public override void BackwardPass(int numStates, int curState)
{
if (DenseFeatureSize > 0)
{
//Update hidden-output weights
Parallel.ForEach(negativeSampleWordList, c =>
{
var err = Err[c];
var featureWeightCol = DenseWeights[c];
var featureWeightsLearningRateCol = DenseWeightsLearningRate[c];
var j = 0;
while (j < DenseFeatureSize - Vector <float> .Count)
{
RNNHelper.UpdateFeatureWeights(DenseFeature, featureWeightCol, featureWeightsLearningRateCol,
err, j);
j += Vector <float> .Count;
}
while (j < DenseFeatureSize)
{
var delta = RNNHelper.NormalizeGradient(err * DenseFeature[j]);
var newLearningRate = RNNHelper.UpdateLearningRate(DenseWeightsLearningRate, c, j, delta);
featureWeightCol[j] += newLearningRate * delta;
j++;
}
});
}
if (SparseFeatureSize > 0)
{
//Update hidden-output weights
Parallel.ForEach(negativeSampleWordList, c =>
{
var er2 = Err[c];
var vector_c = SparseWeights[c];
foreach (var pair in SparseFeature)
{
var pos = pair.Key;
var val = pair.Value;
var delta = RNNHelper.NormalizeGradient(er2 * val);
var newLearningRate = RNNHelper.UpdateLearningRate(SparseWeightsLearningRate, c, pos, delta);
vector_c[pos] += newLearningRate * delta;
}
});
}
}