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Java example source code file (ConvexOptimizer.java)

This example Java source code file (ConvexOptimizer.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Learn more about this Java project at its project page.

Java - Java tags/keywords

computationgraphupdater, convexoptimizer, double, model, neuralnetconfiguration, pair, serializable, util

The ConvexOptimizer.java Java example source code

 *  * Copyright 2015 Skymind,Inc.
 *  *
 *  *    Licensed under the Apache License, Version 2.0 (the "License");
 *  *    you may not use this file except in compliance with the License.
 *  *    You may obtain a copy of the License at
 *  *
 *  *        http://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.

package org.deeplearning4j.optimize.api;

import org.deeplearning4j.berkeley.Pair;
import org.deeplearning4j.nn.api.Model;
import org.deeplearning4j.nn.api.Updater;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.gradient.Gradient;
import org.deeplearning4j.nn.updater.graph.ComputationGraphUpdater;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.learning.AdaGrad;
import org.nd4j.linalg.learning.GradientUpdater;

import java.io.Serializable;
import java.util.Collection;
import java.util.Map;

 * Convex optimizer.
 * @author Adam Gibson
public interface ConvexOptimizer extends Serializable {
     * The score for the optimizer so far
     * @return the score for this optimizer so far
    double score();

    Updater getUpdater();

    ComputationGraphUpdater getComputationGraphUpdater();

    void setUpdater(Updater updater);

    void setUpdaterComputationGraph(ComputationGraphUpdater updater);

    void setListeners(Collection<IterationListener> listeners);

    NeuralNetConfiguration getConf();

     * The gradient and score for this optimizer
     * @return the gradient and score for this optimizer
    Pair<Gradient,Double> gradientAndScore();

     * Calls optimize
     * @return whether the convex optimizer
     * converted or not
    boolean optimize();

     * The batch size for the optimizer
     * @return
    int batchSize();

     * Set the batch size for the optimizer
     * @param batchSize
    void setBatchSize(int batchSize);

     * Pre preProcess a line before an iteration
    void preProcessLine();

     * After the step has been made, do an action
     * @param line
     * */
    void postStep(INDArray line);

     * Based on the gradient and score
     * setup a search state
     * @param pair the gradient and score
    void setupSearchState(Pair<Gradient, Double> pair);

     * Update the gradient according to the configuration such as adagrad, momentum, and sparsity
     * @param gradient the gradient to modify
     * @param model the model with the parameters to update
     * @param batchSize batchSize for update
     * @paramType paramType to update
    void updateGradientAccordingToParams(Gradient gradient, Model model, int batchSize);

     * Check termination conditions
     * setup a search state
     * @param gradient layer gradients
     * @param iteration what iteration the optimizer is on
    boolean checkTerminalConditions(INDArray gradient, double oldScore, double score, int iteration);

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