java tensorflow samples

Java-Tensorflow-samples

رموز عينة Java حول كيفية تحميل ملف النموذج المسبق لـ Tensorflow والتنبؤ بناءً على ملفات النموذج المسبق هذه

الاستخدام

تصنيف الصور باستخدام CIFAR10

يُظهر أدناه الرموز التجريبية لملف طراز CIFAR10ImageClassifier الذي يقوم بتحميل ملف نموذج CNN_CIFAR10.PB ، ويستخدمه لإجراء تصنيف الصورة:

 package com . github . chen0040 . tensorflow . classifiers . demo ;

import com . github . chen0040 . tensorflow . classifiers . utils . ResourceUtils ;
import com . github . chen0040 . tensorflow . classifiers . cifar10 . Cifar10ImageClassifier ;
import org . slf4j . Logger ;
import org . slf4j . LoggerFactory ;

import java . awt . image . BufferedImage ;
import java . io . IOException ;
import java . io . InputStream ;

public class Cifar10ImageClassifierDemo {

    private static final Logger logger = LoggerFactory . getLogger ( Cifar10ImageClassifierDemo . class );

    public static void main ( String [] args ) throws IOException {


        InputStream inputStream = ResourceUtils . getInputStream ( "tf_models/cnn_cifar10.pb" );
        Cifar10ImageClassifier classifier = new Cifar10ImageClassifier ();
        classifier . load_model ( inputStream );

        String [] image_names = new String [] {
                "airplane1" ,
                "airplane2" ,
                "airplane3" ,
                "automobile1" ,
                "automobile2" ,
                "automobile3" ,
                "bird1" ,
                "bird2" ,
                "bird3" ,
                "cat1" ,
                "cat2" ,
                "cat3"
        };

        for ( String image_name : image_names ) {
            String image_path = "images/cifar10/" + image_name + ".png" ;
            BufferedImage img = ResourceUtils . getImage ( image_path );
            String predicted_label = classifier . predict_image ( img );
            logger . info ( "predicted class for {}: {}" , image_name , predicted_label );
        }
    }
}

تصنيف الصور باستخدام التأسيس

يُظهر أدناه الرموز التجريبية لملف طراز inceptionImageClassifier الذي يقوم بتحميل ملف طراز TensorFlow_Inception_graph.pb ، ويستخدمه لإجراء تصنيف الصورة:

 package com . github . chen0040 . tensorflow . classifiers . demo ;

import com . github . chen0040 . tensorflow . classifiers . inception . InceptionImageClassifier ;
import com . github . chen0040 . tensorflow . classifiers . utils . ResourceUtils ;
import org . slf4j . Logger ;
import org . slf4j . LoggerFactory ;

import java . awt . image . BufferedImage ;
import java . io . IOException ;

public class InceptionImageClassifierDemo {

    private static final Logger logger = LoggerFactory . getLogger ( InceptionImageClassifierDemo . class );

    public static void main ( String [] args ) throws IOException {


        InceptionImageClassifier classifier = new InceptionImageClassifier ();
        classifier . load_model ( ResourceUtils . getInputStream ( "tf_models/tensorflow_inception_graph.pb" ));
        classifier . load_labels ( ResourceUtils . getInputStream ( "tf_models/imagenet_comp_graph_label_strings.txt" ));

        String [] image_names = new String [] {
                "tiger" ,
                "lion"
        };

        for ( String image_name : image_names ) {
            String image_path = "images/inception/" + image_name + ".jpg" ;
            BufferedImage img = ResourceUtils . getImage ( image_path );
            String predicted_label = classifier . predict_image ( img );
            logger . info ( "predicted class for {}: {}" , image_name , predicted_label );
        }
    }
}

تحليل المشاعر باستخدام 1D CNN

يُظهر أدناه الرموز التجريبية لملف طراز TensorFlow الذي يعمل على تحميل CNNSentimentClassifier الذي يقوم بتحليل المشاعر:

 import com . github . chen0040 . tensorflow . classifiers . sentiment . models . CnnSentimentClassifier ;
import com . github . chen0040 . tensorflow . classifiers . sentiment . utils . ResourceUtils ;

import java . io . IOException ;
import java . util . List ;

public class CnnSentimentClassifierDemo {
    public static void main ( String [] args ) throws IOException {
        CnnSentimentClassifier classifier = new CnnSentimentClassifier ();
        classifier . load_model ( ResourceUtils . getInputStream ( "tf_models/wordvec_cnn.pb" ));
        classifier . load_vocab ( ResourceUtils . getInputStream ( "tf_models/wordvec_cnn.csv" ));

