public WeightMatrix Perform(WeightMatrix input, WeightMatrix state, ComputeGraph g) { WeightMatrix context; List <WeightMatrix> atten = new List <WeightMatrix>(); var stateRepeat = g.RepeatRows(state, input.Rows); var baiseInput = new WeightMatrix(input.Rows, 1, 1); var inputb = g.concatColumns(input, baiseInput); var uh = g.mul(inputb, Ua); baiseInput = new WeightMatrix(stateRepeat.Rows, 1, 1); stateRepeat = g.concatColumns(stateRepeat, baiseInput); var wc = g.mul(stateRepeat, Wa); var gg = g.tanh(g.add(uh, wc)); var aa = g.mul(gg, V); var res = g.Softmax(aa); var weighted = g.weightRows(input, res);; context = g.sumColumns(weighted); return(context); }
/// <summary> /// 编码 /// </summary> /// <param name="sentIndex">对话索引</param> /// <param name="OutputSentence">输出的样句</param> /// <param name="g"></param> /// <param name="cost"></param> /// <param name="encoded"></param> private void Encode(int sentIndex, out List <string> OutputSentence, out ComputeGraph g, out double cost, ref List <WeightMatrix> encoded) { //var sentIndex = r.Next(0, InputSequences.Count); var inputSentence = InputSequences[sentIndex]; var reversSentence = InputSequences[sentIndex].ToList(); reversSentence.Reverse(); OutputSentence = OutputSequences[sentIndex]; g = new ComputeGraph(); cost = 0.0; for (int i = 0; i < inputSentence.Count; i++) { int ix_source = wordToIndex[inputSentence[i]].i; //顺 int ix_source2 = wordToIndex[reversSentence[i]].i; //逆 var x = g.PeekRow(Embedding, ix_source); //查询指定行数据 var eOutput = encoder.Encode(x, g); var x2 = g.PeekRow(Embedding, ix_source2); var eOutput2 = ReversEncoder.Encode(x2, g); encoded.Add(g.concatColumns(eOutput, eOutput2)); } //if (UseDropout) //{ // encoded = g.Dropout(encoded, 0.2); //} }
/// <summary> /// 预测 /// </summary> /// <param name="inputSeq">输入分词过的语句</param> /// <returns></returns> public ExecuteResult <List <string> > Predict(List <string> inputSeq) { ExecuteResult <List <string> > eresult = new ExecuteResult <List <string> >(); Reset(); List <string> result = new List <string>(); var G2 = new ComputeGraph(false); //反序组 List <string> revseq = inputSeq.ToList(); revseq.Reverse(); List <WeightMatrix> encoded = new List <WeightMatrix>(); // //Console.WriteLine($"keys>{string.Join(",", wordToIndex.Keys.ToArray())}"); for (int i = 0; i < inputSeq.Count; i++) { //索引 if (!wordToIndex.ContainsKey(inputSeq[i])) { return(eresult.SetFail($"抱歉,未能理解 \"{inputSeq[i]}\" 的含义, 请重新训练我吧!")); //return $"抱歉,未能理解 \"{inputSeq[i]}\" 的含义, 请重新训练我吧!".Split(' ').ToList(); //return $"I'm sorry, I can't understand \"{inputSeq[i]}\" the meaning of the word, please you to retrain me!".Split(' ').ToList(); } if (!wordToIndex.ContainsKey(revseq[i])) { return(eresult.SetFail($"抱歉,未能理解 \"{revseq[i]}\" 的含义, 请重新训练我吧!")); //return $"抱歉,未能理解 \"{inputSeq[i]}\" 的含义, 请重新训练我吧!".Split(' ').ToList(); //return $"I'm sorry, I can't understand \"{revseq[i]}\" the meaning of the word, please you to retrain me!".Split(' ').ToList(); } int ix = wordToIndex[inputSeq[i]].i; int ix2 = wordToIndex[revseq[i]].i; var x2 = G2.PeekRow(Embedding, ix); var o = encoder.Encode(x2, G2); var x3 = G2.PeekRow(Embedding, ix2); var eOutput2 = ReversEncoder.Encode(x3, G2); var d = G2.concatColumns(o, eOutput2); encoded.Add(d); } //if (UseDropout) //{ // for (int i = 0; i < encoded.Weight.Length; i++) // { // encoded.Weight[i] *= 0.2; // } //} var ix_input = 1; while (true) { var x = G2.PeekRow(Embedding, ix_input); var eOutput = decoder.Decode(x, encoded, G2); if (UseDropout) { for (int i = 0; i < eOutput.Weight.Length; i++) { eOutput.Weight[i] *= 0.2; } } var o = G2.add( G2.mul(eOutput, this.Whd), this.bd); if (UseDropout) { for (int i = 0; i < o.Weight.Length; i++) { o.Weight[i] *= 0.2; } } var probs = G2.SoftmaxWithCrossEntropy(o); var maxv = probs.Weight[0]; var maxi = 0; for (int i = 1; i < probs.Weight.Length; i++) { if (probs.Weight[i] > maxv) { maxv = probs.Weight[i]; maxi = i; } } var pred = maxi; if (pred == 0) { break; // END token predicted, break out } if (result.Count > max_word) { break; } // something is wrong var letter2 = indexToWord[pred].w; result.Add(letter2); ix_input = pred; } return(eresult.SetData(result).SetOk()); }