StandardTrainersCatalog.OnlineGradientDescent Yöntem

Tanım

Ad Alanı:

Microsoft.ML

Bütünleştirilmiş Kod:

Microsoft.ML.StandardTrainers.dll

Paket:

Microsoft.ML v2.0.0

Aşırı Yüklemeler

OnlineGradientDescent(RegressionCatalog+RegressionTrainers, String, String, IRegressionLoss, Single, Boolean, Single, Int32)	Doğrusal regresyon modeli kullanarak hedefi tahmin eden oluşturma OnlineGradientDescentTrainer.
OnlineGradientDescent(RegressionCatalog+RegressionTrainers, OnlineGradientDescentTrainer+Options)	Doğrusal regresyon modeli kullanarak hedefi tahmin eden gelişmiş seçenekleri kullanarak oluşturma OnlineGradientDescentTrainer .

OnlineGradientDescent(RegressionCatalog+RegressionTrainers, String, String, IRegressionLoss, Single, Boolean, Single, Int32)

Doğrusal regresyon modeli kullanarak hedefi tahmin eden oluşturma OnlineGradientDescentTrainer.

public static Microsoft.ML.Trainers.OnlineGradientDescentTrainer OnlineGradientDescent (this Microsoft.ML.RegressionCatalog.RegressionTrainers catalog, string labelColumnName = "Label", string featureColumnName = "Features", Microsoft.ML.Trainers.IRegressionLoss lossFunction = default, float learningRate = 0.1, bool decreaseLearningRate = true, float l2Regularization = 0, int numberOfIterations = 1);

static member OnlineGradientDescent : Microsoft.ML.RegressionCatalog.RegressionTrainers * string * string * Microsoft.ML.Trainers.IRegressionLoss * single * bool * single * int -> Microsoft.ML.Trainers.OnlineGradientDescentTrainer

<Extension()>
Public Function OnlineGradientDescent (catalog As RegressionCatalog.RegressionTrainers, Optional labelColumnName As String = "Label", Optional featureColumnName As String = "Features", Optional lossFunction As IRegressionLoss = Nothing, Optional learningRate As Single = 0.1, Optional decreaseLearningRate As Boolean = true, Optional l2Regularization As Single = 0, Optional numberOfIterations As Integer = 1) As OnlineGradientDescentTrainer

Parametreler

catalog

RegressionCatalog.RegressionTrainers

Regresyon kataloğu eğitmen nesnesi.

labelColumnName

String

Etiket sütununun adı. Sütun verileri olmalıdır Single.

featureColumnName

String

Özellik sütununun adı. Sütun verileri bilinen boyutlu bir vektör Singleolmalıdır.

lossFunction

IRegressionLoss

Eğitim sürecinde en aza indirgenen kayıp işlevi. Örneğin SquaredLoss kullanarak en az kare eğitmene yol açar.

learningRate

Single

SGD tarafından kullanılan ilk öğrenme oranı.

decreaseLearningRate

Boolean

Yinelemeler ilerledikçe öğrenme hızını azaltın.

l2Regularization

Single

Düzenlileştirme için L2 ağırlığı.

numberOfIterations

Int32

Eğitim veri kümesinden geçenlerin sayısı.

Döndürülenler

OnlineGradientDescentTrainer

Örnekler

using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;

namespace Samples.Dynamic.Trainers.Regression
{
    public static class OnlineGradientDescent
    {
        public static void Example()
        {
            // Create a new context for ML.NET operations. It can be used for
            // exception tracking and logging, as a catalog of available operations
            // and as the source of randomness. Setting the seed to a fixed number
            // in this example to make outputs deterministic.
            var mlContext = new MLContext(seed: 0);

            // Create a list of training data points.
            var dataPoints = GenerateRandomDataPoints(1000);

            // Convert the list of data points to an IDataView object, which is
            // consumable by ML.NET API.
            var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);

            // Define the trainer.
            var pipeline = mlContext.Regression.Trainers.OnlineGradientDescent(
                labelColumnName: nameof(DataPoint.Label),
                featureColumnName: nameof(DataPoint.Features));

            // Train the model.
            var model = pipeline.Fit(trainingData);

            // Create testing data. Use different random seed to make it different
            // from training data.
            var testData = mlContext.Data.LoadFromEnumerable(
                GenerateRandomDataPoints(5, seed: 123));

            // Run the model on test data set.
            var transformedTestData = model.Transform(testData);

            // Convert IDataView object to a list.
            var predictions = mlContext.Data.CreateEnumerable<Prediction>(
                transformedTestData, reuseRowObject: false).ToList();

            // Look at 5 predictions for the Label, side by side with the actual
            // Label for comparison.
            foreach (var p in predictions)
                Console.WriteLine($"Label: {p.Label:F3}, Prediction: {p.Score:F3}");

            // This trainer is not numerically stable.
            // Please see issue #2425.

