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."); }