共用方式為

LearningModelEvaluationResult 類別

  • 參考

定義

命名空間:
Windows.AI.MachineLearning
編輯

取得評估的結果。

public ref class LearningModelEvaluationResult sealed
/// [Windows.Foundation.Metadata.ContractVersion(Windows.AI.MachineLearning.MachineLearningContract, 65536)]
/// [Windows.Foundation.Metadata.MarshalingBehavior(Windows.Foundation.Metadata.MarshalingType.Agile)]
class LearningModelEvaluationResult final
[Windows.Foundation.Metadata.ContractVersion(typeof(Windows.AI.MachineLearning.MachineLearningContract), 65536)]
[Windows.Foundation.Metadata.MarshalingBehavior(Windows.Foundation.Metadata.MarshalingType.Agile)]
public sealed class LearningModelEvaluationResult
Public NotInheritable Class LearningModelEvaluationResult
繼承
Object Platform::Object IInspectable LearningModelEvaluationResult
屬性
ContractVersionAttribute MarshalingBehaviorAttribute

Windows 需求

裝置系列
Windows 10, version 1809 (已於 10.0.17763.0 引進)
API contract
Windows.AI.MachineLearning.MachineLearningContract (已於 v1.0 引進)

範例

下列範例會擷取模型的第一個輸入和輸出功能、建立輸出框架、系結輸入和輸出功能，以及評估模型。

private async Task EvaluateModelAsync(
    VideoFrame _inputFrame, 
    LearningModelSession _session, 
    IReadOnlyList<ILearningModelFeatureDescriptor> _inputFeatures, 
    IReadOnlyList<ILearningModelFeatureDescriptor> _outputFeatures,
    LearningModel _model)
{
    ImageFeatureDescriptor _inputImageDescription;
    TensorFeatureDescriptor _outputImageDescription;
    LearningModelBinding _binding = null;
    VideoFrame _outputFrame = null;
    LearningModelEvaluationResult _results;

    try
    {
        // Retrieve the first input feature which is an image
        _inputImageDescription =
            _inputFeatures.FirstOrDefault(feature => feature.Kind == LearningModelFeatureKind.Image)
            as ImageFeatureDescriptor;

        // Retrieve the first output feature which is a tensor
        _outputImageDescription =
            _outputFeatures.FirstOrDefault(feature => feature.Kind == LearningModelFeatureKind.Tensor)
            as TensorFeatureDescriptor;

        // Create output frame based on expected image width and height
        _outputFrame = new VideoFrame(
            BitmapPixelFormat.Bgra8, 
            (int)_inputImageDescription.Width, 
            (int)_inputImageDescription.Height);

        // Create binding and then bind input/output features
        _binding = new LearningModelBinding(_session);

        _binding.Bind(_inputImageDescription.Name, _inputFrame);
        _binding.Bind(_outputImageDescription.Name, _outputFrame);

        // Evaluate and get the results
        _results = await _session.EvaluateAsync(_binding, "test");
    }
    catch (Exception ex)
    {
        StatusBlock.Text = $"error: {ex.Message}";
        _model = null;
    }
}

備註

Windows Server

若要在 Windows Server 上使用此 API，您必須搭配桌面體驗使用 Windows Server 2019。

執行緒安全

此 API 是安全線程。

屬性

CorrelationId

傳遞至 LearningModelSession.Evaluate的選擇性字串。
ErrorStatus

如果評估失敗，則傳回導致失敗原因的錯誤碼。
Outputs

取得模型的輸出功能。
Succeeded

如果評估成功完成，則為 True;否則為 false。

適用於

另請參閱