/// <summary>
/// Propagates a weight update event upstream through the network.
/// </summary>
/// <param name="properties">Network training properties.</param>
/// <param name="networkTrainer">Network training method.</param>
public override void Update(NetworkTrainingProperties properties, INetworkTrainer networkTrainer)
{
double lm = (properties.Lambda / (int)properties[nameof(GatedRecurrentGenerator.SequenceLength)]);
if (!this.Constrained)
{
this.Rx = networkTrainer.Update(this.NodeId, this.NodeId, nameof(this.Rx), this.Rx, this.DRx, properties);
this.Rh = networkTrainer.Update(this.NodeId, this.NodeId, nameof(this.Rh), this.Rh, this.DRh, properties);
this.Zx = networkTrainer.Update(this.NodeId, this.NodeId, nameof(this.Zx), this.Zx, this.DZx, properties);
this.Zh = networkTrainer.Update(this.NodeId, this.NodeId, nameof(this.Zh), this.Zh, this.DZh, properties);
this.Hh = networkTrainer.Update(this.NodeId, this.NodeId, nameof(this.Hh), this.Hh, this.DHh, properties);
}
for (int edge = 0; edge < this.In.Count; edge++)
{
Delta = (1.0 / properties.Examples) * Delta;
if (!this.In[edge].Source.IsBias)
{
Delta = Delta + (lm * this.In[edge].Weight);
}
if (!this.Constrained)
{
this.In[edge].Weight = networkTrainer.Update(this.In[edge].ParentId, this.In[edge].ChildId,
nameof(Edge.Weight), this.In[edge].Weight, Delta, properties);
}
this.In[edge].Source.Update(properties, networkTrainer);
}
}
/// <summary>
/// Propagates a weight update event upstream through the network.
/// </summary>
/// <param name="properties">Network training properties.</param>
/// <param name="networkTrainer">Network training method.</param>
public virtual void Update(NetworkTrainingProperties properties, INetworkTrainer networkTrainer)
{
for (int edge = 0; edge < this.In.Count; edge++)
{
Delta = (1.0 / properties.Examples) * Delta;
if (edge > 0)
{
Delta = Delta + ((properties.Lambda / properties.Examples) * this.In[edge].Weight);
}
if (!this.Constrained)
{
this.In[edge].Weight = networkTrainer.Update(this.In[edge].ParentId, this.In[edge].ChildId,
nameof(Edge.Weight), this.In[edge].Weight, Delta, properties);
}
this.In[edge].Source.Update(properties, networkTrainer);
}
}
/// <summary>Backpropagates the errors through the network given the supplied sequence label.</summary>
/// <param name="y">Output vector to process.</param>
/// <param name="properties">Network training properties for use in learning.</param>
/// <param name="networkTrainer">Network training method.</param>
/// <param name="update">Indicates whether to update the weights after computing the errors.</param>
public void Back(Vector y, NetworkTrainingProperties properties, INetworkTrainer networkTrainer, bool update = true)
{
this.Cost = this.LossFunction.Compute(this.Output(), y);
// CK
// propagate error gradients
for (int i = 0; i < Out.Length; i++)
{
Out[i].Error(y[i], properties);
}
if (update)
{
// reset weights
for (int i = 0; i < Out.Length; i++)
{
Out[i].Update(properties, networkTrainer);
}
}
}
/// <summary>
/// Train the network using a Network Trainer agent.
/// </summary>
/// <param name="progress">Delegate to update the progress (between 0 and 100).</param>
/// <returns>Whether the training was successful or not.</returns>
public bool AutoTrainNetwork(Progress progress)
{
int attempts = _config.AutoTrainingAttempts.Value;
if (_trainer == null)
{
_trainer = new FeedbackTrainer(); //SimpleTrainer(); //ConditionalTrainer();
}
bool wasQuiet = _config.Basic.QuietModeEnable.Value;
_config.Basic.QuietModeEnable.Value = true;
bool succeeded = _trainer.Train(this, progress);
while (!succeeded && --attempts > 0)
{
RebuildNetwork();
succeeded = _trainer.Train(this, progress);
}
_config.Basic.QuietModeEnable.Value = wasQuiet;
return(succeeded);
}
/// <summary>Backpropagates the errors through the network given the supplied label.</summary>
/// <param name="y">Label to process.</param>
/// <param name="properties">Network training properties for use in learning.</param>
/// <param name="networkTrainer">Network training method.</param>
public void Back(double y, NetworkTrainingProperties properties, INetworkTrainer networkTrainer)
{
this.Back(Vector.Create(this.Out.Length, () => y), properties, networkTrainer);
}
/// <summary>
/// Train the network using a Network Trainer agent.
/// </summary>
/// <param name="progress">Delegate to update the progress (between 0 and 100).</param>
/// <returns>Whether the training was successful or not.</returns>
public bool AutoTrainNetwork(Progress progress)
{
int attempts = _config.AutoTrainingAttempts.Value;
if(_trainer == null)
_trainer = new FeedbackTrainer(); //SimpleTrainer(); //ConditionalTrainer();
bool wasQuiet = _config.Basic.QuietModeEnable.Value;
_config.Basic.QuietModeEnable.Value = true;
bool succeeded = _trainer.Train(this, progress);
while(!succeeded && --attempts > 0)
{
RebuildNetwork();
succeeded = _trainer.Train(this, progress);
}
_config.Basic.QuietModeEnable.Value = wasQuiet;
return succeeded;
}
