Mercurial > pylearn
annotate doc/v2_planning/formulas.txt @ 1104:5e6d7d9e803a
a comment on the GPU issue for datasets
author | Razvan Pascanu <r.pascanu@gmail.com> |
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date | Mon, 13 Sep 2010 20:21:23 -0400 |
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children | 42ddbefd1e03 |
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1 Math formulas |
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2 ============= |
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3 |
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4 Participants |
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5 ------------ |
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6 - Fred* |
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7 - Razvan |
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8 - Aaron |
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9 - Olivier B. |
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10 - Nicolas |
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11 |
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12 TODO |
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13 ---- |
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14 * define a list of search tag to start with |
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15 * propose an interface(many inputs, outputs, doc style, hierrache, to search, html output?) |
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16 * find existing repositories with files for formulas. |
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17 * move existing formulas to pylearn as examples and add other basics ones. |
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18 ** theano.tensor.nnet will probably be copied to pylearn.formulas.nnet and depricated. |
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19 |
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20 Why we need formulas |
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21 -------------------- |
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22 |
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23 Their is a few reasons why having a library of mathematical formula for theano is a good reason: |
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24 |
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25 * Some formula have some special thing needed for the gpu. |
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26 * Sometimes we need to cast to floatX... |
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27 * Some formula have numerical stability problem. |
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28 * Some formula gradiant have numerical stability problem. (Happen more frequently then the previous ones) |
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29 * If theano don't always do some stability optimization, we could do it manually in the formulas |
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30 * Some formula as complex to implement and take many try to do correctly. |
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31 |
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32 Having a library help in that we solve those problem only once. |
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33 |
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34 Formulas definition |
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35 ------------------- |
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36 |
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37 We define formulas as something that don't have a state. They are implemented as python function |
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38 that take theano variable as input and output theano variable. If you want state, look at what the |
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39 learner commity will do. |
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40 |
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41 Formulas doc must have |
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42 ---------------------- |
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43 |
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44 * A latex mathematical description of the formulas(for picture representation in generated documentation) |
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45 * Tags(for searching): |
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46 * a list of lower lovel fct used |
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47 * category(name of the submodule itself) |
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48 * Tell if we did some work to make it more numerical stable. Do theano do the optimization needed? |
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49 * Tell if the grad is numericaly stable? Do theano do the optimization needed? |
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50 * Tell if work on gpu/not/unknow |
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51 * Tell alternate name |
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52 * Tell the domaine, range of the input/output(range should use the english notation of including or excluding) |
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53 |
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54 List of existing repos |
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55 ---------------------- |
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56 |
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57 Olivier B. ? |
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58 Xavier G.: git@github.com:glorotxa/DeepANN.git, see file deepANN/{Activations.py(to nnet),Noise.py,Reconstruction_cost.py(to costs),Regularization.py(to regularization} |
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59 |
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60 Proposed hierarchy |
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61 ------------------ |
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62 |
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63 Here is the proposed hierarchy for formulas |
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64 |
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65 pylearn.formulas.costs: generic / common cost functions, e.g. various cross-entropies, squared error, |
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66 abs. error, various sparsity penalties (L1, Student) |
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67 |
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68 pylearn.formulas.regularization: formulas for regularization |
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69 |
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70 pylearn.formulas.linear: formulas for linear classifier, linear regression, factor analysis, PCA |
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71 |
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72 pylearn.formulas.nnet: formulas for building layers of various kinds, various activation functions, |
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73 layers which could be plugged with various costs & penalties, and stacked |
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74 |
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75 pylearn.formulas.ae: formulas for auto-encoders and denoising auto-encoder variants |
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76 |
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77 pylearn.formulas.noise: formulas for corruption processes |
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78 |
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79 pylearn.formulas.rbm: energies, free energies, conditional distributions, Gibbs sampling |
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80 |
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81 pylearn.formulas.trees: formulas for decision trees |
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82 |
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83 pylearn.formulas.boosting: formulas for boosting variants |
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84 |
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85 pylearn.formulas.maths for other math formulas |
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86 |
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87 pylearn.formulas.scipy.stats: example to implement the same interface as existing lib |
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88 |
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89 etc. |