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StandardTrainersCatalog.OnlineGradientDescent Methode

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Definition

Namespace:
Microsoft.ML
Assembly:
Microsoft.ML.StandardTrainers.dll
Paket:
Microsoft.ML v3.0.1
Paket:
Microsoft.ML v1.0.0
Paket:
Microsoft.ML v1.1.0
Paket:
Microsoft.ML v1.2.0
Paket:
Microsoft.ML v1.3.1
Paket:
Microsoft.ML v1.4.0
Paket:
Microsoft.ML v1.5.5
Paket:
Microsoft.ML v1.6.0
Paket:
Microsoft.ML v1.7.0
Paket:
Microsoft.ML v2.0.0

Überlädt

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

Erstellen Sie OnlineGradientDescentTrainerein Ziel, das ein Ziel mithilfe eines linearen Regressionsmodells vorhersagt.
OnlineGradientDescent(RegressionCatalog+RegressionTrainers, OnlineGradientDescentTrainer+Options)

Erstellen Sie OnlineGradientDescentTrainer erweiterte Optionen, die ein Ziel mithilfe eines linearen Regressionsmodells vorhersagen.

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

Erstellen Sie OnlineGradientDescentTrainerein Ziel, das ein Ziel mithilfe eines linearen Regressionsmodells vorhersagt.

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

Parameter

catalog
RegressionCatalog.RegressionTrainers

Das Regressionskatalog-Trainerobjekt.

labelColumnName
String

Der Name der Bezeichnungsspalte. Die Spaltendaten müssen sein Single.

featureColumnName
String

Der Name der Featurespalte. Die Spaltendaten müssen ein bekannter Vektor von Single.

lossFunction
IRegressionLoss

Die Verlustfunktion wird im Schulungsprozess minimiert. Die Verwendung führt z. B SquaredLoss . zu einem kleinsten quadratischen Trainer.

learningRate
Single

Die anfängliche Lernrate, die von SGD verwendet wird.

decreaseLearningRate
Boolean

Verringern Sie die Lernrate während des Fortschritts der Iterationen.

l2Regularization
Single

Das L2-Gewicht für die Regularisierung.

numberOfIterations
Int32

Die Anzahl der Durchgänge durch das Schulungsdatensatz.

Gibt zurück

OnlineGradientDescentTrainer

Beispiele

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);
        }
    }
}

Gilt für:

OnlineGradientDescent(RegressionCatalog+RegressionTrainers, OnlineGradientDescentTrainer+Options)

Erstellen Sie OnlineGradientDescentTrainer erweiterte Optionen, die ein Ziel mithilfe eines linearen Regressionsmodells vorhersagen.

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

Parameter

catalog
RegressionCatalog.RegressionTrainers

Das Regressionskatalog-Trainerobjekt.

options
OnlineGradientDescentTrainer.Options

Traineroptionen.

Gibt zurück

OnlineGradientDescentTrainer

Beispiele

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);
        }
    }
}

Gilt für: