private IComputeGraph CreateComputGraph(int deviceIdIdx, bool needBack = true)
{
IComputeGraph g;
if (m_archType == ArchTypeEnums.CPU_MKL)
{
g = new ComputeGraphMKL(m_weightFactory[deviceIdIdx], needBack);
}
else if (m_archType == ArchTypeEnums.GPU_CUDA)
{
g = new ComputeGraphTensor(m_weightFactory[deviceIdIdx], m_deviceIds[deviceIdIdx], needBack);
}
else
{
g = new ComputeGraph(m_weightFactory[deviceIdIdx], needBack);
}
return(g);
}
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 IComputeGraph Encode(List <string> inputSentence, out float cost, out SparseWeightMatrix sWM, List <WeightMatrix> encoded, Encoder encoder, Encoder reversEncoder, WeightMatrix Embedding)
{
var reversSentence = inputSentence.ToList();
reversSentence.Reverse();
#if MKL
IComputeGraph g = new ComputeGraphMKL();
#else
IComputeGraph g = new ComputeGraph();
#endif
cost = 0.0f;
SparseWeightMatrix tmpSWM = new SparseWeightMatrix(1, Embedding.Columns);
List <WeightMatrix> forwardOutputs = new List <WeightMatrix>();
List <WeightMatrix> backwardOutputs = new List <WeightMatrix>();
Parallel.Invoke(
() =>
{
for (int i = 0; i < inputSentence.Count; i++)
{
int ix_source = (int)SENTTAGS.UNK;
if (s_wordToIndex.ContainsKey(inputSentence[i]))
{
ix_source = s_wordToIndex[inputSentence[i]];
}
var x = g.PeekRow(Embedding, ix_source);
var eOutput = encoder.Encode(x, g);
forwardOutputs.Add(eOutput);
tmpSWM.AddWeight(0, ix_source, 1.0f);
}
},
() =>
{
for (int i = 0; i < inputSentence.Count; i++)
{
int ix_source2 = (int)SENTTAGS.UNK;
if (s_wordToIndex.ContainsKey(reversSentence[i]))
{
ix_source2 = s_wordToIndex[reversSentence[i]];
}
var x2 = g.PeekRow(Embedding, ix_source2);
var eOutput2 = reversEncoder.Encode(x2, g);
backwardOutputs.Add(eOutput2);
}
});
backwardOutputs.Reverse();
for (int i = 0; i < inputSentence.Count; i++)
{
//encoded.Add(g.concatColumns(forwardOutputs[i], backwardOutputs[i]));
encoded.Add(g.add(forwardOutputs[i], backwardOutputs[i]));
}
sWM = tmpSWM;
return(g);
}