/// <summary>
/// Connect layers from a BasicNetwork. Used internally only.
/// </summary>
/// <param name="network">The BasicNetwork.</param>
/// <param name="fromLayerIdx">The from layer index.</param>
/// <param name="source">The from layer.</param>
/// <param name="target">The target.</param>
private void ConnectLayersFromBasic(BasicNetwork network,
int fromLayerIdx, IFreeformLayer source, IFreeformLayer target)
{
for (int targetNeuronIdx = 0; targetNeuronIdx < target.Count; targetNeuronIdx++)
{
for (int sourceNeuronIdx = 0; sourceNeuronIdx < source.Count; sourceNeuronIdx++)
{
IFreeformNeuron sourceNeuron = source.Neurons[sourceNeuronIdx];
IFreeformNeuron targetNeuron = target.Neurons[targetNeuronIdx];
// neurons with no input (i.e. bias neurons)
if (targetNeuron.InputSummation == null)
{
continue;
}
IFreeformConnection connection = _connectionFactory
.Factor(sourceNeuron, targetNeuron);
sourceNeuron.AddOutput(connection);
targetNeuron.AddInput(connection);
double weight = network.GetWeight(fromLayerIdx,
sourceNeuronIdx, targetNeuronIdx);
connection.Weight = weight;
}
}
}
/// <inheritdoc/>
protected override void LearnConnection(IFreeformConnection connection)
{
double gradient = connection.GetTempTraining(0);
double delta = (gradient * _learningRate)
+ (connection.GetTempTraining(1) * _momentum);
connection.SetTempTraining(1, delta);
connection.Weight += delta;
}
/// <summary>
/// Connect two layers.
/// </summary>
/// <param name="source">The source layer.</param>
/// <param name="target">The target layer.</param>
/// <param name="theActivationFunction">The activation function to use.</param>
/// <param name="biasActivation">The bias activation to use.</param>
/// <param name="isRecurrent">True, if this is a recurrent connection.</param>
public void ConnectLayers(IFreeformLayer source,
IFreeformLayer target,
IActivationFunction theActivationFunction,
double biasActivation, bool isRecurrent)
{
// create bias, if requested
if (biasActivation > EncogFramework.DefaultDoubleEqual)
{
// does the source already have a bias?
if (source.HasBias)
{
throw new FreeformNetworkError(
"The source layer already has a bias neuron, you cannot create a second.");
}
IFreeformNeuron biasNeuron = _neuronFactory
.FactorRegular(null);
biasNeuron.Activation = biasActivation;
biasNeuron.IsBias = true;
source.Add(biasNeuron);
}
// create connections
foreach (IFreeformNeuron targetNeuron in target.Neurons)
{
// create the summation for the target
IInputSummation summation = targetNeuron.InputSummation;
// do not create a second input summation
if (summation == null)
{
summation = _summationFactory.Factor(theActivationFunction);
targetNeuron.InputSummation = summation;
}
// connect the source neurons to the target neuron
foreach (IFreeformNeuron sourceNeuron in source.Neurons)
{
IFreeformConnection connection = _connectionFactory
.Factor(sourceNeuron, targetNeuron);
sourceNeuron.AddOutput(connection);
targetNeuron.AddInput(connection);
}
}
}
/// <inheritdoc/>
public void Add(IFreeformConnection connection)
{
_inputs.Add(connection);
}
/// <inheritdoc />
protected override void LearnConnection(IFreeformConnection connection)
{
// multiply the current and previous gradient, and take the
// sign. We want to see if the gradient has changed its sign.
