TensorflowCatalog.LoadTensorFlowModel Methode
Definition
Wichtig
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Überlädt
| LoadTensorFlowModel(ModelOperationsCatalog, String) |
Laden Sie das TensorFlow-Modell in den Arbeitsspeicher. Dies ist die Komfortmethode, mit der das Modell einmal geladen und anschließend zum Abfragen von Schema und Erstellung der TensorFlowEstimator Verwendung ScoreTensorFlowModel(String, String, Boolean)verwendet werden kann. Die Verwendung dieser API erfordert zusätzliche NuGet-Abhängigkeiten auf TensorFlow-Redist, siehe verknüpftes Dokument für weitere Informationen. TensorFlowModel enthält auch Verweise auf nicht verwaltete Ressourcen, die entweder mit einem expliziten Aufruf von Dispose() oder implizit durch Deklarieren der Variable mit der Syntax "using" freigestellt werden müssen/> |
| LoadTensorFlowModel(ModelOperationsCatalog, String, Boolean) |
Laden Sie das TensorFlow-Modell in den Arbeitsspeicher. Dies ist die Komfortmethode, mit der das Modell einmal geladen und anschließend zum Abfragen von Schema und Erstellung der TensorFlowEstimator Verwendung ScoreTensorFlowModel(String, String, Boolean)verwendet werden kann. Die Verwendung dieser API erfordert zusätzliche NuGet-Abhängigkeiten auf TensorFlow-Redist, siehe verknüpftes Dokument für weitere Informationen. TensorFlowModel enthält auch Verweise auf nicht verwaltete Ressourcen, die entweder mit einem expliziten Aufruf von Dispose() oder implizit durch Deklarieren der Variable mit der Syntax "using" freigestellt werden müssen/> |
LoadTensorFlowModel(ModelOperationsCatalog, String)
Laden Sie das TensorFlow-Modell in den Arbeitsspeicher. Dies ist die Komfortmethode, mit der das Modell einmal geladen und anschließend zum Abfragen von Schema und Erstellung der TensorFlowEstimator Verwendung ScoreTensorFlowModel(String, String, Boolean)verwendet werden kann. Die Verwendung dieser API erfordert zusätzliche NuGet-Abhängigkeiten auf TensorFlow-Redist, siehe verknüpftes Dokument für weitere Informationen. TensorFlowModel enthält auch Verweise auf nicht verwaltete Ressourcen, die entweder mit einem expliziten Aufruf von Dispose() oder implizit durch Deklarieren der Variable mit der Syntax "using" freigestellt werden müssen/>
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 TensorFlowModelParameter
- catalog
- ModelOperationsCatalog
Der Katalog der Transformation.
- modelLocation
- String
Position des TensorFlow-Modells.
Gibt zurück
Beispiele
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; }
}
}
}
Gilt für:
LoadTensorFlowModel(ModelOperationsCatalog, String, Boolean)
Laden Sie das TensorFlow-Modell in den Arbeitsspeicher. Dies ist die Komfortmethode, mit der das Modell einmal geladen und anschließend zum Abfragen von Schema und Erstellung der TensorFlowEstimator Verwendung ScoreTensorFlowModel(String, String, Boolean)verwendet werden kann. Die Verwendung dieser API erfordert zusätzliche NuGet-Abhängigkeiten auf TensorFlow-Redist, siehe verknüpftes Dokument für weitere Informationen. TensorFlowModel enthält auch Verweise auf nicht verwaltete Ressourcen, die entweder mit einem expliziten Aufruf von Dispose() oder implizit durch Deklarieren der Variable mit der Syntax "using" freigestellt werden müssen/>
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 TensorFlowModelParameter
- catalog
- ModelOperationsCatalog
Der Katalog der Transformation.
- modelLocation
- String
Position des TensorFlow-Modells.
- treatOutputAsBatched
- Boolean
Wenn die erste Dimension der Ausgabe unbekannt ist, sollten sie als Batch behandelt werden oder nicht.
Gibt zurück
Beispiele
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; }
}
}
}