public static void PredictSentiment(MLContext mlContext, ITransformer model)
{
// <SnippetCreatePredictionEngine>
var engine = mlContext.Model.CreatePredictionEngine <MovieReview, MovieReviewSentimentPrediction>(model);
// </SnippetCreatePredictionEngine>
// <SnippetCreateTestData>
var review = new MovieReview()
{
ReviewText = "this film is really good"
};
// </SnippetCreateTestData>
// Predict with TensorFlow pipeline.
// <SnippetPredict>
var sentimentPrediction = engine.Predict(review);
// </SnippetPredict>
// <SnippetDisplayPredictions>
Console.WriteLine("Number of classes: {0}", sentimentPrediction.Prediction.Length);
Console.WriteLine("Is sentiment/review positive? {0}", sentimentPrediction.Prediction[1] > 0.5 ? "Yes." : "No.");
Console.WriteLine("Prediction Confidence: {0}", sentimentPrediction.Prediction[1] > 0.5 ? sentimentPrediction.Prediction[1]: sentimentPrediction.Prediction[0]);
// </SnippetDisplayPredictions>
/////////////////////////////////// Expected output ///////////////////////////////////
//
// Name: Features, Type: System.Int32, Size: 600
// Name: Prediction/Softmax, Type: System.Single, Size: 2
//
// Number of classes: 2
// Is sentiment/review positive ? Yes
// Prediction Confidence: 0.65
}
public static void PredictSentiment(MLContext mlContext, ITransformer model)
{
var engine = mlContext.Model.CreatePredictionEngine <MovieReview, MovieReviewSentimentPrediction>(model);
var review = new MovieReview()
{
ReviewText = "really beautiful movie"
};
var sentimentPrediction = engine.Predict(review);
Console.WriteLine("Number of classes: {0}", sentimentPrediction.Prediction.Length);
Console.WriteLine("Is sentiment/review positive? {0}", sentimentPrediction.Prediction[1] > 0.5 ? "Yes." : "No.");
}
