How do I add a already made CoreML model into my playground? I tried what people recommended online -- building a test project and get the .mlmodelc file and put that in the playground along with the autogenerated class for the model. However, I keep on getting so many errors. The errors: Unexpected duplicate tasks Target 'help' (project 'help') has write command with output /Users/cpulipaka/Library/Developer/Xcode/DerivedData/help-appuguzbduqvojfwkaxtnqkozecv/Build/Intermediates.noindex/Previews/help/Intermediates.noindex/help.build/Debug-iphonesimulator/help.build/adc7818afdf4ae03fd98cdd618954541.sb Target 'help' (project 'help') has write command with output /Users/cpulipaka/Library/Developer/Xcode/DerivedData/help-appuguzbduqvojfwkaxtnqkozecv/Build/Intermediates.noindex/Previews/help/Intermediates.noindex/help.build/Debug-iphonesimulator/help.build/adc7818afdf4ae03fd98cdd618954541.sb Unexpected duplicate tasks Showing Recent Issues Target 'help' (project 'help'): CoreMLModelCompile /Users/cpulipaka/Library/Developer/Xcode/DerivedData/help-appuguzbduqvojfwkaxtnqkozecv/Build/Intermediates.noindex/Previews/help/Products/Debug-iphonesimulator/help.app/ /Users/cpulipaka/Desktop/help.swiftpm/Resources/ZooClassifier.mlmodel Target 'help' (project 'help'): CoreMLModelCompile /Users/cpulipaka/Library/Developer/Xcode/DerivedData/help-appuguzbduqvojfwkaxtnqkozecv/Build/Intermediates.noindex/Previews/help/Products/Debug-iphonesimulator/help.app/ /Users/cpulipaka/Desktop/help.swiftpm/Resources/ZooClassifier.mlmodel ZooClassifier.mlmodel: No predominant language detected. Set COREML_CODEGEN_LANGUAGE to preferred language.

Hello! With Swift Student Challenge submissions on Saturday, does anybody mind letting me know how to implement CoreML (I have a model trained in CreateML already) in the SwiftPM format? Thanks!

Hi, I have a an issue with jax.numpy.linalg.inv(a). import jax.numpy.linalg as jnpl B = jnp.identity(2) jnpl.inv(B) Throws the following error: XlaRuntimeError: UNKNOWN: /var/folders/pw/wk5rfkjj6qggqp8r8zb2bw8w0000gn/T/ipykernel_34334/2572982404.py:9:0: error: failed to legalize operation 'mhlo.triangular_solve' /var/folders/pw/wk5rfkjj6qggqp8r8zb2bw8w0000gn/T/ipykernel_34334/2572982404.py:9:0: note: called from /var/folders/pw/wk5rfkjj6qggqp8r8zb2bw8w0000gn/T/ipykernel_34334/2572982404.py:9:0: note: see current operation: %120 = \"mhlo.triangular_solve\"(%42#4, %119) {left_side = true, lower = true, transpose_a = #mhlo<transpose NO_TRANSPOSE>, unit_diagonal = true} : (tensor<2x2xf32>, tensor<2x2xf32>) -> tensor<2x2xf32> Any ideas what could be the issue or how to solve it?

Trying to learn vision apps and I was wondering if the actual .xcodeproj file was available anywhere. I understand there are snippets of code below the video but it's difficult to learn how to build an app with those files since it just focuses on the ML aspect. https://developer.apple.com/videos/play/wwdc2021/10039/ I'm also looking for the code for this video specifically. I'm aware of the drawing code but that is a relatively simple example to understand and the CreateML stuff isn't prevalent in that.

