public void VisualizeNeuralNetwork(string visNNFilePath)
{
(IEncoder encoder, IDecoder decoder, IWeightTensor srcEmbedding, IWeightTensor tgtEmbedding) = GetNetworksOnDeviceAt(-1);
// Build input sentence
List <List <string> > inputSeqs = ParallelCorpus.ConstructInputTokens(null);
int batchSize = inputSeqs.Count;
IComputeGraph g = CreateComputGraph(m_defaultDeviceId, needBack: false, visNetwork: true);
AttentionDecoder rnnDecoder = decoder as AttentionDecoder;
encoder.Reset(g.GetWeightFactory(), batchSize);
rnnDecoder.Reset(g.GetWeightFactory(), batchSize);
// Run encoder
IWeightTensor encodedWeightMatrix = Encode(g, inputSeqs, encoder, srcEmbedding, null, null);
// Prepare for attention over encoder-decoder
AttentionPreProcessResult attPreProcessResult = rnnDecoder.PreProcess(encodedWeightMatrix, batchSize, g);
// Run decoder
IWeightTensor x = g.PeekRow(tgtEmbedding, (int)SENTTAGS.START);
IWeightTensor eOutput = rnnDecoder.Decode(x, attPreProcessResult, batchSize, g);
IWeightTensor probs = g.Softmax(eOutput);
g.VisualizeNeuralNetToFile(visNNFilePath);
}
/// <summary>
/// Given input sentence and generate output sentence by seq2seq model with beam search
/// </summary>
/// <param name="input"></param>
/// <param name="beamSearchSize"></param>
/// <param name="maxOutputLength"></param>
/// <returns></returns>
public List <List <string> > Predict(List <string> input, int beamSearchSize = 1, int maxOutputLength = 100)
{
(IEncoder encoder, IDecoder decoder, IWeightTensor srcEmbedding, IWeightTensor tgtEmbedding) = GetNetworksOnDeviceAt(-1);
List <List <string> > inputSeqs = ParallelCorpus.ConstructInputTokens(input);
int batchSize = 1; // For predict with beam search, we currently only supports one sentence per call
IComputeGraph g = CreateComputGraph(m_defaultDeviceId, needBack: false);
AttentionDecoder rnnDecoder = decoder as AttentionDecoder;
encoder.Reset(g.GetWeightFactory(), batchSize);
rnnDecoder.Reset(g.GetWeightFactory(), batchSize);
// Construct beam search status list
List <BeamSearchStatus> bssList = new List <BeamSearchStatus>();
BeamSearchStatus bss = new BeamSearchStatus();
bss.OutputIds.Add((int)SENTTAGS.START);
bss.CTs = rnnDecoder.GetCTs();
bss.HTs = rnnDecoder.GetHTs();
bssList.Add(bss);
IWeightTensor encodedWeightMatrix = Encode(g, inputSeqs, encoder, srcEmbedding, null, null);
AttentionPreProcessResult attPreProcessResult = rnnDecoder.PreProcess(encodedWeightMatrix, batchSize, g);
List <BeamSearchStatus> newBSSList = new List <BeamSearchStatus>();
bool finished = false;
int outputLength = 0;
while (finished == false && outputLength < maxOutputLength)
{
finished = true;
for (int i = 0; i < bssList.Count; i++)
{
bss = bssList[i];
if (bss.OutputIds[bss.OutputIds.Count - 1] == (int)SENTTAGS.END)
{
newBSSList.Add(bss);
}
else if (bss.OutputIds.Count > maxOutputLength)
{
newBSSList.Add(bss);
}
else
{
finished = false;
int ix_input = bss.OutputIds[bss.OutputIds.Count - 1];
rnnDecoder.SetCTs(bss.CTs);
rnnDecoder.SetHTs(bss.HTs);
IWeightTensor x = g.PeekRow(tgtEmbedding, ix_input);
IWeightTensor eOutput = rnnDecoder.Decode(x, attPreProcessResult, batchSize, g);
using (IWeightTensor probs = g.Softmax(eOutput))
{
List <int> preds = probs.GetTopNMaxWeightIdx(beamSearchSize);
for (int j = 0; j < preds.Count; j++)
{
BeamSearchStatus newBSS = new BeamSearchStatus();
newBSS.OutputIds.AddRange(bss.OutputIds);
newBSS.OutputIds.Add(preds[j]);
newBSS.CTs = rnnDecoder.GetCTs();
