FFNet & PatternList & Categories: Learn...

You can choose this command after selecting one PatternList, one Categories and one FFNet.

## Settings

Maximum number of epochs
  the maximum number of times that the complete PatternList dataset will be presented to the neural net.
Tolerance of minimizer
  when the difference in costs between two successive learning cycles is smaller than this value, the minimization process will be stopped.

Cost function

  Minimum-squared-error:
  costs = ∑allPatterns ∑allOutputs (ok - dk)2, where
  ok : actual output of unit k
  dk : desired output of unit k
  Minimum-cross-entropy:
  costs = - ∑allPatterns ∑allOutputs (dk · ln ok + (1-dk) · ln (1-ok))

## Algorithm

The minimization procedure is a variant of conjugate gradient minimization, see for example Press et al. (1992), chapter 10, or Nocedal & Wright (1999), chapter 5.

### Links to this page

- Feedforward neural networks 1.1. The learning phase
- Feedforward neural networks 2. Quick start
- Feedforward neural networks 4. Command overview
- FFNet & PatternList & ActivationList: Get average costs...
- FFNet & PatternList & ActivationList: Get total costs...
- FFNet & PatternList & Categories: Get average costs...
- FFNet & PatternList & Categories: Get total costs...