/// <summary> /// /// </summary> /// <param name="previewFrame"></param> /// <returns></returns> private async Task EvaluateVideoFrameAsync(VideoFrame previewFrame) { if (previewFrame != null) { try { IList <string> classLabel = new List <string>(); LearningModelBindingPreview binding = new LearningModelBindingPreview(model as LearningModelPreview); binding.Bind(inputImageDescription.Name, previewFrame); binding.Bind(outputMapDescription.Name, prob); binding.Bind(outputTensorDescription.Name, classLabel); var stopwatch = Stopwatch.StartNew(); LearningModelEvaluationResultPreview results = await model.EvaluateAsync(binding, string.Empty); stopwatch.Stop(); await Dispatcher.RunAsync(CoreDispatcherPriority.Normal, () => { fpsTBlock.Text = $"{1000f / stopwatch.ElapsedMilliseconds,4:f1} fps"; statusTBlock.Text = classLabel[0]; }); } catch (Exception ex) { Debug.WriteLine(ex.Message); } } }
public async Task <Results> EvaluateAsync(VideoFrame inputFrame) { if (model != null) { // Create bindings for the input and output buffer var binding = new LearningModelBindingPreview(model as LearningModelPreview); var outputVariableList = new List <float>(); binding.Bind(inputImageDescription.Name, inputFrame); binding.Bind(outputTensorDescription.Name, outputVariableList); // Process the frame through the model var results = await model.EvaluateAsync(binding, String.Empty); var resultProbabilities = results.Outputs[outputTensorDescription.Name] as List <float>; // return the result return(new Results() { Neutral = resultProbabilities[0], Happiness = resultProbabilities[1], Surprise = resultProbabilities[2], Sadness = resultProbabilities[3], Anger = resultProbabilities[4], Disgust = resultProbabilities[5], Fear = resultProbabilities[6], Contempt = resultProbabilities[7] }); } return(null); }
private async Task EvaluateVideoFrameAsync(VideoFrame inputFrame) { if (inputFrame != null) { try { // Create bindings for the input and output buffer var binding = new LearningModelBindingPreview(this.model as LearningModelPreview); // R4 WinML does needs the output pre-allocated for multi-dimensional tensors var outputArray = new List <float>(); outputArray.AddRange(new float[21125]); // Total size of TinyYOLO output binding.Bind(this.inputImageDescription.Name, inputFrame); binding.Bind(this.outputTensorDescription.Name, outputArray); // Process the frame with the model var results = await this.model.EvaluateAsync(binding, "TinyYOLO"); var resultProbabilities = results.Outputs[this.outputTensorDescription.Name] as List <float>; // Use out helper to parse to the YOLO outputs into bounding boxes with labels this.boxes = this.parser.ParseOutputs(resultProbabilities.ToArray(), .3F); await Dispatcher.RunAsync(CoreDispatcherPriority.Normal, () => StatusBlock.Text = "Model Evaluation Completed"); } catch (Exception ex) { await Dispatcher.RunAsync(CoreDispatcherPriority.Normal, () => StatusBlock.Text = $"error: {ex.Message}"); } await Dispatcher.RunAsync(CoreDispatcherPriority.Normal, () => ButtonRun.IsEnabled = true); } }
/// <summary> /// Evaluate the VideoFrame passed in as arg /// </summary> /// <param name="inputFrame"></param> /// <returns></returns> private async Task EvaluateVideoFrameAsync(VideoFrame inputFrame) { if (inputFrame != null) { try { // Create bindings for the input and output buffer LearningModelBindingPreview binding = new LearningModelBindingPreview(_model as LearningModelPreview); binding.Bind(_inputImageDescription.Name, inputFrame); binding.Bind(_outputTensorDescription.Name, _outputVariableList); // Process the frame with the model, and time the operation modeltime_w.Restart(); LearningModelEvaluationResultPreview results = await _model.EvaluateAsync(binding, "test"); modeltime_w.Stop(); List <float> resultProbabilities = results.Outputs[_outputTensorDescription.Name] as List <float>; // Find the result of the evaluation in the bound output (the top classes detected with the max confidence) List <float> topProbabilities = new List <float>() { 0.0f, 0.0f, 0.0f }; List <int> topProbabilityLabelIndexes = new List <int>() { 0, 0, 0 }; for (int i = 0; i < resultProbabilities.Count(); i++) { for (int j = 0; j < 3; j++) { if (resultProbabilities[i] > topProbabilities[j]) { topProbabilityLabelIndexes[j] = i; topProbabilities[j] = resultProbabilities[i]; break; } } } // Display the result var runtimemS = modeltime_w.ElapsedMilliseconds; string message = $"Runtime {runtimemS}mS Predominant objects detected are:"; for (int i = 0; i < 3; i++) { message += $"\n\"{ _labels[topProbabilityLabelIndexes[i]]}\" with confidence of { topProbabilities[i]}"; } StatusBlock.Text = message; } catch (Exception ex) { StatusBlock.Text = $"error: {ex.Message}"; } ButtonRun.IsEnabled = true; } }
/// <summary> /// Evaluate the VideoFrame passed in as arg /// </summary> /// <param name="inputFrame"></param> /// <returns></returns> private async Task EvaluateVideoFrameAsync(VideoFrame inputFrame) { if (inputFrame != null) { try { // Create bindings for the input and output buffer LearningModelBindingPreview binding = new LearningModelBindingPreview(_model as LearningModelPreview); binding.Bind(_inputImageDescription.Name, inputFrame); binding.Bind(_outputTensorDescription.Name, _outputVariableList); // Process the frame with the model LearningModelEvaluationResultPreview results = await _model.EvaluateAsync(binding, "test"); List <float> resultProbabilities = results.Outputs[_outputTensorDescription.Name] as List <float>; // Find the result of the evaluation in the bound output (the top classes detected with the max confidence) List <float> topProbabilities = new List <float>() { 0.0f, 0.0f, 0.0f }; List <int> topProbabilityLabelIndexes = new List <int>() { 0, 0, 0 }; for (int i = 0; i < resultProbabilities.Count(); i++) { for (int j = 0; j < 3; j++) { if (resultProbabilities[i] > topProbabilities[j]) { topProbabilityLabelIndexes[j] = i; topProbabilities[j] = resultProbabilities[i]; break; } } } // Display the result string message = "Predominant objects detected are:"; for (int i = 0; i < 3; i++) { message += $"\n{ _labels[topProbabilityLabelIndexes[i]]} with confidence of { topProbabilities[i]}"; } await Dispatcher.RunAsync(CoreDispatcherPriority.Normal, () => StatusBlock.Text = message); } catch (Exception ex) { await Dispatcher.RunAsync(CoreDispatcherPriority.Normal, () => StatusBlock.Text = $"error: {ex.Message}"); } await Dispatcher.RunAsync(CoreDispatcherPriority.Normal, () => ButtonRun.IsEnabled = true); } }
public async Task <Squeezenet_oldModelOutput> EvaluateAsync(Squeezenet_oldModelInput input) { Squeezenet_oldModelOutput output = new Squeezenet_oldModelOutput(); LearningModelBindingPreview binding = new LearningModelBindingPreview(learningModel); binding.Bind("data_0", input.data_0); binding.Bind("softmaxout_1", output.softmaxout_1); LearningModelEvaluationResultPreview evalResult = await learningModel.EvaluateAsync(binding, string.Empty); return(output); }
public async Task <CNTKGraphModelOutput> EvaluateAsync(CNTKGraphModelInput input) { CNTKGraphModelOutput output = new CNTKGraphModelOutput(); LearningModelBindingPreview binding = new LearningModelBindingPreview(learningModel); binding.Bind("Input3", input.Input3); binding.Bind("Softmax99_Output_0", output.Softmax99_Output_0); LearningModelEvaluationResultPreview evalResult = await learningModel.EvaluateAsync(binding, string.Empty); return(output); }