        List < String > lines = ResourceUtils . getLines ( "data/umich-sentiment-train.txt" );
        for ( String line : lines ){
            String label = line . split ( " t " )[ 0 ];
            String text = line . split ( " t " )[ 1 ];
            float [] predicted = classifier . predict ( text );
            String predicted_label = classifier . predict_label ( text );
            System . out . println ( text );
            System . out . println ( "Outcome: " + predicted [ 0 ] + ", " + predicted [ 1 ]);
            System . out . println ( "Predicted: " + predicted_label + " Actual: " + label );
        }
    }
}

تحليل المشاعر باستخدام LSTM ثنائية الاتجاه

أدناه إظهار الرموز التجريبية لملف طراز TensorFlow الذي يحمل WordVec_Bidirectional_LSTM.PB ، ويستخدمه لإجراء تحليل المعنويات:

 import com . github . chen0040 . tensorflow . classifiers . sentiment . models . BidirectionalLstmSentimentClassifier ;
import com . github . chen0040 . tensorflow . classifiers . sentiment . utils . ResourceUtils ;

import java . io . IOException ;
import java . util . List ;

public class BidirectionalLstmSentimentClassifierDemo {
    public static void main ( String [] args ) throws IOException {
        BidirectionalLstmSentimentClassifier classifier = new BidirectionalLstmSentimentClassifier ();
        classifier . load_model ( ResourceUtils . getInputStream ( "tf_models/bidirectional_lstm_softmax.pb" ));
        classifier . load_vocab ( ResourceUtils . getInputStream ( "tf_models/bidirectional_lstm_softmax.csv" ));

        List < String > lines = ResourceUtils . getLines ( "data/umich-sentiment-train.txt" );

        for ( String line : lines ){
            String label = line . split ( " t " )[ 0 ];
            String text = line . split ( " t " )[ 1 ];
            float [] predicted = classifier . predict ( text );
            String predicted_label = classifier . predict_label ( text );
            System . out . println ( text );
            System . out . println ( "Outcome: " + predicted [ 0 ] + ", " + predicted [ 1 ]);
            System . out . println ( "Predicted: " + predicted_label + " Actual: " + label );
        }
    }
}

تصنيف الصوت باستخدام مصنف الصوت CIFAR10

يُظهر أدناه الرموز التجريبية لملف طراز CIFAR10AudioClassifier الذي يقوم بتحميل ملف طراز CIFAR10.PB ، ويستخدمه للقيام بالتنبؤ بأنواع الموسيقى:

 import com . github . chen0040 . tensorflow . classifiers . audio . models . cifar10 . Cifar10AudioClassifier ;
import com . github . chen0040 . tensorflow . classifiers . audio . utils . ResourceUtils ;
import org . slf4j . Logger ;
import org . slf4j . LoggerFactory ;

import java . io . File ;
import java . io . IOException ;
import java . io . InputStream ;
import java . util . ArrayList ;
import java . util . Collections ;
import java . util . List ;

public class Cifar10AudioClassifierDemo {

    private static final Logger logger = LoggerFactory . getLogger ( Cifar10AudioClassifierDemo . class );

    private static List < String > getAudioFiles () {
        List < String > result = new ArrayList <>();
        File file = new File ( "gtzan/genres" );
        System . out . println ( file . getAbsolutePath ());
        if ( file . isDirectory ()) {
            for ( File class_folder : file . listFiles ()) {
                if ( class_folder . isDirectory ()) {
                    for ( File f : class_folder . listFiles ()) {
                        String file_path = f . getAbsolutePath ();
                        if ( file_path . endsWith ( "au" )) {
                            result . add ( file_path );

                        }
                    }
                }
            }
        }

        return result ;
    }

    public static void main ( String [] args ) throws IOException {
        InputStream inputStream = ResourceUtils . getInputStream ( "tf_models/cifar10.pb" );
        Cifar10AudioClassifier classifier = new Cifar10AudioClassifier ();
        classifier . load_model ( inputStream );

        List < String > paths = getAudioFiles ();

        Collections . shuffle ( paths );

        for ( String path : paths ) {
            System . out . println ( "Predicting " + path + " ..." );
            File f = new File ( path );
            String label = classifier . predict_audio ( f );

