public LSTMGateWeight CloneSharedWeights()
{
LSTMGateWeight gateWeight = new LSTMGateWeight();
gateWeight.InitWeights(layerSize, denseFeatureSize);
gateWeight.weights = weights;
gateWeight.weightsDelta = weightsDelta;
gateWeight.learningRate = learningRate;
return(gateWeight);
}
public override void InitializeWeights(int sparseFeatureSize, int denseFeatureSize)
{
SparseFeatureSize = sparseFeatureSize;
DenseFeatureSize = denseFeatureSize;
if (DenseFeatureSize % Vector <float> .Count != 0)
{
DenseFeatureSize += (Vector <float> .Count - (DenseFeatureSize % Vector <float> .Count));
}
InitializeCellWeights(null);
if (SparseFeatureSize > 0)
{
sparseFeatureWeights = new Vector4[LayerSize][];
for (var i = 0; i < LayerSize; i++)
{
sparseFeatureWeights[i] = new Vector4[SparseFeatureSize];
for (var j = 0; j < SparseFeatureSize; j++)
{
sparseFeatureWeights[i][j] = InitializeLSTMWeight();
}
}
}
if (DenseFeatureSize > 0)
{
wDenseInputGate = new LSTMGateWeight();
wDenseForgetGate = new LSTMGateWeight();
wDenseCellGate = new LSTMGateWeight();
wDenseOutputGate = new LSTMGateWeight();
wDenseInputGate.InitWeights(LayerSize, DenseFeatureSize);
wDenseForgetGate.InitWeights(LayerSize, DenseFeatureSize);
wDenseCellGate.InitWeights(LayerSize, DenseFeatureSize);
wDenseOutputGate.InitWeights(LayerSize, DenseFeatureSize);
}
InitializeInternalTrainingParameters();
Logger.WriteLine(
"Initializing weights, sparse feature size: {0}, dense feature size: {1}, random value is {2}",
SparseFeatureSize, DenseFeatureSize, RNNHelper.rand.NextDouble());
}