TensorflowCatalog.LoadTensorFlowModel 메서드
정의
중요
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오버로드
| LoadTensorFlowModel(ModelOperationsCatalog, String) |
TensorFlow 모델을 메모리에 로드합니다. 이 메서드는 모델을 한 번 로드한 후 스키마를 쿼리하고 사용을 ScoreTensorFlowModel(String, String, Boolean)만드는 TensorFlowEstimator 데 사용할 수 있는 편리한 방법입니다. 이 API를 사용하려면 TensorFlow 재배포에 대한 추가 NuGet 종속성이 필요합니다. 자세한 내용은 연결된 문서를 참조하세요. TensorFlowModel 또한 Dispose()를 명시적으로 호출하거나 "using" 구문으로 변수를 선언하여 암시적으로 해제해야 하는 관리되지 않는 리소스에 대한 참조도 보유합니다.> |
| LoadTensorFlowModel(ModelOperationsCatalog, String, Boolean) |
TensorFlow 모델을 메모리에 로드합니다. 이 메서드는 모델을 한 번 로드한 후 스키마를 쿼리하고 사용을 ScoreTensorFlowModel(String, String, Boolean)만드는 TensorFlowEstimator 데 사용할 수 있는 편리한 방법입니다. 이 API를 사용하려면 TensorFlow 재배포에 대한 추가 NuGet 종속성이 필요합니다. 자세한 내용은 연결된 문서를 참조하세요. TensorFlowModel 또한 Dispose()를 명시적으로 호출하거나 "using" 구문으로 변수를 선언하여 암시적으로 해제해야 하는 관리되지 않는 리소스에 대한 참조도 보유합니다.> |
LoadTensorFlowModel(ModelOperationsCatalog, String)
TensorFlow 모델을 메모리에 로드합니다. 이 메서드는 모델을 한 번 로드한 후 스키마를 쿼리하고 사용을 ScoreTensorFlowModel(String, String, Boolean)만드는 TensorFlowEstimator 데 사용할 수 있는 편리한 방법입니다. 이 API를 사용하려면 TensorFlow 재배포에 대한 추가 NuGet 종속성이 필요합니다. 자세한 내용은 연결된 문서를 참조하세요. TensorFlowModel 또한 Dispose()를 명시적으로 호출하거나 "using" 구문으로 변수를 선언하여 암시적으로 해제해야 하는 관리되지 않는 리소스에 대한 참조도 보유합니다.>
public static Microsoft.ML.Transforms.TensorFlowModel LoadTensorFlowModel (this Microsoft.ML.ModelOperationsCatalog catalog, string modelLocation);static member LoadTensorFlowModel : Microsoft.ML.ModelOperationsCatalog * string -> Microsoft.ML.Transforms.TensorFlowModel<Extension()>
Public Function LoadTensorFlowModel (catalog As ModelOperationsCatalog, modelLocation As String) As TensorFlowModel매개 변수
- catalog
- ModelOperationsCatalog
변환의 카탈로그입니다.
- modelLocation
- String
TensorFlow 모델의 위치입니다.
반환
예제
using System;
using System.IO;
using Microsoft.ML;
using Microsoft.ML.Data;
namespace Samples.Dynamic
{
public static class TextClassification
{
public const int MaxSentenceLength = 600;
/// <summary>
/// Example use of the TensorFlow sentiment classification model.
/// </summary>
public static void Example()
{
// Download an unfrozen (SavedModel format) pre-trained sentiment
// model and return the path to the model directory.
string modelLocation = Microsoft.ML.SamplesUtils.DatasetUtils
.DownloadTensorFlowSentimentModel();
var mlContext = new MLContext();
var data = new[] { new IMDBSentiment() {
Sentiment_Text = "this film was just brilliant casting location " +
"scenery story direction everyone's really suited the part they " +
"played and you could just imagine being there robert is an " +
"amazing actor and now the same being director father came from " +
"the same scottish island as myself so i loved the fact there " +
"was a real connection with this film the witty remarks " +
"throughout the film were great it was just brilliant so much " +
"that i bought the film as soon as it was released for and " +
"would recommend it to everyone to watch and the fly fishing was " +
"amazing really cried at the end it was so sad and you know what " +
"they say if you cry at a film it must have been good and this " +
"definitely was also to the two little boy's that played the of " +
"norman and paul they were just brilliant children are often " +
"left out of the list i think because the stars that play them " +
"all grown up are such a big profile for the whole film but " +
"these children are amazing and should be praised for what " +
"they have done don't you think the whole story was so lovely" +
"because it was true and was someone's life after all that was" +
"shared with us all" } };
var dataView = mlContext.Data.LoadFromEnumerable(data);
// This is the dictionary to convert words into the integer indexes.
