public BackpropAlgorithm(ConvNet net) : base(net)
{
m_EpochCount = DFT_EPOCH_COUNT;
m_LearningRate = DFT_LEARNING_RATE;
m_Stop = DTF_STOP_CRITERIA;
m_BatchSize = DFT_BATCH_SIZE;
m_MaxBatchThreadCount = DFT_BATCH_THREAD_COUNT;
}
private void init()
{
m_EpochCount = DFT_EPOCH_COUNT;
m_BatchSize = DFT_BATCH_SIZE;
m_LearningRate = DFT_LEARNING_RATE;
m_Stop = DTF_STOP_CRITERIA;
m_EpochLength = TrainingSample.Count;
m_InputDim = Result.InputDim;
m_OutputDim = Result[Result.LayerCount - 1].NeuronCount;
m_Errors = new double[Result.LayerCount][];
for (int i = 0; i < Result.LayerCount; i++)
{
var ncount = Result[i].NeuronCount;
m_Errors[i] = new double[ncount];
}
m_Gradient = new double[Result.LayerCount][, ];
for (int i = 0; i < Result.LayerCount; i++)
{
var pcount = (i > 0) ? Result[i - 1].NeuronCount : m_InputDim;
var lcount = Result[i].NeuronCount;
m_Gradient[i] = new double[lcount, pcount + 1]; // take bias into account
}
m_ExpectedOutputs = new Dictionary <Class, double[]>();
var count = Classes.Count;
if (count != OutputDim)
{
throw new MLException("Number of classes must be equal to dimension of output vector");
}
for (int i = 0; i < count; i++)
{
var cls = Classes.FirstOrDefault(p => (int)p.Value.Value == i).Value;
if (cls == null)
{
throw new MLException(string.Format("There is no class with value {0}. It is neccessary to have full set of classes with values from 0 to {1}", i, count));
}
var output = new double[count];
output[i] = 1.0D;
m_ExpectedOutputs[cls] = output;
}
}
private void init()
{
m_EpochCount = DFT_EPOCH_COUNT;
m_BatchSize = DFT_BATCH_SIZE;
m_LearningRate = DFT_LEARNING_RATE;
m_Stop = DTF_STOP_CRITERIA;
m_InputDim = Net.InputDim;
m_OutputDim = Net[Net.LayerCount - 1].NeuronCount;
m_Errors = new double[Net.LayerCount][];
for (int i = 0; i < Net.LayerCount; i++)
{
var ncount = Net[i].NeuronCount;
m_Errors[i] = new double[ncount];
}
m_Gradient = new double[Net.LayerCount][, ];
for (int i = 0; i < Net.LayerCount; i++)
{
var pcount = (i > 0) ? Net[i - 1].NeuronCount : m_InputDim;
var lcount = Net[i].NeuronCount;
m_Gradient[i] = new double[lcount, pcount + 1]; // take bias into account
}
}
public override string ToString()
{
return(string.Format("Selection type: {0} \nStop criteria: {1}\nContest size: {2}\nPopulation size: {3}\n" +
"Generation count: {4}\nTime: {5}\nMin range: {6}\nMax range: {7}", SelectionMode.ToString(), StopCriteria.ToString(),
ContestSize, PopulationSize, GenerationCount, Time, MinRange, MaxRange));
}
public StopDecision RunOnce()
{
Algorithm.RunOnce();
return(StopCriteria.MakeDecision(this, Algorithm.Population));
}
