Microsoft、.NET開発者向けMLフレームワークの最新版「ML.NET 3.0」を公開：ディープラーニング機能を強化

var chain = new EstimatorChain<ITransformer>();
var filteredPipeline = chain. Append(
        mlContext.Transforms.Text.TokenizeIntoWords(labelColumnName, separators: [',']),
        TransformerScope.Training
    )
    .Append(
        mlContext.Transforms.Conversion.MapValueToKey(labelColumnName),
        TransformerScope.Training
    )
    .Append(
        mlContext.Transforms.Text.TokenizeIntoWords(boundingBoxColumnName, separators: [',']),
        TransformerScope.Training
    )
    .Append(
        mlContext.Transforms.Conversion.ConvertType(boundingBoxColumnName),
        TransformerScope.Training
    )
    .Append(mlContext.Transforms.LoadImages("Image", imageFolder, "ImagePath"))
    .Append(
        mlContext.MulticlassClassification.Trainers.ObjectDetection(
            labelColumnName, predictedLabelColumnName, scoreColumnName,
            boundingBoxColumnName, predictedBoundingBoxColumnName,
            imageColumnName, maxEpoch
        )
    )
    .Append(mlContext.Transforms.Conversion.MapKeyToValue(predictedLabelColumnName));
var options = new ObjectDetectionTrainer.Options()
{
    LabelColumnName = labelColumnName,
    BoundingBoxColumnName = boundingBoxColumnName,
    ScoreThreshold = .5,
    MaxEpoch = maxEpoch,
    LogEveryNStep = 1,
};
var pipeline = mlContext.Transforms.Text.TokenizeIntoWords(labelColumnName, separators: [','])
    .Append(mlContext.Transforms.Conversion.MapValueToKey(labelColumnName))
    .Append(mlContext.Transforms.Text.TokenizeIntoWords(boundingBoxColumnName, separators: [',']))
    .Append(mlContext.Transforms.Conversion.ConvertType(boundingBoxColumnName))
    .Append(mlContext.Transforms.LoadImages("Image", imageFolder, "ImagePath"))
    .Append(mlContext.MulticlassClassification.Trainers.ObjectDetection(options))
    .Append(mlContext.Transforms.Conversion.MapKeyToValue(predictedLabelColumnName));
var model = pipeline.Fit(data);
var idv = model.Transform(data);
var metrics = ML.MulticlassClassification.EvaluateObjectDetection(
    idv, idv.Schema[2], idv.Schema[boundingBoxColumnName], idv.Schema[predictedLabelColumnName],
    idv.Schema[predictedBoundingBoxColumnName], idv.Schema[scoreColumnName]
);

// QA trainer
var chain = new EstimatorChain<ITransformer>();
var estimatorQA = chain.Append(mlContext.MulticlassClassification.Trainers.QuestionAnswer(
    contextColumnName, questionColumnName, trainingAnswerColumnName,
    answerIndexColumnName, predictedAnswerColumnName, scoreColumnName,
    topK, batchSize, maxEpochs, architecture, validationSet
));
// NER trainer
var estimatorNER = chain.Append(mlContext.Transforms.Conversion.MapValueToKey("Label", keyData))
    .Append(mlContext.MulticlassClassification.Trainers.NameEntityRecognition(
        labelColumnName, outputColumnName, sentence1ColumnName,
        batchSize, maxEpochs, architecture, validationSet
    ))
    .Append(mlContext.Transforms.Conversion.MapKeyToValue(outputColumn));