Hi Developers, I want to create a Vision app on Swift Playgrounds on iPad. However, Vision does not properly function on Swift Playgrounds on iPad or Xcode Playgrounds. The Vision code only works on a normal Xcode Project. SO can I submit my Swift Student Challenge 2024 Application as a normal Xcode Project rather than Xcode Playgrounds or Swift Playgrounds File. Thanks :)

Hello, I am a new user with an Apple MacBook Pro. I'm experiencing difficulties running my code through the GPU. What do I need to install on my computer to be able to use libraries for machine learning, Computer Vision, PyTorch and Tensor Flow? I already watch lot of tutorials on this subject, but still is looks very complicated and I need mentoring for this task. I would greatly appreciate it if I could receive a response and if someone could guide me on this matter.

I'm exploring my Vision Pro and finding it unclear whether I can even achieve things like body pose detection etc. https://developer.apple.com/videos/play/wwdc2023/111241/ It's clear that I can apply it to self provided images, but how about to the data coming from visionOS SDKs? All I can find is this mesh data from ARKit, https://developer.apple.com/documentation/arkit/arkit_in_visionos - am I missing something or do we not yet have good APIs for this? Appreciate any guidance! Thanks.

After training my dataset, the training, validation, and testing sets all show 0% in detection accuracy and all my test photos show false negative. The dataset has 1032 photos and 2 classes, and I used Roboflow for the image annotation. For network, I choose full network. If there is any way to fix this?

WWDC22 video "Explore the machine learning development experience" provides Python code for an interesting application (real-time ML image colorization), but doesn't provide the complete Xcode project, and assumes viewer knows how to do Python in Xcode (haven't heard of such in 10 years of iOS development!). Any pointers to either the video's example Xcode project, or how to create a suitable Xcode project capable of running Python code?

Hi, In Xcode 14 I was able to train linear regression models with Create ML using large CSV files (I tested on about 30000 items and 5 features): However, in Xcode 15 (I tested on 15.0.1 and 15.1), the training continuously stays in the "Processing" state: When using a dataset with 900 items, everything works fine. I filed a feedback for this issue: FB13516799. Does anybody else have this issue / can reproduce it?

