On accessing the CoreML Model Deployment dashboard with my developer account, the page gives a bad request error saying "Your request was invalid". Also, when I try to create a Model Collection it gives an error saying "One of the fields was invalid".

Hello. I am manually constructing some models with the CoreML protobuf format. When the model has flexible input shapes, I am seeing unexpected output shapes in some cases after running prediction(from:). The model is a single matrix multiplication, A*B (one innerProduct layer), and the dynamic dimension is the first dimension of the only input A (B is constant). What I observe is that sometimes there are additional leading ones in the output shape. Some test program output showing the shapes: running model: dynamic_shape.mlmodel A shape: [1, 2] Y shape: [1, 1, 1, 1, 4] running model: dynamic_shape.mlmodel A shape: [2, 2] Y shape: [1, 1, 1, 2, 4] running model: dynamic_input_shape.mlmodel A shape: [1, 2] Y shape: [1, 4] running model: dynamic_input_shape.mlmodel A shape: [2, 2] Y shape: [1, 1, 1, 2, 4] running model: static_shape.mlmodel A shape: [1, 2] Y shape: [1, 4] I've put the model generation and test code below. Am I specifying the dynamic input/output shapes correctly when creating the .mlmodel? Is the output shape given by CoreML expected, and if so, why are there leading ones? Would appreciate any input. Python script to generate .mlmodel files. coremltools version is 6.3.0. from coremltools.proto.Model_pb2 import Model from coremltools.proto.FeatureTypes_pb2 import ArrayFeatureType from coremltools.proto.NeuralNetwork_pb2 import EXACT_ARRAY_MAPPING def build_model(with_dynamic_input_shape: bool, with_dynamic_output_shape: bool): model = Model() model.specificationVersion = 4 input = model.description.input.add() input.name = "A" input.type.multiArrayType.shape[:] = [1, 2] input.type.multiArrayType.dataType = ArrayFeatureType.FLOAT32 if with_dynamic_input_shape: range = input.type.multiArrayType.shapeRange.sizeRanges.add() range.upperBound = -1 range = input.type.multiArrayType.shapeRange.sizeRanges.add() range.lowerBound = 2 range.upperBound = 2 output = model.description.output.add() output.name = "Y" output.type.multiArrayType.shape[:] = [1, 4] output.type.multiArrayType.dataType = ArrayFeatureType.FLOAT32 if with_dynamic_output_shape: range = output.type.multiArrayType.shapeRange.sizeRanges.add() range.upperBound = -1 range = output.type.multiArrayType.shapeRange.sizeRanges.add() range.lowerBound = 4 range.upperBound = 4 layer = model.neuralNetwork.layers.add() layer.name = "MatMul" layer.input[:] = ["A"] layer.output[:] = ["Y"] layer.innerProduct.inputChannels = 2 layer.innerProduct.outputChannels = 4 layer.innerProduct.weights.floatValue[:] = [0.0, 4.0, 1.0, 5.0, 2.0, 6.0, 3.0, 7.0] model.neuralNetwork.arrayInputShapeMapping = EXACT_ARRAY_MAPPING return model if __name__ == "__main__": model = build_model(with_dynamic_input_shape=True, with_dynamic_output_shape=True) with open("dynamic_shape.mlmodel", mode="wb") as f: f.write(model.SerializeToString(deterministic=True)) model = build_model(with_dynamic_input_shape=True, with_dynamic_output_shape=False) with open("dynamic_input_shape.mlmodel", mode="wb") as f: f.write(model.SerializeToString(deterministic=True)) model = build_model(with_dynamic_input_shape=False, with_dynamic_output_shape=False) with open("static_shape.mlmodel", mode="wb") as f: f.write(model.SerializeToString(deterministic=True)) Swift program to run the models and print the output shape. import Foundation import CoreML func makeFloatShapedArray(shape: [Int]) -> MLShapedArray<Float> { let size = shape.reduce(1, *) let values = (0 ..< size).map { Float($0) } return MLShapedArray(scalars: values, shape: shape) } func runModel(model_path: URL, m: Int) throws { print("running model: \(model_path.lastPathComponent)") let compiled_model_path = try MLModel.compileModel(at: model_path) let model = try MLModel(contentsOf: compiled_model_path) let a = MLMultiArray(makeFloatShapedArray(shape: [m, 2])) print("A shape: \(a.shape)") let inputs = try MLDictionaryFeatureProvider(dictionary: ["A": a]) let outputs = try model.prediction(from: inputs) let y = outputs.featureValue(for: "Y")!.multiArrayValue! print("Y shape: \(y.shape)") } func modelUrl(_ model_file: String) -> URL { return URL(filePath: "/path/to/models/\(model_file)") } try runModel(model_path: modelUrl("dynamic_shape.mlmodel"), m: 1) try runModel(model_path: modelUrl("dynamic_shape.mlmodel"), m: 2) try runModel(model_path: modelUrl("dynamic_input_shape.mlmodel"), m: 1) try runModel(model_path: modelUrl("dynamic_input_shape.mlmodel"), m: 2) try runModel(model_path: modelUrl("static_shape.mlmodel"), m: 1)

I have a dataset with 3 columns "item_id", "user_id", "rating". I created a coreML MLRecommender model from this dataset. I want to use this model to get the top 10 predictions for a new user (not in the original dataset) but who has rated a subset of the items in the dataset. I don't see any API in the Apple docs to do this. Both the recommendations APIs only seem to accept an existing user-id and get recommendations for that user. The WWDC tutorial talks about a prediction API to achieve this. But I dont see this in the Apple API documentation and code below from WWDC tutorial cannot be used since it does not give details on how to create the HikingRouteRecommenderInput class it passes into the prediction API. let hikes : [String : Double] = ["Granite Peak" : 5, "Wildflower Meadows" : 4] let input = HikingRouteRecommenderInput(items: hikes, k: 5) // Get results as sequence of recommended items let results = try model.prediction(input: input) Any pointers on how to get predictions for new user would be greatly appreciated.