int change = EncogMath
.Sign(connection
.GetTempTraining(TempGradient)
*connection
.GetTempTraining(TempLastGradient));
double weightChange = 0;
// if the gradient has retained its sign, then we increase the
// delta so that it will converge faster
if (change > 0)
{
double delta = connection
.GetTempTraining(TempUpdate)
*RPROPConst.PositiveEta;
delta = Math.Min(delta, _maxStep);
weightChange = EncogMath
.Sign(connection
.GetTempTraining(TempGradient))
*delta;
connection.SetTempTraining(
TempUpdate, delta);
connection
.SetTempTraining(
TempLastGradient,
connection
.GetTempTraining(TempGradient));
}
else if (change < 0)
{
// if change<0, then the sign has changed, and the last
// delta was too big
double delta = connection
.GetTempTraining(TempUpdate)
*RPROPConst.NegativeEta;
delta = Math.Max(delta, RPROPConst.DeltaMin);
connection.SetTempTraining(
TempUpdate, delta);
weightChange = -connection
.GetTempTraining(TempLastWeightDelta);
// set the previous gradient to zero so that there will be no
// adjustment the next iteration
connection.SetTempTraining(
TempLastGradient, 0);
}
else if (change == 0)
{
// if change==0 then there is no change to the delta
double delta = connection
.GetTempTraining(TempUpdate);
weightChange = EncogMath
.Sign(connection
.GetTempTraining(TempGradient))
*delta;
connection
.SetTempTraining(
TempLastGradient,
connection
.GetTempTraining(TempGradient));
}
// apply the weight change, if any
connection.Weight += weightChange;
connection.SetTempTraining(
TempLastWeightDelta,
weightChange);
}
/// <inheritdoc />
protected override void LearnConnection(IFreeformConnection connection)
{
// multiply the current and previous gradient, and take the
// sign. We want to see if the gradient has changed its sign.
int change = EncogMath
.Sign(connection
.GetTempTraining(TempGradient)
* connection
.GetTempTraining(TempLastGradient));
double weightChange = 0;
// if the gradient has retained its sign, then we increase the
// delta so that it will converge faster
if (change > 0)
{
double delta = connection
.GetTempTraining(TempUpdate)
* RPROPConst.PositiveEta;
delta = Math.Min(delta, _maxStep);
weightChange = EncogMath
.Sign(connection
.GetTempTraining(TempGradient))
* delta;
connection.SetTempTraining(
TempUpdate, delta);
connection
.SetTempTraining(
TempLastGradient,
connection
.GetTempTraining(TempGradient));
}
else if (change < 0)
{
// if change<0, then the sign has changed, and the last
// delta was too big
double delta = connection
.GetTempTraining(TempUpdate)
* RPROPConst.NegativeEta;
delta = Math.Max(delta, RPROPConst.DeltaMin);
connection.SetTempTraining(
TempUpdate, delta);
weightChange = -connection
.GetTempTraining(TempLastWeightDelta);
// set the previous gradient to zero so that there will be no
// adjustment the next iteration
connection.SetTempTraining(
TempLastGradient, 0);
}
else if (change == 0)
{
// if change==0 then there is no change to the delta
double delta = connection
.GetTempTraining(TempUpdate);
weightChange = EncogMath
.Sign(connection
.GetTempTraining(TempGradient))
* delta;
connection
.SetTempTraining(
TempLastGradient,
connection
.GetTempTraining(TempGradient));
}
// apply the weight change, if any
connection.Weight += weightChange;
connection.SetTempTraining(
TempLastWeightDelta,
weightChange);
}
/// <inheritdoc/>
public void AddOutput(IFreeformConnection connection)
{
_outputConnections.Add(connection);
}
/// <inheritdoc/>
public void AddInput(IFreeformConnection connection)
{
InputSummation.Add(connection);
}
/// <inheritdoc/>
public void Add(IFreeformConnection connection)
{
_inputs.Add(connection);
}
/// <inheritdoc/>
public void AddOutput(IFreeformConnection connection)
{
_outputConnections.Add(connection);
}
/// <inheritdoc/>
public void AddInput(IFreeformConnection connection)
{
InputSummation.Add(connection);
}
/// <summary> /// Learn for a single connection. /// </summary> /// <param name="connection">The connection to learn from.</param> protected abstract void LearnConnection(IFreeformConnection connection);
/// <inheritdoc/>
protected override void LearnConnection(IFreeformConnection connection)
{
double gradient = connection.GetTempTraining(0);
double delta = (gradient * _learningRate)
+ (connection.GetTempTraining(1) * _momentum);
connection.SetTempTraining(1, delta);
connection.Weight += delta;
}
/// <summary> /// Learn for a single connection. /// </summary> /// <param name="connection">The connection to learn from.</param> protected abstract void LearnConnection(IFreeformConnection connection);