            System . out . println ( "Predicted: " + label );
        }
    }
}

تصنيف الصوت باستخدام مصنف الصوت resnetv2

يُظهر أدناه الرموز التجريبية لملف نموذج RESNETV2AudiOclassifier الذي يقوم بتحميل ملف Resnet-V2.PB TensorFlow ، ويستخدمه للقيام بالتنبؤ بأنواع الموسيقى:

 import com . github . chen0040 . tensorflow . classifiers . audio . models . resnet . ResNetV2AudioClassifier ;
import com . github . chen0040 . tensorflow . classifiers . audio . utils . ResourceUtils ;
import org . slf4j . Logger ;
import org . slf4j . LoggerFactory ;

import java . io . File ;
import java . io . IOException ;
import java . io . InputStream ;
import java . util . ArrayList ;
import java . util . Collections ;
import java . util . List ;

public class ResNetV2AudioClassifierDemo {

    private static final Logger logger = LoggerFactory . getLogger ( ResNetV2AudioClassifierDemo . class );

     private static List < String > getAudioFiles () {
        List < String > result = new ArrayList <>();
        File dir = new File ( "music_samples" );
        System . out . println ( dir . getAbsolutePath ());
        if ( dir . isDirectory ()) {

            for ( File f : dir . listFiles ()) {
                String file_path = f . getAbsolutePath ();
                if ( file_path . endsWith ( "au" )) {
                    result . add ( file_path );

                }
            }
        }

        return result ;
    }

    public static void main ( String [] args ) throws IOException {
        InputStream inputStream = ResourceUtils . getInputStream ( "tf_models/resnet-v2.pb" );
        ResNetV2AudioClassifier classifier = new ResNetV2AudioClassifier ();
        classifier . load_model ( inputStream );

        List < String > paths = getAudioFiles ();

        Collections . shuffle ( paths );

        for ( String path : paths ) {
            System . out . println ( "Predicting " + path + " ..." );
            File f = new File ( path );
            String label = classifier . predict_audio ( f );

            System . out . println ( "Predicted: " + label );
        }
    }
}

محرك البحث الصوتي

توضح رموز العينة أدناه كيفية الفهرس والبحث عن ملف الصوت باستخدام فئة AudioSearchEngine:

 AudioSearchEngine searchEngine = new AudioSearchEngine ();
if (! searchEngine . loadIndexDbIfExists ()) {
    searchEngine . indexAll ( new File ( "music_samples" ). listFiles ());
    searchEngine . saveIndexDb ();
}

int pageIndex = 0 ;
int pageSize = 20 ;
boolean skipPerfectMatch = true ;
for ( File f : new File ( "music_samples" ). listFiles ()) {
    System . out . println ( "querying similar music to " + f . getName ());
    List < AudioSearchEntry > result = searchEngine . query ( f , pageIndex , pageSize , skipPerfectMatch );
    for ( int i = 0 ; i < result . size (); ++ i ){
        System . out . println ( "# " + i + ": " + result . get ( i ). getPath () + " (distSq: " + result . get ( i ). getDistanceSq () + ")" );
    }
}

توصي الموسيقى

توضح رموز العينة أدناه كيفية التوصية بالموسيقى بناءً على سجل موسيقى المستخدم باستخدام فئة Knnaudiorecmommender:

 AudioUserHistory userHistory = new AudioUserHistory ();

List < String > audioFiles = FileUtils . getAudioFiles ();
Collections . shuffle ( audioFiles );

for ( int i = 0 ; i < 40 ; ++ i ){
    String filePath = audioFiles . get ( i );
    userHistory . logAudio ( filePath );
    try {
        Thread . sleep ( 100L );
    } catch ( InterruptedException e ) {
        e . printStackTrace ();
    }
}

KnnAudioRecommender recommender = new KnnAudioRecommender ();
if (! recommender . loadIndexDbIfExists ()) {
    recommender . indexAll ( new File ( "music_samples" ). listFiles ( a -> a . getAbsolutePath (). toLowerCase (). endsWith ( ".au" )));
    recommender . saveIndexDb ();
}

System . out . println ( userHistory . head ( 10 ));

int k = 10 ;
List < AudioSearchEntry > result = recommender . recommends ( userHistory . getHistory (), k );

for ( int i = 0 ; i < result . size (); ++ i ){
    AudioSearchEntry entry = result . get ( i );
    System . out . println ( "Search Result #" + ( i + 1 ) + ": " + entry . getPath ());
}