58 lines
2.1 KiB
Java
Executable File
58 lines
2.1 KiB
Java
Executable File
/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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package org.deeplearning4j.optimize.solvers;
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import org.deeplearning4j.nn.api.Model;
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import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
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import org.deeplearning4j.optimize.api.StepFunction;
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import org.deeplearning4j.optimize.api.TrainingListener;
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import org.nd4j.linalg.api.ndarray.INDArray;
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import org.nd4j.linalg.factory.Nd4j;
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import java.util.Collection;
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/**
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* Stochastic Gradient Descent with Line Search
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* @author Adam Gibson
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*
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*/
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public class LineGradientDescent extends BaseOptimizer {
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private static final long serialVersionUID = 6336124657542062284L;
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public LineGradientDescent(NeuralNetConfiguration conf, StepFunction stepFunction,
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Collection<TrainingListener> trainingListeners, Model model) {
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super(conf, stepFunction, trainingListeners, model);
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}
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@Override
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public void preProcessLine() {
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INDArray gradient = (INDArray) searchState.get(GRADIENT_KEY);
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searchState.put(SEARCH_DIR, gradient.dup());
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}
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@Override
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public void postStep(INDArray gradient) {
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double norm2 = Nd4j.getBlasWrapper().level1().nrm2(gradient);
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if (norm2 > stepMax)
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searchState.put(SEARCH_DIR, gradient.dup().muli(stepMax / norm2));
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else
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searchState.put(SEARCH_DIR, gradient.dup());
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searchState.put(GRADIENT_KEY, gradient.dup());
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}
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}
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