var lookupMap = mlContext.Data.LoadFromTextFile(Path.Combine(
modelLocation, "imdb_word_index.csv"),
columns: new[]
{
new TextLoader.Column("Words", DataKind.String, 0),
new TextLoader.Column("Ids", DataKind.Int32, 1),
},
separatorChar: ','
);
// Load the TensorFlow model once.
// - Use it for querying the schema for input and output in the
// model
// - Use it for prediction in the pipeline.
// Unfrozen (SavedModel format) models are loaded by providing the
// path to the directory containing the model file and other model
// artifacts like pre-trained weights.
using var tensorFlowModel = mlContext.Model.LoadTensorFlowModel(
modelLocation);
var schema = tensorFlowModel.GetModelSchema();
var featuresType = (VectorDataViewType)schema["Features"].Type;
Console.WriteLine("Name: {0}, Type: {1}, Shape: (-1, {2})", "Features",
featuresType.ItemType.RawType, featuresType.Dimensions[0]);
var predictionType = (VectorDataViewType)schema["Prediction/Softmax"]
.Type;
Console.WriteLine("Name: {0}, Type: {1}, Shape: (-1, {2})",
"Prediction/Softmax", predictionType.ItemType.RawType,
predictionType.Dimensions[0]);
// The model expects the input feature vector to be a fixed length
// vector.
// In this sample, CustomMappingEstimator is used to resize variable
// length vector to fixed length vector.
// The following ML.NET pipeline
// 1. tokenizes the string into words,
// 2. maps each word to an integer which is an index in the
// dictionary ('lookupMap'),
// 3. Resizes the integer vector to a fixed length vector using
// CustomMappingEstimator ('ResizeFeaturesAction')
// 4. Passes the data to TensorFlow for scoring.
// 5. Retreives the 'Prediction' from TensorFlow and put it into
// ML.NET Pipeline
Action<IMDBSentiment, IntermediateFeatures> ResizeFeaturesAction =
(i, j) =>
{
j.Sentiment_Text = i.Sentiment_Text;
var features = i.VariableLengthFeatures;
Array.Resize(ref features, MaxSentenceLength);
j.Features = features;
};
var model =
mlContext.Transforms.Text.TokenizeIntoWords(
"TokenizedWords",
"Sentiment_Text")
.Append(mlContext.Transforms.Conversion.MapValue(
"VariableLengthFeatures",
lookupMap,
lookupMap.Schema["Words"],
lookupMap.Schema["Ids"],
"TokenizedWords"))
.Append(mlContext.Transforms.CustomMapping(
ResizeFeaturesAction,
"Resize"))
.Append(tensorFlowModel.ScoreTensorFlowModel(
"Prediction/Softmax",
"Features"))
.Append(mlContext.Transforms.CopyColumns(
"Prediction",
"Prediction/Softmax"))
.Fit(dataView);
var engine = mlContext.Model.CreatePredictionEngine<IMDBSentiment,
OutputScores>(model);
// Predict with TensorFlow pipeline.
var prediction = engine.Predict(data[0]);
Console.WriteLine("Number of classes: {0}", prediction.Prediction
.Length);
Console.WriteLine("Is sentiment/review positive? {0}", prediction
.Prediction[1] > 0.5 ? "Yes." : "No.");
Console.WriteLine("Prediction Confidence: {0}", prediction.Prediction[1]
.ToString("0.00"));
///////////////////////////// Expected output //////////////////////////
//
// Name: Features, Type: System.Int32, Shape: (-1, 600)
// Name: Prediction/Softmax, Type: System.Single, Shape: (-1, 2)
//
// Number of classes: 2
// Is sentiment/review positive ? Yes
// Prediction Confidence: 0.65
}
/// <summary>
/// Class to hold original sentiment data.
/// </summary>
public class IMDBSentiment
{
public string Sentiment_Text { get; set; }
/// <summary>
/// This is a variable length vector designated by VectorType attribute.
