org.nd4j.linalg.learning.AdaMaxUpdater Maven / Gradle / Ivy
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* Copyright (c) 2015-2018 Skymind, Inc.
*
* 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.
*
* 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 org.apache.commons.math3.util.FastMath;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.impl.transforms.comparison.OldMax;
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 org.nd4j.linalg.ops.transforms.Transforms;
/**
* The AdaMax updater, a variant of Adam.
* http://arxiv.org/abs/1412.6980
*
* @author Justin Long
*/
@Data
public class AdaMaxUpdater implements GradientUpdater {
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 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
m.muli(config.getBeta1()).addi(gradient.mul(1 - config.getBeta1()));
//u = max(B_2 * u, |grad|)
u.muli(config.getBeta2());
Transforms.abs(gradient, false); //In-place should be OK here, original gradient values aren't used again later
Nd4j.getExecutioner().exec(new OldMax(u, gradient, u));
double beta1t = FastMath.pow(config.getBeta1(), iteration + 1);
double learningRate = config.getLearningRate(iteration, epoch);
double alphat = learningRate / (1.0 - beta1t);
if (Double.isNaN(alphat) || Double.isInfinite(alphat) || alphat == 0.0) {
alphat = config.getEpsilon();
}
u.addi(1e-32); // prevent NaNs in params
gradient.assign(m).muli(alphat).divi(u);
}
}