protected override void InitCore(IChannel ch, int numFeatures, LinearPredictor predictor)
{
base.InitCore(ch, numFeatures, predictor);
// Verify user didn't specify parameters that conflict
Contracts.Check(!Args.DoLazyUpdates || !Args.RecencyGainMulti && Args.RecencyGain == 0,
"Cannot have both recency gain and lazy updates.");
// Do the other initializations by setting the setters as if user had set them
// Initialize the averaged weights if needed (i.e., do what happens when Averaged is set)
if (Args.Averaged)
{
if (Args.AveragedTolerance > 0)
{
VBufferUtils.Densify(ref Weights);
}
Weights.CopyTo(ref TotalWeights);
}
else
{
// It is definitely advantageous to keep weights dense if we aren't adding them
// to another vector with each update.
VBufferUtils.Densify(ref Weights);
}
Gain = 1;
}
private void TrainEx(RoleMappedData data, LinearPredictor predictor)
{
Contracts.AssertValue(data, nameof(data));
Contracts.AssertValueOrNull(predictor);
int numFeatures;
data.CheckFeatureFloatVector(out numFeatures);
CheckLabel(data);
using (var ch = Host.Start("Training"))
{
InitCore(ch, numFeatures, predictor);
// InitCore should set the number of features field.
Contracts.Assert(NumFeatures > 0);
TrainCore(ch, data);
if (NumBad > 0)
{
ch.Warning(
"Skipped {0} instances with missing features during training (over {1} iterations; {2} inst/iter)",
NumBad, Args.NumIterations, NumBad / Args.NumIterations);
}
Contracts.Assert(WeightsScale == 1);
Float maxNorm = Math.Max(VectorUtils.MaxNorm(ref Weights), Math.Abs(Bias));
Contracts.Check(FloatUtils.IsFinite(maxNorm),
"The weights/bias contain invalid values (NaN or Infinite). Potential causes: high learning rates, no normalization, high initial weights, etc.");
ch.Done();
}
}
protected virtual void InitCore(IChannel ch, int numFeatures, LinearPredictor predictor)
{
Contracts.Check(numFeatures > 0, "Can't train with zero features!");
Contracts.Check(NumFeatures == 0, "Can't re-use trainer!");
Contracts.Assert(Iteration == 0);
Contracts.Assert(Bias == 0);
ch.Trace("{0} Initializing {1} on {2} features", DateTime.UtcNow, Name, numFeatures);
NumFeatures = numFeatures;
// We want a dense vector, to prevent memory creation during training
// unless we have a lot of features.
// REVIEW: make a setting
if (predictor != null)
{
predictor.GetFeatureWeights(ref Weights);
VBufferUtils.Densify(ref Weights);
Bias = predictor.Bias;
}
else if (!string.IsNullOrWhiteSpace(Args.InitialWeights))
{
ch.Info("Initializing weights and bias to " + Args.InitialWeights);
string[] weightStr = Args.InitialWeights.Split(',');
if (weightStr.Length != NumFeatures + 1)
{
throw Contracts.Except(
"Could not initialize weights from 'initialWeights': expecting {0} values to initialize {1} weights and the intercept",
NumFeatures + 1, NumFeatures);
}
Weights = VBufferUtils.CreateDense <Float>(NumFeatures);
for (int i = 0; i < NumFeatures; i++)
{
Weights.Values[i] = Float.Parse(weightStr[i], CultureInfo.InvariantCulture);
}
Bias = Float.Parse(weightStr[NumFeatures], CultureInfo.InvariantCulture);
}
else if (Args.InitWtsDiameter > 0)
{
Weights = VBufferUtils.CreateDense <Float>(NumFeatures);
for (int i = 0; i < NumFeatures; i++)
{
Weights.Values[i] = Args.InitWtsDiameter * (Host.Rand.NextSingle() - (Float)0.5);
}
Bias = Args.InitWtsDiameter * (Host.Rand.NextSingle() - (Float)0.5);
}
else if (NumFeatures <= 1000)
{
Weights = VBufferUtils.CreateDense <Float>(NumFeatures);
}
else
{
Weights = VBufferUtils.CreateEmpty <Float>(NumFeatures);
}
WeightsScale = 1;
}
public void Train(RoleMappedData data, IPredictor predictor)
{
Host.CheckValue(data, nameof(data));
Host.CheckValue(predictor, nameof(predictor));
LinearPredictor pred = (predictor as CalibratedPredictorBase)?.SubPredictor as LinearPredictor;
pred = pred ?? predictor as LinearPredictor;
Host.CheckParam(pred != null, nameof(predictor), "Not a linear predictor.");
TrainEx(data, pred);
}
protected override void InitCore(IChannel ch, int numFeatures, LinearPredictor predictor)
{
base.InitCore(ch, numFeatures, predictor);
if (Args.NoBias)
{
Bias = 0;
}
if (predictor == null)
{
VBufferUtils.Densify(ref Weights);
}
_weightsUpdate = VBufferUtils.CreateEmpty <Float>(numFeatures);
}
public WeightsCollection(LinearPredictor pred)
{
Contracts.AssertValue(pred);
_pred = pred;
}
protected abstract TModel TrainCore(IChannel ch, RoleMappedData data, LinearPredictor predictor, int weightSetCount);
