//static readonly string _modelPath = Path.Combine(Environment.CurrentDirectory, "sentiment_model");
internal static void Start()
{
MLContext mlContext = new MLContext();
//код для создания карты подстановки.
var lookupMap = mlContext.Data.LoadFromTextFile(Path.Combine(_modelPath, "imdb_word_index.csv"),
columns: new[]
{
new TextLoader.Column("Words", DataKind.String, 0),
new TextLoader.Column("Ids", DataKind.Int32, 1),
},
separatorChar: ','
);
Action <VariableLength, FixedLength> ResizeFeaturesAction = (s, f) =>
{
var features = s.VariableLengthFeatures;
Array.Resize(ref features, FeatureLength);
f.Features = features;
};
TensorFlowModel tensorFlowModel = mlContext.Model.LoadTensorFlowModel(_modelPath);
DataViewSchema schema = tensorFlowModel.GetModelSchema();
Console.WriteLine(" =============== TensorFlow Model Schema =============== ");
var featuresType = (VectorDataViewType)schema["Features"].Type;
Console.WriteLine($"Name: Features, Type: {featuresType.ItemType.RawType}, Size: ({featuresType.Dimensions[0]})");
var predictionType = (VectorDataViewType)schema["Prediction/Softmax"].Type;
Console.WriteLine($"Name: Prediction/Softmax, Type: {predictionType.ItemType.RawType}, Size: ({predictionType.Dimensions[0]})");
IEstimator <ITransformer> pipeline =
// Split the text into individual words
//разбить текст на слова в виде следующей строки кода
mlContext.Transforms.Text.TokenizeIntoWords("TokenizedWords", "ReviewText")
// Map each word to an integer value. The array of integer makes up the input features.
//Сопоставьте слова с кодировкой целых чисел, используя таблицу подстановки
.Append(mlContext.Transforms.Conversion.MapValue("VariableLengthFeatures", lookupMap,
lookupMap.Schema["Words"], lookupMap.Schema["Ids"], "TokenizedWords"))
// Resize variable length vector to fixed length vector.
//Измените размер кодировок целых чисел переменной длины на фиксированную длину, необходимую для модели
.Append(mlContext.Transforms.CustomMapping(ResizeFeaturesAction, "Resize"))
// Passes the data to TensorFlow for scoring
//Классифицируйте входные данные с помощью загруженной модели TensorFlow:
.Append(tensorFlowModel.ScoreTensorFlowModel("Prediction/Softmax", "Features"))
// Retrieves the 'Prediction' from TensorFlow and and copies to a column
//Создайте новый столбец для прогнозирования выходных данных:
.Append(mlContext.Transforms.CopyColumns("Prediction", "Prediction/Softmax"));
// Create an executable model from the estimator pipeline
//создания модели из конвейера
IDataView dataView = mlContext.Data.LoadFromEnumerable(new List <MovieReview>());
ITransformer model = pipeline.Fit(dataView);
PredictSentiment(mlContext, model);
}
/// <summary>
/// Create pipeline and split input text into words
/// using TokenizeIntoWords transform to break the text into words
/// </summary>
/// <param name="mlContext"></param>
/// <param name="lookupMap"></param>
/// <param name="ResizeFeaturesAction"></param>
/// <param name="tensorFlowModel"></param>
/// <returns></returns>
private static IEstimator <ITransformer> CreatePipeline(MLContext mlContext, IDataView lookupMap, Action <VariableLength, FixedLength> ResizeFeaturesAction, TensorFlowModel tensorFlowModel)
{
return(mlContext.Transforms.Text.TokenizeIntoWords("TokenizedWords", "ReviewText")
.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")));
}
public void TestLoadMultipleModel()
{
var modelFile1 = "model_matmul/frozen_saved_model.pb";
var modelFile2 = "cifar_model/frozen_model.pb";
MLContext context = new MLContext(seed: 1);
TensorFlowModel model1 = context.Model.LoadTensorFlowModel(modelFile1);
TensorFlowModel model2 = context.Model.LoadTensorFlowModel(modelFile2);
model1.ScoreTensorFlowModel(new[] { "c" }, new[] { "a", "b" });
model2.ScoreTensorFlowModel("Output", "Input");
}
static void Main(string[] args)
{
MLContext mlContext = new MLContext();