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 converted a toy Pytorch regression model to CoreML mlmodel using coremltools and set it to be updatable with mean_squared_error_loss. But when testing the training, the context.metrics[.lossValue] can give negative value which is impossible. Further more, context.metrics[.lossValue] result is very different from my own computed training loss as shown in the screenshot attached. I was wondering if I used a wrong way to extract the training loss from context? Does context.metrics[.lossValue] really give MSE if I used coremltools function set_mean_squared_error_loss to set the loss? Any suggestion is appreciated. Since the validation loss decreases as epoch goes, the model should be indeed updated correctly. I am using coremltools==7.0, xcode==15.0.1 Here is my code to convert Pytorch model to updatable CoreML model: import coremltools from coremltools.models.neural_network import NeuralNetworkBuilder, SgdParams, AdamParams from coremltools.models import datatypes # Load the model specification spec = coremltools.utils.load_spec('regression.mlmodel') builder = NeuralNetworkBuilder(spec=spec) builder.inspect_output_features() # Name: linear_1 # Make layers updatable builder.make_updatable(['linear_0', 'linear_1']) # Manually add a mean squared error loss layer feature = ('linear_1', datatypes.Array(1)) builder.set_mean_squared_error_loss(name='lossLayer', input_feature=feature) # define the optimizer (Adam in this example) adam_params = AdamParams(lr=0.01, beta1=0.9, beta2=0.999, eps=1e-8, batch=16) builder.set_adam_optimizer(adam_params) # Set the number of epochs builder.set_epochs(100) # Save the updated model updated_model = coremltools.models.MLModel(spec) updated_model.save('updatable_regression30.mlmodel') Here is the code I use to try to update the saved updatable_regression30.mlmodel: import CoreML import GameKit func generateSampleData(numSamples: Int, seed: UInt64) -> ([MLMultiArray], [MLMultiArray]) { // simple regression: y = 10 * sum(x) + 1 var inputArray = [MLMultiArray]() var outputArray = [MLMultiArray]() // Create a random number generator with a fixed seed let randomSource = GKLinearCongruentialRandomSource(seed: seed) let randomDistribution = GKRandomDistribution(randomSource: randomSource, lowestValue: 0, highestValue: 1000) for _ in 0..<numSamples { do { let input = try MLMultiArray(shape: [1, 2], dataType: .float32) let output = try MLMultiArray(shape: [1], dataType: .float32) var sumInput: Float = 0 for i in 0..<input.shape[1].intValue { // Generate random value using the fixed seed generator let inputValue = Float(randomDistribution.nextInt()) / 1000.0 input[[0, i] as [NSNumber]] = NSNumber(value: inputValue) sumInput += inputValue } output[0] = NSNumber(value: 10.0 * sumInput + 1.0) inputArray.append(input) outputArray.append(output) } catch { print("Error occurred while creating MLMultiArrays: \(error)") } } return (inputArray, outputArray) } func computeLoss(model: MLModel, data: ([MLMultiArray], [MLMultiArray])) -> Double { let (inputData, outputData) = data var totalLoss: Double = 0 for (index, input) in inputData.enumerated() { let output = outputData[index] if let prediction = try? model.prediction(from: MLDictionaryFeatureProvider(dictionary: ["x": MLFeatureValue(multiArray: input)])), let predictedOutput = prediction.featureValue(for: "linear_1")?.multiArrayValue { let loss = (output[0].doubleValue - predictedOutput[0].doubleValue) totalLoss += loss * loss // squared error } } return totalLoss / Double(inputData.count) // mean of squared errors } func trainModel() { // Load the updatable model guard let updatableModelURL = Bundle.main.url(forResource: "updatable_regression30", withExtension: "mlmodelc") else { print("Failed to load the updatable model") return } // Generate sample data let (inputData, outputData) = generateSampleData(numSamples: 200, seed: 8) let validationData = generateSampleData(numSamples: 100, seed:18) // Create an MLArrayBatchProvider from the sample data var featureProviders = [MLFeatureProvider]() for (index, input) in inputData.enumerated() { let output = outputData[index] let dataPointFeatures: [String: MLFeatureValue] = [ "x": MLFeatureValue(multiArray: input), "linear_1_true": MLFeatureValue(multiArray: output) ] if let provider = try? MLDictionaryFeatureProvider(dictionary: dataPointFeatures) { featureProviders.append(provider) } } let batchProvider = MLArrayBatchProvider(array: featureProviders) // Define progress handlers let progressHandlers = MLUpdateProgressHandlers(forEvents: [.trainingBegin, .epochEnd], progressHandler: { context in switch context.event { case .trainingBegin: print("Training began.") case .epochEnd: let loss = context.metrics[.lossValue] as! Double let validationLoss = computeLoss(model: context.model, data: validationData) let computedTrainLoss = computeLoss(model: context.model, data: (inputData, outputData)) print("Epoch \(context.metrics[.epochIndex]!) ended. Training Loss: \(loss), Computed Training Loss: \(computedTrainLoss), Validation Loss: \(validationLoss)") default: break } } ) // Create an update task with progress handlers let updateTask = try! MLUpdateTask(forModelAt: updatableModelURL, trainingData: batchProvider, configuration: nil, progressHandlers: progressHandlers) // Start the update task updateTask.resume() } // call trainModel() to start training

Is there a way to determine finger joint/root circumference, finger length, tip of finger to wrist crease, hand breadth and wrist breadth with Vision hand pose? Or alternative method? Any insight is appreciated.

