I'm using Filemaker, with Monkey Bread Software plugin's CoreML features, to find that it can only write to .mlmodelc. Are these (.mlmodel = .mlmodelc) the same? If not, how do you generate a .mlmodelc using XCode. Please let me know, thanks.

Hey, im training an MLImageClassifier via the train()-method: guard let job = try? MLImageClassifier.train(trainingData: trainingData, parameters: modelParameter, sessionParameters: sessionParameters) else{ debugPrint("Training failed") return } Unfortunately the metrics of my MLProgress, which is created from the returning MLJob while training are empty. Code for listening on Progress: job.progress.publisher(for: \.fractionCompleted) .sink{[weak job] fractionCompleted in guard let job = job else { debugPrint("failure in creating job") return } guard let progress = MLProgress(progress: job.progress) else { debugPrint("failure in creating progress") return } print("ProgressPROGRESS: \(progress)") print("Progress: \(fractionCompleted)") } .store(in: &subscriptions) Printing the Progress ends in: MLProgress(elapsedTime: 2.2328420877456665, phase: CreateML.MLPhase.extractingFeatures, itemCount: 32, totalItemCount: Optional(39), metrics: [:]) Got the Same result when listening to MLCheckpoints, Metrics are empty aswell: MLCheckpoint(url: URLPATH.checkpoint, phase: CreateML.MLPhase.extractingFeatures, iteration: 32, date: 2024-04-18 11:21:18 +0000, metrics: [:]) Can some1 tell me how I can access the metrics while training? Thanks!

Hey, i just created and trained an MLImageClassifier via the MLImageclassifier.train() method (https://developer.apple.com/documentation/createml/mlimageclassifier/train(trainingdata:parameters:sessionparameters:)) For my Trainingdata (MLImageclassifier.DataSource) i am using my directoy structure, so i got an images folder with subfolders of person1, person2, person3 etc. which contain images of the labeled persons (https://developer.apple.com/documentation/createml/mlimageclassifier/datasource/labeleddirectories(at:)) I am saving the checkpoints and sessions in my appdirectory, so i can create an MLIMageClassifier from an exisiting MLSession and/or MLCheckpoint. My question is: is there any way to add new labels, optimally from my directoy strucutre, to an MLImageClassifier which i create from an existing MLCheckpoint/MLSession? So like adding a person4 and training my pretrained Classifier with only that person4. Or is it simply not possible and i have to train from the beginning everytime i want to add a new label? Unfortunately i cannot find anything in the API. Thanks!

I'm training an activity classifier with CreateML and when I add samples to the Preview tab, the length of the sample it displays does not match its actual length. I have set prediction window size to 15 and sample rate to 10. The activity is roughly 1.5 seconds. When I put a 1.49 second sample into preview, it says it is 00:00.06 seconds: and when I put a 12.91 second sample into preview, it says it is 00:00.52 seconds: Here is the code I am using to print out sensor data in csv format: if motionManager.isDeviceMotionAvailable { motionManager.deviceMotionUpdateInterval = 0.1 motionManager.startDeviceMotionUpdates(to: .main) { data, error in guard let data = data, let startTime = self.startTime else { return } let timestamp = Date().timeIntervalSince(startTime) let xAcc = data.userAcceleration.x let yAcc = data.userAcceleration.y let zAcc = data.userAcceleration.z let xRotRate = data.rotationRate.x let yRotRate = data.rotationRate.y let zRotRate = data.rotationRate.z let roll = data.attitude.roll let pitch = data.attitude.pitch let yaw = data.attitude.yaw let row = "\(timestamp),\(xAcc),\(yAcc),\(zAcc),\(xRotRate),\(yRotRate),\(zRotRate),\(roll),\(pitch),\(yaw)" print(row) } } And here is the data for the 1.49 second sample mentioned above:

