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StandardTrainersCatalog.OnlineGradientDescent Método

  • Referencia

Definición

Espacio de nombres:
Microsoft.ML
Ensamblado:
Microsoft.ML.StandardTrainers.dll
Paquete:
Microsoft.ML v3.0.1
Paquete:
Microsoft.ML v1.0.0
Paquete:
Microsoft.ML v1.1.0
Paquete:
Microsoft.ML v1.2.0
Paquete:
Microsoft.ML v1.3.1
Paquete:
Microsoft.ML v1.4.0
Paquete:
Microsoft.ML v1.5.5
Paquete:
Microsoft.ML v1.6.0
Paquete:
Microsoft.ML v1.7.0
Paquete:
Microsoft.ML v2.0.0

Sobrecargas

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

Cree OnlineGradientDescentTrainer, que predice un destino mediante un modelo de regresión lineal.
OnlineGradientDescent(RegressionCatalog+RegressionTrainers, OnlineGradientDescentTrainer+Options)

Cree OnlineGradientDescentTrainer con opciones avanzadas, que predice un destino mediante un modelo de regresión lineal.

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

Cree OnlineGradientDescentTrainer, que predice un destino mediante un modelo de regresión lineal.

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

Parámetros

catalog
RegressionCatalog.RegressionTrainers

Objeto instructor del catálogo de regresión.

labelColumnName
String

Nombre de la columna de etiquetas. Los datos de columna deben ser Single.

featureColumnName
String

Nombre de la columna de característica. Los datos de columna deben ser un vector de tamaño conocido de Single.

lossFunction
IRegressionLoss

La función de pérdida minimizada en el proceso de entrenamiento. El uso de, por ejemplo, SquaredLoss conduce a un entrenador menos cuadrado.

learningRate
Single

Velocidad de aprendizaje inicial utilizada por SGD.

decreaseLearningRate
Boolean

Disminuir la velocidad de aprendizaje a medida que progresan las iteraciones.

l2Regularization
Single

Peso L2 para regularización.

numberOfIterations
Int32

Número de pasos a través del conjunto de datos de entrenamiento.

Devoluciones

OnlineGradientDescentTrainer

Ejemplos

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);
        }
    }
}

Se aplica a

OnlineGradientDescent(RegressionCatalog+RegressionTrainers, OnlineGradientDescentTrainer+Options)

Cree OnlineGradientDescentTrainer con opciones avanzadas, que predice un destino mediante un modelo de regresión lineal.

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

Parámetros

catalog
RegressionCatalog.RegressionTrainers

Objeto instructor del catálogo de regresión.

options
OnlineGradientDescentTrainer.Options

Opciones de entrenador.

Devoluciones

OnlineGradientDescentTrainer

Ejemplos

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);
        }
    }
}

Se aplica a