            // Evaluate the overall metrics
            var metrics = mlContext.Regression.Evaluate(transformedTestData);
            PrintMetrics(metrics);


        }

        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
            int seed = 0)
        {
            var random = new Random(seed);
            for (int i = 0; i < count; i++)
            {
                float label = (float)random.NextDouble();
                yield return new DataPoint
                {
                    Label = label,
                    // Create random features that are correlated with the label.
                    Features = Enumerable.Repeat(label, 50).Select(
                        x => x + (float)random.NextDouble()).ToArray()
                };
            }
        }

        // Example with label and 50 feature values. A data set is a collection of
        // such examples.
        private class DataPoint
        {
            public float Label { get; set; }
            [VectorType(50)]
            public float[] Features { get; set; }
        }

        // Class used to capture predictions.
        private class Prediction
        {
            // Original label.
            public float Label { get; set; }
            // Predicted score from the trainer.
            public float Score { get; set; }
        }

        // Print some evaluation metrics to regression problems.
        private static void PrintMetrics(RegressionMetrics metrics)
        {
            Console.WriteLine("Mean Absolute Error: " + metrics.MeanAbsoluteError);
            Console.WriteLine("Mean Squared Error: " + metrics.MeanSquaredError);
            Console.WriteLine(
                "Root Mean Squared Error: " + metrics.RootMeanSquaredError);

            Console.WriteLine("RSquared: " + metrics.RSquared);
        }
    }
}

OnlineGradientDescent(RegressionCatalog+RegressionTrainers, OnlineGradientDescentTrainer+Options)

Doğrusal regresyon modeli kullanarak hedefi tahmin eden gelişmiş seçenekleri kullanarak oluşturma OnlineGradientDescentTrainer .

public static Microsoft.ML.Trainers.OnlineGradientDescentTrainer OnlineGradientDescent (this Microsoft.ML.RegressionCatalog.RegressionTrainers catalog, Microsoft.ML.Trainers.OnlineGradientDescentTrainer.Options options);

static member OnlineGradientDescent : Microsoft.ML.RegressionCatalog.RegressionTrainers * Microsoft.ML.Trainers.OnlineGradientDescentTrainer.Options -> Microsoft.ML.Trainers.OnlineGradientDescentTrainer

<Extension()>
Public Function OnlineGradientDescent (catalog As RegressionCatalog.RegressionTrainers, options As OnlineGradientDescentTrainer.Options) As OnlineGradientDescentTrainer

Parametreler

catalog

RegressionCatalog.RegressionTrainers

Regresyon kataloğu eğitmen nesnesi.

options

OnlineGradientDescentTrainer.Options

Eğitmen seçenekleri.

Döndürülenler

OnlineGradientDescentTrainer

Örnekler

using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Trainers;

namespace Samples.Dynamic.Trainers.Regression
{
    public static class OnlineGradientDescentWithOptions
    {
        public static void Example()
        {
            // Create a new context for ML.NET operations. It can be used for
            // exception tracking and logging, as a catalog of available operations
            // and as the source of randomness. Setting the seed to a fixed number
            // in this example to make outputs deterministic.
            var mlContext = new MLContext(seed: 0);

            // Create a list of training data points.
            var dataPoints = GenerateRandomDataPoints(1000);

            // Convert the list of data points to an IDataView object, which is
            // consumable by ML.NET API.
            var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);

            // Define trainer options.
            var options = new OnlineGradientDescentTrainer.Options
            {
                LabelColumnName = nameof(DataPoint.Label),
                FeatureColumnName = nameof(DataPoint.Features),
                // Change the loss function.
                LossFunction = new TweedieLoss(),
                // Give an extra gain to more recent updates.
                RecencyGain = 0.1f,
                // Turn off lazy updates.
                LazyUpdate = false,
                // Specify scale for initial weights.
                InitialWeightsDiameter = 0.2f
            };

            // Define the trainer.
            var pipeline =
                mlContext.Regression.Trainers.OnlineGradientDescent(options);

            // Train the model.
            var model = pipeline.Fit(trainingData);

            // Create testing data. Use different random seed to make it different
            // from training data.
            var testData = mlContext.Data.LoadFromEnumerable(
                GenerateRandomDataPoints(5, seed: 123));

            // Run the model on test data set.
            var transformedTestData = model.Transform(testData);

            // Convert IDataView object to a list.
            var predictions = mlContext.Data.CreateEnumerable<Prediction>(
                transformedTestData, reuseRowObject: false).ToList();

            // Look at 5 predictions for the Label, side by side with the actual
            // Label for comparison.
            foreach (var p in predictions)
                Console.WriteLine($"Label: {p.Label:F3}, Prediction: {p.Score:F3}");

            // This trainer is not numerically stable.
            // Please see issue #2425.

            // Evaluate the overall metrics
            var metrics = mlContext.Regression.Evaluate(transformedTestData);
            PrintMetrics(metrics);

            // This trainer is not numerically stable. Please see
            // issue #2425.
        }

        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
            int seed = 0)
        {
            var random = new Random(seed);
            for (int i = 0; i < count; i++)
            {
                float label = (float)random.NextDouble();
                yield return new DataPoint
                {
                    Label = label,
                    // Create random features that are correlated with the label.
                    Features = Enumerable.Repeat(label, 50).Select(
                        x => x + (float)random.NextDouble()).ToArray()
                };
            }
        }

        // Example with label and 50 feature values. A data set is a collection of
        // such examples.
        private class DataPoint
        {
            public float Label { get; set; }
            [VectorType(50)]
            public float[] Features { get; set; }
        }

        // Class used to capture predictions.
        private class Prediction
        {
            // Original label.
            public float Label { get; set; }
            // Predicted score from the trainer.
            public float Score { get; set; }
        }

        // Print some evaluation metrics to regression problems.
        private static void PrintMetrics(RegressionMetrics metrics)
        {
            Console.WriteLine("Mean Absolute Error: " + metrics.MeanAbsoluteError);
            Console.WriteLine("Mean Squared Error: " + metrics.MeanSquaredError);
            Console.WriteLine(
                "Root Mean Squared Error: " + metrics.RootMeanSquaredError);

            Console.WriteLine("RSquared: " + metrics.RSquared);
        }
    }
}