/// Variable length vectors are produced by applying operations such as
/// 'TokenizeWords' on strings resulting in vectors of tokens of
/// variable lengths.
/// </summary>
[VectorType]
public int[] VariableLengthFeatures { get; set; }
}
/// <summary>
/// Class to hold intermediate data. Mostly used by CustomMapping Estimator
/// </summary>
public class IntermediateFeatures
{
public string Sentiment_Text { get; set; }
[VectorType(MaxSentenceLength)]
public int[] Features { get; set; }
}
/// <summary>
/// Class to contain the output values from the transformation.
/// </summary>
class OutputScores
{
[VectorType(2)]
public float[] Prediction { get; set; }
}
}
}
적용 대상
LoadTensorFlowModel(ModelOperationsCatalog, String, Boolean)
TensorFlow 모델을 메모리에 로드합니다. 이 메서드는 모델을 한 번 로드한 후 스키마를 쿼리하고 사용을 ScoreTensorFlowModel(String, String, Boolean)만드는 TensorFlowEstimator 데 사용할 수 있는 편리한 방법입니다. 이 API를 사용하려면 TensorFlow 재배포에 대한 추가 NuGet 종속성이 필요합니다. 자세한 내용은 연결된 문서를 참조하세요. TensorFlowModel 또한 Dispose()를 명시적으로 호출하거나 "using" 구문으로 변수를 선언하여 암시적으로 해제해야 하는 관리되지 않는 리소스에 대한 참조도 보유합니다.>
public static Microsoft.ML.Transforms.TensorFlowModel LoadTensorFlowModel (this Microsoft.ML.ModelOperationsCatalog catalog, string modelLocation, bool treatOutputAsBatched);static member LoadTensorFlowModel : Microsoft.ML.ModelOperationsCatalog * string * bool -> Microsoft.ML.Transforms.TensorFlowModel<Extension()>
Public Function LoadTensorFlowModel (catalog As ModelOperationsCatalog, modelLocation As String, treatOutputAsBatched As Boolean) As TensorFlowModel매개 변수
- catalog
- ModelOperationsCatalog
변환의 카탈로그입니다.
- modelLocation
- String
TensorFlow 모델의 위치입니다.
- treatOutputAsBatched
- Boolean
출력의 첫 번째 차원을 알 수 없는 경우 일괄 처리로 처리해야 합니다.
반환
예제
using System;
using System.IO;
using Microsoft.ML;
using Microsoft.ML.Data;
namespace Samples.Dynamic
{
public static class TextClassification
{
public const int MaxSentenceLength = 600;
/// <summary>
/// Example use of the TensorFlow sentiment classification model.
/// </summary>
public static void Example()
{
// Download an unfrozen (SavedModel format) pre-trained sentiment
// model and return the path to the model directory.
string modelLocation = Microsoft.ML.SamplesUtils.DatasetUtils
.DownloadTensorFlowSentimentModel();
var mlContext = new MLContext();
var data = new[] { new IMDBSentiment() {
Sentiment_Text = "this film was just brilliant casting location " +
"scenery story direction everyone's really suited the part they " +
"played and you could just imagine being there robert is an " +
"amazing actor and now the same being director father came from " +
"the same scottish island as myself so i loved the fact there " +
"was a real connection with this film the witty remarks " +
"throughout the film were great it was just brilliant so much " +
"that i bought the film as soon as it was released for and " +
"would recommend it to everyone to watch and the fly fishing was " +
"amazing really cried at the end it was so sad and you know what " +
"they say if you cry at a film it must have been good and this " +
"definitely was also to the two little boy's that played the of " +
"norman and paul they were just brilliant children are often " +
"left out of the list i think because the stars that play them " +
"all grown up are such a big profile for the whole film but " +
"these children are amazing and should be praised for what " +
"they have done don't you think the whole story was so lovely" +
"because it was true and was someone's life after all that was" +
"shared with us all" } };
var dataView = mlContext.Data.LoadFromEnumerable(data);
// This is the dictionary to convert words into the integer indexes.
var lookupMap = mlContext.Data.LoadFromTextFile(Path.Combine(
modelLocation, "imdb_word_index.csv"),
columns: new[]
{
new TextLoader.Column("Words", DataKind.String, 0),
new TextLoader.Column("Ids", DataKind.Int32, 1),
},
separatorChar: ','
);
// Load the TensorFlow model once.
