BinaryLoaderSaverCatalog.LoadFromBinary 方法
定義
重要
部分資訊涉及發行前產品,在發行之前可能會有大幅修改。 Microsoft 對此處提供的資訊,不做任何明確或隱含的瑕疵擔保。
多載
LoadFromBinary(DataOperationsCatalog, IMultiStreamSource) |
IDataView從 IMultiStreamSource 二進位檔案載入 。 請注意 IDataView , 是延遲的,因此此處不會發生任何實際的載入,只是架構驗證。 |
LoadFromBinary(DataOperationsCatalog, String) |
IDataView從二進位檔案載入 。 請注意 IDataView , 是延遲的,因此此處不會發生任何實際的載入,只是架構驗證。 |
LoadFromBinary(DataOperationsCatalog, IMultiStreamSource)
IDataView從 IMultiStreamSource 二進位檔案載入 。 請注意 IDataView , 是延遲的,因此此處不會發生任何實際的載入,只是架構驗證。
public static Microsoft.ML.IDataView LoadFromBinary (this Microsoft.ML.DataOperationsCatalog catalog, Microsoft.ML.Data.IMultiStreamSource fileSource);
static member LoadFromBinary : Microsoft.ML.DataOperationsCatalog * Microsoft.ML.Data.IMultiStreamSource -> Microsoft.ML.IDataView
<Extension()>
Public Function LoadFromBinary (catalog As DataOperationsCatalog, fileSource As IMultiStreamSource) As IDataView
參數
- catalog
- DataOperationsCatalog
目錄。
- fileSource
- IMultiStreamSource
要從中載入的檔案來源。 例如,這可以是 MultiFileSource 。
傳回
適用於
LoadFromBinary(DataOperationsCatalog, String)
public static Microsoft.ML.IDataView LoadFromBinary (this Microsoft.ML.DataOperationsCatalog catalog, string path);
static member LoadFromBinary : Microsoft.ML.DataOperationsCatalog * string -> Microsoft.ML.IDataView
<Extension()>
Public Function LoadFromBinary (catalog As DataOperationsCatalog, path As String) As IDataView
參數
- catalog
- DataOperationsCatalog
目錄。
- path
- String
要從中載入之檔案的路徑。
傳回
範例
using System;
using System.Collections.Generic;
using System.IO;
using Microsoft.ML;
namespace Samples.Dynamic
{
public static class SaveAndLoadFromBinary
{
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 = new List<DataPoint>()
{
new DataPoint(){ Label = 0, Features = 4},
new DataPoint(){ Label = 0, Features = 5},
new DataPoint(){ Label = 0, Features = 6},
new DataPoint(){ Label = 1, Features = 8},
new DataPoint(){ Label = 1, Features = 9},
};
// Convert the list of data points to an IDataView object, which is
// consumable by ML.NET API.
IDataView data = mlContext.Data.LoadFromEnumerable(dataPoints);
// Create a FileStream object and write the IDataView to it as a binary
// IDV file.
using (FileStream stream = new FileStream("data.idv", FileMode.Create))
mlContext.Data.SaveAsBinary(data, stream);
// Create an IDataView object by loading the binary IDV file.
IDataView loadedData = mlContext.Data.LoadFromBinary("data.idv");
// Inspect the data that is loaded from the previously saved binary file
var loadedDataEnumerable = mlContext.Data
.CreateEnumerable<DataPoint>(loadedData, reuseRowObject: false);
foreach (DataPoint row in loadedDataEnumerable)
Console.WriteLine($"{row.Label}, {row.Features}");
// Preview of the loaded data.
// 0, 4
// 0, 5
// 0, 6
// 1, 8
// 1, 9
}
// Example with label and feature values. A data set is a collection of such
// examples.
private class DataPoint
{
public float Label { get; set; }
public float Features { get; set; }
}
}
}