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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.
* *
* * SPDX-License-Identifier: Apache-2.0
* *****************************************************************************
*/
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.indexing.NDArrayIndex;
import org.nd4j.linalg.learning.config.AdaMax;
import java.util.HashMap;
import java.util.Map;
/**
* The AdaMax updater, a variant of Adam.
* https://arxiv.org/abs/1412.6980
*
* @author Justin Long
*/
@Data
public class AdaMaxUpdater implements GradientUpdater {
public static final String M_STATE = "M";
public static final String U_STATE = "V";
private final AdaMax config;
private INDArray m, u; // moving avg & exponentially weighted infinity norm
private char gradientReshapeOrder;
public AdaMaxUpdater(AdaMax config) {
this.config = config;
}
@Override
public void setState(@NonNull Map stateMap, boolean initialize) {
if(!stateMap.containsKey(M_STATE) || !stateMap.containsKey(U_STATE) || stateMap.size() != 2){
throw new IllegalStateException("State map should contain only keys [" + M_STATE + "," + U_STATE + "] but has keys " + stateMap.keySet());
}
this.m = stateMap.get(M_STATE);
this.u = stateMap.get(U_STATE);
}
@Override
public Map getState() {
Map r = new HashMap<>();
r.put(M_STATE, m);
r.put(U_STATE, u);
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.m = viewArray.get(NDArrayIndex.point(0), NDArrayIndex.interval(0, length / 2));
this.u = viewArray.get(NDArrayIndex.point(0), NDArrayIndex.interval(length / 2, length));
//Reshape to match the expected shape of the input gradient arrays
this.m = Shape.newShapeNoCopy(this.m, gradientShape, gradientOrder == 'f');
this.u = Shape.newShapeNoCopy(this.u, gradientShape, gradientOrder == 'f');
if (m == null || u == null)
throw new IllegalStateException("Could not correctly reshape gradient view arrays");
this.gradientReshapeOrder = gradientOrder;
}
/**
* Calculate the update based on the given gradient
*
* @param gradient the gradient to get the update for
* @param iteration
* @return the gradient
*/
@Override
public void applyUpdater(INDArray gradient, int iteration, int epoch) {
if (m == null || u == null)
throw new IllegalStateException("Updater has not been initialized with view state");
//m = B_1 * m + (1-B_1)*grad
//u = max(B_2 * u, |grad|)
double lr = config.getLearningRate(iteration, epoch);
double b1 = config.getBeta1();
double b2 = config.getBeta2();
double eps = config.getEpsilon();
Nd4j.exec(new org.nd4j.linalg.api.ops.impl.updaters.AdaMaxUpdater(gradient, u, m, lr, b1, b2, eps, iteration));
}
}
