C# (CSharp) Encog.Neural.Networks.Training.Propagation.Resilient ResilientPropagation - 30개의 예제가 발견되었습니다. 이것들은 오픈소스 프로젝트에서 추출된 C# (CSharp)의 Encog.Neural.Networks.Training.Propagation.Resilient.ResilientPropagation에 대한 실세계 최고 등급의 예제들입니다. 예제들을 평가하여 예제의 품질 향상에 도움을 줄 수 있습니다.
One problem with the backpropagation algorithm is that the magnitude of the partial derivative is usually too large or too small. Further, the learning rate is a single value for the entire neural network. The resilient propagation learning algorithm uses a special update value(similar to the learning rate) for every neuron connection. Further these update values are automatically determined, unlike the learning rate of the backpropagation algorithm. For most training situations, we suggest that the resilient propagation algorithm (this class) be used for training. There are a total of three parameters that must be provided to the resilient training algorithm. Defaults are provided for each, and in nearly all cases, these defaults are acceptable. This makes the resilient propagation algorithm one of the easiest and most efficient training algorithms available. The optional parameters are: zeroTolerance - How close to zero can a number be to be considered zero. The default is 0.00000000000000001. initialUpdate - What are the initial update values for each matrix value. The default is 0.1. maxStep - What is the largest amount that the update values can step. The default is 50. Usually you will not need to use these, and you should use the constructor that does not require them.