annotate doc/v2_planning/learner.txt @ 1484:83d3c9ee6d65

* changed MNIST dataset to use config.get_filepath_in_roots mechanism
author gdesjardins
date Tue, 05 Jul 2011 11:01:51 -0400
parents 0e12ea6ba661
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2 Comittee: AB, PL, GM, IG, RP, NB, PV
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3 Leader: PL
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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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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
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34 * learner.train(dataset)
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35 * learner.adapt(data)
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36
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37 * Different types of learners can then exploit their internal state
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38 in order to perform various computations after training is completed,
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39 or in the middle of training, e.g.,
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41 * y=learner.predict(x)
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42 for learners that see (x,y) pairs during training and predict y given x,
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43 or for learners that see only x's and learn a transformation of it (i.e. feature extraction).
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44 Here and below, x and y are tensor-like objects whose first index iterates
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45 over particular examples in a batch or minibatch of examples.
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46
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47 * p=learner.probability(examples)
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48 p=learner.log_probability(examples)
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49 for learners that can estimate probability density or probability functions,
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50 note that example could be a pair (x,y) for learners that expect each example
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51 to represent such a pair. The second form is provided in case the example
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52 is high-dimensional and computations in the log-domain are numerically preferable.
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53 The first dimension of examples or of x and y is an index over a minibatch or a dataset.
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54
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55 * p=learner.free_energy(x)
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56 for learners that can estimate a log unnormalized probability; the output has the same length as the input.
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57
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58 * c=learner.costs(examples)
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59 returns a matrix of costs (one row per example, i.e., again the output has the same length
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60 as the input), the first column of which represents the cost whose expectation
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61 we wish to minimize over new samples from the unknown underlying data distribution.
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64 Some learners may be able to handle x's and y's that contain missing values.
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65
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66 * For convenience, some of these operations could be bundled, e.g.
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67
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68 * [prediction,costs] = learner.predict_and_adapt((x,y))
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70 * Some learners could include in their internal state not only what they
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71 have learned but some information about recently seen examples that conditions
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72 the expected distribution of upcoming examples. In that case, they might
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73 be used, e.g. in an online setting as follows:
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74
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75 .. code-block:: python
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76
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77 for (x,y) in data_stream:
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78 [prediction,costs]=learner.predict((x,y))
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79 accumulate_statistics(prediction,costs)
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80
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81 * In some cases, each example is itself a (possibly variable-size) sequence
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82 or other variable-size object (e.g. an image, or a video)
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93 James's idea for Learner Interface
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94 ===================================
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95
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96 Theory:
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97 -------
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98
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99 Think about the unfolding of a learning algorithm as exploring a path in a vast
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100 directed graph.
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101
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102 There are some source nodes, which are potential initial conditions for the
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103 learning algorithm.
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104
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105 At any node, there are a number of outgoing labeled edges that represent
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106 distinct directions of exploration: like "allocate a model with N hidden units",
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107 or "set the l1 weight decay on such-and-such units to 0.1" or "adapt for T
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108 iterations" or "refresh the GPU dataset memory with the next batch of data".
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109
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110 Not all nodes have the same outgoing edge labels. The dataset, model, and
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111 optimization algorithm implementations may each have their various
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112 hyper-parameters with various restrictions on what values they can take, and
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113 when they can be changed.
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114
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115 Every move in this graph incurs some storage and computational expense, and
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116 explores the graph.
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117
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118 Learners typically engage in goal-directed exploration of this graph - for
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119 example to find the node with the best validation-set performance given a
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120 certain computational budget. We might often be interested in the best node
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121 found.
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123 The predict(), log_probability(), free_energy() etc correspond to costs that we
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124 can measure at any particular node (at some computational expense) to see how we
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125 are doing in our exploration.
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126
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127 Many semantically distinct components come into the definition of this graph:
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128 the model (e.g. DAA) the dataset (e.g. an online one), the inference and
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129 learning strategy. I'm not sure what to call this graph than an 'experiment
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130 graph'... so I'll go with that for now.
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131
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132
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133
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134
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135
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136 Use Cases
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137 ----------
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138
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139 Early stopping
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140 ~~~~~~~~~~~~~~
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141
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142 Early stopping can be implemented as a learner that progresses along a
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143 particular kind of edge (e.g. "train more") until a stopping criterion (in terms
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144 of a cost computed from nodes along the path) is met.
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145
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146
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147 Grid Search
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148 ~~~~~~~~~~~
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149
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150 Grid search is a learner policy that can be implemented in an experiment graph
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151 where all paths have the form:
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152
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153 ( "set param 0 to X",
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154 "set param 1 to Y",
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155 ... ,
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156 "set param N to Z",
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157 adapt,
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158 [early stop...],
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159 test)
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160
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161 It would explore all paths of this form and then return the best node.
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162
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163
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164 Stagewise learning of DBNs combined with early stopping and grid search
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165 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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166
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167 This would be a learner that is effective for experiment graphs that reflect the
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168 greedy-stagewise optimization of DBNs.
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169
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170
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171 Boosting
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172 ~~~~~~~~
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173
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174 Given an ExperimentGraph that permits re-weighting of examples, it is
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175 straightforward to write a meta-ExperimentGraph around it that implements AdaBoost.
