public override void AggragateBatch(DNNRun dnnData)
{
int ParNum = 0;
for (int i = 0; i < dnnData.neurallinks.Count; i++)
{
int mnum = dnnData.neurallinks[i].NeuralLinkModel.Neural_In.Number * dnnData.neurallinks[i].NeuralLinkModel.N_Winsize * dnnData.neurallinks[i].NeuralLinkModel.Neural_Out.Number;
MathOperatorManager.GlobalInstance.Matrix_Add_OFFSET(grad_list[GradIdx % GradHistory], ParNum, dnnData.neurallinks[i].WeightDeriv, 0, mnum, -1.0f);
ParNum += mnum;
if (ParameterSetting.UpdateBias)
{
mnum = dnnData.neurallinks[i].NeuralLinkModel.Neural_Out.Number;
MathOperatorManager.GlobalInstance.Matrix_Add_OFFSET(grad_list[GradIdx % GradHistory], ParNum, dnnData.neurallinks[i].BiasDeriv, 0, mnum, -1.0f);
ParNum += mnum;
}
}
}
void Init(DNN dnn_query, DNN dnn_doc)
{
dnn_model_query = new DNNRun(dnn_query);
dnn_model_doc = new DNNRun(dnn_doc);
Pos_QD_Pair_TOP = new Layer_Output_Deriv_QD_PairTOP(dnn_query, dnn_doc);
Neg_QD_Pair_TOP = new Layer_Output_Deriv_QD_PairTOP_Full(dnn_query, dnn_doc, ParameterSetting.NTRIAL);
alphaCudaPiece = new CudaPieceFloat(ParameterSetting.BATCH_SIZE * (ParameterSetting.NTRIAL + 1), true, true);
distCudaPiece = new CudaPieceFloat(ParameterSetting.BATCH_SIZE * (ParameterSetting.NTRIAL + 1), true, true);
GPU_negative_index_Array = new CudaPieceInt(ParameterSetting.NTRIAL * ParameterSetting.BATCH_SIZE, true, true);
GPU_Inver_negative_index_Array = new CudaPieceInt(ParameterSetting.NTRIAL * ParameterSetting.BATCH_SIZE, true, true);
GPU_Inver_negative_value_Array = new CudaPieceInt(ParameterSetting.NTRIAL * ParameterSetting.BATCH_SIZE, true, true);
if (ParameterSetting.PSEUDO_RANDOM)
{
neg_random = new Random(ParameterSetting.RANDOM_SEED);
}
}
public virtual void AggragateBatch(DNNRun dnnData)
{
}
