Backpropagation
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Overloaded. Initializes a new instance of the Backpropagation class. |
Error (inherited from BasicTraining) | Get the current error percent from the training. |
LearningRate | The learning rate, this is value is essentially a percent. It is the degree to which the gradients are applied to the weight matrix to allow learning. |
Levels (inherited from Propagation) | The propagation levels. |
Momentum | The momentum for training. This is the degree to which changes from which the previous training iteration will affect this training iteration. This can be useful to overcome local minima. |
Network (inherited from Propagation) | Get the current best neural network. |
OutputHolder (inherited from Propagation) | The output holder being used. |
Strategies (inherited from BasicTraining) | The strategies to use. |
Training (inherited from BasicTraining) | The training data to use. |
AddStrategy (inherited from BasicTraining) | Training strategies can be added to improve the training results. There are a number to choose from, and several can be used at once. |
BackwardPass (inherited from Propagation) | Calculate the error for the recognition just done. |
Equals (inherited from Object) | |
GetHashCode (inherited from Object) | |
GetType (inherited from Object) | |
Iteration (inherited from Propagation) | Perform one iteration of training. Note: if you get a StackOverflowError while training, then you have endless recurrent loops. Try inserting no trainable synapses on one side of the loop. |
PostIteration (inherited from BasicTraining) | Call the strategies after an iteration. |
PreIteration (inherited from BasicTraining) | Call the strategies before an iteration. |
ToString (inherited from Object) |
Finalize (inherited from Object) | |
MemberwiseClone (inherited from Object) |
Backpropagation Class | Encog.Neural.Networks.Training.Propagation.Back Namespace