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annotate doc/v2_planning/learner.txt @ 1052:84f62533e7a8
v2planning learner - reply to comments
| author | James Bergstra <bergstrj@iro.umontreal.ca> |
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| date | Wed, 08 Sep 2010 15:43:32 -0400 |
| parents | f1732269bce8 |
| children | 390166ace9e5 |
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2 Comittee: AB, PL, GM, IG, RP, NB, PV |
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3 Leader: ? |
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5 Discussion of Function Specification for Learner Types |
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6 ====================================================== |
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8 In its most abstract form, a learner is an object with the |
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9 following semantics: |
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11 * A learner has named hyper-parameters that control how it learns (these can be viewed |
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12 as options of the constructor, or might be set directly by a user) |
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13 |
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14 * A learner also has an internal state that depends on what it has learned. |
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15 |
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16 * A learner reads and produces data, so the definition of learner is |
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17 intimately linked to the definition of dataset (and task). |
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18 |
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19 * A learner has one or more 'train' or 'adapt' functions by which |
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20 it is given a sample of data (typically either the whole training set, or |
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21 a mini-batch, which contains as a special case a single 'example'). Learners |
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22 interface with datasets in order to obtain data. These functions cause the |
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23 learner to change its internal state and take advantage to some extent |
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24 of the data provided. The 'train' function should take charge of |
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25 completely exploiting the dataset, as specified per the hyper-parameters, |
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26 so that it would typically be called only once. An 'adapt' function |
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27 is meant for learners that can operate in an 'online' setting where |
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28 data continually arrive and the control loop (when to stop) is to |
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29 be managed outside of it. For most intents and purposes, the |
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30 'train' function could also handle the 'online' case by providing |
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31 the controlled iterations over the dataset (which would then be |
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32 seen as a stream of examples). |
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33 * learner.train(dataset) |
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34 * learner.adapt(data) |
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35 |
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36 * Different types of learners can then exploit their internal state |
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37 in order to perform various computations after training is completed, |
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38 or in the middle of training, e.g., |
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39 |
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40 * y=learner.predict(x) |
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41 for learners that see (x,y) pairs during training and predict y given x, |
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42 or for learners that see only x's and learn a transformation of it (i.e. feature extraction). |
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43 Here and below, x and y are tensor-like objects whose first index iterates |
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44 over particular examples in a batch or minibatch of examples. |
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45 |
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46 * p=learner.probability(examples) |
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47 p=learner.log_probability(examples) |
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48 for learners that can estimate probability density or probability functions, |
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49 note that example could be a pair (x,y) for learners that expect each example |
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50 to represent such a pair. The second form is provided in case the example |
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51 is high-dimensional and computations in the log-domain are numerically preferable. |
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52 The first dimension of examples or of x and y is an index over a minibatch or a dataset. |
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53 |
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54 * p=learner.free_energy(x) |
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55 for learners that can estimate a log unnormalized probability; the output has the same length as the input. |
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56 |
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57 * c=learner.costs(examples) |
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58 returns a matrix of costs (one row per example, i.e., again the output has the same length |
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59 as the input), the first column of which represents the cost whose expectation |
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60 we wish to minimize over new samples from the unknown underlying data distribution. |
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61 |
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62 |
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63 Some learners may be able to handle x's and y's that contain missing values. |
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64 |
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65 * For convenience, some of these operations could be bundled, e.g. |
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66 |
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67 * [prediction,costs] = learner.predict_and_adapt((x,y)) |
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68 |
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69 * Some learners could include in their internal state not only what they |
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70 have learned but some information about recently seen examples that conditions |
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71 the expected distribution of upcoming examples. In that case, they might |
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72 be used, e.g. in an online setting as follows: |
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73 for (x,y) in data_stream: |
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74 [prediction,costs]=learner.predict((x,y)) |
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75 accumulate_statistics(prediction,costs) |
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76 |
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77 * In some cases, each example is itself a (possibly variable-size) sequence |
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78 or other variable-size object (e.g. an image, or a video) |
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79 |
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80 |
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82 |
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85 |
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87 |
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89 James's idea for Learner Interface |
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90 =================================== |
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91 |
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92 Theory: |
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93 ------- |
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94 |
