LearningModelEvaluationResult Kelas

Referensi

Definisi

Ruang nama:: Windows.AI.MachineLearning

Penting

Beberapa informasi terkait produk prarilis yang dapat diubah secara signifikan sebelum dirilis. Microsoft tidak memberikan jaminan, tersirat maupun tersurat, sehubungan dengan informasi yang diberikan di sini.

Edit

Dapatkan hasil evaluasi.

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

Warisan: Object Platform::Object IInspectable LearningModelEvaluationResult

Atribut: ContractVersionAttribute MarshalingBehaviorAttribute

Persyaratan Windows

Rangkaian perangkat	Windows 10, version 1809 (diperkenalkan dalam 10.0.17763.0)
API contract	Windows.AI.MachineLearning.MachineLearningContract (diperkenalkan dalam v1.0)

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.

Properti

CorrelationId	String opsional yang diteruskan ke LearningModelSession.Evaluate.
ErrorStatus	Jika evaluasi gagal, mengembalikan kode kesalahan untuk apa yang menyebabkan kegagalan.
Outputs	Mendapatkan fitur output model.
Succeeded	True jika evaluasi berhasil diselesaikan; jika tidak, salah.

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