Running the sample Python keras-ocr example on M3 Max returns incorrect results if tensorflow-metal is installed. Code Example: https://keras-ocr.readthedocs.io/en/latest/examples/using_pretrained_models.html Note: https://upload.wikimedia.org/wikipedia/commons/e/e8/FseeG2QeLXo.jpg not found. Line commented out. Without tensorflow-metal (Correct results): ['toodstande', 's', 'somme', 'srny', 'squadron', 'ds', 'quentn', 'snhnen', 'bnpnone', 'sasne', 'taing', 'yeoms', 'sry', 'the', 'royal', 'wessex', 'yeomanry', 'regiment', 'yeomanry', 'wests', 'south', 'the', 'now', 'recruiting', 'arm', 'blon', 'wxybsqipsacomodn', 'email', '438300', '01722'] ['banana', 'union', 'no', 'no', 'software', 'patents'] With tensorflow-metal (Incorrect results): ['sddoooo', '', 'eamnooss', 'xynrr', 'daanues', 'idd', 'innee', 'iiiinus', 'tnounppanab', 'inla', 'ppnt', 'mmnooexyy', 'yyr', 'ehhtt', 'laayvyoorr', 'xeseww', 'rinamoevy', 'tnemiger', 'yrnamoey', 'sstseww', 'htuwlos', 'fefeahit', 'wwoniia', 'turceedrr', 'ymmrira', 'atate', 'prasbyxwr', 'liamme', '00338803144', '22277100'] ['annnaab', 'noolinnu', 'oon', 'oon', 'wttffoos', 'sttneettaap'] Logs: With tensorflow-metal (Incorrect results) (.venv) <REDACTED> % pip3 install -U tensorflow-metal Collecting tensorflow-metal Using cached tensorflow_metal-1.1.0-cp311-cp311-macosx_12_0_arm64.whl.metadata (1.2 kB) Requirement already satisfied: wheel~=0.35 in ./.venv/lib/python3.11/site-packages (from tensorflow-metal) (0.42.0) Requirement already satisfied: six>=1.15.0 in ./.venv/lib/python3.11/site-packages (from tensorflow-metal) (1.16.0) Using cached tensorflow_metal-1.1.0-cp311-cp311-macosx_12_0_arm64.whl (1.4 MB) Installing collected packages: tensorflow-metal Successfully installed tensorflow-metal-1.1.0 (.venv) <REDACTED> % python3 keras-ocr-bug.py Looking for <REDACTED>/.keras-ocr/craft_mlt_25k.h5 2023-12-16 22:05:05.452493: I metal_plugin/src/device/metal_device.cc:1154] Metal device set to: Apple M3 Max 2023-12-16 22:05:05.452532: I metal_plugin/src/device/metal_device.cc:296] systemMemory: 64.00 GB 2023-12-16 22:05:05.452545: I metal_plugin/src/device/metal_device.cc:313] maxCacheSize: 24.00 GB 2023-12-16 22:05:05.452591: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:306] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support. 2023-12-16 22:05:05.452609: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:272] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>) WARNING:tensorflow:From <REDACTED>/.venv/lib/python3.11/site-packages/tensorflow/python/util/dispatch.py:1260: resize_bilinear (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.image.resize(...method=ResizeMethod.BILINEAR...)` instead. Looking for <REDACTED>/.keras-ocr/crnn_kurapan.h5 2023-12-16 22:05:07.526354: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:117] Plugin optimizer for device_type GPU is enabled. 1/1 [==============================] - 1s 855ms/step 2/2 [==============================] - 1s 140ms/step ['sddoooo', '', 'eamnooss', 'xynrr', 'daanues', 'idd', 'innee', 'iiiinus', 'tnounppanab', 'inla', 'ppnt', 'mmnooexyy', 'yyr', 'ehhtt', 'laayvyoorr', 'xeseww', 'rinamoevy', 'tnemiger', 'yrnamoey', 'sstseww', 'htuwlos', 'fefeahit', 'wwoniia', 'turceedrr', 'ymmrira', 'atate', 'prasbyxwr', 'liamme', '00338803144', '22277100'] ['annnaab', 'noolinnu', 'oon', 'oon', 'wttffoos', 'sttneettaap'] Logs: Valid results, without tensorflow-metal (.venv) <REDACTED> % pip3 uninstall tensorflow-metal Found existing installation: tensorflow-metal 1.1.0 Uninstalling tensorflow-metal-1.1.0: Would remove: <REDACTED>/.venv/lib/python3.11/site-packages/tensorflow-plugins/* <REDACTED>/.venv/lib/python3.11/site-packages/tensorflow_metal-1.1.0.dist-info/* Proceed (Y/n)? Y Successfully uninstalled tensorflow-metal-1.1.0 (.venv) <REDACTED> % python3 keras-ocr-bug.py Looking for <REDACTED>/.keras-ocr/craft_mlt_25k.h5 WARNING:tensorflow:From <REDACTED>/.venv/lib/python3.11/site-packages/tensorflow/python/util/dispatch.py:1260: resize_bilinear (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.image.resize(...method=ResizeMethod.BILINEAR...)` instead. Looking for <REDACTED>/.keras-ocr/crnn_kurapan.h5 1/1 [==============================] - 7s 7s/step 2/2 [==============================] - 1s 71ms/step ['toodstande', 's', 'somme', 'srny', 'squadron', 'ds', 'quentn', 'snhnen', 'bnpnone', 'sasne', 'taing', 'yeoms', 'sry', 'the', 'royal', 'wessex', 'yeomanry', 'regiment', 'yeomanry', 'wests', 'south', 'the', 'now', 'recruiting', 'arm', 'blon', 'wxybsqipsacomodn', 'email', '438300', '01722'] ['banana', 'union', 'no', 'no', 'software', 'patents']