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95 Think about the unfolding of a learning algorithm as exploring a path in a vast |
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96 directed graph. |
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97 |
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98 There are some source nodes, which are potential initial conditions for the |
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99 learning algorithm. |
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100 |
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101 At any node, there are a number of outgoing labeled edges that represent |
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102 distinct directions of exploration: like "allocate a model with N hidden units", |
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103 or "set the l1 weight decay on such-and-such units to 0.1" or "adapt for T |
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104 iterations" or "refresh the GPU dataset memory with the next batch of data". |
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105 |
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106 Not all nodes have the same outgoing edge labels. The dataset, model, and |
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107 optimization algorithm implementations may each have their various |
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108 hyper-parameters with various restrictions on what values they can take, and |
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109 when they can be changed. |
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110 |
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111 Every move in this graph incurs some storage and computational expense, and |
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112 explores the graph. |
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113 |
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114 Learners typically engage in goal-directed exploration of this graph - for |
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115 example to find the node with the best validation-set performance given a |
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116 certain computational budget. We might often be interested in the best node |
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117 found. |
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118 |
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119 The predict(), log_probability(), free_energy() etc correspond to costs that we |
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120 can measure at any particular node (at some computational expense) to see how we |
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121 are doing in our exploration. |
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122 |
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123 Many semantically distinct components come into the definition of this graph: |
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124 the model (e.g. DAA) the dataset (e.g. an online one), the inference and |
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125 learning strategy. I'm not sure what to call this graph than an 'experiment |
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126 graph'... so I'll go with that for now. |
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127 |
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128 |
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129 |
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130 |
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131 |
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132 Use Cases |
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133 ---------- |
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134 |
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135 Early stopping |
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136 ~~~~~~~~~~~~~~ |
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137 |
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138 Early stopping can be implemented as a learner that progresses along a |
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139 particular kind of edge (e.g. "train more") until a stopping criterion (in terms |
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140 of a cost computed from nodes along the path) is met. |
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141 |
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142 |
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143 Grid Search |
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144 ~~~~~~~~~~~ |
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145 |
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146 Grid search is a learner policy that can be implemented in an experiment graph |
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147 where all paths have the form: |
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148 |
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149 ( "set param 0 to X", |
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150 "set param 1 to Y", |
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151 ... , |
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152 "set param N to Z", |
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153 adapt, |
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154 [early stop...], |
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155 test) |
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156 |
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157 It would explore all paths of this form and then return the best node. |
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158 |
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159 |
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160 Stagewise learning of DBNs combined with early stopping and grid search |
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161 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ |
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162 |
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163 This would be a learner that is effective for experiment graphs that reflect the |
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164 greedy-stagewise optimization of DBNs. |
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165 |
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166 |
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167 Boosting |
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168 ~~~~~~~~ |
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169 |
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170 Given an ExperimentGraph that permits re-weighting of examples, it is |
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171 straightforward to write a meta-ExperimentGraph around it that implements AdaBoost. |
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172 A meta-meta-ExperimentGraph around that that does early-stopping would complete |
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173 the picture and make a useful boosting implementation. |
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174 |
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175 |
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176 Using External Hyper-Parameter Optimization Software |
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177 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ |
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178 TODO: use-case - show how we could use the optimizer from |
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179 http://www.cs.ubc.ca/labs/beta/Projects/ParamILS/ |
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180 |
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181 |
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182 Implementation Details / API |
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183 ---------------------------- |
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184 |
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185 Learner |
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186 ~~~~~~~ |
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187 An object that allows us to explore the graph discussed above. Specifically, it represents |
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188 an explored node in that graph. |
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189 |
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190 def active_instructions() |
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191 """ Return a list/set of Instruction instances (see below) that the Learner is prepared |
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192 to handle. |
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193 """ |
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194 |
