/// <summary>
/// The constructor.
/// </summary>
/// <param name="colTop">Specifies the top blobs.</param>
/// <param name="rgbPropagateDown">Specifies whether or not to backpropagate down.</param>
/// <param name="colBottom">Specifies the bottom blobs.</param>
public BackwardArgs(BlobCollection <T> colTop, List <bool> rgbPropagateDown, BlobCollection <T> colBottom)
: base(colBottom, colTop)
{
m_rgbPropagateDown = rgbPropagateDown;
}
/// <summary>
/// The constructor.
/// </summary>
/// <param name="colBottom">Specifies the bottom blobs.</param>
/// <param name="colTop">Specifies the top blobs.</param>
public ForwardArgs(BlobCollection <T> colBottom, BlobCollection <T> colTop)
{
m_colTop = colTop;
m_colBottom = colBottom;
}
/// <summary>
/// Perform the beam-search.
/// </summary>
/// <param name="input">Specifies the input data (e.g. the encoder input)</param>
/// <param name="nK">Specifies the beam width for the search.</param>
/// <param name="dfThreshold">Specifies the threshold where detected items with probabilities less than the threshold are ignored (default = 0.01).</param>
/// <param name="nMax">Specifies the maximum length to process (default = 80)</param>
/// <returns>The list of top sequences is returned.</returns>
/// <remarks>
/// The beam-search algorithm is inspired by the article
/// @see [How to Implement a Beam Search Decoder for Natural Language Processing](https://machinelearningmastery.com/beam-search-decoder-natural-language-processing/) by Jason Brownlee, "Machine Learning Mastery", 2018
/// </remarks>
public List <Tuple <double, bool, List <Tuple <string, int, double> > > > Search(PropertySet input, int nK, double dfThreshold = 0.01, int nMax = 80)
{
List <Tuple <double, bool, List <Tuple <string, int, double> > > > rgSequences = new List <Tuple <double, bool, List <Tuple <string, int, double> > > >();
rgSequences.Add(new Tuple <double, bool, List <Tuple <string, int, double> > >(0, false, new List <Tuple <string, int, double> >()));
BlobCollection <T> colBottom = m_layer.PreProcessInput(input, null);
double dfLoss;
string strInput = input.GetProperty("InputData");
bool bDone = false;
BlobCollection <T> colTop = m_net.Forward(colBottom, out dfLoss);
List <Tuple <string, int, double> > rgRes = m_layer.PostProcessOutput(colTop[0], nK);
rgRes = rgRes.Where(p => p.Item3 >= dfThreshold).ToList();
List <List <Tuple <string, int, double> > > rgrgRes = new List <List <Tuple <string, int, double> > >();
rgrgRes.Add(rgRes);
while (!bDone && nMax > 0)
{
int nProcessedCount = 0;
List <Tuple <double, bool, List <Tuple <string, int, double> > > > rgCandidates = new List <Tuple <double, bool, List <Tuple <string, int, double> > > >();
for (int i = 0; i < rgSequences.Count; i++)
{
if (rgrgRes[i].Count > 0)
{
for (int j = 0; j < rgrgRes[i].Count; j++)
{
if (rgrgRes[i][j].Item1.Length > 0)
{
double dfScore = rgSequences[i].Item1 - Math.Log(rgrgRes[i][j].Item3);
List <Tuple <string, int, double> > rgSequence1 = new List <Tuple <string, int, double> >();
rgSequence1.AddRange(rgSequences[i].Item3);
rgSequence1.Add(rgrgRes[i][j]);
rgCandidates.Add(new Tuple <double, bool, List <Tuple <string, int, double> > >(dfScore, false, rgSequence1));
nProcessedCount++;
}
}
}
else
{
rgCandidates.Add(new Tuple <double, bool, List <Tuple <string, int, double> > >(rgSequences[i].Item1, true, rgSequences[i].Item3));
}
}
if (nProcessedCount > 0)
{
rgSequences = rgCandidates.OrderBy(p => p.Item1).Take(nK).ToList();
rgrgRes = new List <List <Tuple <string, int, double> > >();
for (int i = 0; i < rgSequences.Count; i++)
{
if (!rgSequences[i].Item2)
{
rgRes = new List <Tuple <string, int, double> >();
// Reset state.
m_layer.PreProcessInput(strInput, 1, colBottom);
m_net.Forward(colBottom, out dfLoss, true);
// Re-run through each branch to get correct state at the leaf
for (int j = 0; j < rgSequences[i].Item3.Count; j++)
{
int nIdx = rgSequences[i].Item3[j].Item2;
m_layer.PreProcessInput(strInput, nIdx, colBottom);
colTop = m_net.Forward(colBottom, out dfLoss, true);
if (j == rgSequences[i].Item3.Count - 1)
{
List <Tuple <string, int, double> > rgRes1 = m_layer.PostProcessOutput(colTop[0], nK);
rgRes1 = rgRes1.Where(p => p.Item3 >= dfThreshold).ToList();
for (int k = 0; k < rgRes1.Count; k++)
{
if (rgRes1[k].Item1.Length > 0)
{
rgRes.Add(rgRes1[k]);
}
else
{
Trace.WriteLine("EOS");
}
}
rgrgRes.Add(rgRes);
}
}
}
else
{
rgrgRes.Add(new List <Tuple <string, int, double> >());
}
}
}
else
{
bDone = true;
}
nMax--;
}
return(rgSequences);
}
/// <summary>
/// Adds a Layer and its Bottom and Top blob collections.
/// </summary>
/// <param name="layer">Specifies the Layer.</param>
/// <param name="colBottomBlobs">Specifies the Bottom Blobs flowing into the Layer.</param>
/// <param name="colTopBlobs">Specifies the Top Blobs flowing out of the Layer.</param>
public void Add(Layer <T> layer, BlobCollection <T> colBottomBlobs, BlobCollection <T> colTopBlobs)
{
m_rgLayerInfo.Add(new LayerDebugInformation <T>(layer, colBottomBlobs, colTopBlobs, m_blobWork, m_bDetectNans));
}
/// <summary>
/// The constructor.
/// </summary>
/// <param name="res">Specifies the results returned after running the test.</param>
public TestResultArgs(BlobCollection <T> res)
{
m_results = res;
m_dfAccuracy = 0;
}