I am trying to parse text from an image, split it into words and store the words in a String array. Additionally I want to store the bounding box of each recognized word. My code works but for some reason the bounding boxes of words that are not separated by a space but by an apostrophe come out wrong. Here is the complete code of my VNRecognizeTextRequestHander: let request = VNRecognizeTextRequest { request, error in guard let observations = request.results as? [VNRecognizedTextObservation] else { return } // split recognized text into words and store each word with corresponding observation let wordObservations = observations.flatMap { observation in observation.topCandidates(1).first?.string.unicodeScalars .split(whereSeparator: { CharacterSet.letters.inverted.contains($0) }) .map { (observation, $0) } ?? [] } // store recognized words as strings recognizedWords = wordObservations.map { (observation, word) in String(word) } // calculate bounding box for each word recognizedWordRects = wordObservations.map { (observation, word) in guard let candidate = observation.topCandidates(1).first else { return .zero } let stringRange = word.startIndex..<word.endIndex guard let rect = try? candidate.boundingBox(for: stringRange)?.boundingBox else { return .zero } let bottomLeftOriginRect = VNImageRectForNormalizedRect(rect, Int(captureRect.width), Int(captureRect.height)) // adjust coordinate system to start in top left corner let topLeftOriginRect = CGRect(origin: CGPoint(x: bottomLeftOriginRect.minX, y: captureRect.height - bottomLeftOriginRect.height - bottomLeftOriginRect.minY), size: bottomLeftOriginRect.size) print("BoundingBox for word '\(String(word))': \(topLeftOriginRect)") return topLeftOriginRect } } And here's an example for what's happening. When I'm processing the following image: the code above produces the following output: BoundingBox for word 'In': (23.00069557577264, 5.718113962610181, 45.89460636656961, 32.78087073878238) BoundingBox for word 'un': (71.19064286904202, 6.289275587192936, 189.16024359557852, 34.392966621800475) BoundingBox for word 'intervista': (71.19064286904202, 6.289275587192936, 189.16024359557852, 34.392966621800475) BoundingBox for word 'del': (262.64622870703477, 8.558512219726875, 54.733978711037985, 32.79967358237818) Notice how the bounding boxes of the words 'un' and 'intervista' are exactly the same. This happens consistently for words that are separated by an apostrophe. Why is that? Thank you for any help Elias

What is the accuracy and resolution of the angles measured using Vision?

As the title suggests I am using AVAudioEngine for SpeechRecognition input & AVAudioPlayer for sound output. Apple says in this talk https://developer.apple.com/videos/play/wwdc2019/510 that the setVoiceProcessingEnabled function very usefully cancels the output from speaker to the mic. I set voiceProcessing on the Input and output nodes. It seems to work however the volume is low, even when the system volume is turned up. Any solution to this would be much appreciated.

