private void InitializeLSTMCell(LSTMCell c, LSTMCellWeight cw, LSTMCellWeightDeri deri)
{
c.cellState = 0;
//partial derivatives
deri.dSWPeepholeIn = 0;
deri.dSWPeepholeForget = 0;
deri.dSWCellIn = 0;
deri.dSWCellForget = 0;
deri.dSWCellState = 0;
}
private void InitializeCellWeights(BinaryReader br)
{
CellWeights = new LSTMCellWeight[LayerSize];
CellWeightsDeri = new LSTMCellWeightDeri[LayerSize];
if (br != null)
{
//Load weight from input file
for (var i = 0; i < LayerSize; i++)
{
CellWeights[i] = new LSTMCellWeight();
CellWeights[i].wPeepholeIn = br.ReadDouble();
CellWeights[i].wPeepholeForget = br.ReadDouble();
CellWeights[i].wPeepholeOut = br.ReadDouble();
CellWeights[i].wCellIn = br.ReadDouble();
CellWeights[i].wCellForget = br.ReadDouble();
CellWeights[i].wCellState = br.ReadDouble();
CellWeights[i].wCellOut = br.ReadDouble();
CellWeightsDeri[i] = new LSTMCellWeightDeri();
}
}
else
{
//Initialize weight by random number
for (var i = 0; i < LayerSize; i++)
{
CellWeights[i] = new LSTMCellWeight();
//internal weights, also important
CellWeights[i].wPeepholeIn = RNNHelper.RandInitWeight();
CellWeights[i].wPeepholeForget = RNNHelper.RandInitWeight();
CellWeights[i].wPeepholeOut = RNNHelper.RandInitWeight();
CellWeights[i].wCellIn = RNNHelper.RandInitWeight();
CellWeights[i].wCellForget = RNNHelper.RandInitWeight();
CellWeights[i].wCellState = RNNHelper.RandInitWeight();
CellWeights[i].wCellOut = RNNHelper.RandInitWeight();
CellWeightsDeri[i] = new LSTMCellWeightDeri();
}
}
}
public override void UpdateWeights()
{
wDenseInputGate.UpdateWeights();
wDenseForgetGate.UpdateWeights();
wDenseCellGate.UpdateWeights();
wDenseOutputGate.UpdateWeights();
for (var i = 0; i < LayerSize; i++)
{
LSTMCellWeight cellWeights_i = CellWeights[i];
//Normalize cell peephole weights delta
var vecPeepholeDelta = peepholeDelta[i];
peepholeDelta[i] = Vector3.Zero;
vecPeepholeDelta = vecPeepholeDelta / RNNHelper.MiniBatchSize;
vecPeepholeDelta = Vector3.Clamp(vecPeepholeDelta, vecMinGrad3, vecMaxGrad3);
//Normalize cell weights delta
var vecCellDelta = cellDelta[i];
cellDelta[i] = Vector4.Zero;
vecCellDelta = vecCellDelta / RNNHelper.MiniBatchSize;
vecCellDelta = Vector4.Clamp(vecCellDelta, vecMinGrad, vecMaxGrad);
//Computing actual learning rate
var vecPeepholeLearningRate = ComputeLearningRate(vecPeepholeDelta, ref peepholeLearningRate[i]);
vecPeepholeDelta = vecPeepholeLearningRate * vecPeepholeDelta;
var vecCellLearningRate = ComputeLearningRate(vecCellDelta, ref cellLearningRate[i]);
vecCellDelta = vecCellLearningRate * vecCellDelta;
cellWeights_i.wPeepholeIn += vecPeepholeDelta.X;
cellWeights_i.wPeepholeForget += vecPeepholeDelta.Y;
cellWeights_i.wPeepholeOut += vecPeepholeDelta.Z;
cellWeights_i.wCellIn += vecCellDelta.X;
cellWeights_i.wCellForget += vecCellDelta.Y;
cellWeights_i.wCellState += vecCellDelta.Z;
cellWeights_i.wCellOut += vecCellDelta.W;
//Update weights for sparse features
var wlr_i = sparseFeatureLearningRate[i];
var sparseFeatureWeightsDelta_i = sparseFeatureWeightsDelta[i];
var sparseFeatureWeights_i = sparseFeatureWeights[i];
for (var j = 0; j < SparseFeatureSize; j++)
{
if (sparseFeatureWeightsDelta_i[j] != Vector4.Zero)
{
Vector4 vecDelta = sparseFeatureWeightsDelta_i[j];
sparseFeatureWeightsDelta_i[j] = Vector4.Zero;
vecDelta = vecDelta / RNNHelper.MiniBatchSize;
vecDelta = Vector4.Clamp(vecDelta, vecMinGrad, vecMaxGrad);
var vecLearningRate = ComputeLearningRate(vecDelta, ref wlr_i[j]);
sparseFeatureWeights_i[j] += vecDelta * vecLearningRate;
}
}
}
}