// 4. Bind & evaluate var session = new LearningModelSession(model); var binding = new LearningModelBinding(session); binding.Bind("data", tensor);
var info = LearningModelDevice.FindAllDevices(); foreach (var d in info) Console.WriteLine(d.AdapterId); | Model Type | Input Shape | Output Shape | |------------|-------------|---------------| | Image classification | [1,3,224,224] | [1,1000] | | Object detection (YOLO) | [1,3,640,640] | [1,84,8400] | | BERT text | [1,128] (ids) + [1,128] (mask) | [1,2] (logits) | 7. Debugging & Performance Enable diagnostics: windows.ai.machinelearning
// 5. Map to label return Labels[ArgMax(classId)]; Windows ML automatically uses DirectML – you don’t need to change code. But you can select the device: var binding = new LearningModelBinding(session)
// Get output var outputTensor = results.Outputs["output"] as TensorFloat; var outputArray = outputTensor.GetAsVectorView(); public async Task<string> ClassifyImage(SoftwareBitmap bitmap) var info = LearningModelDevice.FindAllDevices()