When the input dimension is 600w, the operator runs on ANE. But when the input shape is 100w or 200w, this operator can only run on the CPU. The data dimension has decreased, but it does not run on ANE. What is the reason for this and what are the ways to avoid it

x = tf.Variable(tf.ones(3)) x[1].assign(5) Above code results in: tensorflow.python.framework.errors_impl.InvalidArgumentError: Cannot assign a device for operation ResourceStridedSliceAssign: Could not satisfy explicit device specification '/job:localhost/replica:0/task:0/device:GPU:0' because no supported kernel for GPU devices is available. Colocation Debug Info: Colocation group had the following types and supported devices: Root Member(assigned_device_name_index_=1 requested_device_name_='/job:localhost/replica:0/task:0/device:GPU:0' assigned_device_name_='/job:localhost/replica:0/task:0/device:GPU:0' resource_device_name_='/job:localhost/replica:0/task:0/device:GPU:0' supported_device_types_=[CPU] possible_devices_=[] ResourceStridedSliceAssign: CPU _Arg: GPU CPU Colocation members, user-requested devices, and framework assigned devices, if any: ref (_Arg) framework assigned device=/job:localhost/replica:0/task:0/device:GPU:0 ResourceStridedSliceAssign (ResourceStridedSliceAssign) /job:localhost/replica:0/task:0/device:GPU:0 Op: ResourceStridedSliceAssign Node attrs: ellipsis_mask=0, Index=DT_INT32, T=DT_FLOAT, shrink_axis_mask=1, end_mask=0, begin_mask=0, new_axis_mask=0 Registered kernels: device='XLA_CPU_JIT'; Index in [DT_INT32, DT_INT64]; T in [DT_FLOAT, DT_DOUBLE, DT_INT32, DT_UINT8, DT_INT16, DT_INT8, DT_COMPLEX64, DT_INT64, DT_BOOL, DT_QINT8, DT_QUINT8, DT_QINT32, DT_BFLOAT16, DT_UINT16, DT_COMPLEX128, DT_HALF, DT_UINT32, DT_UINT64, DT_FLOAT8_E5M2, DT_FLOAT8_E4M3FN, DT_INT4, DT_UINT4] device='DEFAULT'; T in [DT_INT32] device='CPU'; T in [DT_UINT64] device='CPU'; T in [DT_INT64] device='CPU'; T in [DT_UINT32] device='CPU'; T in [DT_UINT16] device='CPU'; T in [DT_INT16] device='CPU'; T in [DT_UINT8] device='CPU'; T in [DT_INT8] device='CPU'; T in [DT_INT32] device='CPU'; T in [DT_HALF] device='CPU'; T in [DT_BFLOAT16] device='CPU'; T in [DT_FLOAT] device='CPU'; T in [DT_DOUBLE] device='CPU'; T in [DT_COMPLEX64] device='CPU'; T in [DT_COMPLEX128] device='CPU'; T in [DT_BOOL] device='CPU'; T in [DT_STRING] device='CPU'; T in [DT_RESOURCE] device='CPU'; T in [DT_VARIANT] device='CPU'; T in [DT_QINT8] device='CPU'; T in [DT_QUINT8] device='CPU'; T in [DT_QINT32] device='CPU'; T in [DT_FLOAT8_E5M2] device='CPU'; T in [DT_FLOAT8_E4M3FN] [[{{node ResourceStridedSliceAssign}}]] [Op:ResourceStridedSliceAssign] name: strided_slice/_assign I am starting to regret my Macbook purchase. There are so many issues with tensorflow-metal: ADAM is slow Inconsistent values with CPU And now this, I saw a post regarding this but that was one year old. So, Macbooks are not even good for learning anymore?

I'm building up a data frame for the sole purpose of using that lovely textual grid output. I'm getting output without any issue, but I'm trying to sort out how I might apply a formatter to a specific column so that print(dataframeInstance) "just works" nicely. In my use case, I'm running a function, collecting its output - appending that into a frame, and then using TabularData to get a nice output in a unit test, so I can see the patterns within the output. I found https://developer.apple.com/documentation/tabulardata/column/description(options:), but wasn't able to find any way to "pre-bind" that to a dataframe Column when I was creating it. (I have some double values that get a bit "excessive" in length due to the joys of floating point rounding) Is there a way of setting a formatter on a column at creation time, or after (using a property) that could basically use the same pattern as that description method above?

