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annotate doc/v2_planning/architecture_NB.txt @ 1396:310e22d7e44b

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new file about datalearn in pytables.
author Frederic Bastien <nouiz@nouiz.org>
date Mon, 10 Jan 2011 14:55:39 -0500
parents d9f93923765f
children
rev   line source
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1
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2 Here is how I think how the Pylearn library could be organized simply and
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3 efficiently.
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5 We said the main goals for a library are:
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6 1. Easily connect new learners with new datasets
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7 2. Easily build new formula-based learners
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8 3. Have "hyper" learning facilities such as hyper optimization, model selection,
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9 experiments design, etc.
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10
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11 We should focus on those features. They are 80% of our use cases and the other
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12 20% will always comprise new developments which should not be predictable.
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13 Focusing on the 80% is relatively simple and implementation could be done in a
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14 matter of weeks.
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16 Let's say we have a DBN learner and we want to plan ahead for possible
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17 modifications and decompose it in small "usable" chunks. When a new student
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18 wants to modify the learning procedure, we envisioned either:
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20 1. A pre-made hyper-learning graph of a DBN that he can "conveniently" adapt to
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21 his need
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23 2. A hooks or messages system that allows custom actions at various set points
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24 in the file (pre-defined but can also be "easily" added)
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26 However, consider that it is CODE that he wants to modify. Intricate details of
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27 new learning algorithms possibly include modifying ANY parts of the code, adding
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28 loops, changing algorithms, etc. There are two well time-tested methods for
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29 dealing with this:
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30
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31 1. Change the code. Add a new parameter that optionnally does the job. OR, if
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32 changes are substantial:
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33
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34 2. Copy the DBN code, modify and save your forked version of it. Each learner
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35 or significantly new experiment should have its own file. We should not try to
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36 generalize what is not generalizable. In other words, small loops and
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37 mini-algorithms inside learners may not be worthy of being encapsulated.
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38
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39 Based on the above three main goals, two objects need well-defined
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40 encapsulation: datasets and learners.
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41 (Visualization should be included in the learners. The hard part is not the
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42 print or pylab.plot statements, it's the statistics gathering.)
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43 Here is the basic interface we talked about, and how we would work out some
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44 special cases.
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45
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46 Datasets: fetch mini-batches as numpy arrays in the usual format.
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47 Learners: "standalone" interface: a train function that includes optional
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48 visualization, "advanced" interface for more control: adapt and predict
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49 functions.
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50
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51 - K-fold cross-validation? Write a generic "hyper"-learner that does this for
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52 arbitrary learners via their "advanced" interface. ... and if multiple
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53 similar datasets can be learned more efficiently for a particular learner?
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54 Include an option inside the learner to cross-validate.
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55 - Optimizers? Have a generic "Theano formula"-based learner for each optimizer
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56 you want (SGD, momentum, delta-bar-delta, etc.). Of course combine similar
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57 optimizers with compatible parameters. A set of helper functions should also
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58 be provided for building the actual Theano formula.
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59 - Early stopping? This has to be included inside the train function for each
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60 learner where applicable (probably only the formula-based generic ones anyway)
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61 - Generic hyper parameters optimizer? Write a generic hyper-learner that does
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62 this. And a simple "grid" one. Require supported learners to provide the
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63 list/distribution of their applicable hyper-parameters which will be supplied
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64 to their constructor at the hyper-learner discretion.
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65 - Visualization? Each learner defines what can be visualized and how.
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66 - Early stopping curves? The early stopping learner optionally shows this.
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67 - Complex hyper-parameters 2D-subsets curves? Add this as an option in the
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68 hyper-parameter optimizer.
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69 - Want a dataset that sits in RAM? Write a custom class that still outputs numpy
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70 arrays in usual format.
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71 - Want an infinite auto-generated dataset? Write a custom class that generates
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72 and outputs numpy arrays on the fly.
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73 - Dealing with time series with multi-dimensional input? This requires
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74 cooperation between learner and dataset. Use 3-dimensional numpy arrays. Write
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75 dataset that outputs these and learner that understands it. OR write dataset
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76 that converts to one-dimensional input and use any learner.
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77 - Sophisticated performance evaluation function? This evaluation function should
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78 be suppliable to every learner.
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79 - Have a multi-steps complex learning procedure using gradient-based learning in
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80 some steps? Write a "hyper"-learner that successively calls formula-based
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81 learners and directly accesses the weights member variables for
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82 initializations of subsequent learners.
