shubham0204

@Throws(IOException::class)
private fun loadModelFile(): MappedByteBuffer {
    val MODEL_ASSETS_PATH = "model.tflite"
    val assetFileDescriptor = assets.openFd(MODEL_ASSETS_PATH)
    val fileInputStream = FileInputStream(assetFileDescriptor.getFileDescriptor())
    val fileChannel = fileInputStream.getChannel()
    val startoffset = assetFileDescriptor.getStartOffset()
    val declaredLength = assetFileDescriptor.getDeclaredLength()
    return fileChannel.map(FileChannel.MapMode.READ_ONLY, startoffset, declaredLength)
}

shubham0204

fun classifySequence ( sequence : IntArray ): FloatArray {
    val interpreter = Interpreter( loadModelFile() )
    val inputs : Array<FloatArray> = arrayOf( sequence.map{ it.toFloat() }.toFloatArray() )
    val outputs : Array<FloatArray> = arrayOf( floatArrayOf( 0.0f , 0.0f ) )
    interpreter.run( inputs , outputs )
    return outputs[0]
}

val tokenizedMessage = tokenize( message_text.text.toString().toLowerCase().trim() )
val paddedMessage = padSequence( tokenizedMessage )

shubham0204

import tensorflow.keras.backend as K

input_shape = ( (DIMEN**2) * 3 , )
convolution_shape = ( DIMEN , DIMEN , 3 )
kernel_size_1 = ( 8 , 8 )
kernel_size_2 = ( 6 , 6 )
kernel_size_3 = ( 4 , 4 )
pool_size_1 = ( 6 , 6 )
pool_size_2 = ( 4 , 4 )

shubham0204

model.save( 'model.h5' ) 
converter = tf.lite.TFLiteConverter.from_keras_model_file( 'model.h5' ) 
converter.post_training_quantize = True
tflite_model = converter.convert()
size = open( 'recog_model.tflite' , 'wb' ).write( tflite_model ) 
print( 'Model size > {} Megabytes'.format( size / 1024**2))

shubham0204

public float getSimilarity ( Bitmap image1 , Bitmap image2 ) {
        try {
            Interpreter interpreter = new Interpreter( loadModelFile() ) ;
            int INPUT_DIM = 32;
            float[][] x1 = new float[][]{mapBitmapToFloatArray( resizeBitmap( image1 , INPUT_DIM) , INPUT_DIM) };
            float[][] x2 = new float[][]{mapBitmapToFloatArray( resizeBitmap( image2 , INPUT_DIM) , INPUT_DIM) };
            Object[] inputs = { x1 , x2 } ;
            Map<Integer, Object> outputs = new HashMap<>();
            outputs.put(0, new float[1][1] );
            interpreter.runForMultipleInputsOutputs( inputs , outputs ) ;

shubham0204

companion object {
    val CLASS_POSITIVE = 0 // Spam
    val CLASS_NEGATIVE = 1 // Ham
    private val englishStopWords = arrayOf(
        "i", "me", "my", "myself", "we", "our", "ours", "ourselves", "you", "your", "yours", "yourself", "yourselves",
        "he", "him", "his", "himself", "she", "her", "hers", "herself", "it", "its", "itself", "they", "them", "their",
        "theirs", "themselves", "what", "which", "who", "whom", "this", "that", "these", "those", "am", "is", "are", "was",
        "were", "be", "been", "being", "have", "has", "had", "having", "do", "does", "did", "doing", "a", "an", "the",
        "and", "but", "if", "or", "because", "as", "until", "while", "of", "at", "by", "for", "with", "about", "against",
        "between", "into", "through", "during", "before", "after", "above", "below", "to", "from", "up", "down", "in",

shubham0204

class Vocabulary {
    companion object {
        val positiveBagOfWords = arrayOf( ... ) // Some words here
        val negativeBagOfWords = arrayOf( ... ) // Some words here
    }
}

shubham0204

private fun findClassProbability(classVocab1 : Array<String>, classVocab2 : Array<String> , classVocab : Array<String>) : Float{
    val total_words = (classVocab1.count() + classVocab2.count()).toFloat()
    val class_count = (classVocab.count()).toFloat()
    return ( class_count / total_words )
}

shubham0204

private fun findProbabilityGivenClass( x : String , classVocab : Array<String> ) : Float {
    val x_count = classVocab.count { it.contains(x) or x.contains(it) }.toFloat()
    val class_count = classVocab.count().toFloat()
    return (( x_count / class_count ) + 1)
}

shubham0204

private fun findProbabilityGivenSample( document : String , classVocab : Array<String> ) : Float {
    val tokens = getTokens( document )
    var probability_given_class = 1.0f
    for ( token in tokens ) {
        probability_given_class *= findProbabilityGivenClass( token , classVocab )
    }
    return probability_given_class * findClassProbability( positiveBagOfWords , negativeBagOfWords , classVocab )
}