public override void Build()
{
// init fields
m_InputDepth = Net.InputDepth;
m_InputHeight = Net.InputHeight;
m_InputWidth = Net.InputWidth;
m_OutputDepth = Net[Net.LayerCount - 1].OutputDepth;
m_Gradient = new double[Net.LayerCount][];
m_Values = new double[Net.LayerCount][][, ];
m_Errors = new double[Net.LayerCount][][, ];
for (int l = 0; l < Net.LayerCount; l++)
{
var layer = Net[l];
m_Gradient[l] = new double[layer.ParamCount];
m_Values[l] = new double[layer.OutputDepth][, ];
m_Errors[l] = new double[layer.OutputDepth][, ];
for (int p = 0; p < layer.OutputDepth; p++)
{
m_Values[l][p] = new double[layer.OutputHeight, layer.OutputWidth];
m_Errors[l][p] = new double[layer.OutputHeight, layer.OutputWidth];
}
}
// init optimizer
if (m_Optimizer == null)
{
m_Optimizer = Registry.Optimizer.SGD;
}
// init scheduler
if (m_LearningRateScheduler == null)
{
m_LearningRateScheduler = Registry.LearningRateScheduler.Constant(m_LearningRate);
}
// init batch context
if (m_UseBatchParallelization)
{
m_BatchContext = new BatchContext(this, m_MaxBatchThreadCount);
}
}
public override void Build()
{
// init fields
m_EpochLength = TrainingSample.Count;
m_InputDepth = Net.InputDepth;
m_InputHeight = Net.InputHeight;
m_InputWidth = Net.InputWidth;
m_OutputDepth = Net[Net.LayerCount - 1].OutputDepth;
m_Gradient = new double[Net.LayerCount][];
m_Values = new double[Net.LayerCount][][, ];
m_Errors = new double[Net.LayerCount][][, ];
for (int l = 0; l < Net.LayerCount; l++)
{
var layer = Net[l];
m_Gradient[l] = new double[layer.ParamCount];
m_Values[l] = new double[layer.OutputDepth][, ];
m_Errors[l] = new double[layer.OutputDepth][, ];
for (int p = 0; p < layer.OutputDepth; p++)
{
m_Values[l][p] = new double[layer.OutputHeight, layer.OutputWidth];
m_Errors[l][p] = new double[layer.OutputHeight, layer.OutputWidth];
}
}
// init expected outputs
m_ExpectedOutputs = new Dictionary <Class, double[]>();
var count = Classes.Count;
if (count != OutputDepth)
{
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;
}
// init optimizer
if (m_Optimizer == null)
{
m_Optimizer = Registry.Optimizer.SGD;
}
// init scheduler
if (m_LearningRateScheduler == null)
{
m_LearningRateScheduler = Registry.LearningRateScheduler.Constant(m_LearningRate);
}
// init batch context
if (m_UseBatchParallelization)
{
m_BatchContext = new BatchContext(this, m_MaxBatchThreadCount);
}
}
