How do you integrate a custom image classification model created in keras in your iOS App?

Using the tutorial found at blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html, I trained a Keras model to recognize the difference between cats and dogs.

''' Directory structure: 
     data/ 
          train/ 
               dogs/ 
                 dog001.jpg 
                 dog002.jpg 
                 ...
               cats/ 
                 cat001.jpg 
                 cat002.jpg 
                  ... 
          validation/ 
               dogs/ 
                 dog001.jpg 
                 dog002.jpg
                 ... 
               cats/ 
                  cat001.jpg 
                  cat002.jpg
                  ... 
'''


from keras.preprocessing.image import ImageDataGenerator 
from keras.models import Sequential 
from keras.layers import Conv2D, MaxPooling2D 
from keras.layers import Activation, Dropout, Flatten, Dense 
from keras import backend as K 
from PIL import Image import numpy as np 

# dimensions of our images. 
img_width, img_height = 150, 150 

train_data_dir = 'data/train' 
validation_data_dir = 'data/validation' 
nb_train_samples = 2000 
nb_validation_samples = 800 
epochs = 50 
batch_size = 16 

if K.image_data_format() == 'channels_first': 
     input_shape = (3, img_width, img_height) 
else: 
     input_shape = (img_width, img_height, 3) 

model = Sequential() 
model.add(Conv2D(32, (3, 3), input_shape=input_shape)) 
model.add(Activation('relu')) 
model.add(MaxPooling2D(pool_size=(2, 2))) 

model.add(Conv2D(32, (3, 3))) 
model.add(Activation('relu')) 
model.add(MaxPooling2D(pool_size=(2, 2))) 

model.add(Conv2D(64, (3, 3))) 
model.add(Activation('relu')) 
model.add(MaxPooling2D(pool_size=(2, 2))) 

model.add(Flatten()) model.add(Dense(64)) 
model.add(Activation('relu')) 
model.add(Dropout(0.5)) 
model.add(Dense(1)) 
model.add(Activation('sigmoid'))

model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy']) 

# this is the augmentation configuration we will use for training
train_datagen = ImageDataGenerator( rescale=1. / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True) 

# this is the augmentation configuration we will use for testing: 
# only rescaling 
test_datagen = ImageDataGenerator(rescale=1. / 255) 

train_generator = train_datagen.flow_from_directory( train_data_dir, target_size=(img_width, img_height), batch_size=batch_size, class_mode='binary') 

validation_generator = test_datagen.flow_from_directory( validation_data_dir, target_size=(img_width, img_height), batch_size=batch_size, class_mode='binary') 

model.fit_generator( train_generator, steps_per_epoch=nb_train_samples / epochs=epochs, validation_data=validation_generator, validation_steps=nb_validation_samples / 

model.save('first_try.h5')

Using the coremltools documentation as a guide, I tried converting my model to the coreml format:

import coremltools 
import h5py 
coreml_model = coremltools.converters.keras.convert('first_try.h5',input_names='image',output_names='class',image_input_names = 'image',class_labels = ['cat', 'dog'], is_bgr=True) 
coreml_model.save('cats_dogs.mlmodel')

When I import the model into XCode and run it with the following code (which works with the resnet50 and inceptionv3 models found on Apple's website), the snippet at line 86 "guard let prediction = try? model.prediction(image: pixelBuffer!) else { print("Error!") return}" outputs "Error!" and the snippet "textView.text = "I think this is a \(prediction.classLabel)." never gets executed.

import UIKit
import Vision
import CoreML
class ViewController: UIViewController, UINavigationControllerDelegate {

    /
    var model: cats_dogs!

    override func viewWillAppear(_ animated: Bool) {
        model = cats_dogs()
    }

    @IBOutlet weak var imageView: UIImageView!
    @IBOutlet weak var textView: UITextView!

    let imagePicker = UIImagePickerController()

    /

    override func viewDidLoad() {
        super .viewDidLoad()
        self.imagePicker.delegate = self
    }

    @IBAction func openImagePicker(_ sender: Any) {
        imagePicker.allowsEditing = false
        imagePicker.sourceType = .photoLibrary
        present(imagePicker, animated: true, completion: nil)
    }

    @IBAction func camera(_ sender: Any) {
    
        if !UIImagePickerController.isSourceTypeAvailable(.camera) {
            return
        }
    
        let cameraPicker = UIImagePickerController()
        cameraPicker.delegate = self
        cameraPicker.sourceType = .camera
        cameraPicker.allowsEditing = false
    
        present(cameraPicker, animated: true)
    }

}
extension ViewController: UIImagePickerControllerDelegate {
    func imagePickerControllerDidCancel(_ picker: UIImagePickerController) {
        dismiss(animated: true, completion: nil)
    }

    func imagePickerController(_ picker: UIImagePickerController, didFinishPickingMediaWithInfo info: [String : Any]) {
        picker.dismiss(animated: true)
        textView.text = "Analyzing Image..."
        guard let image = info["UIImagePickerControllerOriginalImage"] as? UIImage else {
            return
        }
    
        UIGraphicsBeginImageContextWithOptions(CGSize(width: 150, height: 150), true, 2.0)
        image.draw(in: CGRect(x: 0, y: 0, width: 150, height: 150))
        let newImage = UIGraphicsGetImageFromCurrentImageContext()!
        UIGraphicsEndImageContext()
    
        let attrs = [kCVPixelBufferCGImageCompatibilityKey: kCFBooleanTrue, kCVPixelBufferCGBitmapContextCompatibilityKey: kCFBooleanTrue] as CFDictionary
        var pixelBuffer : CVPixelBuffer?
        let status = CVPixelBufferCreate(kCFAllocatorDefault, Int(newImage.size.width), Int(newImage.size.height), kCVPixelFormatType_32ARGB, attrs, &pixelBuffer)
        guard (status == kCVReturnSuccess) else {
            return
        }
    
        CVPixelBufferLockBaseAddress(pixelBuffer!, CVPixelBufferLockFlags(rawValue: 0))
        let pixelData = CVPixelBufferGetBaseAddress(pixelBuffer!)
    
        let rgbColorSpace = CGColorSpaceCreateDeviceRGB()
        let context = CGContext(data: pixelData, width: Int(newImage.size.width), height: Int(newImage.size.height), bitsPerComponent: 8, bytesPerRow: CVPixelBufferGetBytesPerRow(pixelBuffer!), space: rgbColorSpace, bitmapInfo: CGImageAlphaInfo.noneSkipFirst.rawValue) /
    
        context?.translateBy(x: 0, y: newImage.size.height)
        context?.scaleBy(x: 1.0, y: -1.0)
    
        UIGraphicsPushContext(context!)
        newImage.draw(in: CGRect(x: 0, y: 0, width: newImage.size.width, height: newImage.size.height))
        UIGraphicsPopContext()
        CVPixelBufferUnlockBaseAddress(pixelBuffer!, CVPixelBufferLockFlags(rawValue: 0))
        imageView.image = newImage
    
        guard let prediction = try? model.prediction(image: pixelBuffer!) else {
            print("Error!")
            return
        }
        textView.text = "I think this is a \(prediction.classLabel)."
    }
}

I have searched the web extensively to solve this issue. Help to fix this issue would be much appreciated!