ModelOperationsCatalog.Load Metode

Definisi

Ruang nama:

Microsoft.ML

Rakitan:

Microsoft.ML.Data.dll

Paket:

Microsoft.ML v2.0.0

Overload

Load(Stream, DataViewSchema)	Muat model dan skema inputnya dari aliran.
Load(String, DataViewSchema)	Muat model dan skema inputnya dari file.

Load(Stream, DataViewSchema)

Muat model dan skema inputnya dari aliran.

public Microsoft.ML.ITransformer Load (System.IO.Stream stream, out Microsoft.ML.DataViewSchema inputSchema);

member this.Load : System.IO.Stream * DataViewSchema -> Microsoft.ML.ITransformer

Public Function Load (stream As Stream, ByRef inputSchema As DataViewSchema) As ITransformer

Parameter

stream

Stream

Aliran yang dapat dibaca dan dapat dicari untuk dimuat.

inputSchema

DataViewSchema

Akan berisi skema input untuk model. Jika model disimpan tanpa deskripsi input, tidak akan ada skema input. Dalam hal ini bisa .null

Mengembalikan

ITransformer

Model yang dimuat.

Contoh

using System;
using System.Collections.Generic;
using System.IO;
using Microsoft.ML;

namespace Samples.Dynamic.ModelOperations
{
    public class SaveLoadModel
    {
        public static void Example()
        {
            // Create a new ML context, for ML.NET operations. It can be used for
            // exception tracking and logging, as well as the source of randomness.
            var mlContext = new MLContext();

            // Generate sample data.
            var data = new List<Data>()
            {
                new Data() { Value="abc" }
            };

            // Convert data to IDataView.
            var dataView = mlContext.Data.LoadFromEnumerable(data);
            var inputColumnName = nameof(Data.Value);
            var outputColumnName = nameof(Transformation.Key);

            // Transform.
            ITransformer model = mlContext.Transforms.Conversion
                .MapValueToKey(outputColumnName, inputColumnName).Fit(dataView);

            // Save model.
            mlContext.Model.Save(model, dataView.Schema, "model.zip");

            // Load model.
            using (var file = File.OpenRead("model.zip"))
                model = mlContext.Model.Load(file, out DataViewSchema schema);

            // Create a prediction engine from the model for feeding new data.
            var engine = mlContext.Model
                .CreatePredictionEngine<Data, Transformation>(model);

            var transformation = engine.Predict(new Data() { Value = "abc" });

            // Print transformation to console.
            Console.WriteLine("Value: {0}\t Key:{1}", transformation.Value,
                transformation.Key);

            // Value: abc       Key:1

        }

        private class Data
        {
            public string Value { get; set; }
        }

        private class Transformation
        {
            public string Value { get; set; }
            public uint Key { get; set; }
        }
    }
}

Load(String, DataViewSchema)

Muat model dan skema inputnya dari file.

public Microsoft.ML.ITransformer Load (string filePath, out Microsoft.ML.DataViewSchema inputSchema);

member this.Load : string * DataViewSchema -> Microsoft.ML.ITransformer

Public Function Load (filePath As String, ByRef inputSchema As DataViewSchema) As ITransformer

Parameter

filePath

String

Jalur ke file tempat model harus dibaca.

inputSchema

DataViewSchema

Akan berisi skema input untuk model. Jika model disimpan tanpa deskripsi input, tidak akan ada skema input. Dalam hal ini bisa .null

Mengembalikan

ITransformer

Model yang dimuat.

Contoh

using System;
using System.Collections.Generic;
using System.IO;
using Microsoft.ML;

namespace Samples.Dynamic.ModelOperations
{
    public class SaveLoadModelFile
    {
        public static void Example()
        {
            // Create a new ML context, for ML.NET operations. It can be used for
            // exception tracking and logging, as well as the source of randomness.
            var mlContext = new MLContext();

            // Generate sample data.
            var data = new List<Data>()
            {
                new Data() { Value="abc" }
            };

            // Convert data to IDataView.
            var dataView = mlContext.Data.LoadFromEnumerable(data);
            var inputColumnName = nameof(Data.Value);
            var outputColumnName = nameof(Transformation.Key);

            // Transform.
            ITransformer model = mlContext.Transforms.Conversion
                .MapValueToKey(outputColumnName, inputColumnName).Fit(dataView);

            // Save model.
            mlContext.Model.Save(model, dataView.Schema, "model.zip");

            // Load model.
            model = mlContext.Model.Load("model.zip", out DataViewSchema schema);

            // Create a prediction engine from the model for feeding new data.
            var engine = mlContext.Model
                .CreatePredictionEngine<Data, Transformation>(model);

            var transformation = engine.Predict(new Data() { Value = "abc" });

            // Print transformation to console.
            Console.WriteLine("Value: {0}\t Key:{1}", transformation.Value,
                transformation.Key);

            // Value: abc       Key:1

        }

        private class Data
        {
            public string Value { get; set; }
        }

        private class Transformation
        {
            public string Value { get; set; }
            public uint Key { get; set; }
        }
    }
}