.AdaDeltaUpdater Maven / Gradle / Ivy
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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
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* * SPDX-License-Identifier: Apache-2.0
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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.factory.Nd4j;
import org.nd4j.linalg.indexing.NDArrayIndex;
import org.nd4j.linalg.learning.config.AdaDelta;
import java.util.HashMap;
import java.util.Map;
@Data
public class AdaDeltaUpdater implements GradientUpdater {
public static final String MSG_STATE = "msg";
public static final String MSDX_STATE = "msdx";
private final AdaDelta config;
private INDArray msg; //E[g^2]_t by arxiv paper, algorithm 1
private INDArray msdx; //E[delta x^2]_t by arxiv paper, algorithm 1
public AdaDeltaUpdater(AdaDelta config) {
this.config = config;
}
@Override
public void setState(Map stateMap, boolean initialize) {
if(!stateMap.containsKey(MSG_STATE) || !stateMap.containsKey(MSDX_STATE) || stateMap.size() != 2){
throw new IllegalStateException("State map should contain only keys [" + MSG_STATE + "," + MSDX_STATE + "] but has keys " + stateMap.keySet());
}
this.msg = stateMap.get(MSG_STATE);
this.msdx = stateMap.get(MSDX_STATE);
}
@Override
public Map getState() {
Map r = new HashMap<>();
r.put(MSG_STATE, msg);
r.put(MSDX_STATE, msdx);
return r;
}
@Override
public void setStateViewArray(INDArray viewArray, long[] gradientShape, char gradientOrder, boolean initialize) {
if (!viewArray.isRowVector())
throw new IllegalArgumentException("Invalid input: expect row vector input");
if (initialize)
viewArray.assign(0);
long length = viewArray.length();
this.msg = viewArray.get(NDArrayIndex.point(0), NDArrayIndex.interval(0, length / 2));
this.msdx = viewArray.get(NDArrayIndex.point(0), NDArrayIndex.interval(length / 2, length));
//Reshape to match the expected shape of the input gradient arrays
this.msg = Shape.newShapeNoCopy(this.msg, gradientShape, gradientOrder == 'f');
this.msdx = Shape.newShapeNoCopy(this.msdx, gradientShape, gradientOrder == 'f');
if (msg == null || msdx == null)
throw new IllegalStateException("Could not correctly reshape gradient view arrays");
}
/**
* Get the updated gradient for the given gradient
* and also update the state of ada delta.
*
* @param gradient the gradient to get the
* updated gradient for
* @param iteration
* @return the update gradient
*/
@Override
public void applyUpdater(INDArray gradient, int iteration, int epoch) {
if (msg == null || msdx == null)
throw new IllegalStateException("Updater has not been initialized with view state");
double rho = config.getRho();
double epsilon = config.getEpsilon();
//Line 4 of Algorithm 1: https://arxiv.org/pdf/1212.5701v1.pdf
//E[g^2]_t = rho * E[g^2]_{t-1} + (1-rho)*g^2_t
//Calculate update:
//dX = - g * RMS[delta x]_{t-1} / RMS[g]_t
//Note: negative is applied in the DL4J step function: params -= update rather than params += update
//Accumulate gradients: E[delta x^2]_t = rho * E[delta x^2]_{t-1} + (1-rho)* (delta x_t)^2
Nd4j.exec(new org.nd4j.linalg.api.ops.impl.updaters.AdaDeltaUpdater(gradient, msg, msdx, rho, epsilon));
}
}