Objective: I am in the process of developing an application that utilizes machine learning (Core ML) to interact with photographs of documents, specifically focusing on those containing tables. Step 1: Capturing the Image The application will initiate by allowing users to take photos of documents. The key here is not just any part of the document, but specifically the sections where tables are present. Step 2: Image Analysis through Machine Learning Upon capturing the image, the next phase involves a machine learning model. Using Apple's Create ML tool with Swift, the application will analyze the image. The model's task is two-fold: Identifying the Table: Distinguish the table from other document information, ensuring it recognizes and isolates the table structure within the photograph. Ignoring Irrelevant Information: Concurrently, the model will disregard all non-table content, focusing the application's resources on the table data. Step 3: Data Extraction and Training Once the table is identified, the real work begins. The application will engage in detailed scrutiny, where it's trained to understand and recognize row and column data based on specific datasets. This training will enable the application to 'read' the table accurately, much like a human would, by identifying the organization of information into rows and columns. Step 4: Information Storage Post-analysis, the application will extract this critical data, storing it in a structured format. Each piece of identifiable information from the rows and columns will be systematically organized into a Dictionary or an Object. This structure is not just for immediate use but also efficient for future data operations within the app. Conclusion: Through these sequential steps, the application transitions from merely capturing an image to intelligently recognizing, deciphering, and storing table data from within a physical document. This streamlined process is all courtesy of integrating machine learning into the app's functionality, promising significant efficiency and accuracy in data handling. Realistically, I have not found any good examples out there so I am attempting to create my own ML (with no experience 😅), so any guidance or help would be very much appreciated.

I followed the video of Composing advanced models with Create ML Components. I have created the model with let urlParameter = URL(fileURLWithPath: "/path/to/model.pkg") let (training, validation) = dataFrame.randomSplit(by: 0.8) let model = try await transformer.fitted(to: DataFrame(training), validateOn: DataFrame(validation)) { event in guard let tAccuracy = event.metrics[.trainingAccuracy] as? Double else { return } print(tAccuracy) } try transformer.write(model, to: url) print("done") Next goal is to read the model and update it with new dataFrame let urlCSV = URL(fileURLWithPath: "path/to/newData.csv") var model = try transformer.read(from: urlParameters) // loading created model let newDataFrame = try DataFrame(contentsOfCSVFile: urlCSV ) // new dataFrame with features and annotations try await transformer.update(&model, with: newDataFrame) // I want to keep previous learned data and update the model with new try transformer.write(model, to: urlParameters) // the model saves but the only last added dataFrame are saved. Previous one just replaced with new one But looks like I only replace old data with new one. **The Question ** How can add new data to model I created without losing old one ?

My app allows the user to select different stable diffusion models, and I noticed a very strange issue concerning memory management. When using the StableDiffusionPipeline (https://github.com/apple/ml-stable-diffusion) with cpu+gpu, around 1.5 GB of memory is not properly released after generateImages is called and the pipeline is released. When generating more images with a new StableDiffusionPipeline object, memory is reused and stays stable at around 1.5 GB after inference is complete. Everything, especially MLModels, are released properly. Guessing, MLModel seems to create a persistent cache. Here is the problem: When using a different MLModel afterwards, another 1.5 GB is not released and stays resident. Using a third model, this totales to 4.5 GB of unreleased, persistent memory. At first I thought that would be a bug in the StableDiffusionPipeline – but I was able to reproduce this behaviour in a very minimal objective-c sample without ARC: MLArrayBatchProvider *batchProvider = [[MLArrayBatchProvider alloc] initWithFeatureProviderArray:@[<VALID FEATURE PROVIDER>]]; MLModelConfiguration *config = [[MLModelConfiguration alloc] init]; config.computeUnits = MLComputeUnitsCPUAndGPU; MLModel *model = [[MLModel modelWithContentsOfURL:[NSURL fileURLWithPath:<VALID PATH TO .mlmodelc SD 1.5 FILE>] configuration:config error:&error] retain]; id<MLBatchProvider> returnProvider = [model predictionsFromBatch:batchProvider error:&error]; [model release]; [config release]; [batchProvider release]; After running this minimal code, 1.5 GB of persistent memory is present that is not released during the lifetime of the app. This only happens on macOS 14(.1) Sonoma and on iOS 17(.1), but not on macOS 13 Ventura. On Ventura, everything works as expected and the memory is released when predictionsFromBatch: is done and the model is released. Some observations: This only happens using cpu+gpu, not cpu+ane (since the memory is allocated out of process) and not using cpu-only It does not matter which stable diffusion model is used, I tried custom sd-derived models as well as the apple-provided sd 1.5 models I reproduced the issue on MBP 16" M1 Max with macOS 14.1, iPhone 12 mini with iOS 17.0.3 and iPad Pro M2 with iPadOS 17.1 The memory that "leaks" are mostly huge malloc block of 100-500 MB of size OR IOSurfaces This memory is allocated during predictionsFromBatch, not while loading the model Loading and unloading a model does not leak memory – only when predictionsFromBatch is called, the huge memory chunk is allocated and never freed during the lifetime of the app Does anybody have any clue what is going on? I highly suspect that I am missing something crucial, but my colleagues and me looked everywhere trying to find a method of releasing this leaked/cached memory.