// - Use it for querying the schema for input and output in the
// model
// - Use it for prediction in the pipeline.
// Unfrozen (SavedModel format) models are loaded by providing the
// path to the directory containing the model file and other model
// artifacts like pre-trained weights.
using var tensorFlowModel = mlContext.Model.LoadTensorFlowModel(
modelLocation);
var schema = tensorFlowModel.GetModelSchema();
var featuresType = (VectorDataViewType)schema["Features"].Type;
Console.WriteLine("Name: {0}, Type: {1}, Shape: (-1, {2})", "Features",
featuresType.ItemType.RawType, featuresType.Dimensions[0]);
var predictionType = (VectorDataViewType)schema["Prediction/Softmax"]
.Type;
Console.WriteLine("Name: {0}, Type: {1}, Shape: (-1, {2})",
"Prediction/Softmax", predictionType.ItemType.RawType,
predictionType.Dimensions[0]);
// The model expects the input feature vector to be a fixed length
// vector.
// In this sample, CustomMappingEstimator is used to resize variable
// length vector to fixed length vector.
// The following ML.NET pipeline
// 1. tokenizes the string into words,
// 2. maps each word to an integer which is an index in the
// dictionary ('lookupMap'),
// 3. Resizes the integer vector to a fixed length vector using
// CustomMappingEstimator ('ResizeFeaturesAction')
// 4. Passes the data to TensorFlow for scoring.
// 5. Retreives the 'Prediction' from TensorFlow and put it into
// ML.NET Pipeline
Action<IMDBSentiment, IntermediateFeatures> ResizeFeaturesAction =
(i, j) =>
{
j.Sentiment_Text = i.Sentiment_Text;
var features = i.VariableLengthFeatures;
Array.Resize(ref features, MaxSentenceLength);
j.Features = features;
};
var model =
mlContext.Transforms.Text.TokenizeIntoWords(
"TokenizedWords",
"Sentiment_Text")
.Append(mlContext.Transforms.Conversion.MapValue(
"VariableLengthFeatures",
lookupMap,
lookupMap.Schema["Words"],
lookupMap.Schema["Ids"],
"TokenizedWords"))
.Append(mlContext.Transforms.CustomMapping(
ResizeFeaturesAction,
"Resize"))
.Append(tensorFlowModel.ScoreTensorFlowModel(
"Prediction/Softmax",
"Features"))
.Append(mlContext.Transforms.CopyColumns(
"Prediction",
"Prediction/Softmax"))
.Fit(dataView);
var engine = mlContext.Model.CreatePredictionEngine<IMDBSentiment,
OutputScores>(model);
// Predict with TensorFlow pipeline.
var prediction = engine.Predict(data[0]);
Console.WriteLine("Number of classes: {0}", prediction.Prediction
.Length);
Console.WriteLine("Is sentiment/review positive? {0}", prediction
.Prediction[1] > 0.5 ? "Yes." : "No.");
Console.WriteLine("Prediction Confidence: {0}", prediction.Prediction[1]
.ToString("0.00"));
///////////////////////////// Expected output //////////////////////////
//
// Name: Features, Type: System.Int32, Shape: (-1, 600)
// Name: Prediction/Softmax, Type: System.Single, Shape: (-1, 2)
//
// Number of classes: 2
// Is sentiment/review positive ? Yes
// Prediction Confidence: 0.65
}
/// <summary>
/// Class to hold original sentiment data.
/// </summary>
public class IMDBSentiment
{
public string Sentiment_Text { get; set; }
/// <summary>
/// This is a variable length vector designated by VectorType attribute.
/// Variable length vectors are produced by applying operations such as
/// 'TokenizeWords' on strings resulting in vectors of tokens of
/// variable lengths.
/// </summary>
[VectorType]
public int[] VariableLengthFeatures { get; set; }
}
/// <summary>
/// Class to hold intermediate data. Mostly used by CustomMapping Estimator
/// </summary>
public class IntermediateFeatures
{
public string Sentiment_Text { get; set; }
[VectorType(MaxSentenceLength)]
public int[] Features { get; set; }
}
/// <summary>
/// Class to contain the output values from the transformation.
/// </summary>
class OutputScores
{
[VectorType(2)]
public float[] Prediction { get; set; }
}
}
}