共用方式為

LearningModelSession.EvaluateAsync(LearningModelBinding, String) 方法

  • 參考

定義

命名空間:
Windows.AI.MachineLearning
編輯

使用系 結中已系結的功能值，以非同步方式評估機器學習模型。

public:
 virtual IAsyncOperation<LearningModelEvaluationResult ^> ^ EvaluateAsync(LearningModelBinding ^ bindings, Platform::String ^ correlationId) = EvaluateAsync;
/// [Windows.Foundation.Metadata.RemoteAsync]
IAsyncOperation<LearningModelEvaluationResult> EvaluateAsync(LearningModelBinding const& bindings, winrt::hstring const& correlationId);
[Windows.Foundation.Metadata.RemoteAsync]
public IAsyncOperation<LearningModelEvaluationResult> EvaluateAsync(LearningModelBinding bindings, string correlationId);
function evaluateAsync(bindings, correlationId)
Public Function EvaluateAsync (bindings As LearningModelBinding, correlationId As String) As IAsyncOperation(Of LearningModelEvaluationResult)

參數

bindings
LearningModelBinding

系結至具名輸入和輸出特徵的值。

correlationId
String

Platform::String

winrt::hstring

選擇性的使用者提供字串，以連接輸出結果。

傳回

IAsyncOperation<LearningModelEvaluationResult>

來自評估的 LearningModelEvaluationResult 。

屬性
RemoteAsyncAttribute

範例

下列範例會從模型擷取第一個輸入和輸出功能、建立輸出框架、系結輸入和輸出特徵，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。

適用於