Thank you for the insight! Looks pretty useful, I guess it's time to
read the paper and write some code...
On 11/11/2015 11:39 AM, Ben Goertzel wrote:
Also, tensorflow has a distributed variation, which allegedly will be
OSS'd at some point ... and has a more flexible OO architecture, i.e.
Theano's architecture is all about multi-D arrays, whereas
tensorflow's object structure would in principle let it be used more
broadly (with a lot of work)
I sent this to the OpenCog list...
****
some quasi-random thoughts on tensorflow, after reading the paper and
API (but not actually using the code yet) ...
An update on the release of the distributed version of tensorflow is here:
https://github.com/tensorflow/tensorflow/issues/23#issuecomment-155608002
Regarding the relationship btw tensorflow and OpenCog -- most simply,
an OpenCog Atom is a persistent store of information, whereas a
TensorFlow graph is a collection of Operations (each translating input
into output).
On the face of it, TensorFlow is best for procedural knowledge,
whereas Atomspace is best for declarative knowledge.... As it looks
to me, the "declarative knowledge" in a TensorFlow graph is pretty
much contained in the numerical tensors that the Operations pass around...
In terms of OpenCog integration, the most straightforward thing would
be to implement
-- TensorNode ... with subtypes as appropriate
-- GroundedSchemaNodes that wrap up TensorFlow "Operations"
This would allow us to basically embed TensorFlow graphs inside the
Atomspace...
Deep learning operations like convolution are represented as opaque
operations in tensorflow, and would also be opaque operations (wrapped
inside GSNs) in OpenCog....
The purported advantage over Theano would be that TensorFlow is
supposed to be faster (we'll test), whereas Theano has an elegant
interface (but not really more elegant than TensorFlow) but is slower
than Caffe ...
Wrapping Operations inside GSN would add a level of
indirection/inefficiency, but if the Operations are expensive things
like running convolutions on images or multiplying big matrices, this
doesn't matter much...
Now if one considered tensorflow as a general dataflow approach rather
than just a deep learning architecture, one could potentially use it
for more than just perception processing
For instance, the URE (Universal Rule Engine) in OpenCog (which
underlies our PLN inference algorithms, for example) consists of a set
of operations for mapping inputs into outputs; each of these
operations could be implemented as a TensorFlow Operation, if one
wanted to....
But to do this, TensorFlow would need to be generalized so that
"sub-hypergraph" was a Tensor type, basically. Right now TensorFlow
is for setting up operations that pass around numerical tensors. But
we would need operations that pass around complex sub-hypergraphs...
Whether going this route is a good idea, I don't know.... It's more
clear that tensorflow will be useful for wrapping up deep learning
perception algorithms...
Interesting times ;)
\****
***
On Wed, Nov 11, 2015 at 12:36 PM, Ben Goertzel <[email protected]
<mailto:[email protected]>> wrote:
Interface-wise, they're both elegant, it seems...
My guess would be that tensorflow will do faster learning, but
gotta test to find out...
On Wed, Nov 11, 2015 at 12:34 PM, J Rao <[email protected]
<mailto:[email protected]>> wrote:
Website: http://tensorflow.org/
Source code for single machine version:
https://github.com/tensorflow/tensorflow
I would be interested to know how this compares to Theano.
On 11/11/2015 2:55 AM, Peter Christiansen wrote:
http://www.wired.com/2015/11/googles-open-source-ai-tensorflow-signals-fast-changing-hardware-world/
Sent from my iPad
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