| Name |
Description |
| Greedy |
A simple greedy strategy. If the last iteration did not improve training, then discard it. Care must be taken with this strategy, as sometimes a training algorithm may need to temporarily decrease the error level before improving it. |
| HybridStrategy |
A hybrid stragey allows a secondary training algorithm to be used. Once the primary algorithm is no longer improving by much, the secondary will be used. Using simulated annealing in as a secondary to one of the propagation methods is often a very efficient combination as it can help the propagation method escape a local minimum. This is particularly true with backpropagation. |
| RequiredImprovementStrategy |
This reset strategy will reset the weights if the neural network fails to improve by the specified amount over a number of cycles. |
| ResetStrategy |
The reset strategy will reset the weights if the neural network fails to fall below a specified error by a specified number of cycles. This can be useful to throw out initially "bad/hard" random initializations of the weight matrix. |
| SmartLearningRate |
|
| SmartMomentum |
Attempt to automatically set a momentum in a training algorithm that supports momentum. |
| StopTrainingStrategy |
This strategy will indicate once training is no longer improving the neural network by a specified amount, over a specified number of cycles. This allows the program to automatically determine when to stop training. |