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195 def copy(), deepcopy() |
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196 """ Learners should be serializable """ |
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197 |
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198 |
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199 To make the implementation easier, I found it was helpful to introduce a string-valued |
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200 `fsa_state` member attribute and associate methods to these states. That made it |
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201 syntactically easy to build relatively complex finite-state transition graphs to describe |
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202 which instructions were active at which times in the life-cycle of a learner. |
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203 |
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204 |
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205 Instruction |
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206 ~~~~~~~~~~~ |
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207 An object that represents a potential edge in the graph discussed above. It is an |
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208 operation that a learner can perform. |
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209 |
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210 arg_types |
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211 """a list of Type object (see below) indicating what args are required by execute""" |
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212 |
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213 def execute(learner, args, kwargs): |
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214 """ Perform some operation on the learner (follow an edge in the graph discussed above) |
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215 and modify the learner in-place. Calling execute 'moves' the learner from one node in |
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216 the graph along an edge. To have the old learner as well, it must be copied prior to |
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217 calling execute(). |
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218 """ |
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219 |
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220 def expense(learner, args, kwargs, resource_type='CPUtime'): |
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221 """ Return an estimated cost of performing this instruction (calling execute), in time, |
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222 space, number of computers, disk requierement, etc. |
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223 """ |
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224 |
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225 Type |
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226 ~~~~ |
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227 An object that describes a parameter domain for a call to Instruction.execute. |
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228 It is not necessary that a Type specifies exactly which arguments are legal, but it should |
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229 `include` all legal arguments, and exclude as many illegal ones as possible. |
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230 |
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231 def includes(value): |
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232 """return True if value is a legal argument""" |
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233 |
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234 |
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235 To make things a bit more practical, there are some Type subclasses like Int, Float, Str, |
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236 ImageDataset, SgdOptimizer, that include additional attributes (e.g. min, max, default) so |
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237 that automatic graph exploration algorithms can generate legal arguments with reasonable |
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238 efficiency. |
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239 |
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240 |
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241 |
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242 The proxy pattern is a powerful way to combine learners. Especially when proxy Learner |
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243 instances also introduce Proxy Instruction classes. |
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244 |
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245 For example, it is straightforward to implement a hyper-learner by implementing a Learner with |
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246 another learner (sub-learner) as a member attribute. The hyper-learner makes some |
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247 modifications to the instruction_set() return value of the sub-learner, typically to introduce |
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248 more powerful instructions and hide simpler ones. |
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249 |
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250 It is less straightforward, but consistent with the design to implement a Learner that |
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251 encompasses job management. Such a learner would retain the semantics of the |
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252 instruction_set of the sub-learner, but would replace the Instruction objects themselves with |
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253 Instructions that arranged for remote procedure calls (e.g. jobman, multiprocessing, bqtools, |
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254 etc.) Such a learner would replace synchronous instructions (return on completion) with |
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255 asynchronous ones (return after scheduling) and the active instruction set would also change |
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256 asynchronously, but neither of these things is inconsistent with the Learner API. |
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257 |
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258 |
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259 TODO |
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260 ~~~~ |
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261 |
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262 I feel like something is missing from the API - and that is an interface to the graph structure |
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263 discussed above. The nodes in this graph are natural places to store meta-information for |
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264 visualization, statistics-gathering etc. But none of the APIs above corresponds to the graph |
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265 itself. In other words, there is no API through which to attach information to nodes. It is |
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266 not good to say that the Learner instance *is* the node because (a) learner instances change |
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267 during graph exploration and (b) learner instances are big, and we don't want to have to keep a |
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268 whole saved model just to attach meta-info e.g. validation score. Choosing this API spills |
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269 over into other committees, so we should get their feedback about how to resolve it. |
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270 |
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271 Comments |
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272 ~~~~~~~~ |
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273 OD asks: (I hope it's ok to leave comments even though I'm not in committee... I'm |
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274 interested to see how the learner interface is shaping up so I'll be keeping |
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275 an eye on this file) |
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276 I'm wondering what's the benefit of such an API compared to simply defining a |
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277 new method for each instruction. It seems to me that typically, the 'execute' |
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278 method would end up being something like |
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279 if instruction == 'do_x': |
