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annotate doc/v2_planning/neural_net.txt @ 1212:478bb1f8215c

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plugin_JB - added SPAWN control element and demo program
author James Bergstra <bergstrj@iro.umontreal.ca>
date Wed, 22 Sep 2010 01:37:55 -0400
parents 0e12ea6ba661
children
rev   line source
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1 Neural Net committee
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2 ====================
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3
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4 Members:
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5 - Razvan Pascanu
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6 - James Bergstra
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7 - Xavier Glorot
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8 - Guillaume Desjardins
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9
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10 (Add your name here if you want)
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12
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13 Objective ( Razvan)
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14 -------------------
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16 Come up with a description of how to write learners ( how to combine
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17 optimizer, structure, error measure, how to talk to datasets, tasks ( if there
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18 is anything like a dataset object in your view) and so on).
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19 o The way I see it personaly, we should pick "random" interfaces for any component
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20 for which there is no one yet, or change the interface to answer our needs.
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21 If our description of how these things get together. I would say come up with
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22 pseudo-code for some tasks ( that vary as much as possible) + text describing
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23 all the missing details.
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24
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25 Link with PLearn
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26 ----------------
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27
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28 OD: This is basically what the OnlineLearningModule framework was doing in
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29 PLearn (c.f. PLearn/plearn_learners/online). Basically, the idea was that a
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30 module was a "box" with so-called "ports" representing inputs / outputs. So
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31 for instance you could think of an RBM as a module with "visible" and
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32 "hidden" ports, but also "log_p_visible", "energy", etc. You would use
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33 such a module by calling an fprop method where you would give some values for
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34 input ports (not necessarily all of them), and would ask some output ports
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35 (not necessarily all of them). Some ports could be used either as inputs or
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36 outputs (e.g. the "hidden" port could be used as input to compute
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37 P(visible|hidden), or as output to compute E[hidden|visible]). Optimization
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38 was achieved independently within each module, who would be provided a
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39 gradient w.r.t. some of its ports (considered outputs), and asked to update
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40 its internal parameters and compute accodingly a gradient w.r.t. to its input
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41 ports.
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42
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43 Although it worked, it had some issues:
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44 - The biggest problem was that as you added more ports and options to do
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45 different computations, the fprop method would grow and grow and become very
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46 difficult to write properly to handle all possible combinations of inputs /
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47 outputs, while remaining efficient. Hopefully this is where Theano can be a
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48 big help (note: a "lazy if" could be required to handle situations where the
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49 same port is computed in very different ways depending on what is given as
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50 input).
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51 - We had to introduce a notion of 'states' that were ports whose values had to
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52 be computed, even if they were not asked by the user. The reason was that
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53 those values were required to perform the optimization (bprop phase) without
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54 re-doing some computations. Hopefully again Theano could take care of it
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55 (those states were potentially confusing to the user, who had to manipulate
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56 them without necessarily understanding what they were for).
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57
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58 Besides that, there are at least 3 design decisions that could be done
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59 differently:
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60 - How to connect those modules together: in those OnlineLearningModules, each
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61 module had no idea of who it was connected to. A higher level entity was
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62 responsible for grabbing the output of some module and forwarding it to its
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63 target destination. This is to be contrasted with the design of PLearn
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64 Variables, where each variable was explicitely constructed given its input
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65 variables (Theano-like), and would directly ask them to provide data. I am not
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66 sure what are the pros vs. cons of these two approaches.
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67 - How to perform optimization. The OnlineLearningModule way is nice to plug
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68 together pieces that are optimized very differently, because each module is
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69 responsible for its own optimizatin. However, this also means it is difficult
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70 to easily try different global optimizers (again, this is in contrast with
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71 PLearn variables).
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72 - One must think about the issue of RNG for stochastic modules. Here we had
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73 one single RNG per module. This makes it diffiult to easily try different
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74 seeds for everyone. On another hand, sharing a single RNG is not neceassarily
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75 a good idea because of potentially unwanted side-effects.
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76