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83 - Want to combine early stopping curves for many hyper-parameter values? Modify
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84 the optimization-based learners to save the early stopping curve as a member
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85 variable and use this in the hyper-parameter learner visualization routine.
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86 - Curriculum learning? This requires cooperation between learner and dataset.
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87 Require supported datasets to understand a function call "set_experience" or
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88 anything you decide.
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89 - Filters visualization on selected best hyper-parameters set? Include code in
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90 the formula-based learners to look for the weights applied on input and
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91 activate visualization in hyper-learner only for the chosen hyper-parameters.
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92
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93
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94 >> to demonstrate architecture designs on kfold dbn training - how would you
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95 >> propose that the library help to do that?
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96
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97 By providing a K-fold cross-validation generic "hyper"-learner that controls an
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98 arbitrary learner via their advanced interface (train, adapt) and their exposed
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99 hyper-parameters which would be fixed on the behalf of the user.
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100
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101 JB asks:
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102 What interface should the learner expose in order for the hyper-parameter to
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103 be generic (work for many/most/all learners)
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104
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105 NB: In the case of a K-fold hyper-learner, I would expect the user to
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106 completely specify the hyper-parameters and the hyper-learner could just
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107 blindly pass them along to the sub-learner. For more complex hyper-learners
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108 like hyper-optimizer or hyper-grid we would require supported sub-learners
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109 to define a function "get_hyperparam" that returns a
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110 dict(name1: [default, range], name2: ...). These hyper-parameters are
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111 supplied to the learner constructor.
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112
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113 This K-fold learner, since it is generic, would work by launching multiple
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114 experiments and would support doing so in parallel inside of a job (python MPI
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115 ?) or by launching on the cluster multiple owned scripts that write results on
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116 disk in the way specified by the K-fold learner.
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117
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118 JB asks:
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119 This is not technically possible if the worker nodes and the master node do
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120 not all share a filesystem. There is a soft requirement that the library
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121 support this so that we can do job control from DIRO without messing around
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122 with colosse, mammouth, condor, angel, etc. all separately.
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123
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124 NB: The hyper-learner would have to support launching jobs on remote servers
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125 via ssh. Common functionality for this could of course be reused between
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126 different hyper-learners.
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127
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128 JB asks:
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129 The format used to communicate results from 'learner' jobs with the kfold loop
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130 and with the stats collectors, and the experiment visualization code is not
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131 obvious - any ideas how to handle this?
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132
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133 NB: The DBN is responsible for saving/viewing results inside a DBN experiment.
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134 The hyper-learner controls DBN execution (even in a script on a remote
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135 machine) and collects evaluation measurements after its dbn.predict call.
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136 For K-fold it would typically just save the evaluation distribution and
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137 average in whatever way (internal convention) that can be transfered over ssh.
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138 The K-fold hyper-learner would only expose its train interface (no adapt,
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139 predict) since it cannot always be decomposed in many steps depending on the
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140 sublearner.
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141
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142 The library would also have a DBN learner with flexible hyper-parameters that
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143 control its detailed architecture.
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144
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145 JB asks:
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146 What kind of building blocks should make this possible - how much flexibility
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147 and what kinds are permitted?
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148
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149 NB: Things like number of layers, hidden units and any optional parameters
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150 that affect initialization or training (i.e. AE or RBM variant) that the DBN
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151 developer can think of. The final user would have to specify those
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152 hyper-parameters to the K-fold learner anyway.
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153
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154 The interface of the provided dataset would have to conform to possible inputs
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155 that the DBN module understands, i.e. by
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156 default 2D numpy arrays. If more complex dataset needs arise, either subclass a
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157 converter for the known format or add this functionality to the DBN learner
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158 directly. Details of the DBN learner core would resemble the tutorials, would
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159 typically be included in one straigthforward code file and could potentially use
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160 "Theano-formula"-based learners as intermediate steps.
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161
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162 JB asks:
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163
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164 One of the troubles with straightforward code is that it is neither easy to
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165 stop and start (as in long-running jobs) nor control via a hyper-parameter
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166 optimizer. So I don't think code in the style of the curren tutorials is very
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167 useful in the library.
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168
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169 NB: I could see how we could require all learners to define stop and restart
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170 methods so they would be responsible to save and restore themselves.
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171 A hyper-learner's stop and restart method would in addition call recursively
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172 its subleaners' stop and restart methods.
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173