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280 self.do_x(..) |
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281 elif instruction == 'do_y': |
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282 self.do_y(..) |
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283 ... |
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284 so why not directly call do_x / do_y instead? |
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285 |
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286 |
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287 JB replies: I agree with you, and in the implementation of a Learner I suggest |
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288 using Python decorators to get the best of both worlds: |
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289 |
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290 class NNet(Learner): |
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291 |
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292 ... |
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293 |
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294 @Instruction.new(arg_types=(Float(min=-8, max=-1, default=-4),)) |
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295 def set_log_lr(self, log_lr): |
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296 self.lr.value = numpy.exp(log_lr) |
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297 |
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298 ... |
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299 |
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300 The Learner base class can implement a instruction_set() that walks through the |
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301 methods of 'self' and pick out the ones that have corresponding instructions. |
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302 But anyone can call the method normally. The NNet class can also have methods |
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303 that are not instructions. |
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304 |
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305 |
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306 |
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307 RP asks: James correct me if I'm wrong, but I think each instruction has a execute |
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308 command. The job of the learner is to traverse the graph and for each edge |
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309 that it decides to cross to call the execute of that edge. Maybe James has |
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310 something else in mind, but this was my understanding. |
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311 |
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312 JB replies: close, but let me make a bit of a clarification. The job of a |
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313 Learner is simply to implement the API of a Learner - to list what edges are |
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314 available and to be able to cross them if asked. The code *using* the Learner |
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315 (client) decides which edges to cross. The client may also be a Learner, but |
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316 maybe not. |
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317 |
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318 |
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319 |
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320 Just another view/spin on the same idea (Razvan) |
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321 ================================================ |
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322 |
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323 |
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324 My idea is probably just a spin off from what James wrote. It is an extension |
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325 of what I send on the mailing list some time ago. |
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326 |
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327 Big Picture |
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328 ----------- |
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329 |
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330 What do we care about ? |
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331 ~~~~~~~~~~~~~~~~~~~~~~~ |
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332 |
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333 This is the list of the main points that I have in mind : |
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334 |
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335 * Re-usability |
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336 * Extensibility |
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337 * Simplicity or easily readable code ( connected to re-usability ) |
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338 * Modularity ( connected to extensibility ) |
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339 * Fast to write code ( - sort of comes out of simplicity) |
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340 * Efficient code |
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341 |
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342 |
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343 Composition |
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344 ~~~~~~~~~~~ |
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345 |
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346 To me this reads as code generated by composing pieces. Imagine this : |
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347 you start of with something primitive that I will call a "variable", which |
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348 probably is a very unsuitable name. And then you compose those intial |
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349 "variables" or transform them through several "functions". Each such |
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350 "function" hides some logic, that you as the user don't care about. |
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351 You can have low-level or micro "functions" and high-level or macro |
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352 "functions", where a high-level function is just a certain compositional |
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353 pattern of low-level "functions". There are several classes of "functions" |
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354 and "variables" that can be inter-changable. This is how modularity is |
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355 obtained, by chainging between functions from a certain class. |
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356 |
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357 Now when you want to research something, what you do is first select |
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358 the step you want to look into. If you are lucky you can re-write this |
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359 step as certain decomposition of low-level transformations ( there can be |
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360 multiple such decompositions). If not you have to implement such a |
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361 decompositions acording to your needs. Pick the low-level transformations you want |
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362 to change and write new versions that implement your logic. |
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363 |
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364 I think the code will be easy to read, because it is just applying a fixed |
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365 set of transformations, one after the other. The one who writes the code can |
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366 decide how explicit he wants to write things by switching between high-level |
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367 and low-level functions. |
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368 |
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369 I think the code this way is re-usable, because you can just take this chain |
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370 of transformation and replace the one you care about, without looking into |
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371 the rest. |
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372 |
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373 You get this fractal property of the code. Zooming in, you always get just |
