public TiedFeedForward(IFeedForward layer, IWeightInitialisation weightInit, string name = null)
: base(name)
{
_layer = layer;
_layerId = layer.Id;
_bias = weightInit.CreateBias(layer.InputSize);
}
public override void OnDeserialise(IReadOnlyDictionary <string, INode> graph)
{
_layer = graph[_layerId] as IFeedForward;
Debug.Assert(_layer != null);
}
/// <summary>
/// Creates a layer whose weights are shared with another layer (but transposed)
/// </summary>
/// <param name="layer">The layer that shares weights</param>
/// <param name="name">Optional name to give the node</param>
/// <returns></returns>
public INode CreateTiedFeedForward(IFeedForward layer, string name = null)
{
var weightInit = _GetWeightInitialisation();
return(new TiedFeedForward(layer, weightInit, name));
}
/// <summary>
/// Adds a feed forward layer whose weights are tied to a previous layer
/// </summary>
/// <param name="layer">The layer whose weights are tied</param>
/// <param name="name">Optional name to give the node</param>
/// <returns></returns>
public WireBuilder AddTiedFeedForward(IFeedForward layer, string name = null)
{
_SetNode(_factory.CreateTiedFeedForward(layer, name));
return(SetNewSize(layer.InputSize));
}
//double stopTraining_lastOutputDeviation = double.MaxValue;
public ValidationSet(IFeedForward feedForward, IOuputDeviation ouputDeviation)
{
_ouputDeviation = ouputDeviation;
_feedForward = feedForward;
}
public NeuralNetworkRunner(IFeedForward feedForward)
{
_feedForward = feedForward;
}
public TrainSet(IFeedForward feedForward, IBackPropagate backPropagate)
{
_backPropagate = backPropagate;
_feedForward = feedForward;
}
public TestSet(IFeedForward feedForward, IOuputDeviation ouputDeviation)
{
_ouputDeviation = ouputDeviation;
_feedForward = feedForward;
}