When I add AppEnity to my model, I receive this error that is still repeated for each attribute in the model. The models are already marked for Widget Extension in Target Membership. I have already cleaned and restarted, nothing works. Will anyone know what I'm doing wrong? Unable to find matching source file for path "@_swiftmacro_21HabitWidgetsExtension0A05ModelfMm.swift" import SwiftData import AppIntents enum FrecuenciaCumplimiento: String, Codable { case diario case semanal case mensual } @Model final class Habit: AppEntity { @Attribute(.unique) var id: UUID var nombre: String var descripcion: String var icono: String var color: String var esHabitoPositivo: Bool var valorObjetivo: Double var unidadObjetivo: String var frecuenciaCumplimiento: FrecuenciaCumplimiento static var typeDisplayRepresentation: TypeDisplayRepresentation = "Hábito" static var defaultQuery = HabitQuery() var displayRepresentation: DisplayRepresentation { DisplayRepresentation(title: "\(nombre)") } static var allHabits: [Habit] = [ Habit(id: UUID(), nombre: "uno", descripcion: "", icono: "circle", color: "#BF0000", esHabitoPositivo: true, valorObjetivo: 1.0, unidadObjetivo: "", frecuenciaCumplimiento: .mensual), Habit(id: UUID(), nombre: "dos", descripcion: "", icono: "circle", color: "#BF0000", esHabitoPositivo: true, valorObjetivo: 1.0, unidadObjetivo: "", frecuenciaCumplimiento: .mensual) ] /* static func loadAllHabits() async throws { do { let modelContainer = try ModelContainer(for: Habit.self) let descriptor = FetchDescriptor<Habit>() allHabits = try await modelContainer.mainContext.fetch(descriptor) } catch { // Manejo de errores si es necesario print("Error al cargar hábitos: \(error)") throw error } } */ init(id: UUID = UUID(), nombre: String, descripcion: String, icono: String, color: String, esHabitoPositivo: Bool, valorObjetivo: Double, unidadObjetivo: String, frecuenciaCumplimiento: FrecuenciaCumplimiento) { self.id = id self.nombre = nombre self.descripcion = descripcion self.icono = icono self.color = color self.esHabitoPositivo = esHabitoPositivo self.valorObjetivo = valorObjetivo self.unidadObjetivo = unidadObjetivo self.frecuenciaCumplimiento = frecuenciaCumplimiento } @Relationship(deleteRule: .cascade) var habitRecords: [HabitRecord] = [] } struct HabitQuery: EntityQuery { func entities(for identifiers: [Habit.ID]) async throws -> [Habit] { //try await Habit.loadAllHabits() return Habit.allHabits.filter { identifiers.contains($0.id) } } func suggestedEntities() async throws -> [Habit] { //try await Habit.loadAllHabits() return Habit.allHabits// .filter { $0.isAvailable } } func defaultResult() async -> Habit? { try? await suggestedEntities().first } }

macbook pro m2 max/ 64G / macos:13.2.1 (22D68) import tensorflow as tf def runMnist(device = '/device:CPU:0'): with tf.device(device): #tf.config.set_default_device(device) mnist = tf.keras.datasets.mnist (x_train, y_train), (x_test, y_test) = mnist.load_data() x_train, x_test = x_train / 255.0, x_test / 255.0 model = tf.keras.models.Sequential([ tf.keras.layers.Flatten(input_shape=(28, 28)), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(10) ]) loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) model.compile(optimizer='adam', loss=loss_fn, metrics=['accuracy']) model.fit(x_train, y_train, epochs=10) runMnist(device = '/device:CPU:0') runMnist(device = '/device:GPU:0')

