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Hi, I try the activity classifier and want to show the results with an app on a IPhone SE. I try the example analog https://apple.github.io/turicreate/docs/userguide/activity_classifier/export_coreml.html When calling the prediction function: ...    func performModelPrediction () -> String? { ... EXC_BAD_ACCESS (code=1, address=0x0) was thrown in line:         let modelPrediction = try! activityClassificationModel.prediction( ^^^^^^^^here was the Error shown   I can trak it down to the source of the mlmodel:     func prediction(input: MyActivityClassifier2Input, options: MLPredictionOptions) throws -> MyActivityClassifier2Output {         let outFeatures = try model.prediction(from: input, options:options)         return MyActivityClassifier2Output(features: outFeatures)     } I created the model with ml creator, working with Xcode 13 beta. What I am doing wrong ? Do you have any hints ? Is there a better example for activity classifier ? Don't hesitate to ask for further details. -Hans here my code: // //  ViewController.swift //  motionstor4yboard // //  Created by Hans Regler on 19.07.21. // import Foundation import UIKit import CoreML import CoreMotion class ViewController: UIViewController {     override func viewDidLoad() {         debugPrint("info: start viewDidLoad ... ")         super.viewDidLoad()         // Do any additional setup after loading the view, typically from a nib.                 // Connect data:         self.startDeviceMotion()     }      // Define some ML Model constants for the recurrent network   struct ModelConstants {     static let numOfFeatures = 6     // Must be the same value you used while training     static let predictionWindowSize = 100     static let sensorsUpdateInterval = 1.0 / 10.0     static let stateInLength = 400   }      // Initialize the model, layers, and sensor data arrays     let activityClassificationModel = MyActivityClassifier2()     var currentIndexInPredictionWindow = 0     let accelDataX = try! MLMultiArray(shape: [ModelConstants.predictionWindowSize] as [NSNumber], dataType: MLMultiArrayDataType.double)     let accelDataY = try! MLMultiArray(shape: [ModelConstants.predictionWindowSize] as [NSNumber], dataType: MLMultiArrayDataType.double)     let accelDataZ = try! MLMultiArray(shape: [ModelConstants.predictionWindowSize] as [NSNumber], dataType: MLMultiArrayDataType.double)     let gyroDataX = try! MLMultiArray(shape: [ModelConstants.predictionWindowSize] as [NSNumber], dataType: MLMultiArrayDataType.double)     let gyroDataY = try! MLMultiArray(shape: [ModelConstants.predictionWindowSize] as [NSNumber], dataType: MLMultiArrayDataType.double)     let gyroDataZ = try! MLMultiArray(shape: [ModelConstants.predictionWindowSize] as [NSNumber], dataType: MLMultiArrayDataType.double)     var stateOutput = try! MLMultiArray(shape:[ModelConstants.stateInLength as NSNumber], dataType: MLMultiArrayDataType.double)     // Initialize CoreMotion Manager   let motionManager = CMMotionManager()      func startDeviceMotion() { //         guard motionManager.isDeviceMotionAvailable else {     guard motionManager.isAccelerometerAvailable, motionManager.isGyroAvailable else {             debugPrint("Core Motion Data Unavailable!")             return         }          motionManager.accelerometerUpdateInterval = TimeInterval(ModelConstants.sensorsUpdateInterval)     motionManager.gyroUpdateInterval = TimeInterval(ModelConstants.sensorsUpdateInterval)     motionManager.startAccelerometerUpdates(to: .main) { accelerometerData, error in         guard let accelerometerData = accelerometerData else {             print("Error: accelerometerData = accelerometerData")             return }         // Add the current data sample to the data array         self.addAccelSampleToDataArray(accelSample: accelerometerData)     }             }      func addAccelSampleToDataArray (accelSample: CMAccelerometerData) {     // Add the current accelerometer reading to the data array     accelDataX[[currentIndexInPredictionWindow] as [NSNumber]] = accelSample.acceleration.x as NSNumber     accelDataY[[currentIndexInPredictionWindow] as [NSNumber]] = accelSample.acceleration.y as NSNumber     accelDataZ[[currentIndexInPredictionWindow] as [NSNumber]] = accelSample.acceleration.z as NSNumber     // Update the index in the prediction window data array     currentIndexInPredictionWindow += 1     // If the data array is full, call the prediction method to get a new model prediction.     // We assume here for simplicity that the Gyro data was added to the data arrays as well.     if (currentIndexInPredictionWindow == ModelConstants.predictionWindowSize) {         if let predictedActivity = performModelPrediction() {             // Use the predicted activity here             // ...             // Start a new prediction window             currentIndexInPredictionWindow = 0         }     } }          func performModelPrediction () -> String? {         // Perform model prediction         let modelPrediction = try! activityClassificationModel.prediction(             acceleration_x: accelDataX,             acceleration_y: accelDataY,             acceleration_z: accelDataZ,             rotation_x: gyroDataX,             rotation_y: gyroDataY,             rotation_z: gyroDataZ,             stateIn: stateOutput)         // Update the state vector         stateOutput = modelPrediction.stateOut         // Return the predicted activity - the activity with the highest probability         return modelPrediction.label     }