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annotate doc/v2_planning/learner.txt @ 1002:f82093bf4405

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adding learner.txt and dataset.txt in v2_planning/
author Yoshua Bengio <bengioy@iro.umontreal.ca>
date Wed, 01 Sep 2010 16:43:24 -0400
parents
children 38f799f8b6cd
rev   line source
1002
f82093bf4405 adding learner.txt and dataset.txt in v2_planning/
Yoshua Bengio <bengioy@iro.umontreal.ca>
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f82093bf4405 adding learner.txt and dataset.txt in v2_planning/
Yoshua Bengio <bengioy@iro.umontreal.ca>
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2 Discussion of Function Specification for Learner Types
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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3 ======================================================
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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5 In its most abstract form, a learner is an object with the
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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6 following semantics:
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7
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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8 * A learner has named hyper-parameters that control how it learns (these can be viewed
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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9 as options of the constructor, or might be set directly by a user)
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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11 * A learner also has an internal state that depends on what it has learned.
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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13 * A learner reads and produces data, so the definition of learner is
f82093bf4405 adding learner.txt and dataset.txt in v2_planning/
Yoshua Bengio <bengioy@iro.umontreal.ca>
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14 intimately linked to the definition of dataset (and task).
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16 * A learner has one or more 'train' or 'adapt' functions by which
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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17 it is given a sample of data (typically either the whole training set, or
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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18 a mini-batch, which contains as a special case a single 'example'). Learners
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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19 interface with datasets in order to obtain data. These functions cause the
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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20 learner to change its internal state and take advantage to some extent
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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21 of the data provided. The 'train' function should take charge of
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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22 completely exploiting the dataset, as specified per the hyper-parameters,
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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23 so that it would typically be called only once. An 'adapt' function
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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24 is meant for learners that can operate in an 'online' setting where
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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25 data continually arrive and the control loop (when to stop) is to
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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26 be managed outside of it. For most intents and purposes, the
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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27 'train' function could also handle the 'online' case by providing
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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28 the controlled iterations over the dataset (which would then be
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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29 seen as a stream of examples).
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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30 * learner.train(dataset)
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31 * learner.adapt(data)
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32
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33 * Different types of learners can then exploit their internal state
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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34 in order to perform various computations after training is completed,
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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35 or in the middle of training, e.g.,
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36
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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37 * y=learner.predict(x)
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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38 for learners that see (x,y) pairs during training and predict y given x,
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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39 or for learners that see only x's and learn a transformation of it (i.e. feature extraction).
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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40 Here and below, x and y are tensor-like objects whose first index iterates
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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41 over particular examples in a batch or minibatch of examples.
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42
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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43 * p=learner.probability(examples)
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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44 p=learner.log_probability(examples)
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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45 for learners that can estimate probability density or probability functions,
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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46 note that example could be a pair (x,y) for learners that expect each example
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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47 to represent such a pair. The second form is provided in case the example
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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48 is high-dimensional and computations in the log-domain are numerically preferable.
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49 The first dimension of examples or of x and y is an index over a minibatch or a dataset.
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50
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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51 * p=learner.free_energy(x)
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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52 for learners that can estimate a log unnormalized probability; the output has the same length as the input.
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53
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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54 * c=learner.costs(examples)
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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55 returns a matrix of costs (one row per example, i.e., again the output has the same length
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56 as the input), the first column of which represents the cost whose expectation
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57 we wish to minimize over new samples from the unknown underlying data distribution.
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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60 Some learners may be able to handle x's and y's that contain missing values.
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61
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62 * For convenience, some of these operations could be bundled, e.g.
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63
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64 * [prediction,costs] = learner.predict_and_adapt((x,y))
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65
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66 * Some learners could include in their internal state not only what they
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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67 have learned but some information about recently seen examples that conditions
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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68 the expected distribution of upcoming examples. In that case, they might
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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69 be used, e.g. in an online setting as follows:
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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70 for (x,y) in data_stream:
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71 [prediction,costs]=learner.predict((x,y))
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72 accumulate_statistics(prediction,costs)
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73
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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74 * In some cases, each example is itself a (possibly variable-size) sequence
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75 or other variable-size object (e.g. an image, or a video)
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