public async Task <TinyYOLOModelModelOutput> EvaluateAsync(TinyYOLOModelModelInput input) { TinyYOLOModelModelOutput output = new TinyYOLOModelModelOutput(); LearningModelBindingPreview binding = new LearningModelBindingPreview(learningModel); binding.Bind("image", input.image); binding.Bind("grid", output.grid); LearningModelEvaluationResultPreview evalResult = await learningModel.EvaluateAsync(binding, string.Empty); return(output); }
public async Task <MpsnnGraphModelOutput> EvaluateAsync(MpsnnGraphModelInput input) { MpsnnGraphModelOutput output = new MpsnnGraphModelOutput(); LearningModelBindingPreview binding = new LearningModelBindingPreview(_learningModel); binding.Bind("image", input.Image); binding.Bind("grid", output.Grid); LearningModelEvaluationResultPreview evalResult = await _learningModel.EvaluateAsync(binding, string.Empty); return(output); }
public async Task <MNISTModelOutput> EvaluateAsync(MNISTModelInput input) { MNISTModelOutput output = new MNISTModelOutput(); LearningModelBindingPreview binding = new LearningModelBindingPreview(learningModel); binding.Bind("Input3", input.Input3); binding.Bind("Plus214_Output_0", output.Plus214_Output_0); LearningModelEvaluationResultPreview evalResult = await learningModel.EvaluateAsync(binding, string.Empty); return(output); }
public async Task <TinyYoloV2ModelOutput> EvaluateAsync(TinyYoloV2ModelInput input) { var output = new TinyYoloV2ModelOutput(); var binding = new LearningModelBindingPreview(_learningModel); binding.Bind("image", input.Image); binding.Bind("grid", output.Grid); var evalResult = await _learningModel.EvaluateAsync(binding, string.Empty); return(output); }
public async Task <YOLO2ModelOutput> EvaluateAsync(YOLO2ModelInput input) { YOLO2ModelOutput output = new YOLO2ModelOutput(); LearningModelBindingPreview binding = new LearningModelBindingPreview(learningModel); binding.Bind("input__0", input.input__0); binding.Bind("output__0", output.output__0); LearningModelEvaluationResultPreview evalResult = await learningModel.EvaluateAsync(binding, string.Empty); return(output); }
public async Task <Vgg19ModelOutput> EvaluateAsync(Vgg19ModelInput input) { Vgg19ModelOutput output = new Vgg19ModelOutput(); LearningModelBindingPreview binding = new LearningModelBindingPreview(learningModel); binding.Bind("data_0", input.data_0); binding.Bind("prob_1", output.prob_1); LearningModelEvaluationResultPreview evalResult = await learningModel.EvaluateAsync(binding, string.Empty); return(output); }
public async Task <Af4dec16_x002D_0900_x002D_4f35_x002D_8d03_x002D_28743433843e_cb36632a_x002D_770d_x002D_4ef8_x002D_a996_x002D_d3b067ff0e11ModelOutput> EvaluateAsync(Af4dec16_x002D_0900_x002D_4f35_x002D_8d03_x002D_28743433843e_cb36632a_x002D_770d_x002D_4ef8_x002D_a996_x002D_d3b067ff0e11ModelInput input) { Af4dec16_x002D_0900_x002D_4f35_x002D_8d03_x002D_28743433843e_cb36632a_x002D_770d_x002D_4ef8_x002D_a996_x002D_d3b067ff0e11ModelOutput output = new Af4dec16_x002D_0900_x002D_4f35_x002D_8d03_x002D_28743433843e_cb36632a_x002D_770d_x002D_4ef8_x002D_a996_x002D_d3b067ff0e11ModelOutput(); LearningModelBindingPreview binding = new LearningModelBindingPreview(learningModel); binding.Bind("data", input.data); binding.Bind("classLabel", output.classLabel); binding.Bind("loss", output.loss); LearningModelEvaluationResultPreview evalResult = await learningModel.EvaluateAsync(binding, string.Empty); return(output); }
