Specifies that a training algorithm has the concept of a learning rate. This allows it to be used with strategies that automatically adjust the learning rate.
For a list of all members of this type, see ILearningRate Members .
| Type | Description |
|---|---|
| CompetitiveTraining | This class implements competitive training, which would be used in a winner-take-all neural network, such as the self organizing map (SOM). This is an unsupervised training method, no ideal data is needed on the training set. If ideal data is provided, it will be ignored. Training is done by looping over all of the training elements and calculating a "best matching unit" (BMU). This BMU output neuron is then adjusted to better "learn" this pattern. Additionally, this training may be applied to othr "nearby" output neurons. The degree to which nearby neurons are update is defined by the neighborhood function. A neighborhood function is required to determine the degree to which neighboring neurons (to the winning neuron) are updated by each training iteration. Because this is unsupervised training, calculating an error to measure progress by is difficult. The error is defined to be the "worst", or longest, Euclidean distance of any of the BMU's. This value should be minimized, as learning progresses. Because only the BMU neuron and its close neighbors are updated, you can end up with some output neurons that learn nothing. By default these neurons are forced to win patterns that are not represented well. This spreads out the workload among all output neurons. This feature is used by default, but can be disabled by setting the "forceWinner" property. |
| TrainInstar | Used for Instar training of a CPN neural network. A CPN network is a hybrid supervised/unsupervised network. The Instar training handles the unsupervised portion of the training. |
| TrainOutstar | Used for Instar training of a CPN neural network. A CPN network is a hybrid supervised/unsupervised network. The Outstar training handles the supervised portion of the training. |
| Backpropagation | This class implements a backpropagation training algorithm for feed forward neural networks. It is used in the same manner as any other training class that implements the Train interface. Backpropagation is a common neural network training algorithm. It works by analyzing the error of the output of the neural network. Each neuron in the output layer's contribution, according to weight, to this error is determined. These weights are then adjusted to minimize this error. This process continues working its way backwards through the layers of the neural network. This implementation of the backpropagation algorithm uses both momentum and a learning rate. The learning rate specifies the degree to which the weight matrixes will be modified through each iteration. The momentum specifies how much the previous learning iteration affects the current. To use no momentum at all specify zero. One primary problem with backpropagation is that the magnitude of the partial derivative is often detrimental to the training of the neural network. The other propagation methods of Manhatten and Resilient address this issue in different ways. In general, it is suggested that you use the resilient propagation technique for most Encog training tasks over back propagation. |
| ManhattanPropagation | One problem that the backpropagation technique has is that the magnitude of the partial derivative may be calculated too large or too small. The Manhattan update algorithm attempts to solve this by using the partial derivative to only indicate the sign of the update to the weight matrix. The actual amount added or subtracted from the weight matrix is obtained from a simple constant. This constant must be adjusted based on the type of neural network being trained. In general, start with a higher constant and decrease it as needed. The Manhattan update algorithm can be thought of as a simplified version of the resilient algorithm. The resilient algorithm uses more complex techniques to determine the update value. |
| TrainAdaline | Train an ADALINE neural network. |
Namespace: Encog.Neural.Networks.Training
Assembly: encog-core-cs (in encog-core-cs.dll)
ILearningRate Members | Encog.Neural.Networks.Training Namespace