private void Reset(Encoder encoder, Encoder reversEncoder, AttentionDecoder decoder)
{
encoder.Reset();
reversEncoder.Reset();
decoder.Reset();
}
/// <summary>
/// Encode source sentences and output encoded weights
/// </summary>
/// <param name="g"></param>
/// <param name="inputSentences"></param>
/// <param name="encoder"></param>
/// <param name="reversEncoder"></param>
/// <param name="Embedding"></param>
/// <returns></returns>
private IWeightMatrix Encode(IComputeGraph g, List <List <string> > inputSentences, Encoder encoder, Encoder reversEncoder, IWeightMatrix Embedding)
{
PadSentences(inputSentences);
List <IWeightMatrix> forwardOutputs = new List <IWeightMatrix>();
List <IWeightMatrix> backwardOutputs = new List <IWeightMatrix>();
int seqLen = inputSentences[0].Count;
List <IWeightMatrix> forwardInput = new List <IWeightMatrix>();
for (int i = 0; i < seqLen; i++)
{
for (int j = 0; j < inputSentences.Count; j++)
{
var inputSentence = inputSentences[j];
int ix_source = (int)SENTTAGS.UNK;
if (m_srcWordToIndex.ContainsKey(inputSentence[i]))
{
ix_source = m_srcWordToIndex[inputSentence[i]];
}
var x = g.PeekRow(Embedding, ix_source);
forwardInput.Add(x);
}
}
var forwardInputsM = g.ConcatRows(forwardInput);
List <IWeightMatrix> attResults = new List <IWeightMatrix>();
for (int i = 0; i < seqLen; i++)
{
var emb_i = g.PeekRow(forwardInputsM, i * inputSentences.Count, inputSentences.Count);
attResults.Add(emb_i);
}
for (int i = 0; i < seqLen; i++)
{
var eOutput = encoder.Encode(attResults[i], g);
forwardOutputs.Add(eOutput);
var eOutput2 = reversEncoder.Encode(attResults[seqLen - i - 1], g);
backwardOutputs.Add(eOutput2);
}
backwardOutputs.Reverse();
var encodedOutput = g.ConcatRowColumn(forwardOutputs, backwardOutputs);
return(encodedOutput);
}
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);
}
private void Reset(IWeightFactory weightFactory, Encoder encoder, Encoder reversEncoder, AttentionDecoder decoder)
{
encoder.Reset(weightFactory);
reversEncoder.Reset(weightFactory);
decoder.Reset(weightFactory);
}