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TrainNetworkWithPCA.java
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60 lines (41 loc) · 2.09 KB
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package io.bhagat.projects.handwrittendigits;
import java.io.IOException;
import java.util.ArrayList;
import io.bhagat.ai.supervised.NeuralNetwork;
import io.bhagat.ai.unsupervised.PrincipalComponentAnalysis;
import io.bhagat.math.linearalgebra.Matrix;
import io.bhagat.util.ArrayUtil;
import io.bhagat.util.SerializableUtil;
import io.bhagat.util.Timer;
public class TrainNetworkWithPCA {
public static void main(String[] args) throws IOException {
Timer totalTimer = new Timer();
totalTimer.start();
Timer t = new Timer();
ArrayList<double[]> images = new ArrayList<>();
ArrayList<double[]> labels = new ArrayList<>();
System.out.println("Reading data . . .");
t.start();
ReadData.read("csv/train/images.csv", "csv/train/labels.csv", images, labels);
System.out.println("Done Reading data: " + t.elapsed() + " ms");
System.out.println("Reducting dimensions of input data . . .");
t.restart();
Matrix inputs = new Matrix(ArrayUtil.newArrayFromArrayList(images, new double[images.size()][images.get(0).length]));
PrincipalComponentAnalysis pca = new PrincipalComponentAnalysis(inputs);
Matrix newInputs = pca.dimensionReduction();
double[][] inputArr = newInputs.getData();
NeuralNetwork neuralNetwork = new NeuralNetwork(inputArr[0].length, 64, 10);
System.out.println("Done Reducing Dimensions to " + inputArr[0].length + ": " + t.elapsed() + " ms");
System.out.println("Training . . .");
t.restart();
for(int i = 0; i < inputArr.length; i++)
neuralNetwork.train(inputArr[i], labels.get(i));
System.out.println("Done Training: " + t.elapsed() + " ms");
System.out.println("Serializing . . .");
t.restart();
neuralNetwork.serialize("mnist/network-pca.ser");
SerializableUtil.serialize(pca, "mnist/pca.ser");
System.out.println("Done Serializing: " + t.elapsed() + " ms");
System.out.println("Total Program Done: " + totalTimer.elapsed() + " ms");
}
}