TensorFlowModel.ScoreTensorFlowModel Méthode
Définition
Important
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Surcharges
| ScoreTensorFlowModel(String, String, Boolean) |
Score un jeu de données à l’aide d’un modèle TensorFlow préentraîné . |
| ScoreTensorFlowModel(String[], String[], Boolean) |
Score un jeu de données à l’aide d’un modèle TensorFlow préentraîné. |
ScoreTensorFlowModel(String, String, Boolean)
Score un jeu de données à l’aide d’un modèle TensorFlow préentraîné .
public Microsoft.ML.Transforms.TensorFlowEstimator ScoreTensorFlowModel (string outputColumnName, string inputColumnName, bool addBatchDimensionInput = false);member this.ScoreTensorFlowModel : string * string * bool -> Microsoft.ML.Transforms.TensorFlowEstimatorPublic Function ScoreTensorFlowModel (outputColumnName As String, inputColumnName As String, Optional addBatchDimensionInput As Boolean = false) As TensorFlowEstimatorParamètres
- outputColumnName
- String
Nom de la sortie du modèle demandé. Le type de données est un vecteur de Single
- addBatchDimensionInput
- Boolean
Ajoutez une dimension de lot à l’entrée, par exemple, input = [224, 224, 3] => [-1, 224, 224, 3]. Ce paramètre est utilisé pour traiter les modèles qui ont une forme inconnue, mais les opérateurs internes du modèle nécessitent également des données pour avoir une dimension de lot.
Retours
Exemples
using System;
using System.IO;
using System.Linq;
using System.Net;
using System.Net.Http;
using System.Text;
using System.Threading.Tasks;
using ICSharpCode.SharpZipLib.GZip;
using ICSharpCode.SharpZipLib.Tar;
using Microsoft.ML;
using Microsoft.ML.Data;
namespace Samples.Dynamic
{
public static class ImageClassification
{
/// <summary>
/// Example use of the TensorFlow image model in a ML.NET pipeline.
/// </summary>
public static void Example()
{
// Download the ResNet 101 model from the location below.
// https://storage.googleapis.com/download.tensorflow.org/models/tflite_11_05_08/resnet_v2_101.tgz
string modelLocation = "resnet_v2_101_299_frozen.pb";
if (!File.Exists(modelLocation))
{
var downloadTask = Download(@"https://storage.googleapis.com/download.tensorflow.org/models/tflite_11_05_08/resnet_v2_101.tgz", @"resnet_v2_101_299_frozen.tgz");
downloadTask.Wait();
modelLocation = downloadTask.Result;
Unzip(Path.Join(Directory.GetCurrentDirectory(), modelLocation),
Directory.GetCurrentDirectory());
modelLocation = "resnet_v2_101_299_frozen.pb";
}
var mlContext = new MLContext();
var data = GetTensorData();
var idv = mlContext.Data.LoadFromEnumerable(data);
// Create a ML pipeline.
using var model = mlContext.Model.LoadTensorFlowModel(modelLocation);
var pipeline = model.ScoreTensorFlowModel(
new[] { nameof(OutputScores.output) },
new[] { nameof(TensorData.input) }, addBatchDimensionInput: true);
// Run the pipeline and get the transformed values.
var estimator = pipeline.Fit(idv);
var transformedValues = estimator.Transform(idv);
// Retrieve model scores.
var outScores = mlContext.Data.CreateEnumerable<OutputScores>(
transformedValues, reuseRowObject: false);
// Display scores. (for the sake of brevity we display scores of the
// first 3 classes)
foreach (var prediction in outScores)
{
int numClasses = 0;
foreach (var classScore in prediction.output.Take(3))
{
Console.WriteLine(
$"Class #{numClasses++} score = {classScore}");
}
Console.WriteLine(new string('-', 10));
}
// Results look like below...
