TensorFlowModel.ScoreTensorFlowModel Methode

Definition

Namespace:

Microsoft.ML.Transforms

Assembly:

Microsoft.ML.TensorFlow.dll

Paket:

Microsoft.ML.TensorFlow v2.0.0

Überlädt

ScoreTensorFlowModel(String, String, Boolean)	Bewertet ein Dataset mithilfe eines vorab trainierten TensorFlow-Modells .
ScoreTensorFlowModel(String[], String[], Boolean)	Bewertet ein Dataset mithilfe eines vorab trainierten TensorFlow-Modells.

ScoreTensorFlowModel(String, String, Boolean)

Bewertet ein Dataset mithilfe eines vorab trainierten TensorFlow-Modells .

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

Parameter

outputColumnName

String

Der Name der angeforderten Modellausgabe. Der Datentyp ist ein Vektor von Single

inputColumnName

String

Der Name der Modelleingabe. Der Datentyp ist ein Vektor von Single.

addBatchDimensionInput

Boolean

Fügen Sie der Eingabe eine Batchdimension hinzu, z. B. eingabe = [224, 224, 3] => [-1, 224, 3]. Dieser Parameter wird verwendet, um Modelle zu behandeln, die unbekannte Form haben, aber die internen Operatoren im Modell erfordern daten, die Batchdimension haben.

Gibt zurück

TensorFlowEstimator

Beispiele

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();
        }
    }
}

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

Bewertet ein Dataset mithilfe eines vorab trainierten TensorFlow-Modells.

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

Parameter

outputColumnNames

String[]

Die Namen der angeforderten Modellausgabe.

inputColumnNames

String[]

Die Namen der Modelleingaben.

addBatchDimensionInput

Boolean

Fügen Sie der Eingabe eine Batchdimension hinzu, z. B. eingabe = [224, 224, 3] => [-1, 224, 3]. Dieser Parameter wird verwendet, um Modelle zu behandeln, die unbekannte Form haben, aber die internen Operatoren im Modell erfordern daten, die Batchdimension haben.

Gibt zurück

TensorFlowEstimator

Beispiele

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();
        }
    }
}