TensorFlowModel.ScoreTensorFlowModel メソッド
定義
重要
一部の情報は、リリース前に大きく変更される可能性があるプレリリースされた製品に関するものです。 Microsoft は、ここに記載されている情報について、明示または黙示を問わず、一切保証しません。
オーバーロード
| ScoreTensorFlowModel(String, String, Boolean) |
事前トレーニング済みの TensorFlow モデルを使用してデータセットをスコア付けします。 |
| ScoreTensorFlowModel(String[], String[], Boolean) |
事前トレーニング済みの TensorFlow モデルを使用してデータセットをスコア付けします。 |
ScoreTensorFlowModel(String, String, Boolean)
事前トレーニング済みの TensorFlow モデルを使用してデータセットをスコア付けします。
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 TensorFlowEstimatorパラメーター
- addBatchDimensionInput
- Boolean
入力にバッチ ディメンションを追加します (例: input = [224, 224, 3] => [-1, 224, 224, 3])。 このパラメーターは、不明な形状を持つモデルを処理するために使用されますが、モデル内の内部演算子もバッチ ディメンションを持つデータを必要とします。
戻り値
例
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();
}
}
}
適用対象
ScoreTensorFlowModel(String[], String[], Boolean)
事前トレーニング済みの TensorFlow モデルを使用してデータセットをスコア付けします。
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 TensorFlowEstimatorパラメーター
- outputColumnNames
- String[]
要求されたモデルの名前が出力されます。
- inputColumnNames
- String[]
モデル入力の名前。
- addBatchDimensionInput
- Boolean
入力にバッチ ディメンションを追加します (例: input = [224, 224, 3] => [-1, 224, 224, 3])。 このパラメーターは、不明な形状を持つモデルを処理するために使用されますが、モデル内の内部演算子もバッチ ディメンションを持つデータを必要とします。
戻り値
例
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();
}
}
}
適用対象
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