//Class #0 score = -0.8092947
//Class #1 score = -0.3310375
//Class #2 score = 0.1119193
//----------
//Class #0 score = -0.7807726
//Class #1 score = -0.2158062
//Class #2 score = 0.1153686
//----------
}
private const int imageHeight = 224;
private const int imageWidth = 224;
private const int numChannels = 3;
private const int inputSize = imageHeight * imageWidth * numChannels;
/// <summary>
/// A class to hold sample tensor data.
/// Member name should match the inputs that the model expects (in this
/// case, input).
/// </summary>
public class TensorData
{
[VectorType(imageHeight, imageWidth, numChannels)]
public float[] input { get; set; }
}
/// <summary>
/// Method to generate sample test data. Returns 2 sample rows.
/// </summary>
public static TensorData[] GetTensorData()
{
// This can be any numerical data. Assume image pixel values.
var image1 = Enumerable.Range(0, inputSize).Select(
x => (float)x / inputSize).ToArray();
var image2 = Enumerable.Range(0, inputSize).Select(
x => (float)(x + 10000) / inputSize).ToArray();
return new TensorData[] { new TensorData() { input = image1 },
new TensorData() { input = image2 } };
}
/// <summary>
/// Class to contain the output values from the transformation.
/// </summary>
class OutputScores
{
public float[] output { get; set; }
}
private static async Task<string> Download(string baseGitPath, string dataFile)
{
if (File.Exists(dataFile))
return dataFile;
using (HttpClient client = new HttpClient())
{
var response = await client.GetStreamAsync(new Uri($"{baseGitPath}")).ConfigureAwait(false);
using (var fs = new FileStream(dataFile, FileMode.CreateNew))
{
await response.CopyToAsync(fs).ConfigureAwait(false);
}
}
return dataFile;
}
/// <summary>
/// Taken from
/// https://github.com/icsharpcode/SharpZipLib/wiki/GZip-and-Tar-Samples.
/// </summary>
private static void Unzip(string path, string targetDir)
{
Stream inStream = File.OpenRead(path);
Stream gzipStream = new GZipInputStream(inStream);
TarArchive tarArchive = TarArchive.CreateInputTarArchive(gzipStream, Encoding.ASCII);
tarArchive.ExtractContents(targetDir);
tarArchive.Close();
gzipStream.Close();
inStream.Close();
}
}
}
S’applique à
ScoreTensorFlowModel(String[], String[], Boolean)
Score un jeu de données à l’aide d’un modèle TensorFlow préentraîné.
public Microsoft.ML.Transforms.TensorFlowEstimator ScoreTensorFlowModel (string[] outputColumnNames, string[] inputColumnNames, bool addBatchDimensionInput = false);member this.ScoreTensorFlowModel : string[] * string[] * bool -> Microsoft.ML.Transforms.TensorFlowEstimatorPublic Function ScoreTensorFlowModel (outputColumnNames As String(), inputColumnNames As String(), Optional addBatchDimensionInput As Boolean = false) As TensorFlowEstimatorParamètres
- outputColumnNames
- String[]
Noms des sorties du modèle demandé.
- inputColumnNames
- String[]
Noms des entrées du modèle.
- addBatchDimensionInput
- Boolean
Ajoutez une dimension de lot à l’entrée, par exemple, input = [224, 224, 3] => [-1, 224, 224, 3]. Ce paramètre est utilisé pour traiter les modèles qui ont une forme inconnue, mais les opérateurs internes du modèle nécessitent également des données pour avoir une dimension de lot.
Retours
Exemples
using System;
using System.IO;
using System.Linq;
using System.Net;
using System.Net.Http;
using System.Text;
using System.Threading.Tasks;
using ICSharpCode.SharpZipLib.GZip;
using ICSharpCode.SharpZipLib.Tar;
using Microsoft.ML;
using Microsoft.ML.Data;
namespace Samples.Dynamic
{
public static class ImageClassification
{
/// <summary>
/// Example use of the TensorFlow image model in a ML.NET pipeline.
/// </summary>
public static void Example()
{
// Download the ResNet 101 model from the location below.
