what am I not understanding here. in short the view loads text from the jsons descriptions and then should filter out the words. and return and display a list of most used words, debugging shows words being identified by the code but does not filter them out private func loadWordCounts() { DispatchQueue.global(qos: .background).async { let fileManager = FileManager.default guard let documentsDirectory = try? fileManager.url(for: .documentDirectory, in: .userDomainMask, appropriateFor: nil, create: false) else { return } let descriptions = loadDescriptions(fileManager: fileManager, documentsDirectory: documentsDirectory) var counts = countWords(in: descriptions) let tagsToRemove: Set<NLTag> = [ .verb, .pronoun, .determiner, .particle, .preposition, .conjunction, .interjection, .classifier ] for (word, _) in counts { let tagger = NLTagger(tagSchemes: [.lexicalClass]) tagger.string = word let (tag, _) = tagger.tag(at: word.startIndex, unit: .word, scheme: .lexicalClass) if let unwrappedTag = tag, tagsToRemove.contains(unwrappedTag) { counts[word] = 0 } } DispatchQueue.main.async { self.wordCounts = counts } } }

I'm using the iOS 18.2 beta on my iPhone 15 Pro Max, but can't find Apple Intelligence, and the Settings app still shows the Old Siri logo.

Almost all the functions in Accelerate are for single precision (Float) and double precision (Double) operations. However, I stumbled upon three integer arithmetic functions which operate on Int32 values. Are there any more functions in Accelerate that operate on integer values? If not, then why aren't there more functions that work with integers?

hi, I am currently running LSTM on TensorFlow. However, when i switched from keras2 to keras3. code running time has increased 10 times -- it seems there is no GPU acceleration. Here is my code: batch size = 256 optimiser = adam activation = tanh _______________________________________________ Layer (type) Output Shape Param # ============================================= input_1 (InputLayer) [(None, 7, 16)] 0 bidirectional (Bidirection (None, 7, 320) 226560 al) bidirectional_1 (Bidirecti (None, 7, 512) 1181696 onal) bidirectional_2 (Bidirecti (None, 256) 656384 onal) dense (Dense) (None, 1) 257 ============================================== Total params: 2064897 (7.88 MB) Trainable params: 2064897 (7.88 MB) Non-trainable params: 0 (0.00 Byte) ______________________________________________ This is keras 3.6.0 + tensorflow 2.17.0 + tensorflow-metal 1.1.0 training status: Training------------ Epoch 1/200 28/681 ━━━━━━━━━━━━━━━━━━━━ 8:13 756ms/step - loss: 0.5901 - mape: 338.6876 - mse: 0.8591 This is keras 2.14.0 + tensorflow 2.14.0 + tensorflow-metal 1.1.0 training status: Training------------ Epoch 1/200 681/681 [==============================] - 37s 49ms/step - loss: 3.6345 - mape: 499038.7500 - mse: 34.4148 - val_loss: 3.5452 - val_mape: 41.7964 - val_mse: 32.0133 - lr: 0.0010 Is that because keras3 has no GPU support on macos? Apart from that, if I change LSTM activation from tanh to sigmoid in keras2, it does not have GPU support as well. My system is 15.0.1 and the code was running on python3.11 I am not sure why these happen. Thanks

Hello. I can't find anything about the SSML that is used in Apple's speech synthesis. SSML from Google, Amazon and W3C either don't work or work incorrectly. Where is Apple's documentation for their implementation of SSML?

Hi All, Is it possible to record a video using the Object Capture instead of taking a series of pictures ? Is it possible to get the bounding box coordinates of the object we capture ?

I am using Apple’s Vision framework with DetectHorizonRequest to detect the horizon in an image. Here is my code: func processHorizonImage(_ ciImage: CIImage) async { let request = DetectHorizonRequest() do { let result = try await request.perform(on: ciImage) print(result) } catch { print(error) } } After calling the perform method, I am getting result as nil. To ensure the request's correctness, I have verified the following: The input CIImage is valid and contains a visible horizon. No errors are being thrown. The relevant frameworks are properly imported. Given that my image contains a clear horizon, why am I still not getting any results? I would appreciate any help or suggestions to resolve this issue. Thank you for your support! This is the image

Hi Ty for playing

WWDC 2024 mentioned that the OCR feature from the Vision framework has support for "Korean, Swedish, and Chinese", but the Swedish support does not seem to be available... Running either print(try? VNRecognizeTextRequest().supportedRecognitionLanguages()) or var ocrRequest = RecognizeTextRequest(.revision3) print(ocrRequest.supportedRecognitionLanguages) did not print out Swedish as one of the supported languages, but Korean and Chinese are. Tested on early versions of iOS 18 developer beta, and the latest version of iOS 18.1 (22B5054e).

I was working on my project and when I tried to train a model the kernel crashed, so I restarted the kernel and tried the same and still I got the same crashing issue. Then I read one of the thread having the same issue where the apple support was saying to install tensorflow-macos and tensorflow-metal and read the guide from this site: https://developer.apple.com/metal/tensorflow-plugin/ and I did so, I tried every single thing and when I tried the test code provided in the site, I got the same error, here's the code and the output. Code: import tensorflow as tf cifar = tf.keras.datasets.cifar100 (x_train, y_train), (x_test, y_test) = cifar.load_data() model = tf.keras.applications.ResNet50( include_top=True, weights=None, input_shape=(32, 32, 3), classes=100,) loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False) model.compile(optimizer="adam", loss=loss_fn, metrics=["accuracy"]) model.fit(x_train, y_train, epochs=5, batch_size=64) and here's the output: Epoch 1/5 The Kernel crashed while executing code in the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click here for more info. View Jupyter log for further details. And here's the half of log file as it was not fully coming: metal_plugin/src/device/metal_device.cc:1154] Metal device set to: Apple M1 2024-10-06 23:30:49.894405: I metal_plugin/src/device/metal_device.cc:296] systemMemory: 8.00 GB 2024-10-06 23:30:49.894420: I metal_plugin/src/device/metal_device.cc:313] maxCacheSize: 2.67 GB 2024-10-06 23:30:49.894444: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support. 2024-10-06 23:30:49.894460: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] 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: ) 2024-10-06 23:30:56.701461: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:117] Plugin optimizer for device_type GPU is enabled. [libprotobuf FATAL google/protobuf/message_lite.cc:353] CHECK failed: target + size == res: libc++abi: terminating due to uncaught exception of type google::protobuf::FatalException: CHECK failed: target + size == res: Please respond to this post as soon as possible as I am working on my project now and getting this error again n again. Device: Apple MacBook Air M1.