public List <float> Forward(int [] feaidx, float[] feaval)
{
Layer_Output output = new Layer_Output(this);
List <Sample_Input> inputList = new List <Sample_Input>();
Sample_Input input = new Sample_Input();
if (TYPE == 0 || TYPE == 2) //DSSM -- bag of word feature
{
Dictionary <int, float> fea = new Dictionary <int, float>();
for (int i = 0; i < feaidx.Length; i++)
{
fea.Add(feaidx[i], feaval[i]);
}
input.Load_BOW(fea);
}
else if (TYPE == 1 || TYPE == 3) //CDSSM -- seq of word feature
{
throw new Exception("cdssm for this kind of feature NOT SUPPORTED yet!");
}
inputList.Add(input);
forward_activate(inputList, output);
List <float> result = new List <float>();
for (int i = 0; i < output.layerOutputs[output.Layer_TOP].Length; i++)
{
result.Add(output.layerOutputs[output.Layer_TOP][i]);
}
return(result);
}
public List <float> Forward(string text, Dictionary <string, Dictionary <int, float> > dic, FeatureList featureList)
{
Layer_Output output = new Layer_Output(this);
List <Sample_Input> inputList = new List <Sample_Input>();
string[] sentenceList = PoolIdx >= 1 ?
text.Split(new char[] { '.', '\r', '\n' }, StringSplitOptions.RemoveEmptyEntries)
: new string[] { text };
if (sentenceList.Length > MaxPoolSentenceNumber)
{
string[] sentenceListTmp = new string[MaxPoolSentenceNumber];
for (int i = 0; i < MaxPoolSentenceNumber - 1; ++i)
{
sentenceListTmp[i] = sentenceList[i];
}
sentenceListTmp[MaxPoolSentenceNumber - 1] = string.Join(" ", sentenceList.Skip(MaxPoolSentenceNumber - 1));
sentenceList = sentenceListTmp;
}
foreach (string sentence in sentenceList)
{
Sample_Input input = new Sample_Input();
if (TYPE == 0 || TYPE == 2) //DSSM -- bag of word feature
{
Dictionary <int, float> fea = new Dictionary <int, float>();
int pos = 0;
if (featureList.l3g == true)
{
Dictionary <int, float> tmp = LFE.Feature_Extract_BOW(sentence, pos); //pos
fea = fea.Concat(tmp).ToDictionary(k => k.Key, v => v.Value);
pos += LFE.vocab_dict.Count;
}
if (featureList.root == true)
{
int count = 0;
var featStrFeq = TextUtils.String2FeatStrSeq(sentence, 3, 20, FeatureType.root); // list of root
List <Dictionary <int, double> > tmpList = TextUtils.StrFreq2IdFreq(featStrFeq, FeatureType.root, pos, ref count);
Dictionary <int, float> tmp = TextUtils.MergeList(tmpList).ToDictionary(k => k.Key, v => (float)(v.Value));
fea = fea.Concat(tmp).ToDictionary(k => k.Key, v => v.Value);
pos += count;
}
if (featureList.infl == true)
{
int count = 0;
var featStrFeq = TextUtils.String2FeatStrSeq(sentence, 3, 20, FeatureType.infl); // list of inflections
List <Dictionary <int, double> > tmpList = TextUtils.StrFreq2IdFreq(featStrFeq, FeatureType.infl, pos, ref count);
Dictionary <int, float> tmp = TextUtils.MergeList(tmpList).ToDictionary(k => k.Key, v => (float)(v.Value));
fea = fea.Concat(tmp).ToDictionary(k => k.Key, v => v.Value);
pos += count;
}
input.Load_BOW(fea);
}
//need to updata
else if (TYPE == 1 || TYPE == 3) //CDSSM -- seq of word feature
{
List <Dictionary <int, float> > feas = new List <Dictionary <int, float> >();
//need to update
//if (featureType == FeatureType.we)
//{
// feas = LFE.Feature_Extractor_SOW(sentence, dic);
//}
//else
{
feas = LFE.Feature_Extractor_SOW(sentence);
}
input.Load_SOW(feas);
}
inputList.Add(input);
}
forward_activate(inputList, output);
List <float> result = new List <float>();
for (int i = 0; i < output.layerOutputs[output.Layer_TOP].Length; i++)
{
result.Add(output.layerOutputs[output.Layer_TOP][i]);
}
return(result);
}
public void forward_activate(List <Sample_Input> dataList, Layer_Output output)
{
if (dataList.Count == 0)
{
return;
}
int layerIndex = 0;
int batchsize = dataList.First().batchsize;
foreach (NeuralLink neurallink in neurallinks)
{
if (layerIndex < PoolIdx)
{
for (int i = 0; i < dataList.Count; ++i)
{
Sample_Input data = dataList[i];
///first layer.