public async Task <C2302845_x002D_023d_x002D_4c61_x002D_9b48_x002D_25d860a0c350_c6ad3001_x002D_dc81_x002D_45fd_x002D_a943_x002D_e7afa90c07f5ModelOutput> EvaluateAsync(C2302845_x002D_023d_x002D_4c61_x002D_9b48_x002D_25d860a0c350_c6ad3001_x002D_dc81_x002D_45fd_x002D_a943_x002D_e7afa90c07f5ModelInput input) { C2302845_x002D_023d_x002D_4c61_x002D_9b48_x002D_25d860a0c350_c6ad3001_x002D_dc81_x002D_45fd_x002D_a943_x002D_e7afa90c07f5ModelOutput output = new C2302845_x002D_023d_x002D_4c61_x002D_9b48_x002D_25d860a0c350_c6ad3001_x002D_dc81_x002D_45fd_x002D_a943_x002D_e7afa90c07f5ModelOutput(); LearningModelBindingPreview binding = new LearningModelBindingPreview(learningModel); binding.Bind("data", input.data); binding.Bind("classLabel", output.classLabel); binding.Bind("loss", output.loss); LearningModelEvaluationResultPreview evalResult = await learningModel.EvaluateAsync(binding, string.Empty); return(output); }
public async Task <ResNet50ModelOutput> EvaluateAsync(ResNet50ModelInput input) { ResNet50ModelOutput output = new ResNet50ModelOutput(); LearningModelBindingPreview binding = new LearningModelBindingPreview(learningModel); binding.Bind("image", input.image); binding.Bind("classLabel", output.classLabel); binding.Bind("classLabelProbs", output.classLabelProbs); LearningModelEvaluationResultPreview evalResult = await learningModel.EvaluateAsync(binding, string.Empty); return(output); }
public async Task <_x0036_fba5602_x002D_2416_x002D_4582_x002D_8f3b_x002D_931bc0eadc24_c97c9b55_x002D_21f5_x002D_48b8_x002D_9a25_x002D_83f235a0d8d7ModelOutput> EvaluateAsync(_x0036_fba5602_x002D_2416_x002D_4582_x002D_8f3b_x002D_931bc0eadc24_c97c9b55_x002D_21f5_x002D_48b8_x002D_9a25_x002D_83f235a0d8d7ModelInput input) { _x0036_fba5602_x002D_2416_x002D_4582_x002D_8f3b_x002D_931bc0eadc24_c97c9b55_x002D_21f5_x002D_48b8_x002D_9a25_x002D_83f235a0d8d7ModelOutput output = new _x0036_fba5602_x002D_2416_x002D_4582_x002D_8f3b_x002D_931bc0eadc24_c97c9b55_x002D_21f5_x002D_48b8_x002D_9a25_x002D_83f235a0d8d7ModelOutput(); LearningModelBindingPreview binding = new LearningModelBindingPreview(learningModel); binding.Bind("data", input.data); binding.Bind("classLabel", output.classLabel); binding.Bind("loss", output.loss); LearningModelEvaluationResultPreview evalResult = await learningModel.EvaluateAsync(binding, string.Empty); return(output); }
public async Task <InkShapesModelOutput> EvaluateAsync(InkShapesModelInput input) { InkShapesModelOutput output = new InkShapesModelOutput(_lossCount); LearningModelBindingPreview binding = new LearningModelBindingPreview(learningModel); binding.Bind("data", input.data); binding.Bind("classLabel", output.classLabel); binding.Bind("loss", output.loss); LearningModelEvaluationResultPreview evalResult = await learningModel.EvaluateAsync(binding, string.Empty); return(output); }
public async Task <GoogLeNetPlacesModelModelOutput> EvaluateAsync(GoogLeNetPlacesModelModelInput input) { GoogLeNetPlacesModelModelOutput output = new GoogLeNetPlacesModelModelOutput(); LearningModelBindingPreview binding = new LearningModelBindingPreview(learningModel); binding.Bind("sceneImage", input.sceneImage); binding.Bind("sceneLabel", output.sceneLabel); binding.Bind("sceneLabelProbs", output.sceneLabelProbs); LearningModelEvaluationResultPreview evalResult = await learningModel.EvaluateAsync(binding, string.Empty); return(output); }
public async Task <E6c82f6e_x002D_c60f_x002D_422a_x002D_97b6_x002D_e0406cba82da_6ed0259c_x002D_001e_x002D_4895_x002D_be7a_x002D_4a930321a307ModelOutput> EvaluateAsync(E6c82f6e_x002D_c60f_x002D_422a_x002D_97b6_x002D_e0406cba82da_6ed0259c_x002D_001e_x002D_4895_x002D_be7a_x002D_4a930321a307ModelInput input) { E6c82f6e_x002D_c60f_x002D_422a_x002D_97b6_x002D_e0406cba82da_6ed0259c_x002D_001e_x002D_4895_x002D_be7a_x002D_4a930321a307ModelOutput output = new E6c82f6e_x002D_c60f_x002D_422a_x002D_97b6_x002D_e0406cba82da_6ed0259c_x002D_001e_x002D_4895_x002D_be7a_x002D_4a930321a307ModelOutput(); LearningModelBindingPreview binding = new LearningModelBindingPreview(learningModel); binding.Bind("data", input.data); binding.Bind("classLabel", output.classLabel); binding.Bind("loss", output.loss); LearningModelEvaluationResultPreview evalResult = await learningModel.EvaluateAsync(binding, string.Empty); return(output); }
