-----------------ann_mlp manual 

by davide morelli - www.davidemorelli.it


-----------What is a neural network?

To be shure you fully understand what is and why to use a ANN, read
http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html

-----------Why use a ANN?
Because they are useful in Pattern recognition, gesure recognition (patterns
over time), associative recall of data (images, sounds, etc), predictions
(e.g. time-series forecasting), complex data handling, etc..

--ANNs can handle noisy inputs: 
if you trained your ANN that [1,1] -> 1
Then if you pass [1.1, 0.9] -> 1 probably..

--ANNs can be trained without writing code: 
see ann/examples/ann_mlp_example2 (in CVS), you can teach the ANN to tell
you when all these balls are close together or still.. How could you do this
coding? You'd have to compute the distance of every ball from every other
ball, then sum all the distances and ... Very complex and difficult! With
ANN you simply teach when the balls are close and when they are not, you
don't have to write code at all, you just have to use pd.

-----------How to build a ANN?
INPUTS:
You must code your input data as a list of float. 
E.g. If you want timbre recognition you must fft a signal then build a list
with fft's partial and feed ann_mlp with it
E.g. if you want midi chord recognition and you played A4 C5 E6 then use the
midi values of the notes of the chord to build a list with 3 integers (57 60
64) 

Tip: inputs should be 0 centered
the example of chord recognition should not work well (hard to train)
because possible input values go from 30 to 90, you should remap them so
they go from -30 to 30
Notice how the inputs in ann/examples/ann_mlp_example2 go from -1 to 1
If you can't make inputs 0 centered they should at least start from 0

Tip: inputs should be normalized
If you have one input that goes from -10 to 10 and another input that goes
from -1 to 1 the first input will be more important than the second input

OUTPUTS:
Each "meaning" you want your ANN to detect should have its own output.
Notice ann/examples/ann_mlp_example2:
"Calm" and "chaos" have their outputs even if they are related.
I could have set only 1 output 0 for calm and 1 for chaos.
But having separated outputs I can see if my ANN has been trained well or
not, but also could be that a situation is neither calm nor chaotic, or
somehow calm AND chaotic..

-----------TRAINING ON THE FLY:
It is much easier to train the ANN on the fly rather than using a train
file.
To train on the fly you simply must pass a list with 
[inputs + expected outputs(
If you have 3 inputs and 2 outputs then you must pass a list with 5 floats
E.g. You want to train a ANN for a simple logical function: OR
You build a ann_mlp with 2 inputs and 1 output
You set |train(
You pass lists like 
|0 0 0(  inputs are 0 0 output is 0
|1 0 1(  inputs are 1 0 output is 1
|0 1 1(  inputs are 0 1 output is 1
And so on.. Repeating until MSE is low enough

MSE tells you the general error the ANN currently has with the inputs and
outputs you are giving

When you are ready set |run(
And start passing lists with only inputs values
|0 0(
|1 0(
Etc..
The left outlet of ann_mlp will start sending lists of float, in this case a
list with only 1 float

-----------PUTTING IT TOGETHER:

1) Create a ANN passing ann_mlp a message with num_inputs and num_outputs
E.g. (the simple ann for logical function OR)
|create 2 1(
|
[ann_mlp]

2) set train mode
|train(
|
[ann_mlp]

3) train the ANN passing lists num_inputs+num_outputs long
|0 0 0(
|
[ann_mlp]
(repeat at will using different inputs until mse is low, see right outlet)

4) set run mode
|run(
|
[ann_mlp]

5) run the net passing lists num_inputs long, the left outlet will send a
list with the results
|0 0(
|
[ann_mlp]

When everything is fine you can save it to a file
|save filename(
|
[ann_mlp]

Can be loaded in 2 ways
|load filename(
|
[ann_mlp]

Or as argument

[ann_mlp filename]

-----------

For a more in-depth sight over technical issues:
http://fann.sourceforge.net/report/report.html
See fann manual for details on advanced params 
(activation functions,training params, etc..)
http://fann.sourceforge.net/

-----------

questions and suggestions to info(a)davidemorelli.it
