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Java example source code file (Model.java)

This example Java source code file (Model.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

convexoptimizer, gradient, indarray, map, model, neuralnetconfiguration, pair, string, util

The Model.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.nn.api;

import org.deeplearning4j.berkeley.Pair;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.gradient.Gradient;
import org.deeplearning4j.optimize.api.ConvexOptimizer;
import org.nd4j.linalg.api.ndarray.INDArray;

import java.util.Map;

/**
 * A Model is meant for predicting something from data.
 * Note that this is not like supervised learning where
 * there are labels attached to the examples.
 *
 */
public interface Model {

    /**
     * All models have a fit method
     */
    void fit();

    /**
     * Perform one update  applying the gradient
     * @param gradient the gradient to apply
     */
    void update(INDArray gradient, String paramType);


    /**
     * The score for the model
     * @return the score for the model
     */
    double score();


    /**
     * Update the score
     */
    void computeGradientAndScore();

    /**
     * Sets a rolling tally for the score. This is useful for mini batch learning when
     * you are accumulating error across a dataset.
     * @param accum the amount to accum
     */
    void accumulateScore(double accum);


    /**
     * Parameters of the model (if any)
     * @return the parameters of the model
     */
    INDArray params();

    /**
     * the number of parameters for the model
     * @return the number of parameters for the model
     *
     */
    int numParams();


    /**
     * the number of parameters for the model
     * @return the number of parameters for the model
     *
     */
    int numParams(boolean backwards);

    /**
     * Set the parameters for this model.
     * This expects a linear ndarray which then be unpacked internally
     * relative to the expected ordering of the model
     * @param params the parameters for the model
     */
    void setParams(INDArray params);

    /**
     * Set the initial parameters array as a view of the full (backprop) network parameters
     * NOTE: this is intended to be used internally in MultiLayerNetwork and ComputationGraph, not by users.
     * @param params a 1 x nParams row vector that is a view of the larger (MLN/CG) parameters array
     */
    void setParamsViewArray(INDArray params);

    /**
     * Set the gradients array as a view of the full (backprop) network parameters
     * NOTE: this is intended to be used internally in MultiLayerNetwork and ComputationGraph, not by users.
     * @param gradients a 1 x nParams row vector that is a view of the larger (MLN/CG) gradients array
     */
    void setBackpropGradientsViewArray(INDArray gradients);

    /**
     * Update learningRate using for this model.
     * Use the learningRateScoreBasedDecay to adapt the score
     * if the Eps termination condition is met
     */
    void applyLearningRateScoreDecay();


    /**
     * Fit the model to the given data
     * @param data the data to fit the model to
     */
    void fit(INDArray data);


    /**
     * Run one iteration
     * @param input the input to iterate on
     */
    void iterate(INDArray input);


    /**
     * Calculate a gradient
     * @return the gradient for this model
     */
    Gradient gradient();

    /**
     * Get the gradient and score
     * @return the gradient and score
     */
    Pair<Gradient,Double> gradientAndScore();

    /**
     * The current inputs batch size
     * @return the current inputs batch size
     */
    int batchSize();


    /**
     * The configuration for the neural network
     * @return the configuration for the neural network
     */
    NeuralNetConfiguration conf();

    /**
     * Setter for the configuration
     * @param conf
     */
    void setConf(NeuralNetConfiguration conf);

    /**
     * The input/feature matrix for the model
     * @return the input/feature matrix for the model
     */
    INDArray input();


    /**
     * Validate the input
     */
    void validateInput();

    /**
     * Returns this models optimizer
     * @return this models optimizer
     */
    ConvexOptimizer getOptimizer();

    /**
     * Get the parameter
     * @param param the key of the parameter
     * @return the parameter vector/matrix with that particular key
     */
    INDArray getParam(String param);

    /**
     * Initialize the parameters
     */
    void initParams();

    /**
     * The param table
     * @return
     */
    Map<String,INDArray> paramTable();

    /**
     * Setter for the param table
     * @param paramTable
     */
    void setParamTable(Map<String,INDArray> paramTable);


    /**
     * Set the parameter with a new ndarray
     * @param key the key to se t
     * @param val the new ndarray
     */
    void setParam(String key,INDArray val);

    /**
     * Clear input
     */
    void clear();


}

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