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374 a set of functions applied to a set of variables. In the begining those might |
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375 not be there, and you would have to create new "low level" decompositions, |
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376 maybe even new "variables" that get data between those decompositions. |
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377 |
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378 The thing with variables here, is that I don't want this "functions" to have |
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379 a state. All the information is passed along through these variables. This |
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380 way understanding the graph is easy, debugging it is also easier ( then having |
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381 all these hidden states ..) |
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382 |
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383 Note that while doing so we might ( and I strongly think we should) create |
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384 a (symbolic) DAG of operations. ( this is where it becomes what James was saying). |
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385 In such a DAG the "variables" will the nodes and the functions will be edges. |
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386 I think having a DAG is useful in many ways (all this are things that one |
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387 might think about implementing in a far future, I'm not proposing to implement |
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388 them unless we want to use them - like the reconstruction ): |
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389 * there exist the posibility of writing optimizations ( theano style ) |
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390 * there exist the posibility to add global view utility functions ( like |
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391 a reconstruction function for SdA - extremely low level here), or global |
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392 view diagnostic tools |
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393 * the posibility of creating a GUI ( where you just create the Graph by |
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394 picking transforms and variables from a list ) or working interactively |
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395 and then generating code that will reproduce the graph |
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396 * you can view the graph and different granularity levels to understand |
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397 things ( global diagnostics) |
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398 |
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399 We should have a taxonomy of possible classes of functions and possible |
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400 classes of variables, but those should not be exclusive. We can work at a high |
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401 level for now, and decompose those high level functions to lower level when |
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402 we need to. We can introduce new classes of functions or intermediate |
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403 variables between those low level functions. |
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404 |
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405 |
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406 Similarities with James' idea |
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407 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ |
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408 |
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409 As I said before, this is I think just another view on what James proposed. |
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410 The learner in his case is the module that traverses the graph of this |
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411 operations, which makes sense here as well. |
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412 |
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413 The 'execute' command in his api is just applying a function to some variables in |
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414 my case. |
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415 |
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416 The learner keeps track of the graph that is formed I think in both cases. |
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417 |
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418 His view is a bit more general. I see the graph as fully created by the user, |
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419 and the learner just has to go from the start to the end. In his case the |
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420 traversal is conditioned on some policies. I think these ideas can be mixed / |
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421 united. What I would see in my case to have this functionality is something |
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422 similar to the lazy linker for Theano. |
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my first draft of my own views which are close to be just a reformulation of what James proposes
Razvan Pascanu <r.pascanu@gmail.com>
parents:
1043
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changeset
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423 |
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3b1fd599bafd
my first draft of my own views which are close to be just a reformulation of what James proposes
Razvan Pascanu <r.pascanu@gmail.com>
parents:
1043
diff
changeset
|
424 |
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3b1fd599bafd
my first draft of my own views which are close to be just a reformulation of what James proposes
Razvan Pascanu <r.pascanu@gmail.com>
parents:
1043
diff
changeset
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425 |
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1052
84f62533e7a8
v2planning learner - reply to comments
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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426 JB asks: There is definitely a strong theme of graphs in both suggestions, |
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v2planning learner - reply to comments
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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427 furthermore graphs that have heavy-duty nodes and light-weight edges. But I |
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v2planning learner - reply to comments
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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428 don't necessarily think that we're proposing the same thing. One difference is |
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v2planning learner - reply to comments
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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429 that the graph I talked about would be infinite in most cases of interest, so |
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v2planning learner - reply to comments
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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430 it's not going to be representable by Theano's data structures (even with lazy |
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v2planning learner - reply to comments
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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431 if). Another difference is that the graph I had in mind doesn't feel fractal - |
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v2planning learner - reply to comments
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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432 it would be very common for a graph edge to be atomic. A proxy pattern, such as |
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v2planning learner - reply to comments
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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433 in a hyper-learner would create a notion of being able to zoom in, but other |
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v2planning learner - reply to comments
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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434 than that, i'm not sure what you mean. |
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my first draft of my own views which are close to be just a reformulation of what James proposes
Razvan Pascanu <r.pascanu@gmail.com>
parents:
1043
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435 |