[pdp-discuss] Leabra: learning continous values over time?

[Frank Leoné]

Hi all,

Again I want to expres my gratitude: it now works! Especially this, for me 
previous undiscovered, layertype really opens new interesting possibilities, 
I like it!

It does not work flawlessly though: the error first goes down quite fast, 
but after some time rises even faster, to really high levels, just to go 
down again (not as far down as it went previously) and to rise later on, 
etcetera. Needles to say, it is quite hard to reach low error levels with 
this kind of behaviour. Does anyone got an idea what the reason might be? 
I'm using a leabra network with a context layer (made with the wizard).

Also I have one other minor problem: for the learning over time I want the 
retinal input to switch off after the first trial. So, the first trial the 
input is a value between a minimum and a maximum, converted in a distributed 
code. But in the rest of the trials no such input should be given: the input 
layer should give a zero input. But if I enter zero as input, it is 
converted to the center of my distributed code, which is ofcourse normally 
the correct behavior. So I thought: lets take a number far outside the 
borders, but that point is just moved to the border itself.

So: how can I input nothing into a scalarvallayer? I now fixed it by just 
clamping the entire pattern, but it would be nice to use the scalarvallayer 
again.

Thanks in advance!

Frank

PS. I can't seem to reach the latest archives. Is there something wrong or 
is it just me? :)

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