org.nd4j.linalg.learning.AdaGradUpdater Maven / Gradle / Ivy
/*******************************************************************************
* 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.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.shape.Shape;
import org.nd4j.linalg.learning.config.AdaGrad;
import static org.nd4j.linalg.ops.transforms.Transforms.sqrt;
/**
* Vectorized Learning Rate used per Connection Weight
*
* Adapted from: http://xcorr.net/2014/01/23/adagrad-eliminating-learning-rates-in-stochastic-gradient-descent/
* See also http://cs231n.github.io/neural-networks-3/#ada
*
* @author Adam Gibson
*/
@Data
public class AdaGradUpdater implements GradientUpdater {
public INDArray historicalGradient;
public int[] shape;
protected double learningRate = 1e-1; // learning rate
protected int numIterations = 0;
private double epsilon = AdaGrad.DEFAULT_ADAGRAD_EPSILON;
private char gradientReshapeOrder;
private AdaGrad config;
public AdaGradUpdater(AdaGrad 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(epsilon);
this.historicalGradient = viewArray;
//Reshape to match the expected shape of the input gradient arrays
this.historicalGradient = Shape.newShapeNoCopy(this.historicalGradient, gradientShape, gradientOrder == 'f');
if (historicalGradient == null)
throw new IllegalStateException("Could not correctly reshape gradient view array");
this.gradientReshapeOrder = gradientOrder;
}
/**
* Gets feature specific learning rates
* Adagrad keeps a history of gradients being passed in.
* Note that each gradient passed in becomes adapted over time, hence the opName adagrad
*
* @param gradient the gradient to get learning rates for
* @param iteration
*/
@Override
public void applyUpdater(INDArray gradient, int iteration, int epoch) {
if (historicalGradient == null)
throw new IllegalStateException("Updater has not been initialized with view state");
double learningRate = config.getLearningRate(iteration, epoch);
double epsilon = config.getEpsilon();
historicalGradient.addi(gradient.mul(gradient));
INDArray sqrtHistory = sqrt(historicalGradient.dup(gradientReshapeOrder), false).addi(epsilon);
// lr * gradient / (sqrt(sumSquaredGradients) + epsilon)
gradient.muli(sqrtHistory.rdivi(learningRate));
}
}