one.empty3.neuralnetwork.of.Run Maven / Gradle / Ivy

Go to download
Show more of this group Show more artifacts with this name
Show all versions of empty3-library-3d Show documentation
3D rendering engine. Plus modelling. Expected glsl textures 3d and 2d rendering3D primitives, and a lot of scenes' samples to test.+ Game Jogl reworked, Calculator (numbers and vectors). Java code parser implementation starts (<=1.2)
The newest version!
/*
 *
 *  * Copyright (c) 2024. Manuel Daniel Dahmen
 *  *
 *  *
 *  *    Copyright 2024 Manuel Daniel Dahmen
 *  *
 *  *    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 one.empty3.neuralnetwork.of;

import javaAnd.awt.image.imageio.ImageIO;
import one.empty3.feature.M;
import one.empty3.feature.PixM;
import one.empty3.library.core.math.Matrix;
import one.empty3.library.core.nurbs.F;
import one.empty3.neuralnetwork.HiddenNeuron;
import one.empty3.neuralnetwork.LossFunction;
import one.empty3.neuralnetwork.Neuron;

import java.awt.geom.RectangularShape;
import java.awt.image.BufferedImage;
import java.io.File;
import java.util.function.Function;

public class Run {
    private static File dir = null;
    public static int MAX_RES = 36;
    private Neuron inputNeuron = new Neuron(MAX_RES);

    public static void main(String[] args) {
        String file = null;
        if (args.length < 1) {
            file = "C:\\Users\\manue\\OneDrive\\Bureau\\Dropbox\\Dropbox\\Chargements appareil photo\\IMG_20231202_223159.jpg";
        } else
            file = args[0];

        int sqrt = (int) Math.sqrt(MAX_RES);
        System.out.println("sqrt: " + sqrt);

        dir = new File(file);
        Run run = new Run();
        run.inputNeuron = new Neuron(MAX_RES);

        double[] x = new double[MAX_RES];
        for (int i = 0; i < (MAX_RES); i++) {
            run.inputNeuron.getW()[i] = Math.random() - 0.5;
        }

        Matrix matrixRandomInput = new Matrix(x, sqrt, sqrt);
        run.loadImageInput(ImageIO.read(new File(file)), sqrt, run.inputNeuron);
        Matrix matrixNeuron = new Matrix(run.inputNeuron.getW(), sqrt, sqrt);
        Matrix actual =
                matrixNeuron.multiply(matrixRandomInput).softmax();
        Matrix expectedMatrix = Util.createExpectedMatrix(sqrt, sqrt);
        Matrix loss = LossFunction.crossEntropy(actual, expectedMatrix);

        Matrix calculateError = actual.apply((index, value) -> value - expectedMatrix.get(index));

        System.out.println("Actual result\n" + actual);
        System.out.println("Matrix neuron image\n" + matrixNeuron);
        System.out.println("Loss Matrix\n" + loss);
        System.out.println("Calculate error Matrix\n" + calculateError);
    }

    public Matrix weightTransform(Matrix weights, Function transform) {
        final double INC = 0.0001;
        Matrix loss1 = transform.apply(weights);
        Matrix result = new Matrix(weights.getColumns(), weights.getLines(), i -> 0);
        weights.forEach(((row, col, index, value) -> {
            Matrix incremented = weights.addIncrement(row, col, INC);
            Matrix loss2 = transform.apply(incremented);
            double rate = (loss2.get(0) - loss1.get(0)) / INC;
            result.set(row, col, rate);
        }));
        return result;
    }

    public Run() {
    }

    public void train(File inputFile, File outputFile) {
        loadImageInput(ImageIO.read(dir), MAX_RES, inputNeuron
        );
    }

    private void loadImageInput(BufferedImage read, int maxRes, Neuron inputNeuron) {
        PixM pixM = PixM.getPixM(read, maxRes);
        for (int i = 0; i < pixM.getColumns(); i++) {
            for (int j = 0; j < pixM.getLines(); j++) {
                inputNeuron.getInput()[pixM.index(i, j) / 3] = pixM.luminance(i, j);
            }
        }
    }
}