// </SnippetDeclareGlobalVariables>
static void Main(string[] args)
{
// 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 integer vector 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 <MovieReview, FixedLengthFeatures> 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>
}
/// <summary>
/// Run a TensorFlow model provided through <paramref name="tensorFlowModel"/> on the input column and extract one output column.
/// The inputs and outputs are matched to TensorFlow graph nodes by name.
/// </summary>
public static Vector <float> ApplyTensorFlowGraph(this Vector <float> input, TensorFlowModel tensorFlowModel)
{
Contracts.CheckValue(input, nameof(input));
Contracts.CheckValue(tensorFlowModel, nameof(tensorFlowModel));
return(new OutColumn(input, tensorFlowModel));
}
public Reconciler(string modelFile)
{
Contracts.AssertNonEmpty(modelFile);
_modelFile = modelFile;
_tensorFlowModel = null;
}
public OutColumn(Vector <float> input, TensorFlowModel tensorFlowModel)
: base(new Reconciler(tensorFlowModel), input)
{
Input = input;
}
/// <summary>
/// Run a TensorFlow model provided through <paramref name="tensorFlowModel"/> on the input column and extract one output column.
/// The inputs and outputs are matched to TensorFlow graph nodes by name.
/// </summary>
public static Vector <float> ApplyTensorFlowGraph(this Vector <float> input, TensorFlowModel tensorFlowModel, bool addBatchDimensionInput = false)
{
Contracts.CheckValue(input, nameof(input));
Contracts.CheckValue(tensorFlowModel, nameof(tensorFlowModel));
return(new OutColumn(input, tensorFlowModel, addBatchDimensionInput));
}
public OutColumn(Vector <float> input, TensorFlowModel tensorFlowModel, bool addBatchDimensionInput)
: base(new Reconciler(tensorFlowModel, addBatchDimensionInput), input)
{
Input = input;
}
