scala-tensorflow-samples

Scala samples codes on how to load tensorflow pb model and use them to predict

Usage

Image Classification using Cifar10

Below show the demo codes of the Cifar10ImageClassifier which loads the cnn_cifar10.pb tensorflow model file, and uses it to do image classification:

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

import java.io.IOException

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

class Cifar10ImageClassifierDemo() {

}

object Cifar10ImageClassifierDemo {
  private val logger = LoggerFactory.getLogger(classOf[Cifar10ImageClassifierDemo])

  @throws[IOException]
  def main(args: Array[String]): Unit = {
    val inputStream = ResourceUtils.getInputStream("tf_models/cnn_cifar10.pb")
    val classifier = new Cifar10ImageClassifier
    classifier.load_model(inputStream)
    val image_names = Array[String]("airplane1", "airplane2", "airplane3", "automobile1", "automobile2", "automobile3", "bird1", "bird2", "bird3", "cat1", "cat2", "cat3")
    for (image_name <- image_names) {
      val image_path = "images/cifar10/" + image_name + ".png"
      val img = ResourceUtils.getImage(image_path)
      val predicted_label = classifier.predict_image(img)
      System.out.println("predicted class for " + image_name + ": " + predicted_label)
    }
  }
}

Image Classification using Inception

Below show the demo codes of the InceptionImageClassifier which loads the tensorflow_inception_graph.pb tensorflow model file, and uses it to do image classification:

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

import java.io.IOException

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

class InceptionImageClassifierDemo {

}

object InceptionImageClassifierDemo {
  private val logger = LoggerFactory.getLogger(classOf[InceptionImageClassifierDemo])

  @throws[IOException]
  def main(args: Array[String]): Unit = {
    val 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"))
    val image_names = Array[String]("tiger", "lion")
    for (image_name <- image_names) {
      val image_path = "images/inception/" + image_name + ".jpg"
      val img = ResourceUtils.getImage(image_path)
      val predicted_label = classifier.predict_image(img)
      System.out.println("predicted class for " + image_name + ": " + predicted_label)
    }
  }
}

Sentiment Analysis using 1D CNN

Below show the demo codes of the CnnSentimentClassifier which loads the wordvec_cnn.pb tensorflow model file, and uses it to do sentiment analysis:

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

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

import scala.collection.JavaConversions._

object BidirectionalLstmSentimentClassifierDemo {
  def main(args: Array[String]): Unit = {
    val 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"))

    val lines = ResourceUtils.getLines("data/umich-sentiment-train.txt")

    for(line <- lines){
      val label = line.split("\t")(0)
      val text = line.split("\t")(1)
      val predicted = classifier.predict(text)
      val 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)
    }
  }
}

Sentiment Analysis using Bi-directional LSTM

Below show the demo codes of the BidirectionalLstmSentimentClassifier which loads the wordvec_bidirectional_lstm.pb tensorflow model file, and uses it to do sentiment analysis:

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

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

import scala.collection.JavaConversions._

object BidirectionalLstmSentimentClassifierDemo {
  def main(args: Array[String]): Unit = {
    val 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"))

    val lines = ResourceUtils.getLines("data/umich-sentiment-train.txt")

    for(line <- lines){
      val label = line.split("\t")(0)
      val text = line.split("\t")(1)
      val predicted = classifier.predict(text)
      val 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)
    }
  }
}