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def modelFitGenerator(fitModel):

    num_train_samples = sum([len(files) for r, d, files in os.walk(train_data_dir)])
    num_valid_samples = sum([len(files) for r, d, files in os.walk(validation_data_dir)])

    num_train_steps = math.floor(num_train_samples/batch_size)
    num_valid_steps = math.floor(num_valid_samples/batch_size)
    
    train_datagen = ImageDataGenerator(  
      rotation_range=90,      

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def saveCoreMLModel(kerasModel):
    coreml_model = coremltools.converters.keras.convert(kerasModel,
                                                    input_names=['input'],
                                                    output_names=['probs'],
                                                    image_input_names='input',
                                                    predicted_feature_name='predictedMoney',
                                                    class_labels = 'drive/Resnet/labels.txt')
    coreml_model.save('resnet50custom.mlmodel') 
    print('CoreML model saved')

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import CreateML
import Foundation


// 1. CSV'den veri alma
var data = try MLDataTable(contentsOf: URL(fileURLWithPath: "/Users/ozgur/Documents/CreateMLFiles/dataset/bitmojiReviews.csv"))

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let parsingOptions = MLDataTable.ParsingOptions(containsHeader: true, delimiter: ",", comment: "", escape: "", doubleQuote: false, quote: "", skipInitialSpaces: false, missingValues: [], lineTerminator: "\r", selectColumns:["author","rating","review"], maxRows: nil, skipRows: 0)

var table = try MLDataTable(contentsOf: URL(fileURLWithPath: "/Users/ozgur/Documents/CreateMLFiles/dataset/bitmojiReviews.csv"), options: parsingOptions)

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let newColumn = data.map { row -> Double in
    guard   let deger1 = row["deg1"]?.intValue,
        let deger2 = row["deg2"]?.intValue,
        let deger3 = row["deg3"]?.doubleValue
        else {
            fatalError("Missing or invalid columns in row.")
    }
    let sum = Double(deger1)+Double(deger2)+deger3
    return sum
}

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let data: [String: MLDataValueConvertible] = [
    "Title": ["Alice in Wonderland", "Hamlet", "Treasure Island", "Peter Pan"],
    "Author": ["Lewis Carroll", "William Shakespeare", "Robert L. Stevenson", "J. M. Barrie"],
    "Pages": [124, 98, 280, 94],
]

var bookTable = try MLDataTable(dictionary: data)

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let pagesColumn = MLDataColumn([124, 98, 280, 94])
bookTable.addColumn(pagesColumn, named: "Pages")

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let newColumn = data.map { row -> String in
    guard
        let average = row["deg3"]?.doubleValue
        else {
            fatalError("Missing or invalid columns in row.")
    }
    
    var sentiment = "negative"

    if average < 0

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import NaturalLanguage

let text = "All human beings are born free and equal in dignity and rights. They are endowed with reason and conscience and should act towards one another in a spirit of brotherhood."

let tokenizer = NLTokenizer(unit: .word)
tokenizer.string = text
//let tokenArray = tokenizer.tokens(for: strRange)
tokenizer.enumerateTokens(in: text.startIndex..<text.endIndex) { tokenRange, _ in
    print(text[tokenRange])
    return true

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import NaturalLanguage
let recognizer = NLLanguageRecognizer()
recognizer.processString("oduncu")
let lang = recognizer.dominantLanguage
let hypotheses = recognizer.languageHypotheses(withMaximum:2)
//convenience method: NLLanguageRecognizer.dominantLanguage(for: "oduncu")