On Mon, Jul 5, 2010 at 1:28 PM, Ronan Lamy <ronan.l...@gmail.com> wrote:
> Le lundi 05 juillet 2010 à 19:17 +0200, Øyvind Jensen a écrit :
>> > Yep.  I just got back from SciPy and I talked a lot with one of the
>> > devs of theano:
>> >
>> > http://deeplearning.net/software/theano/
>>
>> Thanks for bringing theano to my attention!
>
> Yes, that's very interesting!
>
>> > Are you familiar with this?  It looks quite impressive and does code
>> > generation for both CPUs and GPUs.  They actually have a lightweight
>> > symbolic engine and they were interested in seeing if they could use
>> > sympy instead.  It is definitely worst looking at.  I can contact them
>> > if you want to explore this further.
>> >
>>
>> Theano looks very nice, especially the GPU functionality is cool.  I
>> will play with it and see what I can learn from it.  However, they use
>> expression trees that are different from ours, so I fear it will be
>> nontrivial to use it directly with sympy.  Whereas our tree consist of
>> operations and operands, they have Apply-nodes that contain an operation
>> instance and the operands.  I wonder how much their optimization
>> framework depend on the form of the theano expression tree.
>
> Theirs is the obviously correct way of dealing with mathematical
> functions in a CAS, but sympy will get there in the end (that's issue
> 1688). In the mean time, there's an obvious bijection between f(x, y, z)
> and Apply(f, (x, y, z)) so this doesn't seem to be a difficult problem.
> On the other hand, theano uses types and variables, which sympy doesn't
> have: I think this is a more significant barrier to translating between
> the two.

I talked to James, one of the Theano devs, and I think it wouldn't be
too difficult to use Sympy for the top level stuff, because of the
bijection between the two data structures.  In terms of the types, my
idea was to use the assumptions system.

> In any case, merging the two code bases seems a useful and achievable
> medium-term goal.

Definitely.

Cheers,

Brian

> Ronan
>
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-- 
Brian E. Granger, Ph.D.
Assistant Professor of Physics
Cal Poly State University, San Luis Obispo
bgran...@calpoly.edu
elliso...@gmail.com

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