/// <summary>
/// This function returns the Learning rate using linear scale rule and LR decay.
/// </summary>
internal override float GetLearningRate(DnnTrainState trainstate)
{
float learningrate;
float initialLearningRate = BaseLearningRate * trainstate.BatchSize / 128;
learningrate = initialLearningRate * GetLearningRateScheduleMultiplier(trainstate.CurrentEpoch);
return(learningrate);
}
/// <summary>
/// Computes exponentially decayed learning rate
/// </summary>
internal override float GetLearningRate(DnnTrainState trainstate)
{
int numSamplesPerEpoch = trainstate.BatchSize * trainstate.BatchesPerEpoch;
DecaySteps = (int)(numSamplesPerEpoch * NumEpochsPerDecay / trainstate.BatchSize);
GlobalStep = (trainstate.CurrentEpoch) * (trainstate.BatchesPerEpoch) + trainstate.CurrentBatchIndex;
float decayPower = (float)GlobalStep / DecaySteps;
decayPower = Staircase ? (float)Math.Floor(decayPower) : decayPower;
float decayedLearningRate = LearningRate * (float)Math.Pow(DecayRate, decayPower);
return(decayedLearningRate);
}
internal override float GetLearningRate(DnnTrainState trainstate)
{
int numSamplesPerEpoch = trainstate.BatchSize * trainstate.BatchesPerEpoch;
int decaySteps = (int)(numSamplesPerEpoch * NumEpochsPerDecay / trainstate.BatchSize);
int globalStep = (trainstate.CurrentEpoch) * (trainstate.BatchesPerEpoch) + trainstate.CurrentBatchIndex;
float decayedLearningRate;
if (Cycle && globalStep > decaySteps)
{
float calculatedStep = (float)decaySteps * (float)Math.Ceiling((double)globalStep / (double)decaySteps);
decayedLearningRate = (LearningRate - EndLearningRate) * ((float)Math.Pow((1 - (float)globalStep / calculatedStep), Power)) + EndLearningRate;
}
else
{
float calculatedStep = Math.Min(globalStep, decaySteps);
decayedLearningRate = (LearningRate - EndLearningRate) * ((float)Math.Pow((1 - calculatedStep / (float)decaySteps), Power)) + EndLearningRate;
}
return(decayedLearningRate);
}
internal abstract float GetLearningRate(DnnTrainState options);