MnistImagePipelineExampleLoadAndChooseCleanUp.java

package org.deeplearning4j.examples.dataExamples;

import org.datavec.api.io.labels.ParentPathLabelGenerator;
import org.datavec.api.split.FileSplit;
import org.datavec.image.loader.NativeImageLoader;
import org.datavec.image.recordreader.ImageRecordReader;
import org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.util.ModelSerializer;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.api.DataSet;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
import org.nd4j.linalg.dataset.api.preprocessor.DataNormalization;
import org.nd4j.linalg.dataset.api.preprocessor.ImagePreProcessingScaler;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import javax.swing.*;
import java.io.File;
import java.util.Arrays;
import java.util.HashMap;
import java.util.List;
import java.util.Random;

/**
 * Created by tomhanlon on 11/7/16.
 */
public class MnistImagePipelineExampleLoadAndChooseCleanUp {
    private static Logger log = LoggerFactory.getLogger(MnistImagePipelineExampleLoadAndChooseCleanUp.class);

    public static String fileChose()
    {
        JFileChooser fc= new JFileChooser();
        int ret = fc.showOpenDialog(null);

        if (ret== JFileChooser.APPROVE_OPTION)
        {
            File file = fc.getSelectedFile();
            String filename= file.getAbsolutePath();
            return filename;
        }
        else
            return null;
    }


    public static void main(String[] args) throws Exception {
        // image information
        // 28 * 28 grayscale
        // grayscale implies single channel
        // Black background white digit
        // is what was trained on
        int height = 28;
        int width = 28;
        int channels = 1;
        int rngseed = 123;
        Random randNumGen = new Random(rngseed);
        int batchSize = 128;
        int outputNum = 10;

        // This is the file that
        // the user choses from
        // the popup chooser window

        String filechose = fileChose().toString();


        // Define the File Paths
        File testData = new File(filechose);

        // Define the FileSplit(PATH, ALLOWED FORMATS,random)

        // Build a list from the labels list
        // [2, 3, 7, 1, 6, 4, 0, 5, 8]
        // these are the digit labels as a list,
        // retrieved from RecordReaderDataSetIterator.GetLabels
        // when the model was trained
        List<Integer> labelList = Arrays.asList(2, 3, 7, 1, 6, 4, 0, 5, 8);

        FileSplit test = new FileSplit(testData,NativeImageLoader.ALLOWED_FORMATS);

        // Extract the parent path as the image label
        ParentPathLabelGenerator labelMaker = new ParentPathLabelGenerator();
        ImageRecordReader recordReader = new ImageRecordReader(height,width,channels,labelMaker);


        // Scale pixel values to 0-1

        DataNormalization scaler = new ImagePreProcessingScaler(0,1);

        // Load Saved Neural Network

        log.info("******LOAD TRAINED MODEL******");
        // Details

        // Where Model was saved
        File locationToSave = new File("trained_mnist_model.zip");

        // Restore the Model
        MultiLayerNetwork model = ModelSerializer.restoreMultiLayerNetwork(locationToSave);

        log.info("******Test your input image against model******");

        //recordReader.reset();

        recordReader.initialize(test);
        DataSetIterator testIter = new RecordReaderDataSetIterator(recordReader,batchSize,1,outputNum);
        scaler.fit(testIter);
        testIter.setPreProcessor(scaler);

        // Create Eval object with 10 possible classes
        //Evaluation eval = new Evaluation(outputNum);

        /*
        If I wanted to zip up the two lists
        for (int i = 0; i < strings.size(); i++) {
         map.put(strings.get(i), integers.get(i));
        }

         */


        while(testIter.hasNext()){
            DataSet next = testIter.next();
            INDArray output = model.output(next.getFeatureMatrix());
            //log.info(next.toString());
            log.info("## THE FILE YOU CHOSE ## " + filechose);
            log.info("## Neural Net's Prediction ##");
            log.info("## list of probabilities per label ##");
            log.info("## list of Labels in order ##");
            log.info(output.toString());
            log.info(labelList.toString());

        }
        

    }
}