I see a lot of crashes on iOS 17 beta regarding some problem of "Text To Speech". Does anybody has a clue why TTS crashes? Anybody else seeing the same problem? Exception Type: EXC_BAD_ACCESS (SIGSEGV) Exception Subtype: KERN_INVALID_ADDRESS at 0x000000037f729380 Exception Codes: 0x0000000000000001, 0x000000037f729380 VM Region Info: 0x37f729380 is not in any region. Bytes after previous region: 3748828033 Bytes before following region: 52622617728 REGION TYPE START - END [ VSIZE] PRT/MAX SHRMOD REGION DETAIL MALLOC_NANO 280000000-2a0000000 [512.0M] rw-/rwx SM=PRV ---> GAP OF 0xd20000000 BYTES commpage (reserved) fc0000000-1000000000 [ 1.0G] ---/--- SM=NUL ...(unallocated) Termination Reason: SIGNAL 11 Segmentation fault: 11 Terminating Process: exc handler [36389] Triggered by Thread: 9 ..... Thread 9 name: Thread 9 Crashed: 0 libobjc.A.dylib 0x000000019eeff248 objc_retain_x8 + 16 1 AudioToolboxCore 0x00000001b2da9d80 auoop::RenderPipeUser::~RenderPipeUser() + 112 (AUOOPRenderPipePool.mm:400) 2 AudioToolboxCore 0x00000001b2e110b4 -[AUAudioUnit_XPC internalDeallocateRenderResources] + 92 (AUAudioUnit_XPC.mm:904) 3 AVFAudio 0x00000001bfa4cc04 AUInterfaceBaseV3::Uninitialize() + 60 (AUInterface.mm:524) 4 AVFAudio 0x00000001bfa894bc AVAudioEngineGraph::PerformCommand(AUGraphNodeBaseV3&, AVAudioEngineGraph::ENodeCommand, void*, unsigned int) const + 772 (AVAudioEngineGraph.mm:3317) 5 AVFAudio 0x00000001bfa93550 AVAudioEngineGraph::_Uninitialize(NSError**) + 132 (AVAudioEngineGraph.mm:1469) 6 AVFAudio 0x00000001bfa4b50c AVAudioEngineImpl::Stop(NSError**) + 396 (AVAudioEngine.mm:1081) 7 AVFAudio 0x00000001bfa4b094 -[AVAudioEngine stop] + 48 (AVAudioEngine.mm:193) 8 TextToSpeech 0x00000001c70b3c5c __55-[TTSSynthesisProviderAudioEngine renderSpeechRequest:]_block_invoke + 1756 (TTSSynthesisProviderAudioEngine.m:613) 9 libdispatch.dylib 0x00000001ae4b0740 _dispatch_call_block_and_release + 32 (init.c:1519) 10 libdispatch.dylib 0x00000001ae4b2378 _dispatch_client_callout + 20 (object.m:560) 11 libdispatch.dylib 0x00000001ae4b990c _dispatch_lane_serial_drain + 748 (queue.c:3885) 12 libdispatch.dylib 0x00000001ae4ba470 _dispatch_lane_invoke + 432 (queue.c:3976) 13 libdispatch.dylib 0x00000001ae4c5074 _dispatch_root_queue_drain_deferred_wlh + 288 (queue.c:6913) 14 libdispatch.dylib 0x00000001ae4c48e8 _dispatch_workloop_worker_thread + 404 (queue.c:6507) ... Thread 9 crashed with ARM Thread State (64-bit): x0: 0x0000000283309360 x1: 0x0000000000000000 x2: 0x0000000000000000 x3: 0x00000002833093c0 x4: 0x00000002833093c0 x5: 0x0000000101737740 x6: 0x0000000000000013 x7: 0x00000000ffffffff x8: 0x0000000283309360 x9: 0x3c788942d067009a x10: 0x0000000101547000 x11: 0x0000000000000000 x12: 0x00000000000007fb x13: 0x00000000000007fd x14: 0x000000001ee24020 x15: 0x0000000000000020 x16: 0x0000b1037f729360 x17: 0x000000037f729360 x18: 0x0000000000000000 x19: 0x0000000000000000 x20: 0x00000001016a8de8 x21: 0x0000000283e21d00 x22: 0x0000000283b3f1f8 x23: 0x0000000283098000 x24: 0x00000001bfb4fc35 x25: 0x00000001bfb4fc43 x26: 0x000000028033a688 x27: 0x0000000280c93090 x28: 0x0000000000000000 fp: 0x000000016fc86490 lr: 0x00000001b2da9d80 sp: 0x000000016fc863e0 pc: 0x000000019eeff248 cpsr: 0x1000 esr: 0x92000006 (Data Abort) byte read Translation fault

I want to add shortcut and Siri support using the new AppIntents framework. Running my intent using shortcuts or from spotlight works fine, as the touch based UI for the disambiguation is shown. However, when I ask Siri to perform this action, she gets into a loop of asking me the question to set the parameter. My AppIntent is implemented as following: struct StartSessionIntent: AppIntent { static var title: LocalizedStringResource = "start_recording" @Parameter(title: "activity", requestValueDialog: IntentDialog("which_activity")) var activity: ActivityEntity @MainActor func perform() async throws -> some IntentResult & ProvidesDialog { let activityToSelect: ActivityEntity = self.activity guard let selectedActivity = Activity[activityToSelect.name] else { return .result(dialog: "activity_not_found") } ... return .result(dialog: "recording_started \(selectedActivity.name.localized())") } } The ActivityEntity is implemented like this: struct ActivityEntity: AppEntity { static var typeDisplayRepresentation = TypeDisplayRepresentation(name: "activity") typealias DefaultQuery = MobilityActivityQuery static var defaultQuery: MobilityActivityQuery = MobilityActivityQuery() var id: String var name: String var icon: String var displayRepresentation: DisplayRepresentation { DisplayRepresentation(title: "\(self.name.localized())", image: .init(systemName: self.icon)) } } struct MobilityActivityQuery: EntityQuery { func entities(for identifiers: [String]) async throws -> [ActivityEntity] { Activity.all()?.compactMap({ activity in identifiers.contains(where: { $0 == activity.name }) ? ActivityEntity(id: activity.name, name: activity.name, icon: activity.icon) : nil }) ?? [] } func suggestedEntities() async throws -> [ActivityEntity] { Activity.all()?.compactMap({ activity in ActivityEntity(id: activity.name, name: activity.name, icon: activity.icon) }) ?? [] } } Has anyone an idea what might be causing this and how I can fix this behavior? Thanks in advance

