It might be interesting to add a Clojure frontend to Nengo. Having
said that, Nengo is very "opinionated" in the sense that you have to
buy in to the kind of neural simulation Eliasmith is doing (and the
way he's doing it), which Eric may not be interested in doing. There
are many other kinds of
You might also consider using your DSL as a frontend to the Nengo
neural simulator (http://nengo.ca). Nengo (which is written in Java)
has recently added a Python
scripting interface (http://www.frontiersin.org/neuroinformatics/
10.3389/neuro.11/007.2009/abstract). Nengo has a lot to recommend it
#+begin_src clojure
(let [n {:phi identity
:accum (comp (partial reduce +) (partial map *))
:weights [2 2 2]}]
[(repeat 3 n) (repeat 5 n) (assoc n :weights (vec (repeat 5 1)))])
#+end_src
would result in the following connection pattern
[[file:/tmp/layers.png]]
> Howe
On Nov 13, 9:12 am, "Eric Schulte" wrote:
> Albert Cardona writes:
>
> > Your neural network DSL looks great. One minor comment: why use lists
> > instead of sets? ...
>
> I used lists because I want to be able to specify a network in which (at
> least initially) all neurons in a hidden layer a
Hi Albert,
Albert Cardona writes:
> Hi Eric,
>
> Your neural network DSL looks great. One minor comment: why use lists
> instead of sets? In the webpage you state:
>
> "Lists are used to represent a unordered series"
>
I used lists because I want to be able to specify a network in which (at
lea
Carson writes:
> Hi! That looks interesting. I'm curious how big a network are you
> intending to experiment with? (ie, # of layers, size of layers?).
>
I haven't really thought about limits on the size of the networks,
although I suppose with very large networks it may become a good idea to
On Fri, Nov 12, 2010 at 9:15 AM, Albert Cardona wrote:
> Hi Eric,
>
> Your neural network DSL looks great. One minor comment: why use lists
> instead of sets? In the webpage you state:
>
> "Lists are used to represent a unordered series"
>
> ... but lists are generally considered data structures w
Hi Eric,
Your neural network DSL looks great. One minor comment: why use lists
instead of sets? In the webpage you state:
"Lists are used to represent a unordered series"
... but lists are generally considered data structures whose elements
are accessible by index. The closest representation to
Hi! That looks interesting. I'm curious how big a network are you
intending to experiment with? (ie, # of layers, size of layers?).
Carson
On Nov 11, 8:17 am, "Eric Schulte" wrote:
> Hi Saul,
>
> Saul Hazledine writes:
> > On Nov 10, 11:20 pm, "Eric Schulte" wrote:
> >> Hi,
>
> >> Inspired
Sean Grove writes:
> Very elegant. From the example, it looks like it takes away a great
> deal of the tedium of neural networks.
>
Thanks, that is certainly the goal.
>
> Do you have any more significant code examples than those listed on [3]?
>
At the bottom of [3] I link to both a perceptro
Hi Saul,
Saul Hazledine writes:
> On Nov 10, 11:20 pm, "Eric Schulte" wrote:
>> Hi,
>>
>> Inspired by cgrand's regexp example [1], I've implemented a simple DSL
>> for specifying neural networks using Clojure data types.
>
> This is really clear. The web page documentation is awesome.
>
Grea
On Nov 10, 11:20 pm, "Eric Schulte" wrote:
> Hi,
>
> Inspired by cgrand's regexp example [1], I've implemented a simple DSL
> for specifying neural networks using Clojure data types.
This is really clear. The web page documentation is awesome.
> Construction of this simple language involved a
Awesome! Thanks for sharing! :-)
On Nov 10, 2010, at 2:20 PM, Eric Schulte wrote:
> Hi,
>
> Inspired by cgrand's regexp example [1], I've implemented a simple DSL
> for specifying neural networks using Clojure data types. The code is
> available in this gist [2], and a brief introduction with s
Very elegant. From the example, it looks like it takes away a great deal of the
tedium of neural networks.
Do you have any more significant code examples than those listed on [3]?
Sean
On Nov 10, 2010, at 2:20 PM, Eric Schulte wrote:
> Hi,
>
> Inspired by cgrand's regexp example [1], I've imp
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