var lookupMap = mlContext.Data.LoadFromTextFile(Path.Combine(_modelPath, "imdb_word_index.csv"),
columns: new[]
{
new TextLoader.Column("Words", DataKind.String, 0),
new TextLoader.Column("Ids", DataKind.Int32, 1),
},
separatorChar: ','
);
Action <VariableLength, FixedLength> ResizeFeaturesAction = (s, f) =>
{
var features = s.VariableLengthFeatures;
Array.Resize(ref features, FeatureLength);
f.Features = features;
};
TensorFlowModel tensorFlowModel = mlContext.Model.LoadTensorFlowModel(_modelPath);
DataViewSchema schema = tensorFlowModel.GetModelSchema();
Console.WriteLine(" =============== TensorFlow Model Schema =============== ");
var featuresType = (VectorDataViewType)schema["Features"].Type;
Console.WriteLine($"Name: Features, Type: {featuresType.ItemType.RawType}, Size: ({featuresType.Dimensions[0]})");
var predictionType = (VectorDataViewType)schema["Prediction/Softmax"].Type;
Console.WriteLine($"Name: Prediction/Softmax, Type: {predictionType.ItemType.RawType}, Size: ({predictionType.Dimensions[0]})");
IEstimator <ITransformer> pipeline =
// Split the text into individual words
mlContext.Transforms.Text.TokenizeIntoWords("TokenizedWords", "ReviewText")
// Map each word to an integer value. The array of integer makes up the input features.
.Append(mlContext.Transforms.Conversion.MapValue("VariableLengthFeatures", lookupMap,
lookupMap.Schema["Words"], lookupMap.Schema["Ids"], "TokenizedWords"))
// Resize variable length vector to fixed length vector.
.Append(mlContext.Transforms.CustomMapping(ResizeFeaturesAction, "Resize"))
// Passes the data to TensorFlow for scoring
.Append(tensorFlowModel.ScoreTensorFlowModel("Prediction/Softmax", "Features"))
// Retrieves the 'Prediction' from TensorFlow and and copies to a column
.Append(mlContext.Transforms.CopyColumns("Prediction", "Prediction/Softmax"));
// Create an executable model from the estimator pipeline
IDataView dataView = mlContext.Data.LoadFromEnumerable(new List <MovieReview>());
ITransformer model = pipeline.Fit(dataView);
PredictSentiment(mlContext, model);
}
static void Main(string[] args)
{
MLContext mlContext = new MLContext();
var lookupMap = mlContext.Data.LoadFromTextFile(Path.Combine(_modelPath, "imdb_word_index.csv"),
columns: new[]
{
new TextLoader.Column("Words", DataKind.String, 0),
new TextLoader.Column("Ids", DataKind.Int32, 1),
},
separatorChar: ','
);
Action <VariableLength, FixedLength> ResizeFeaturesAction = (s, f) =>
{
var features = s.VariableLengthFeatures;
Array.Resize(ref features, FeatureLength);
f.Features = features;
};
TensorFlowModel tensorFlowModel = mlContext.Model.LoadTensorFlowModel(_modelPath);
DataViewSchema schema = tensorFlowModel.GetModelSchema();
Console.WriteLine(" =============== TensorFlow Model Schema =============== ");
var featuresType = (VectorDataViewType)schema["Features"].Type;
Console.WriteLine($"Name: Features, Type: {featuresType.ItemType.RawType}, Size: ({featuresType.Dimensions[0]})");
var predictionType = (VectorDataViewType)schema["Prediction/Softmax"].Type;
Console.WriteLine($"Name: Prediction/Softmax, Type: {predictionType.ItemType.RawType}, Size: ({predictionType.Dimensions[0]})");
IEstimator <ITransformer> pipeline =
mlContext.Transforms.Text.TokenizeIntoWords("TokenizedWords", "ReviewText")
.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"));
IDataView dataView = mlContext.Data.LoadFromEnumerable(new List <MovieReview>());
ITransformer model = pipeline.Fit(dataView);
PredictSentiment(mlContext, model);
}
// </SnippetDeclareGlobalVariables>
static void Main(string[] args)
{
Console.WriteLine($"modelPath: ${_modelPath}");
// Create MLContext to be shared across the model creation workflow objects
// <SnippetCreateMLContext>
MLContext mlContext = new MLContext();
// </SnippetCreateMLContext>
// Dictionary to encode words as integers.