// https://storage.googleapis.com/download.tensorflow.org/models/tflite_11_05_08/resnet_v2_101.tgz
string modelLocation = "resnet_v2_101_299_frozen.pb";
if (!File.Exists(modelLocation))
{
var downloadTask = Download(@"https://storage.googleapis.com/download.tensorflow.org/models/tflite_11_05_08/resnet_v2_101.tgz", @"resnet_v2_101_299_frozen.tgz");
downloadTask.Wait();
modelLocation = downloadTask.Result;
Unzip(Path.Join(Directory.GetCurrentDirectory(), modelLocation),
Directory.GetCurrentDirectory());
modelLocation = "resnet_v2_101_299_frozen.pb";
}
var mlContext = new MLContext();
var data = GetTensorData();
var idv = mlContext.Data.LoadFromEnumerable(data);
// Create a ML pipeline.
using var model = mlContext.Model.LoadTensorFlowModel(modelLocation);
var pipeline = model.ScoreTensorFlowModel(
new[] { nameof(OutputScores.output) },
new[] { nameof(TensorData.input) }, addBatchDimensionInput: true);
// Run the pipeline and get the transformed values.
var estimator = pipeline.Fit(idv);
var transformedValues = estimator.Transform(idv);
// Retrieve model scores.
var outScores = mlContext.Data.CreateEnumerable<OutputScores>(
transformedValues, reuseRowObject: false);
// Display scores. (for the sake of brevity we display scores of the
// first 3 classes)
foreach (var prediction in outScores)
{
int numClasses = 0;
foreach (var classScore in prediction.output.Take(3))
{
Console.WriteLine(
$"Class #{numClasses++} score = {classScore}");
}
Console.WriteLine(new string('-', 10));
}
// Results look like below...
//Class #0 score = -0.8092947
//Class #1 score = -0.3310375
//Class #2 score = 0.1119193
//----------
//Class #0 score = -0.7807726
//Class #1 score = -0.2158062
//Class #2 score = 0.1153686
//----------
}
private const int imageHeight = 224;
private const int imageWidth = 224;
private const int numChannels = 3;
private const int inputSize = imageHeight * imageWidth * numChannels;
/// <summary>
/// A class to hold sample tensor data.
/// Member name should match the inputs that the model expects (in this
/// case, input).
/// </summary>
public class TensorData
{
[VectorType(imageHeight, imageWidth, numChannels)]
public float[] input { get; set; }
}
/// <summary>
/// Method to generate sample test data. Returns 2 sample rows.
/// </summary>
public static TensorData[] GetTensorData()
{
// This can be any numerical data. Assume image pixel values.
var image1 = Enumerable.Range(0, inputSize).Select(
x => (float)x / inputSize).ToArray();
var image2 = Enumerable.Range(0, inputSize).Select(
x => (float)(x + 10000) / inputSize).ToArray();
return new TensorData[] { new TensorData() { input = image1 },
new TensorData() { input = image2 } };
}
/// <summary>
/// Class to contain the output values from the transformation.
/// </summary>
class OutputScores
{
public float[] output { get; set; }
}
private static async Task<string> Download(string baseGitPath, string dataFile)
{
if (File.Exists(dataFile))
return dataFile;
using (HttpClient client = new HttpClient())
{
var response = await client.GetStreamAsync(new Uri($"{baseGitPath}")).ConfigureAwait(false);
using (var fs = new FileStream(dataFile, FileMode.CreateNew))
{
await response.CopyToAsync(fs).ConfigureAwait(false);
}
}
return dataFile;
}
/// <summary>
/// Taken from
/// https://github.com/icsharpcode/SharpZipLib/wiki/GZip-and-Tar-Samples.
/// </summary>
private static void Unzip(string path, string targetDir)
{
Stream inStream = File.OpenRead(path);
Stream gzipStream = new GZipInputStream(inStream);
TarArchive tarArchive = TarArchive.CreateInputTarArchive(gzipStream, Encoding.ASCII);
tarArchive.ExtractContents(targetDir);
tarArchive.Close();
gzipStream.Close();
inStream.Close();
}
}
}
S’applique à
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