本文整理汇总了C#中Encog.Neural.Networks.BasicNetwork.GetLayer方法的典型用法代码示例。如果您正苦于以下问题：C# BasicNetwork.GetLayer方法的具体用法？C# BasicNetwork.GetLayer怎么用？C# BasicNetwork.GetLayer使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类Encog.Neural.Networks.BasicNetwork的用法示例。
在下文中一共展示了BasicNetwork.GetLayer方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C#代码示例。
示例1: Randomize
/// <summary>
/// The Nguyen-Widrow initialization algorithm is the following :
///
/// 1. Initialize all weight of hidden layers with (ranged) random values
/// 2. For each hidden layer
/// 2.1 calculate beta value, 0.7 * Nth(#neurons of input layer) root of
/// #neurons of current layer
/// 2.2 for each synapse
/// 2.1.1 for each weight
/// 2.1.2 Adjust weight by dividing by norm of weight for neuron and
/// multiplying by beta value
/// </summary>
/// <param name="network">The network to randomize.</param>
public override void Randomize(BasicNetwork network)
{
base.Randomize(network);
int neuronCount = 0;
foreach (ILayer layer in network.Structure.Layers)
{
neuronCount += layer.NeuronCount;
}
ILayer inputLayer = network.GetLayer(BasicNetwork.TAG_INPUT);
ILayer outputLayer = network.GetLayer(BasicNetwork.TAG_OUTPUT);
if (inputLayer == null)
throw new EncogError("Must have an input layer for Nguyen-Widrow.");
if (outputLayer == null)
throw new EncogError("Must have an output layer for Nguyen-Widrow.");
int hiddenNeurons = neuronCount - inputLayer.NeuronCount
- outputLayer.NeuronCount;
if (hiddenNeurons < 1)
throw new EncogError("Must have hidden neurons for Nguyen-Widrow.");
double beta = 0.7 * Math.Pow(hiddenNeurons, 1.0 / inputLayer
.NeuronCount);
foreach (ISynapse synapse in network.Structure.Synapses)
{
Randomize(beta, synapse);
}
network.Structure.FlatUpdate = FlatUpdateNeeded.Flatten;
network.Structure.FlattenWeights();
}