if (layerIndex == 0)
{
if (neurallink.Nt == N_Type.Fully_Connected)
{
//????data.Norm_BOW(2);
BasicMathlib.Sparse_Matrix_Multiply_INTEX(data.Sample_Idx, data.Fea_Idx, data.Fea_Value, data.elementsize,
neurallink.Back_Weight, output.layerPoolingSecondary[layerIndex], data.batchsize,
neurallink.Neural_Out.Number, neurallink.Neural_In.Number, i * neurallink.Neural_Out.Number);
}
else if (neurallink.Nt == N_Type.Convolution_layer)
{
BasicMathlib.Convolution_Sparse_Matrix_Multiply_INTEX(data.Sample_Idx, data.batchsize, data.Seg_Idx, data.Seg_Margin, data.segsize, data.Fea_Idx, data.Fea_Value, data.elementsize,
neurallink.Back_Weight, output.layerPooling[layerIndex], neurallink.Neural_In.Number, neurallink.Neural_Out.Number, neurallink.N_Winsize);
BasicMathlib.Max_Pooling(output.layerPooling[layerIndex], data.Sample_Idx, data.batchsize, output.layerPoolingSecondary[layerIndex], output.layerMaxPooling_Index[layerIndex], neurallink.Neural_Out.Number, i * neurallink.Neural_Out.Number);
}
}
else
{
BasicMathlib.Matrix_Multiply(output.layerPoolingSecondary[layerIndex - 1], neurallink.Back_Weight, output.layerPoolingSecondary[layerIndex], batchsize, neurallink.Neural_Out.Number, neurallink.Neural_In.Number, i * neurallink.Neural_In.Number, i * neurallink.Neural_Out.Number);
}
}
if (neurallink.Af == A_Func.Tanh)
{
BasicMathlib.Matrix_Add_Tanh(output.layerPoolingSecondary[layerIndex], neurallink.Back_Bias, batchsize, neurallink.Neural_Out.Number * dataList.Count);
}
if (layerIndex == PoolIdx - 1)
{
BasicMathlib.Max_PoolingDense(output.layerPoolingSecondary[layerIndex], batchsize, output.layerOutputs[layerIndex], output.layerMaxPooling_IndexSecondary[layerIndex], dataList.Count, neurallink.Neural_Out.Number);
}
}
else
{
///first layer.
if (layerIndex == 0)
{
if (neurallink.Nt == N_Type.Fully_Connected)
{
BasicMathlib.Sparse_Matrix_Multiply_INTEX(dataList[0].Sample_Idx, dataList[0].Fea_Idx, dataList[0].Fea_Value, dataList[0].elementsize,
neurallink.Back_Weight, output.layerOutputs[layerIndex], dataList[0].batchsize,
neurallink.Neural_Out.Number, neurallink.Neural_In.Number, 0);
}
else if (neurallink.Nt == N_Type.Convolution_layer)
{
BasicMathlib.Convolution_Sparse_Matrix_Multiply_INTEX(dataList[0].Sample_Idx, dataList[0].batchsize, dataList[0].Seg_Idx, dataList[0].Seg_Margin, dataList[0].segsize, dataList[0].Fea_Idx, dataList[0].Fea_Value, dataList[0].elementsize,
neurallink.Back_Weight, output.layerPooling[layerIndex], neurallink.Neural_In.Number, neurallink.Neural_Out.Number, neurallink.N_Winsize);
BasicMathlib.Max_Pooling(output.layerPooling[layerIndex], dataList[0].Sample_Idx, dataList[0].batchsize, output.layerOutputs[layerIndex], output.layerMaxPooling_Index[layerIndex], neurallink.Neural_Out.Number, 0);
}
}
else
{
BasicMathlib.Matrix_Multiply(output.layerOutputs[layerIndex - 1], neurallink.Back_Weight, output.layerOutputs[layerIndex], dataList[0].batchsize, neurallink.Neural_Out.Number, neurallink.Neural_In.Number, 0, 0);
}
if (neurallink.Af == A_Func.Tanh)
{
BasicMathlib.Matrix_Add_Tanh(output.layerOutputs[layerIndex], neurallink.Back_Bias, dataList[0].batchsize, neurallink.Neural_Out.Number);
}
}
layerIndex += 1;
}
}
