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Readme work.

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Lewis Van Winkle 2016-02-09 22:09:21 -06:00
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README.md
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@ -79,7 +79,8 @@ Call `genann_free()` when you're finished with an ANN returned by `genann_init()
###Training ANNs ###Training ANNs
```C ```C
void genann_train(GENANN const *ann, double const *inputs, double const *desired_outputs, double learning_rate); void genann_train(GENANN const *ann, double const *inputs,
double const *desired_outputs, double learning_rate);
``` ```
`genann_train()` will preform one update using standard backpropogation. It `genann_train()` will preform one update using standard backpropogation. It
@ -117,6 +118,12 @@ double const *genann_run(GENANN const *ann, double const *inputs);
Call `genann_run()` on a trained ANN to run a feed-forward pass on a given set of inputs. `genann_run()` Call `genann_run()` on a trained ANN to run a feed-forward pass on a given set of inputs. `genann_run()`
will provide a pointer to the array of predicted outputs (of `ann->outputs` length). will provide a pointer to the array of predicted outputs (of `ann->outputs` length).
##Hints
- All functions start with `genann_`.
- The code is simple. Dig in and change things.
##Extra Resources ##Extra Resources
The [comp.ai.neural-nets The [comp.ai.neural-nets
@ -128,9 +135,3 @@ I highly recommend the [FANN library](http://leenissen.dk/fann/wp/). Another
good library is Peter van Rossum's [Lightweight Neural good library is Peter van Rossum's [Lightweight Neural
Network](http://lwneuralnet.sourceforge.net/), which despite its name, is Network](http://lwneuralnet.sourceforge.net/), which despite its name, is
heavier and has more features than GENANN. heavier and has more features than GENANN.
##Hints
- All functions start with `genann_`.
- The code is simple. Dig in and change things.