Hi everyone, I might need some help with on-device recognition. It seems that the speech recognition task will discard whatever it has transcribed after a new sentence starts (or it believes it becomes a new sentence) during a single audio session, with requiresOnDeviceRecognition is set to true. This doesn't happen with requiresOnDeviceRecognition set to false. System environment: macOS 14 with Xcode 15, deploying to iOS 17 Thank you all!

I need a simple text-to-speech avatar in my iOS app. iOS already has Memojis ready to go - but I cannot find anywhere in the dev docs on how to access Memojis to use in as a tool in app development. Am I missing something? Also - can anyone point me to any resources besides the Apple docs for using AVSpeechSynthesis?

I am using SFSpeechRecognizer to perform speech recognition, but I am getting the following error. [SpeechFramework] -[SFSpeechRecognitionTask localSpeechRecognitionClient:speechRecordingDidFail:]_block_invoke Ignoring subsequent local speech recording error: Error Domain=kAFAssistantErrorDomain Code=1101 "(null)" Setting requiresOnDeviceRecognition to False works correctly, but previously it worked with True with no error. The value of supportsOnDeviceRecognition was True, so the device is recognizing that it supports speech recognition. iPad Pro 11inch iOS 16.5. Is this expected behavior?

I am working on the neural network classifier provided on the coremltools.readme.io in the updatable->neural network section(https://coremltools.readme.io/docs/updatable-neural-network-classifier-on-mnist-dataset). I am using the same code but I get an error saying that the coremltools.converters.keras.convert does not exist. But this I know can be coreml version issue. Right know I am using coremltools version 6.2. I converted this model to mlmodel with .convert only. It got converted successfully. But I face an error in the make_updatable function saying the loss layer must be softmax output. Even the coremlt package API reference there I found its because the layer name is softmaxND but it should be softmax. Now the problem is when I convert the model from Keras sequential model to coreml model. the layer name and type change. And the softmax changes to softmaxND. Does anyone faced this issue? if I execute this builder.inspect_layers(last=4) I get this output [Id: 32], Name: sequential/dense_1/Softmax (Type: softmaxND) Updatable: False Input blobs: ['sequential/dense_1/MatMul'] Output blobs: ['Identity'] [Id: 31], Name: sequential/dense_1/MatMul (Type: batchedMatmul) Updatable: False Input blobs: ['sequential/dense/Relu'] Output blobs: ['sequential/dense_1/MatMul'] [Id: 30], Name: sequential/dense/Relu (Type: activation) Updatable: False Input blobs: ['sequential/dense/MatMul'] Output blobs: ['sequential/dense/Relu'] In the make_updatable function when I execute builder.set_categorical_cross_entropy_loss(name='lossLayer', input='Identity') I get this error ValueError: Categorical Cross Entropy loss layer input (Identity) must be a softmax layer output.

I am using NLTagger to tag lexical classes of words, but it suddenly just stopped working. I boiled my code down to the most basic version, but it's never executing the closure of the enumerateTags() function. What do I have to change or what should I try? for e in sentenceArray { let cupcake = "I like you, have a cupcake" tagger.string = cupcake tagger.enumerateTags(in: cupcake.startIndex..<cupcake.endIndex, unit: .word, scheme: .nameTypeOrLexicalClass) { tag, range in print("TAG") return true }

in iOS 15, on stopSpeaking of AVSpeechSynthesizer, didFinish delegate method getting called instead of didCancel which is working fine in iOS 14 and below version.

Can access to SoundAnalysis (sound classifier built into next version of MacOS, iOS, WatchOS) be provided to my app running in the background on iPhone or Apple Watch? I want to monitor local sounds from Apple Watch and iPhones and take remote action for out of band data (ie. send alert to caregiver if coughing rate is too high, or if someone is knocking on the door for more than a minute, etc.)