newBSS.HTs = rnnDecoder.GetHTs();
float score = probs.GetWeightAt(preds[j]);
newBSS.Score = bss.Score;
newBSS.Score += (float)(-Math.Log(score));
//var lengthPenalty = Math.Pow((5.0f + newBSS.OutputIds.Count) / 6, 0.6);
//newBSS.Score /= (float)lengthPenalty;
newBSSList.Add(newBSS);
}
}
}
}
bssList = BeamSearch.GetTopNBSS(newBSSList, beamSearchSize);
newBSSList.Clear();
outputLength++;
}
// Convert output target word ids to real string
List <List <string> > results = new List <List <string> >();
for (int i = 0; i < bssList.Count; i++)
{
results.Add(m_modelMetaData.Vocab.ConvertTargetIdsToString(bssList[i].OutputIds));
}
return(results);
}
private void Reset(Encoder encoder, Encoder reversEncoder, AttentionDecoder decoder)
{
encoder.Reset();
reversEncoder.Reset();
decoder.Reset();
}
public List <string> Predict(List <string> input)
{
reversEncoder.Reset();
encoder.Reset();
decoder.Reset();
List <string> result = new List <string>();
#if MKL
var G2 = new ComputeGraphMKL(false);
#else
var G2 = new ComputeGraph(false);
#endif
List <string> inputSeq = new List <string>();
// inputSeq.Add(m_START);
inputSeq.AddRange(input);
// inputSeq.Add(m_END);
List <string> revseq = inputSeq.ToList();
revseq.Reverse();
List <WeightMatrix> forwardEncoded = new List <WeightMatrix>();
List <WeightMatrix> backwardEncoded = new List <WeightMatrix>();
List <WeightMatrix> encoded = new List <WeightMatrix>();
SparseWeightMatrix sparseInput = new SparseWeightMatrix(1, s_Embedding.Columns);
Parallel.Invoke(
() =>
{
for (int i = 0; i < inputSeq.Count; i++)
{
int ix = (int)SENTTAGS.UNK;
if (s_wordToIndex.ContainsKey(inputSeq[i]) == false)
{
Logger.WriteLine($"Unknow input word: {inputSeq[i]}");
}
else
{
ix = s_wordToIndex[inputSeq[i]];
}
var x2 = G2.PeekRow(s_Embedding, ix);
var o = encoder.Encode(x2, G2);
forwardEncoded.Add(o);
sparseInput.AddWeight(0, ix, 1.0f);
}
},
() =>
{
for (int i = 0; i < inputSeq.Count; i++)
{
int ix = (int)SENTTAGS.UNK;
if (s_wordToIndex.ContainsKey(revseq[i]) == false)
{
Logger.WriteLine($"Unknow input word: {revseq[i]}");
}
else
{
ix = s_wordToIndex[revseq[i]];
}
var x2 = G2.PeekRow(s_Embedding, ix);
var o = reversEncoder.Encode(x2, G2);
backwardEncoded.Add(o);
}
});
backwardEncoded.Reverse();
for (int i = 0; i < inputSeq.Count; i++)
{
//encoded.Add(G2.concatColumns(forwardEncoded[i], backwardEncoded[i]));
encoded.Add(G2.add(forwardEncoded[i], backwardEncoded[i]));
}
//if (UseDropout)
//{
// for (int i = 0; i < encoded.Weight.Length; i++)
// {
// encoded.Weight[i] *= 0.2;
// }
//}
var ix_input = (int)SENTTAGS.START;
while (true)
{
var x = G2.PeekRow(t_Embedding, ix_input);
var eOutput = decoder.Decode(sparseInput, x, encoded, G2);
if (UseDropout)
{
for (int i = 0; i < eOutput.Weight.Length; i++)
{
eOutput.Weight[i] *= 0.2f;
}
}
var o = G2.muladd(eOutput, this.Whd, this.bd);
if (UseDropout)
{
for (int i = 0; i < o.Weight.Length; i++)
{
o.Weight[i] *= 0.2f;
}
}
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 == (int)SENTTAGS.END)
{
break; // END token predicted, break out
}
if (result.Count > max_word)
{
break;
} // something is wrong
var letter2 = m_UNK;
if (t_indexToWord.ContainsKey(pred))
{
letter2 = t_indexToWord[pred];
}
result.Add(letter2);
ix_input = pred;
}
return(result);
}
private void Reset(IWeightFactory weightFactory, Encoder encoder, Encoder reversEncoder, AttentionDecoder decoder)
{
encoder.Reset(weightFactory);
reversEncoder.Reset(weightFactory);
decoder.Reset(weightFactory);
}
