annotate doc/v2_planning/main_plan.txt @ 1245:808e38dce8d6

Replied to YB's comment on JB's system
author Olivier Delalleau <delallea@iro>
date Thu, 23 Sep 2010 12:18:39 -0400
parents 0e12ea6ba661
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James Bergstra <bergstrj@iro.umontreal.ca>
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1
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James Bergstra <bergstrj@iro.umontreal.ca>
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2 Motivation
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James Bergstra <bergstrj@iro.umontreal.ca>
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3 ==========
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James Bergstra <bergstrj@iro.umontreal.ca>
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4
1007
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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5 Yoshua (points discussed Thursday Sept 2, 2010 at LISA tea-talk)
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Frederic Bastien <nouiz@nouiz.org>
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6 ----------------------------------------------------------------
1007
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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7
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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8 ****** Why we need to get better organized in our code-writing ******
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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9
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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10 - current state of affairs on top of Theano is anarchic and does not lend itself to easy code re-use
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 1001
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11 - the lab is growing and will continue to grow significantly, and more people outside the lab are using Theano
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 1001
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12 - we have new industrial partners and funding sources that demand deliverables, and more/better collectively organized efforts
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 1001
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13
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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14 *** Who can take advantage of this ***
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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15
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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16 - us, directly, taking advantage of the different advances made by different researchers in the lab to yield better models
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 1001
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17 - us, easier to compare different models and different datasets with different metrics on different computing platforms available to us
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 1001
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18 - future us, new students, able to quickly move into 'production' mode without having to reinvent the wheel
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 1001
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19 - students in the two ML classes, able to play with the library to explore new ML variants
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 1001
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20 - other ML researchers in academia, able to play with our algorithms, try new variants, cite our papers
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 1001
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21 - non-ML users in or out of academia, and our user-partners
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22
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23
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24 *** Move with care ***
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25
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26 - Write down use-cases, examples for each type of module, do not try to be TOO general
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27 - Want to keep ease of exploring and flexibility, not create a prison
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28 - Too many constraints can lead to paralysis, especially in C++ object-oriented model
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29 - Too few guidelines lead to code components that are not interchangeable
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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30 - Poor code practice leads to buggy, spaguetti code
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31
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32 *** What ***
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33
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34 - define standards
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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35 - write-up a few instances of each basic type (dataset, learner, optimizer, hyper-parameter exploration boilerplate, etc.) enough to implement some of the basic algorithms we use often (e.g. like those in the tutorials)
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36 - let the library grow according to our needs
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37 - keep tight reins on it to control quality
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38
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39 *** Content and Form ***
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40
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41 We need to establish guidelines and conventions for
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42
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43 * Content: what are the re-usable components? define conventions or API for each, make sure they fit with each other
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44 * Form: social engineering, coding practices and conventions, code review, incentives
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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45
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46 Yoshua:
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47 -------
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48
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James Bergstra <bergstrj@iro.umontreal.ca>
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49 We are missing a *Theano Machine Learning library*.
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50
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51 The deep learning tutorials do a good job but they lack the following features, which I would like to see in a ML library:
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52
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53 - a well-organized collection of Theano symbolic expressions (formulas) for handling most of
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James Bergstra <bergstrj@iro.umontreal.ca>
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54 what is needed either in implementing existing well-known ML and deep learning algorithms or
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James Bergstra <bergstrj@iro.umontreal.ca>
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55 for creating new variants (without having to start from scratch each time), that is the
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56 mathematical core,
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57
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58 - a well-organized collection of python modules to help with the following:
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James Bergstra <bergstrj@iro.umontreal.ca>
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59 - several data-access models that wrap around learning algorithms for interfacing with various types of data (static vectors, images, sound, video, generic time-series, etc.)
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60 - generic utility code for optimization
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61 - stochastic gradient descent variants
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62 - early stopping variants
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63 - interfacing to generic 2nd order optimization methods
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64 - 2nd order methods tailored to work on minibatches
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65 - optimizers for sparse coefficients / parameters
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66 - generic code for model selection and hyper-parameter optimization (including the use and coordination of multiple jobs running on different machines, e.g. using jobman)
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67 - generic code for performance estimation and experimental statistics
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68 - visualization tools (using existing python libraries) and examples for all of the above
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69 - learning algorithm conventions and meta-learning algorithms (bagging, boosting, mixtures of experts, etc.) which use them
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70
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71 [Note that many of us already use some instance of all the above, but each one tends to reinvent the wheel and newbies don't benefit from a knowledge base.]
