public void TestEstimatorSaveLoad()
{
using (var env = new TlcEnvironment())
{
var dataFile = GetDataPath("images/images.tsv");
var imageFolder = Path.GetDirectoryName(dataFile);
var data = env.CreateLoader("Text{col=ImagePath:TX:0 col=Name:TX:1}", new MultiFileSource(dataFile));
var pipe = new ImageLoaderEstimator(env, imageFolder, ("ImagePath", "ImageReal"))
.Append(new ImageResizerEstimator(env, "ImageReal", "ImageReal", 100, 100))
.Append(new ImagePixelExtractorEstimator(env, "ImageReal", "ImagePixels"))
.Append(new ImageGrayscaleEstimator(env, ("ImageReal", "ImageGray")));
pipe.GetOutputSchema(Core.Data.SchemaShape.Create(data.Schema));
var model = pipe.Fit(data);
using (var file = env.CreateTempFile())
{
using (var fs = file.CreateWriteStream())
model.SaveTo(env, fs);
var model2 = TransformerChain.LoadFrom(env, file.OpenReadStream());
var newCols = ((ImageLoaderTransform)model2.First()).Columns;
var oldCols = ((ImageLoaderTransform)model.First()).Columns;
Assert.True(newCols
.Zip(oldCols, (x, y) => x == y)
.All(x => x));
}
}
Done();
}
public void TrainSaveModelAndPredict()
{
var dataPath = GetDataPath(SentimentDataPath);
var testDataPath = GetDataPath(SentimentTestPath);
using (var env = new TlcEnvironment(seed: 1, conc: 1))
{
// Pipeline
var loader = new TextLoader(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(dataPath));
var trans = TextTransform.Create(env, MakeSentimentTextTransformArgs(), loader);
// Train
var trainer = new LinearClassificationTrainer(env, new LinearClassificationTrainer.Arguments
{
NumThreads = 1
});
var cached = new CacheDataView(env, trans, prefetch: null);
var trainRoles = new RoleMappedData(cached, label: "Label", feature: "Features");
var predictor = trainer.Train(new Runtime.TrainContext(trainRoles));
PredictionEngine <SentimentData, SentimentPrediction> model;
using (var file = env.CreateTempFile())
{
// Save model.
var scoreRoles = new RoleMappedData(trans, label: "Label", feature: "Features");
using (var ch = env.Start("saving"))
TrainUtils.SaveModel(env, ch, file, predictor, scoreRoles);
// Load model.
using (var fs = file.OpenReadStream())
model = env.CreatePredictionEngine <SentimentData, SentimentPrediction>(fs);
}
// Take a couple examples out of the test data and run predictions on top.
var testLoader = new TextLoader(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(GetDataPath(SentimentTestPath)));
var testData = testLoader.AsEnumerable <SentimentData>(env, false);
foreach (var input in testData.Take(5))
{
var prediction = model.Predict(input);
// Verify that predictions match and scores are separated from zero.
Assert.Equal(input.Sentiment, prediction.Sentiment);
Assert.True(input.Sentiment && prediction.Score > 1 || !input.Sentiment && prediction.Score < -1);
}
}
}
public void New_TrainSaveModelAndPredict()
{
var dataPath = GetDataPath(SentimentDataPath);
var testDataPath = GetDataPath(SentimentTestPath);
using (var env = new TlcEnvironment(seed: 1, conc: 1))
{
var reader = new TextLoader(env, MakeSentimentTextLoaderArgs());
var data = reader.Read(new MultiFileSource(dataPath));
// Pipeline.
var pipeline = new TextTransform(env, "SentimentText", "Features")
.Append(new LinearClassificationTrainer(env, new LinearClassificationTrainer.Arguments {
NumThreads = 1
}, "Features", "Label"));
// Train.
var model = pipeline.Fit(data);
ITransformer loadedModel;
using (var file = env.CreateTempFile())
{
// Save model.
using (var fs = file.CreateWriteStream())
model.SaveTo(env, fs);
// Load model.
loadedModel = TransformerChain.LoadFrom(env, file.OpenReadStream());
}
// Create prediction engine and test predictions.
var engine = loadedModel.MakePredictionFunction <SentimentData, SentimentPrediction>(env);
// Take a couple examples out of the test data and run predictions on top.
var testData = reader.Read(new MultiFileSource(GetDataPath(SentimentTestPath)))
.AsEnumerable <SentimentData>(env, false);
foreach (var input in testData.Take(5))
{
var prediction = engine.Predict(input);
// Verify that predictions match and scores are separated from zero.
Assert.Equal(input.Sentiment, prediction.Sentiment);
Assert.True(input.Sentiment && prediction.Score > 1 || !input.Sentiment && prediction.Score < -1);
}
}
}
