jamesonthecrow

import coremltools

# Scrape the 100 newest posts from each subreddit
max_posts = 100
posts = []
for subreddit in subreddits:
    posts.extend(get_n_posts(max_posts, subreddit, sort='new'))

# Apply the same preprocessing to the data
new_df = pandas.DataFrame(posts, columns=['subreddit', 'title'])

jamesonthecrow

// Drag your subredditClassifier.mlmodel to the Xcode project navigator.
// Use the model with the following code.
import NaturalLanguage

let subredditPredictor = try NLModel(mlModel: subredditClassifier().model)
subredditPredictor.predictedLabel(for: "TIL you can use Core ML to suggest subreddits to users.")

jamesonthecrow

@classmethod
def build(
        cls,
        image_size,
        alpha=1.0,
        input_tensor=None,
        checkpoint_file=None):
    """Build a Transfer Network Model using keras' functional API.
    Args:
        image_size - the size of the input and output image (H, W)

jamesonthecrow

@classmethod
def build(
        cls,
        image_size,
        alpha=1.0,
        input_tensor=None,
        checkpoint_file=None):
    """Build a Small Transfer Network Model using keras' functional API.
    This architecture removes some blocks of layers and reduces the size
    of convolutions to save on computation.

jamesonthecrow

private var machineIdentifier: String {
    // Returns a machine identifier string. E.g. iPhone10,3 or iPhone7,1
    // A full list of machine identifiers can be found here:
    // https://gist.github.com/adamawolf/3048717
    if let simulatorModelIdentifier = ProcessInfo().environment["SIMULATOR_MODEL_IDENTIFIER"] { return simulatorModelIdentifier }
    var systemInfo = utsname()
    uname(&systemInfo)
    return withUnsafeMutablePointer(to: &systemInfo.machine) {
        ptr in String(cString: UnsafeRawPointer(ptr).assumingMemoryBound(to: CChar.self))
    }

jamesonthecrow

import Fritz

// Fetch all of the models matching the tag
// we chose based on the machine identifier
let tagManager = ModelTagManager(tags: [tag])

// Loop through all of the models returned and download each model.
// In this case, we should only have a single model for each tag.
var allModels: [FritzMLModel] = []
tagManager.fetchManagedModelsForTags { managedModels, error in

jamesonthecrow

class ViewController: UIViewController {

    var cameraView: UIImageView!
    var maskView: UIImageView!

    override func viewDidLoad() {
        // ...
        
        cameraView = UIImageView(frame: view.bounds)
        cameraView.contentMode = .scaleAspectFill

jamesonthecrow

class MainActivity : AppCompatActivity() {

    override fun onCreate(savedInstanceState: Bundle?) {
        val onDeviceModel = new ObjectDetectionOnDeviceModel();
        val objectPredictor = FritzVision.ObjectDetection.getPredictor(onDeviceModel);
        var fritzVisionImage: FritzVisionImage
        cameraView.addFrameProcessor { frame ->
            if (yuvDataLength == 0) {
                //Run this only once
                initializeData()

jamesonthecrow

class MainActivity : AppCompatActivity() {

    private lateinit var renderScript: RenderScript
    private lateinit var yuvToRGB: ScriptIntrinsicYuvToRGB
    private var yuvDataLength: Int = 0
    private lateinit var allocationIn: Allocation
    private lateinit var allocationOut: Allocation
    private lateinit var bitmapOut: Bitmap

    //Rest of the code

jamesonthecrow

class MainActivity : AppCompatActivity() {

    private lateinit var renderScript: RenderScript
    private lateinit var yuvToRGB: ScriptIntrinsicYuvToRGB
    private var yuvDataLength: Int = 0
    private lateinit var allocationIn: Allocation
    private lateinit var allocationOut: Allocation
    private lateinit var bitmapOut: Bitmap

    private val itemMap by lazy {