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72
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73 - a well-documented set of python scripts using the above library to show how to run the most
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74 common ML algorithms (possibly with examples showing how to run multiple experiments with
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75 many different models and collect statistical comparative results). This is particularly
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76 important for pure users to adopt Theano in the ML application work.
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77
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78 Ideally, there would be one person in charge of this project, making sure a coherent and
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79 easy-to-read design is developed, along with many helping hands (to implement the various
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80 helper modules, formulae, and learning algorithms).
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81
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82
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83 James:
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84 -------
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85
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James Bergstra <bergstrj@iro.umontreal.ca>
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86 I am interested in the design and implementation of the "well-organized collection of Theano
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87 symbolic expressions..."
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88
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89 I would like to explore algorithms for hyper-parameter optimization, following up on some
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90 "high-throughput" work. I'm most interested in the "generic code for model selection and
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91 hyper-parameter optimization..." and "generic code for performance estimation...".
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92
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93 I have some experiences with the data-access requirements, and some lessons I'd like to share
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94 on that, but no time to work on that aspect of things.
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95
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96 I will continue to contribute to the "well-documented set of python scripts using the above to
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97 showcase common ML algorithms...". I have an Olshausen&Field-style sparse coding script that
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98 could be polished up. I am also implementing the mcRBM and I'll be able to add that when it's
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99 done.
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100
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101
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102
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103 Suggestions for how to tackle various desiderata
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104 ================================================
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105
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106
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107 Theano Symbolic Expressions for ML
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108 ----------------------------------
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109
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110 We could make this a submodule of pylearn: ``pylearn.nnet``.
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111
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112 Yoshua: I would use a different name, e.g., "pylearn.formulas" to emphasize that it is not just
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113 about neural nets, and that this is a collection of formulas (expressions), rather than
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114 completely self-contained classes for learners. We could have a "nnet.py" file for
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115 neural nets, though.
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116
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117 There are a number of ideas floating around for how to handle classes /
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118 modules (LeDeepNet, pylearn.shared.layers, pynnet, DeepAnn) so lets implement as much
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119 math as possible in global functions with no classes. There are no models in
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120 the wish list that require than a few vectors and matrices to parametrize.
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121 Global functions are more reusable than classes.
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122
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123
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124 Data access
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125 -----------
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126
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127 A general interface to datasets from the perspective of an experiment driver
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128 (e.g. kfold) is to see them as a function that maps index (typically integer)
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129 to example (whose type and nature depends on the dataset, it could for
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130 instance be an (image, label) pair). This interface permits iterating over
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131 the dataset, shuffling the dataset, and splitting it into folds. For
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132 efficiency, it is nice if the dataset interface supports looking up several
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133 index values at once, because looking up many examples at once can sometimes
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134 be faster than looking each one up in turn. In particular, looking up
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135 a consecutive block of indices, or a slice, should be well supported.
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136
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137 Some datasets may not support random access (e.g. a random number stream) and
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138 that's fine if an exception is raised. The user will see a NotImplementedError
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139 or similar, and try something else. We might want to have a way to test
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140 that a dataset is random-access or not without having to load an example.
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141
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142
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143 A more intuitive interface for many datasets (or subsets) is to load them as
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144 matrices or lists of examples. This format is more convenient to work with at
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145 an ipython shell, for example. It is not good to provide only the "dataset
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146 as a function" view of a dataset. Even if a dataset is very large, it is nice
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147 to have a standard way to get some representative examples in a convenient
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148 structure, to be able to play with them in ipython.
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149
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150
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151 Another thing to consider related to datasets is that there are a number of
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152 other efforts to have standard ML datasets, and we should be aware of them,
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153 and compatible with them when it's easy:
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154
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155 - mldata.org (they have a file format, not sure how many use it)
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156 - weka (ARFF file format)
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157 - scikits.learn
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158 - hdf5 / pytables
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159
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160
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161 pylearn.datasets uses a DATA_ROOT environment variable to locate a filesystem
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162 folder that is assumed to have a standard form across different installations.
