public override void CleanLearningRate()
{
if (SparseFeatureSize > 0)
{
for (var i = 0; i < LayerSize; i++)
{
Array.Clear(sparseFeatureToHiddenLearningRate[i], 0, SparseFeatureSize);
}
}
if (DenseFeatureSize > 0)
{
wDenseCellGate.CleanLearningRate();
wDenseForgetGate.CleanLearningRate();
wDenseInputGate.CleanLearningRate();
wDenseOutputGate.CleanLearningRate();
}
peepholeLearningRate = new Vector3[LayerSize];
cellLearningRate = new Vector4[LayerSize];
vecNormalLearningRate = new Vector4(RNNHelper.LearningRate, RNNHelper.LearningRate, RNNHelper.LearningRate,
RNNHelper.LearningRate);
vecNormalLearningRate3 = new Vector3(RNNHelper.LearningRate, RNNHelper.LearningRate, RNNHelper.LearningRate);
vecNormalLearningRateFloat = new Vector <float>(RNNHelper.LearningRate);
vecMaxGrad = new Vector4(RNNHelper.GradientCutoff, RNNHelper.GradientCutoff, RNNHelper.GradientCutoff,
RNNHelper.GradientCutoff);
vecMinGrad = new Vector4(-RNNHelper.GradientCutoff, -RNNHelper.GradientCutoff, -RNNHelper.GradientCutoff,
-RNNHelper.GradientCutoff);
vecMaxGrad3 = new Vector3(RNNHelper.GradientCutoff, RNNHelper.GradientCutoff, RNNHelper.GradientCutoff);
vecMinGrad3 = new Vector3(-RNNHelper.GradientCutoff, -RNNHelper.GradientCutoff, -RNNHelper.GradientCutoff);
}
public override void CleanLearningRate()
{
if (SparseFeatureSize > 0)
{
sparseFeatureToHiddenLearningRate = new Vector4[LayerSize][];
}
if (DenseFeatureSize > 0)
{
wDenseCellGate.CleanLearningRate();
wDenseForgetGate.CleanLearningRate();
wDenseInputGate.CleanLearningRate();
wDenseOutputGate.CleanLearningRate();
}
peepholeLearningRate = new Vector3[LayerSize];
cellLearningRate = new Vector4[LayerSize];
Parallel.For(0, LayerSize, parallelOption, i =>
{
if (SparseFeatureSize > 0)
{
sparseFeatureToHiddenLearningRate[i] = new Vector4[SparseFeatureSize];
}
});
vecNormalLearningRate = new Vector4(RNNHelper.LearningRate, RNNHelper.LearningRate, RNNHelper.LearningRate,
RNNHelper.LearningRate);
vecNormalLearningRate3 = new Vector3(RNNHelper.LearningRate, RNNHelper.LearningRate, RNNHelper.LearningRate);
vecNormalLearningRateFloat = new Vector <float>(RNNHelper.LearningRate);
vecMaxGrad = new Vector4(RNNHelper.GradientCutoff, RNNHelper.GradientCutoff, RNNHelper.GradientCutoff,
RNNHelper.GradientCutoff);
vecMinGrad = new Vector4(-RNNHelper.GradientCutoff, -RNNHelper.GradientCutoff, -RNNHelper.GradientCutoff,
-RNNHelper.GradientCutoff);
vecMaxGrad3 = new Vector3(RNNHelper.GradientCutoff, RNNHelper.GradientCutoff, RNNHelper.GradientCutoff);
vecMinGrad3 = new Vector3(-RNNHelper.GradientCutoff, -RNNHelper.GradientCutoff, -RNNHelper.GradientCutoff);
}