public LRBMTrainer(MnistDataMgr dataMgr, LRBM lrbm)
{
_dataMgr = dataMgr;
_lrbm = lrbm;
_rnd = new Random();
}
public LRBMTester(LRBM lrbm, MnistDataMgr dataMgr)
{
_lrbm = lrbm;
_dataMgr = dataMgr;
_testSetSize = _dataMgr.count;
}
void InitLRBM(LRBM lrbm)
{
int lrbmLayerCnt = lrbm.layerCnt;
for (int i = 0; i < lrbmLayerCnt; i++)
{
_layers.Add(new double[lrbm._rbms[i].visibleNbr]);
_weights.Add(lrbm._rbms[i].GetWeightVisibleHidden());
_bias.Add(lrbm._rbms[i].GetHiddenBias());
}
_layers.Add(new double[lrbm._rbms[lrbmLayerCnt - 1].hiddenNbr]);
}
public DeepBeliefNet(double learningRate, LRBM lrbm, int outputNbr)
{
_rnd = new Random();
_learningRate = learningRate;
//_lrbm = lrbm;
_outputNbr = outputNbr;
_layers = new List <double[]>();
_weights = new List <double[, ]>();
_bias = new List <double[]>();
InitLRBM(lrbm);
InitOutput(lrbm);
_mlp = new MLP(_layers, _weights, _bias, _learningRate);
}
void InitOutputStruct(int[] outputStruct, LRBM lrbm)
{
int currentLevelNbr = lrbm._rbms[lrbm.layerCnt - 1].hiddenNbr;
int nextLevelNbr = outputStruct[0];
double[,] weight = new double[currentLevelNbr, nextLevelNbr];
double[] bias = new double[nextLevelNbr];
for (int j = 0; j < nextLevelNbr; j++)
{
for (int k = 0; k < currentLevelNbr; k++)
{
weight[k, j] = _rnd.NextDouble() - 0.5;
}
bias[j] = 0.0;
}
_weights.Add(weight);
_bias.Add(bias);
_layers.Add(new double[nextLevelNbr]);
for (int i = 1; i < outputStruct.Length; i++)
{
currentLevelNbr = outputStruct[i - 1];
nextLevelNbr = outputStruct[i];
weight = new double[currentLevelNbr, nextLevelNbr];
bias = new double[nextLevelNbr];
for (int j = 0; j < nextLevelNbr; j++)
{
for (int k = 0; k < currentLevelNbr; k++)
{
weight[k, j] = _rnd.NextDouble() - 0.5;
}
bias[j] = 0.0;
}
_weights.Add(weight);
_bias.Add(bias);
_layers.Add(new double[nextLevelNbr]);
}
}
public DeepBeliefNet(double learningRate, LRBM lrbm, int[] outputStruct)
{
_rnd = new Random();
_learningRate = learningRate;
//_lrbm = lrbm;
int outputDepth = outputStruct.Length;
_outputNbr = outputStruct[outputDepth - 1];
_layers = new List <double[]>();
_weights = new List <double[, ]>();
_bias = new List <double[]>();
InitLRBM(lrbm);
InitOutputStruct(outputStruct, lrbm);
_mlp = new MLP(_layers, _weights, _bias, _learningRate);
}
void InitOutput(LRBM lrbm)
{
int lrbmLayerCnt = lrbm.layerCnt;
double [,] weight = new double[lrbm._rbms[lrbmLayerCnt - 1].hiddenNbr, _outputNbr];
double[] bias = new double[_outputNbr];
for (int j = 0; j < _outputNbr; j++)
{
for (int k = 0; k < lrbm._rbms[lrbmLayerCnt - 1].hiddenNbr; k++)
{
weight[k, j] = _rnd.NextDouble() - 0.5;
}
bias[j] = 0.0;
}
_weights.Add(weight);
_bias.Add(bias);
_layers.Add(new double[_outputNbr]);
}