// <SnippetCreateLookupMap>
var lookupMap = mlContext.Data.LoadFromTextFile(Path.Combine(_modelPath, "imdb_word_index.csv"),
columns: new[]
{
new TextLoader.Column("Words", DataKind.String, 0),
new TextLoader.Column("Ids", DataKind.Int32, 1),
},
separatorChar: ','
);
// </SnippetCreateLookupMap>
// The model expects the input feature vector to be a fixed length vector.
// This action resizes the variable length array generated by the lookup map
// to a fixed length vector. If there are less than 600 words in the sentence,
// the remaining indices will be filled with zeros. If there are more than
// 600 words in the sentence, then the array is truncated at 600.
// <SnippetResizeFeatures>
Action <VariableLength, FixedLength> ResizeFeaturesAction = (s, f) =>
{
var features = s.VariableLengthFeatures;
Array.Resize(ref features, FeatureLength);
f.Features = features;
};
// </SnippetResizeFeatures>
// Load the TensorFlow model.
// <SnippetLoadTensorFlowModel>
TensorFlowModel tensorFlowModel = mlContext.Model.LoadTensorFlowModel(_modelPath);
// </SnippetLoadTensorFlowModel>
// <SnippetGetModelSchema>
DataViewSchema schema = tensorFlowModel.GetModelSchema();
Console.WriteLine(" =============== TensorFlow Model Schema =============== ");
var featuresType = (VectorDataViewType)schema["Features"].Type;
Console.WriteLine($"Name: Features, Type: {featuresType.ItemType.RawType}, Size: ({featuresType.Dimensions[0]})");
var predictionType = (VectorDataViewType)schema["Prediction/Softmax"].Type;
Console.WriteLine($"Name: Prediction/Softmax, Type: {predictionType.ItemType.RawType}, Size: ({predictionType.Dimensions[0]})");
// </SnippetGetModelSchema>
// <SnippetTokenizeIntoWords>
IEstimator <ITransformer> pipeline =
// Split the text into individual words
mlContext.Transforms.Text.TokenizeIntoWords("TokenizedWords", "ReviewText")
// </SnippetTokenizeIntoWords>
// <SnippetMapValue>
// Map each word to an integer value. The array of integer makes up the input features.
.Append(mlContext.Transforms.Conversion.MapValue("VariableLengthFeatures", lookupMap,
lookupMap.Schema["Words"], lookupMap.Schema["Ids"], "TokenizedWords"))
// </SnippetMapValue>
// <SnippetCustomMapping>
// Resize variable length vector to fixed length vector.
.Append(mlContext.Transforms.CustomMapping(ResizeFeaturesAction, "Resize"))
// </SnippetCustomMapping>
// <SnippetScoreTensorFlowModel>
// Passes the data to TensorFlow for scoring
.Append(tensorFlowModel.ScoreTensorFlowModel("Prediction/Softmax", "Features"))
// </SnippetScoreTensorFlowModel>
// <SnippetCopyColumns>
// Retrieves the 'Prediction' from TensorFlow and and copies to a column
.Append(mlContext.Transforms.CopyColumns("Prediction", "Prediction/Softmax"));
// </SnippetCopyColumns>
// <SnippetCreateModel>
// Create an executable model from the estimator pipeline
IDataView dataView = mlContext.Data.LoadFromEnumerable(new List <MovieReview>());
ITransformer model = pipeline.Fit(dataView);
// </SnippetCreateModel>
// <SnippetCallPredictSentiment>
PredictSentiment(mlContext, model);
// </SnippetCallPredictSentiment>
}