public async Task <ModelOutput> EvaluateAsync(VideoFrame videoFrame) { ModelOutput output = new ModelOutput(_labels); LearningModelBindingPreview binding = new LearningModelBindingPreview(_learningModel); binding.Bind("data", videoFrame); binding.Bind("classLabel", output.ClassLabel); binding.Bind("loss", output.Loss); LearningModelEvaluationResultPreview evalResult = await _learningModel.EvaluateAsync(binding, string.Empty); return(output); }
public async Task <_x0035_3979b10_x002D_4cb6_x002D_417f_x002D_9f57_x002D_a85d70709fbc_44105f29_x002D_1f46_x002D_48b2_x002D_b0ad_x002D_7d42d639cb20ModelOutput> EvaluateAsync(_x0035_3979b10_x002D_4cb6_x002D_417f_x002D_9f57_x002D_a85d70709fbc_44105f29_x002D_1f46_x002D_48b2_x002D_b0ad_x002D_7d42d639cb20ModelInput input) { _x0035_3979b10_x002D_4cb6_x002D_417f_x002D_9f57_x002D_a85d70709fbc_44105f29_x002D_1f46_x002D_48b2_x002D_b0ad_x002D_7d42d639cb20ModelOutput output = new _x0035_3979b10_x002D_4cb6_x002D_417f_x002D_9f57_x002D_a85d70709fbc_44105f29_x002D_1f46_x002D_48b2_x002D_b0ad_x002D_7d42d639cb20ModelOutput(); LearningModelBindingPreview binding = new LearningModelBindingPreview(learningModel); binding.Bind("data", input.data); binding.Bind("classLabel", output.classLabel); binding.Bind("loss", output.loss); LearningModelEvaluationResultPreview evalResult = await learningModel.EvaluateAsync(binding, string.Empty); return(output); }
public async Task <_x0039_3deb65e_x002D_dfa1_x002D_49a3_x002D_8a7c_x002D_b802a3a909e1_28f9b6ec_x002D_eae5_x002D_49e6_x002D_8da7_x002D_2b70beb4c498ModelOutput> EvaluateAsync(_x0039_3deb65e_x002D_dfa1_x002D_49a3_x002D_8a7c_x002D_b802a3a909e1_28f9b6ec_x002D_eae5_x002D_49e6_x002D_8da7_x002D_2b70beb4c498ModelInput input) { _x0039_3deb65e_x002D_dfa1_x002D_49a3_x002D_8a7c_x002D_b802a3a909e1_28f9b6ec_x002D_eae5_x002D_49e6_x002D_8da7_x002D_2b70beb4c498ModelOutput output = new _x0039_3deb65e_x002D_dfa1_x002D_49a3_x002D_8a7c_x002D_b802a3a909e1_28f9b6ec_x002D_eae5_x002D_49e6_x002D_8da7_x002D_2b70beb4c498ModelOutput(); LearningModelBindingPreview binding = new LearningModelBindingPreview(learningModel); binding.Bind("data", input.data); binding.Bind("classLabel", output.classLabel); binding.Bind("loss", output.loss); LearningModelEvaluationResultPreview evalResult = await learningModel.EvaluateAsync(binding, string.Empty); return(output); }
public async Task <_x0034_7c46b62_x002D_55d4_x002D_418f_x002D_9667_x002D_bca58c105516_df5a0f61_x002D_d714_x002D_4789_x002D_8077_x002D_27f227995765ModelOutput> EvaluateAsync(_x0034_7c46b62_x002D_55d4_x002D_418f_x002D_9667_x002D_bca58c105516_df5a0f61_x002D_d714_x002D_4789_x002D_8077_x002D_27f227995765ModelInput input) { _x0034_7c46b62_x002D_55d4_x002D_418f_x002D_9667_x002D_bca58c105516_df5a0f61_x002D_d714_x002D_4789_x002D_8077_x002D_27f227995765ModelOutput output = new _x0034_7c46b62_x002D_55d4_x002D_418f_x002D_9667_x002D_bca58c105516_df5a0f61_x002D_d714_x002D_4789_x002D_8077_x002D_27f227995765ModelOutput(); LearningModelBindingPreview binding = new LearningModelBindingPreview(learningModel); binding.Bind("data", input.data); binding.Bind("classLabel", output.classLabel); binding.Bind("loss", output.loss); LearningModelEvaluationResultPreview evalResult = await learningModel.EvaluateAsync(binding, string.Empty); return(output); }
