TensorFlowModel.ScoreTensorFlowModel Metoda

  • Odwołanie

Definicja

Przestrzeń nazw:
Microsoft.ML.Transforms
Zestaw:
Microsoft.ML.TensorFlow.dll
Pakiet:
Microsoft.ML.TensorFlow v3.0.1
Pakiet:
Microsoft.ML.TensorFlow v1.2.0
Pakiet:
Microsoft.ML.TensorFlow v1.3.1
Pakiet:
Microsoft.ML.TensorFlow v1.4.0
Pakiet:
Microsoft.ML.TensorFlow v1.5.5
Pakiet:
Microsoft.ML.TensorFlow v1.6.0
Pakiet:
Microsoft.ML.TensorFlow v1.7.0
Pakiet:
Microsoft.ML.TensorFlow v2.0.0

Przeciążenia

ScoreTensorFlowModel(String, String, Boolean)

Ocenia zestaw danych przy użyciu wstępnie wytrenowanego modelu TensorFlow .
ScoreTensorFlowModel(String[], String[], Boolean)

Ocenia zestaw danych przy użyciu wstępnie wytrenowanego modelu TensorFlow.

ScoreTensorFlowModel(String, String, Boolean)

Ocenia zestaw danych przy użyciu wstępnie wytrenowanego modelu TensorFlow .

public Microsoft.ML.Transforms.TensorFlowEstimator ScoreTensorFlowModel (string outputColumnName, string inputColumnName, bool addBatchDimensionInput = false);
member this.ScoreTensorFlowModel : string * string * bool -> Microsoft.ML.Transforms.TensorFlowEstimator
Public Function ScoreTensorFlowModel (outputColumnName As String, inputColumnName As String, Optional addBatchDimensionInput As Boolean = false) As TensorFlowEstimator

Parametry

outputColumnName
String

Nazwa żądanych danych wyjściowych modelu. Typ danych jest wektorem Single

inputColumnName
String

Nazwa danych wejściowych modelu. Typ danych jest wektorem .Single

addBatchDimensionInput
Boolean

Dodaj wymiar wsadowy do danych wejściowych, np. input = [224, 224, 3] => [-1, 224, 224, 3]. Ten parametr służy do obsługi modeli, które mają nieznany kształt, ale operatory wewnętrzne w modelu wymagają, aby dane miały również wymiar wsadowy.

Zwraca

TensorFlowEstimator

Przykłady

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();
        }
    }
}

Dotyczy

ScoreTensorFlowModel(String[], String[], Boolean)

Ocenia zestaw danych przy użyciu wstępnie wytrenowanego modelu TensorFlow.

public Microsoft.ML.Transforms.TensorFlowEstimator ScoreTensorFlowModel (string[] outputColumnNames, string[] inputColumnNames, bool addBatchDimensionInput = false);
member this.ScoreTensorFlowModel : string[] * string[] * bool -> Microsoft.ML.Transforms.TensorFlowEstimator
Public Function ScoreTensorFlowModel (outputColumnNames As String(), inputColumnNames As String(), Optional addBatchDimensionInput As Boolean = false) As TensorFlowEstimator

Parametry

outputColumnNames
String[]

Nazwy żądanych danych wyjściowych modelu.

inputColumnNames
String[]

Nazwy danych wejściowych modelu.

addBatchDimensionInput
Boolean

Dodaj wymiar wsadowy do danych wejściowych, np. input = [224, 224, 3] => [-1, 224, 224, 3]. Ten parametr służy do obsługi modeli, które mają nieznany kształt, ale operatory wewnętrzne w modelu wymagają, aby dane miały również wymiar wsadowy.

Zwraca

TensorFlowEstimator

Przykłady

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();
        }
    }
}

Dotyczy