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163 That's where the data files are. The correct format of this folder is currently
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164 defined implicitly by the contents of /data/lisa/data at DIRO, but it would be
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165 better to document in pylearn what the contents of this folder should be as
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166 much as possible. It should be possible to rebuild this tree from information
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167 found in pylearn.
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168
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169 Yoshua (about ideas proposed by Pascal Vincent a while ago):
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170
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171 - we may want to distinguish between datasets and tasks: a task defines
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172 not just the data but also things like what is the input and what is the
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173 target (for supervised learning), and *importantly* a set of performance metrics
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174 that make sense for this task (e.g. those used by papers solving a particular
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175 task, or reported for a particular benchmark)
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176
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177 - we should discuss about a few "standards" that datasets and tasks may comply to, such as
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178 - "input" and "target" fields inside each example, for supervised or semi-supervised learning tasks
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179 (with a convention for the semi-supervised case when only the input or only the target is observed)
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180 - "input" for unsupervised learning
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181 - conventions for missing-valued components inside input or target
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182 - how examples that are sequences are treated (e.g. the input or the target is a sequence)
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183 - how time-stamps are specified when appropriate (e.g., the sequences are asynchronous)
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184 - how error metrics are specified
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185 * example-level statistics (e.g. classification error)
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186 * dataset-level statistics (e.g. ROC curve, mean and standard error of error)
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187
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188
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189 Model Selection & Hyper-Parameter Optimization
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190 ----------------------------------------------
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191
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192 Driving a distributed computing job for a long time to optimize
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193 hyper-parameters using one or more clusters is the goal here.
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194 Although there might be some library-type code to write here, I think of this
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195 more as an application template. The user would use python code to describe
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196 the experiment to run and the hyper-parameter space to search. Then this
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197 application-driver would take control of scheduling jobs and running them on
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198 various computers... I'm imagining a potentially ugly brute of a hack that's
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199 not necessarily something we will want to expose at a low-level for reuse.
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200
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201 Yoshua: We want both the library-defined driver that takes instructions about how to generate
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202 new hyper-parameter combinations (e.g. implicitly providing a prior distribution from which
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203 to sample them), and examples showing how to use it in typical cases.
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204 Note that sometimes we just want to find the best configuration of hyper-parameters,
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205 but sometimes we want to do more subtle analysis. Often a combination of both.
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206 In this respect it could be useful for the user to define hyper-parameters over
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207 which scientific questions are sought (e.g. depth of an architecture) vs
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208 hyper-parameters that we would like to marginalize/maximize over (e.g. learning rate).
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209 This can influence both the sampling of configurations (we want to make sure that all
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210 combinations of question-driving hyper-parameters are covered) and the analysis
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211 of results (we may be willing to estimate ANOVAs or averaging or quantiles over
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212 the non-question-driving hyper-parameters).
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213
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214 Python scripts for common ML algorithms
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215 ---------------------------------------
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216
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217 The script aspect of this feature request makes me think that what would be
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218 good here is more tutorial-type scripts. And the existing tutorials could
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219 potentially be rewritten to use some of the pylearn.nnet expressions. More
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220 tutorials / demos would be great.
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221
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222 Yoshua: agreed that we could write them as tutorials, but note how the
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223 spirit would be different from the current deep learning tutorials: we would
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224 not mind using library code as much as possible instead of trying to flatten
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225 out everything in the interest of pedagogical simplicity. Instead, these
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226 tutorials should be meant to illustrate not the algorithms but *how to take
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227 advantage of the library*. They could also be used as *BLACK BOX* implementations
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228 by people who don't want to dig lower and just want to run experiments.
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229
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230 Functional Specifications
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231 =========================
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232
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233 TODO:
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234 Put these into different text files so that this one does not become a monster.
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235 For each thing with a functional spec (e.g. datasets library, optimization library) make a
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236 separate file.
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237
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238 Indexing Convention
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239 ===================
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240
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241 Something to decide on - Fortran-style or C-style indexing. Although we have
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242 often used c-style indexing in the past (for efficiency in c!) this is no
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243 longer an issue with numpy because the physical layout is independent of the
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244 indexing order. The fact remains that Fortran-style indexing follows linear
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245 algebra conventions, while c-style indexing does not. If a global function
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246 includes a lot of math derivations, it would be *really* nice if the code used
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247 the same convention for the orientation of matrices, and endlessly annoying to
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248 have to be always transposing everything.
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249