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

Minimumsquarederror:

costs = ∑_{allPatterns} ∑_{allOutputs} (o_{k}  d_{k})^{2}, where

o_{k} : actual output of unit k

d_{k} : desired output of unit k

Minimumcrossentropy:

costs =  ∑_{allPatterns} ∑_{allOutputs} (d_{k} · ln o_{k} + (1d_{k}) · ln (1o_{k}))
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.
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