LearningModelSession.EvaluateAsync(LearningModelBinding, String) Metode

Referensi

Definisi

Ruang nama:: Windows.AI.MachineLearning

Penting

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Edit

Evaluasi model pembelajaran mesin secara asinkron menggunakan nilai fitur yang sudah terikat dalam pengikatan.

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)

Parameter

bindings: LearningModelBinding

Nilai yang terikat ke fitur input dan output bernama.

correlationId: String
Platform::String

winrt::hstring

String opsional yang disediakan pengguna untuk menyambungkan hasil output.

Mengembalikan

IAsyncOperation<LearningModelEvaluationResult>

LearningModelEvaluationResult dari evaluasi.

Atribut: RemoteAsyncAttribute

Contoh

Contoh berikut mengambil fitur input dan output pertama dari model, membuat bingkai output, mengikat fitur input dan output, dan mengevaluasi model.

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;
    }
}

Keterangan

Windows Server

Untuk menggunakan API ini di Windows Server, Anda harus menggunakan Windows Server 2019 dengan Pengalaman Desktop.

Keamanan utas

API ini aman untuk utas.

Berlaku untuk

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