public unsafe void UpdateWeights(Trainable v, Tensor g)
{
//Console.WriteLine("Updating The Variable with ID " + v.UniqueId); trainable should have uniqueID
//Console.Write("Updating The Variable with ID " + v.UniqueId);
//Console.WriteLine(" -> " + g);
if (g.Shape.EqualShape(v.Weights.Shape))
{
float *ptr_v = (float *)v.Weights.Array;
float *ptr_m = (float *)g.Array;
VectorizationFloat.ElementWiseAddAVXBetaB(ptr_v, ptr_m, ptr_v, v.Weights.Shape.TotalSize, -v.LearningRateMultiplier * Hyperparameters.LearningRate);
}
else
{
throw new DimensionIncompability("The shapes of Variable and Gradient are different!");
}
}
public override int GetHashCode()
{
int hash = 1;
if (VariableName.Length != 0)
{
hash ^= VariableName.GetHashCode();
}
if (InitialValueName.Length != 0)
{
hash ^= InitialValueName.GetHashCode();
}
if (InitializerName.Length != 0)
{
hash ^= InitializerName.GetHashCode();
}
if (SnapshotName.Length != 0)
{
hash ^= SnapshotName.GetHashCode();
}
if (saveSliceInfoDef_ != null)
{
hash ^= SaveSliceInfoDef.GetHashCode();
}
if (IsResource != false)
{
hash ^= IsResource.GetHashCode();
}
if (Trainable != false)
{
hash ^= Trainable.GetHashCode();
}
if (Synchronization != global::Tensorflow.VariableSynchronization.Auto)
{
hash ^= Synchronization.GetHashCode();
}
if (Aggregation != global::Tensorflow.VariableAggregation.None)
{
hash ^= Aggregation.GetHashCode();
}
if (_unknownFields != null)
{
hash ^= _unknownFields.GetHashCode();
}
return(hash);
}