Really excited after got some experiences with MPS backends for torch. But when I try to install Horovod due to problems related to c++17. Needs help.

Hi, When I try to train resnet-50 with tensorflow-metal I found the l2 regularizer makes each epoch take almost 4x as long (~220ms instead of 60ms). I'm on a M1 Max 16" MBP. It seems like regularization shouldn't add that much time, is there anything I can do to make it faster? Here's some sample code that reproduces the issue: import tensorflow as tf from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, ZeroPadding2D,\ Flatten, BatchNormalization, AveragePooling2D, Dense, Activation, Add from tensorflow.keras.regularizers import l2 from tensorflow.keras.models import Model from tensorflow.keras import activations import random import numpy as np random.seed(1234) np.random.seed(1234) tf.random.set_seed(1234) batch_size = 64 (train_im, train_lab), (test_im, test_lab) = tf.keras.datasets.cifar10.load_data() train_im, test_im = train_im/255.0 , test_im/255.0 train_lab_categorical = tf.keras.utils.to_categorical( train_lab, num_classes=10, dtype='uint8') train_DataGen = tf.keras.preprocessing.image.ImageDataGenerator() train_set_data = train_DataGen.flow(train_im, train_lab, batch_size=batch_size, shuffle=False) # Change this to l2 for it to train much slower regularizer = None # l2(0.001) def res_identity(x, filters): x_skip = x f1, f2 = filters x = Conv2D(f1, kernel_size=(1, 1), strides=(1, 1), padding='valid', use_bias=False, kernel_regularizer=regularizer)(x) x = BatchNormalization()(x) x = Activation(activations.relu)(x) x = Conv2D(f1, kernel_size=(3, 3), strides=(1, 1), padding='same', use_bias=False, kernel_regularizer=regularizer)(x) x = BatchNormalization()(x) x = Activation(activations.relu)(x) x = Conv2D(f2, kernel_size=(1, 1), strides=(1, 1), padding='valid', use_bias=False, kernel_regularizer=regularizer)(x) x = BatchNormalization()(x) x = Add()([x, x_skip]) x = Activation(activations.relu)(x) return x def res_conv(x, s, filters): x_skip = x f1, f2 = filters x = Conv2D(f1, kernel_size=(1, 1), strides=(s, s), padding='valid', use_bias=False, kernel_regularizer=regularizer)(x) x = BatchNormalization()(x) x = Activation(activations.relu)(x) x = Conv2D(f1, kernel_size=(3, 3), strides=(1, 1), padding='same', use_bias=False, kernel_regularizer=regularizer)(x) x = BatchNormalization()(x) x = Activation(activations.relu)(x) x = Conv2D(f2, kernel_size=(1, 1), strides=(1, 1), padding='valid', use_bias=False, kernel_regularizer=regularizer)(x) x = BatchNormalization()(x) x_skip = Conv2D(f2, kernel_size=(1, 1), strides=(s, s), padding='valid', use_bias=False, kernel_regularizer=regularizer)(x_skip) x_skip = BatchNormalization()(x_skip) x = Add()([x, x_skip]) x = Activation(activations.relu)(x) return x input = Input(shape=(train_im.shape[1], train_im.shape[2], train_im.shape[3]), batch_size=batch_size) x = ZeroPadding2D(padding=(3, 3))(input) x = Conv2D(64, kernel_size=(7, 7), strides=(2, 2), use_bias=False)(x) x = BatchNormalization()(x) x = Activation(activations.relu)(x) x = MaxPooling2D((3, 3), strides=(2, 2))(x) x = res_conv(x, s=1, filters=(64, 256)) x = res_identity(x, filters=(64, 256)) x = res_identity(x, filters=(64, 256)) x = res_conv(x, s=2, filters=(128, 512)) x = res_identity(x, filters=(128, 512)) x = res_identity(x, filters=(128, 512)) x = res_identity(x, filters=(128, 512)) x = res_conv(x, s=2, filters=(256, 1024)) x = res_identity(x, filters=(256, 1024)) x = res_identity(x, filters=(256, 1024)) x = res_identity(x, filters=(256, 1024)) x = res_identity(x, filters=(256, 1024)) x = res_identity(x, filters=(256, 1024)) x = res_conv(x, s=2, filters=(512, 2048)) x = res_identity(x, filters=(512, 2048)) x = res_identity(x, filters=(512, 2048)) x = AveragePooling2D((2, 2), padding='same')(x) x = Flatten()(x) x = Dense(10, activation='softmax', kernel_initializer='he_normal')(x) model = Model(inputs=input, outputs=x, name='Resnet50') opt = tf.keras.optimizers.legacy.SGD(learning_rate = 0.01) model.compile(loss=tf.keras.losses.CategoricalCrossentropy(reduction=tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE), optimizer=opt) model.fit(x=train_im, y=train_lab_categorical, batch_size=batch_size, epochs=150, steps_per_epoch=train_im.shape[0]/batch_size)