public async Task <MobilenetModelOutput> EvaluateAsync(MobilenetModelInput input) { MobilenetModelOutput output = new MobilenetModelOutput(); LearningModelBindingPreview binding = new LearningModelBindingPreview(learningModel); binding.Bind("data", input.data); binding.Bind("classLabel", output.classLabel); binding.Bind("prob", output.prob); LearningModelEvaluationResultPreview evalResult = await learningModel.EvaluateAsync(binding, string.Empty); return(output); }
public async Task <MemeClassifierModelOutput> EvaluateAsync(MemeClassifierModelInput input) { MemeClassifierModelOutput output = new MemeClassifierModelOutput(); LearningModelBindingPreview binding = new LearningModelBindingPreview(learningModel); binding.Bind("data", input.Data); binding.Bind("classLabel", output.ClassLabel); binding.Bind("loss", output.Loss); LearningModelEvaluationResultPreview evalResult = await learningModel.EvaluateAsync(binding, string.Empty); return(output); }
public async Task <ViscoreitanoModelOutput> EvaluateAsync(ViscoreitanoModelInput input) { ViscoreitanoModelOutput output = new ViscoreitanoModelOutput(); LearningModelBindingPreview binding = new LearningModelBindingPreview(learningModel); binding.Bind("data", input.data); binding.Bind("classLabel", output.classLabel); binding.Bind("loss", output.loss); LearningModelEvaluationResultPreview evalResult = await learningModel.EvaluateAsync(binding, string.Empty); return(output); }
public async Task <_x0035_f11ca47_x002D_2841_x002D_4ece_x002D_af83_x002D_e7bc2b5e8603_fe5629e0_x002D_49e2_x002D_4e9c_x002D_be74_x002D_076aa419dcddModelOutput> EvaluateAsync(_x0035_f11ca47_x002D_2841_x002D_4ece_x002D_af83_x002D_e7bc2b5e8603_fe5629e0_x002D_49e2_x002D_4e9c_x002D_be74_x002D_076aa419dcddModelInput input) { _x0035_f11ca47_x002D_2841_x002D_4ece_x002D_af83_x002D_e7bc2b5e8603_fe5629e0_x002D_49e2_x002D_4e9c_x002D_be74_x002D_076aa419dcddModelOutput output = new _x0035_f11ca47_x002D_2841_x002D_4ece_x002D_af83_x002D_e7bc2b5e8603_fe5629e0_x002D_49e2_x002D_4e9c_x002D_be74_x002D_076aa419dcddModelOutput(); LearningModelBindingPreview binding = new LearningModelBindingPreview(learningModel); binding.Bind("data", input.data); binding.Bind("classLabel", output.classLabel); binding.Bind("loss", output.loss); LearningModelEvaluationResultPreview evalResult = await learningModel.EvaluateAsync(binding, string.Empty); return(output); }
public async Task <IMachineLearningOutput> EvaluateAsync(IMachineLearningInput input) { var modelInput = input as TinyYOLOModelModelInput; TinyYOLOModelModelOutput output = new TinyYOLOModelModelOutput(); LearningModelBindingPreview binding = new LearningModelBindingPreview(LearningModel); binding.Bind("image", modelInput.image); binding.Bind("grid", output.grid); LearningModelEvaluationResultPreview evalResult = await LearningModel.EvaluateAsync(binding, string.Empty); return(output); }
public async Task <Ee7165d4_x002D_31e3_x002D_468e_x002D_ba0c_x002D_565f0b33d377_1bc49a81_x002D_827d_x002D_48b3_x002D_84bf_x002D_3a9555b4c54eModelOutput> EvaluateAsync(Ee7165d4_x002D_31e3_x002D_468e_x002D_ba0c_x002D_565f0b33d377_1bc49a81_x002D_827d_x002D_48b3_x002D_84bf_x002D_3a9555b4c54eModelInput input) { Ee7165d4_x002D_31e3_x002D_468e_x002D_ba0c_x002D_565f0b33d377_1bc49a81_x002D_827d_x002D_48b3_x002D_84bf_x002D_3a9555b4c54eModelOutput output = new Ee7165d4_x002D_31e3_x002D_468e_x002D_ba0c_x002D_565f0b33d377_1bc49a81_x002D_827d_x002D_48b3_x002D_84bf_x002D_3a9555b4c54eModelOutput(); LearningModelBindingPreview binding = new LearningModelBindingPreview(learningModel); binding.Bind("data", input.data); binding.Bind("classLabel", output.classLabel); binding.Bind("loss", output.loss); LearningModelEvaluationResultPreview evalResult = await learningModel.EvaluateAsync(binding, string.Empty); return(output); }