public override void Execute()
{
// Reset the input sample
Owner.ResetSample();
// For every sample...
for (uint i = 0; i < Owner.ForwardSamplesPerStep; i++)
{
// Forward propagation
Owner.ForwardPropagation();
if (NetworkMode == MyFeedForwardMode.TRAINING)
{
// Backward propagation
BackPropAgent.Execute(m_trainingStep);
}
// Switch to the next input
if (i < Owner.ForwardSamplesPerStep - 1)
{
Owner.NextSample();
}
m_trainingStep++;
}
}
public override void Execute()
{
// Reset the input sample
Owner.ResetSample();
Owner.InputLayer.SetInputMemoryBlock(Owner.TrainingData);
// For every sample of TrainingData
// Do forward pass on TrainingData and backward pass
if (!UseTrainingSignal || Owner.TrainingSignal.IsIncomingRised())
{
for (uint i = 0; i < Owner.ForwardSamplesPerStep; i++)
{
// Forward propagation
Owner.ForwardPropagation();
// Backward propagation
BackPropAgent.Execute(m_trainingStep);
// Switch to the next input
if (i < Owner.ForwardSamplesPerStep - 1)
{
Owner.NextSample();
}
m_trainingStep++;
}
}
if (!UseForwardSignal || Owner.ForwardSignal.IsIncomingRised())
{
Owner.ResetSample();
Owner.InputLayer.SetInputMemoryBlock(Owner.DataInput);
// For every sample
// Do forward pass on first data input
for (uint i = 0; i < Owner.ForwardSamplesPerStep; i++)
{
// Forward propagation
Owner.ForwardPropagation();
// Copy the end layer output to the node output
Owner.CopyResult();
// Switch to the next input
if (i < Owner.ForwardSamplesPerStep - 1)
{
Owner.NextSample();
}
}
}
if (RepeatTrainingSignal && UseTrainingSignal && Owner.TrainingSignal.IsIncomingRised())
{
Owner.TrainingSignal.Raise();
}
else
{
Owner.TrainingSignal.Drop();
}
if (RepeatForwardSignal && UseForwardSignal && Owner.ForwardSignal.IsIncomingRised())
{
Owner.ForwardSignal.Raise();
}
else
{
Owner.ForwardSignal.Drop();
}
}