Hi everyone, I found that the performance of GPU is not good as I expected (as slow as a turtle), I wanna switch from GPU to CPU. but mlcompute module cannot be found, so wired. The same code ran on colab and my computer (jupyter lab) take 156s vs 40 minutes per epoch, respectively. I only used a small dataset (a few thousands of data points), and each epoch only have 20 baches. I am so disappointing and it seems like the "powerful" GPU is a joke. I am using 12.0.1 macOS and the version of tensorflow-macos is 2.6.0 Can anyone tell me why this happens?

Hi, I installed skearn successfully and ran the MINIST toy example successfully. then I started to run my project. The finning thing everything seems good at the start point (at least no ImportError occurs). but when I made some changes of my code and try to run all cells (I use jupyter lab) again, ImportError occurs..... ImportError: dlopen(/Users/a/miniforge3/lib/python3.9/site-packages/scipy/spatial/qhull.cpython-39-darwin.so, 0x0002): Library not loaded: @rpath/liblapack.3.dylib   Referenced from: /Users/a/miniforge3/lib/python3.9/site-packages/scipy/spatial/qhull.cpython-39-darwin.so   Reason: tried: '/Users/a/miniforge3/lib/liblapack.3.dylib' (no such file), '/Users/a/miniforge3/lib/liblapack.3.dylib' (no such file), '/Users/a/miniforge3/lib/python3.9/site-packages/scipy/spatial/../../../../liblapack.3.dylib' (no such file), '/Users/a/miniforge3/lib/liblapack.3.dylib' (no such file), '/Users/a/miniforge3/lib/liblapack.3.dylib' (no such file), '/Users/a/miniforge3/lib/python3.9/site-packages/scipy/spatial/../../../../liblapack.3.dylib' (no such file), '/Users/a/miniforge3/lib/liblapack.3.dylib' (no such file), '/Users/a/miniforge3/bin/../lib/liblapack.3.dylib' (no such file), '/Users/a/miniforge3/lib/liblapack.3.dylib' (no such file), '/Users/a/miniforge3/bin/../lib/liblapack.3.dylib' (no such file), '/usr/local/lib/liblapack.3.dylib' (no such file), '/usr/lib/liblapack.3.dylib' (no such file) then I have to uninstall scipy, sklearn, etc and reinstall all of them. and my code can be ran again..... Magically I hate to say, anyone knows how to permanently solve this problem? make skearn more stable?