Hello, I have created a Neural Network → K Nearest Neighbors Classifier with python. # followed by k-Nearest Neighbors for classification. import coremltools import coremltools.proto.FeatureTypes_pb2 as ft from coremltools.models.nearest_neighbors import KNearestNeighborsClassifierBuilder import copy # Take the SqueezeNet feature extractor from the Turi Create model. base_model = coremltools.models.MLModel("SqueezeNet.mlmodel") base_spec = base_model._spec layers = copy.deepcopy(base_spec.neuralNetworkClassifier.layers) # Delete the softmax and innerProduct layers. The new last layer is # a "flatten" layer that outputs a 1000-element vector. del layers[-1] del layers[-1] preprocessing = base_spec.neuralNetworkClassifier.preprocessing # The Turi Create model is a classifier, which is treated as a special # model type in Core ML. But we need a general-purpose neural network. del base_spec.neuralNetworkClassifier.layers[:] base_spec.neuralNetwork.layers.extend(layers) # Also copy over the image preprocessing options. base_spec.neuralNetwork.preprocessing.extend(preprocessing) # Remove other classifier stuff. base_spec.description.ClearField("metadata") base_spec.description.ClearField("predictedFeatureName") base_spec.description.ClearField("predictedProbabilitiesName") # Remove the old classifier outputs. del base_spec.description.output[:] # Add a new output for the feature vector. output = base_spec.description.output.add() output.name = "features" output.type.multiArrayType.shape.append(1000) output.type.multiArrayType.dataType = ft.ArrayFeatureType.FLOAT32 # Connect the last layer to this new output. base_spec.neuralNetwork.layers[-1].output[0] = "features" # Create the k-NN model. knn_builder = KNearestNeighborsClassifierBuilder(input_name="features", output_name="label", number_of_dimensions=1000, default_class_label="???", number_of_neighbors=3, weighting_scheme="inverse_distance", index_type="linear") knn_spec = knn_builder.spec knn_spec.description.input[0].shortDescription = "Input vector" knn_spec.description.output[0].shortDescription = "Predicted label" knn_spec.description.output[1].shortDescription = "Probabilities for each possible label" knn_builder.set_number_of_neighbors_with_bounds(3, allowed_range=(1, 10)) # Use the same name as in the neural network models, so that we # can use the same code for evaluating both types of model. knn_spec.description.predictedProbabilitiesName = "labelProbability" knn_spec.description.output[1].name = knn_spec.description.predictedProbabilitiesName # Put it all together into a pipeline. pipeline_spec = coremltools.proto.Model_pb2.Model() pipeline_spec.specificationVersion = coremltools._MINIMUM_UPDATABLE_SPEC_VERSION pipeline_spec.isUpdatable = True pipeline_spec.description.input.extend(base_spec.description.input[:]) pipeline_spec.description.output.extend(knn_spec.description.output[:]) pipeline_spec.description.predictedFeatureName = knn_spec.description.predictedFeatureName pipeline_spec.description.predictedProbabilitiesName = knn_spec.description.predictedProbabilitiesName # Add inputs for training. pipeline_spec.description.trainingInput.extend([base_spec.description.input[0]]) pipeline_spec.description.trainingInput[0].shortDescription = "Example image" pipeline_spec.description.trainingInput.extend([knn_spec.description.trainingInput[1]]) pipeline_spec.description.trainingInput[1].shortDescription = "True label" pipeline_spec.pipelineClassifier.pipeline.models.add().CopyFrom(base_spec) pipeline_spec.pipelineClassifier.pipeline.models.add().CopyFrom(knn_spec) pipeline_spec.pipelineClassifier.pipeline.names.extend(["FeatureExtractor", "kNNClassifier"]) coremltools.utils.save_spec(pipeline_spec, "../Models/FaceDetection.mlmodel") it is from the following tutorial: https://machinethink.net/blog/coreml-training-part3/ It Works and I were am to include it into my project: I want to train the model via the MLUpdateTask: ar batchInputs: [MLFeatureProvider] = [] let imageconstraint = (model.model.modelDescription.inputDescriptionsByName["image"]?.imageConstraint) let imageOptions: [MLFeatureValue.ImageOption: Any] = [ .cropAndScale: VNImageCropAndScaleOption.scaleFill.rawValue] var featureProviders = [MLFeatureProvider]() //URLS where images are stored let trainingData = ImageManager.getImagesAndLabel() for data in trainingData{ let label = data.key for imgURL in data.value{ let featureValue = try MLFeatureValue(imageAt: imgURL, constraint: imageconstraint!, options: imageOptions) if let pixelBuffer = featureValue.imageBufferValue{ let featureProvider = FaceDetectionTrainingInput(image: pixelBuffer, label: label) batchInputs.append(featureProvider)}} let trainingData = MLArrayBatchProvider(array: batchInputs) When calling the MLUpdateTask as follows, the context.model from completionHandler is null. Unfortunately there is no other Information available from the compiler. do{ debugPrint(context) try context.model.write(to: ModelManager.targetURL) } catch{ debugPrint("Error saving the model \(error)") } }) updateTask.resume() I get the following error when I want to access the context.model: Thread 5: EXC_BAD_ACCESS (code=1, address=0x0) Can some1 more experienced tell me how to fix this? It seems like I am missing some parameters? I am currently not splitting the Data when training into train and test data. only preprocessing im doing is scaling the image down to 227x227 pixels. Thanks!

Hi, I try to create some machine learning model for each stock in S&P500 index. When creating the model(Boosted tree model) I try to make it more successfully by doing hyper parameters using GridSearchCV. It takes so long to create one model so I don't want to think of creating all stocks models. I tried to work with CreateML and swift but it looks like it takes longer to run than sklearn on python. My question is how can I make the process faster? is there any hyper parameters on CreateML on swift (I couldn't find it at docs) and how can I run this code on my GPU? (should be much faster).