// static methods
private static void ExecuteOneTrainingBatch(SentimentModel model, IList <Tree> trainingBatch, double[] sumGradSquare)
{
SentimentCostAndGradient gcFunc = new SentimentCostAndGradient(model, trainingBatch);
double[] theta = model.ParamsToVector();
// AdaGrad
double eps = 1e-3;
// TODO: do we want to iterate multiple times per batch?
double[] gradf = gcFunc.DerivativeAt(theta);
double currCost = gcFunc.ValueAt(theta);
log.Info("batch cost: " + currCost);
for (int feature = 0; feature < gradf.Length; feature++)
{
sumGradSquare[feature] = sumGradSquare[feature] + gradf[feature] * gradf[feature];
theta[feature] = theta[feature] - (model.op.trainOptions.learningRate * gradf[feature] / (Math.Sqrt(sumGradSquare[feature]) + eps));
}
model.VectorToParams(theta);
}
public static bool RunGradientCheck(SentimentModel model, IList <Tree> trees)
{
SentimentCostAndGradient gcFunc = new SentimentCostAndGradient(model, trees);
return(gcFunc.GradientCheck(model.TotalParamSize(), 50, model.ParamsToVector()));
}