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176 A meta-meta-ExperimentGraph around that that does early-stopping would complete
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177 the picture and make a useful boosting implementation.
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178
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179
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180 Using External Hyper-Parameter Optimization Software
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181 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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182 TODO: use-case - show how we could use the optimizer from
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183 http://www.cs.ubc.ca/labs/beta/Projects/ParamILS/
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184
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185
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186 Implementation Details / API
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187 ----------------------------
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188
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189 Learner
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190 ~~~~~~~
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191 An object that allows us to explore the graph discussed above. Specifically, it represents
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192 an explored node in that graph.
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193
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194 .. code-block:: python
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195
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196 def active_instructions()
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197 """ Return a list/set of Instruction instances (see below) that the Learner is prepared
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198 to handle.
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199 """
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200
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201 def copy(), deepcopy()
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202 """ Learners should be serializable """
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203
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204
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205 To make the implementation easier, I found it was helpful to introduce a string-valued
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206 `fsa_state` member attribute and associate methods to these states. That made it
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207 syntactically easy to build relatively complex finite-state transition graphs to describe
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208 which instructions were active at which times in the life-cycle of a learner.
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209
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210
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211 Instruction
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212 ~~~~~~~~~~~
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213 An object that represents a potential edge in the graph discussed above. It is an
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214 operation that a learner can perform.
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215
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216 .. code-block:: python
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217
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218 arg_types
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219 """a list of Type object (see below) indicating what args are required by execute"""
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220
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221 def execute(learner, args, kwargs):
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222 """ Perform some operation on the learner (follow an edge in the graph discussed above)
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223 and modify the learner in-place. Calling execute 'moves' the learner from one node in
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224 the graph along an edge. To have the old learner as well, it must be copied prior to
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225 calling execute().
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226 """
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227
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228 def expense(learner, args, kwargs, resource_type='CPUtime'):
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229 """ Return an estimated cost of performing this instruction (calling execute), in time,
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230 space, number of computers, disk requierement, etc.
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231 """
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232
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233 Type
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234 ~~~~
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235 An object that describes a parameter domain for a call to Instruction.execute.
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236 It is not necessary that a Type specifies exactly which arguments are legal, but it should
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237 `include` all legal arguments, and exclude as many illegal ones as possible.
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238
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239 .. code-block:: python
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240
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241 def includes(value):
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242 """return True if value is a legal argument"""
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243
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244
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245 To make things a bit more practical, there are some Type subclasses like Int, Float, Str,
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246 ImageDataset, SgdOptimizer, that include additional attributes (e.g. min, max, default) so
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247 that automatic graph exploration algorithms can generate legal arguments with reasonable
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248 efficiency.
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249
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250
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251
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252 The proxy pattern is a powerful way to combine learners. Especially when proxy Learner
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253 instances also introduce Proxy Instruction classes.
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254
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255 For example, it is straightforward to implement a hyper-learner by implementing a Learner with
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256 another learner (sub-learner) as a member attribute. The hyper-learner makes some
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257 modifications to the instruction_set() return value of the sub-learner, typically to introduce
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258 more powerful instructions and hide simpler ones.
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259
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260 It is less straightforward, but consistent with the design to implement a Learner that
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261 encompasses job management. Such a learner would retain the semantics of the
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262 instruction_set of the sub-learner, but would replace the Instruction objects themselves with
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263 Instructions that arranged for remote procedure calls (e.g. jobman, multiprocessing, bqtools,
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264 etc.) Such a learner would replace synchronous instructions (return on completion) with
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265 asynchronous ones (return after scheduling) and the active instruction set would also change
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266 asynchronously, but neither of these things is inconsistent with the Learner API.
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267
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268
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269 TODO - Experiment API?
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270 ~~~~~~~~~~~~~~~~~~~~~~
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271
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272 I feel like something is missing from the API - and that is an interface to the graph structure
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273 discussed above. The nodes in this graph are natural places to store meta-information for
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274 visualization, statistics-gathering etc. But none of the APIs above corresponds to the graph
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275 itself. In other words, there is no API through which to attach information to nodes. It is
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276 not good to say that the Learner instance *is* the node because (a) learner instances change
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277 during graph exploration and (b) learner instances are big, and we don't want to have to keep a
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278 whole saved model just to attach meta-info e.g. validation score. Choosing this API spills
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279 over into other committees, so we should get their feedback about how to resolve
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280 it. Maybe we need an 'Experiment' API to stand for this graph?
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281
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282
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283 TODO: Validation & Monitoring Costs
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284 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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285
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286 Even if we do have the Experiment API as a structure to hang validation and
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287 monitoring results, what should be the mechanism for extracting those results.
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288 The Learner API is not right because extracting a monitoring cost doesn't change
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289 the model, doesn't change the legal instructions/edges etc. Maybe we should use
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290 a similar mechanism to Instruction, called something like Measurement? Any node
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291 / learner can report the list of instructions (for moving) and the list of
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292 measurements (and the cost of computing them too)
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293
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294
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295 TODO - Parameter Distributions
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296 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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297
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298 YB asks: it seems to me that what we really need from "Type" is not just
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299 testing that a value is legal, but more practically a function that specifies the
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300 prior distribution for the hyper-parameter, i.e., how to sample from it,
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301 and possibly some representation of it that could be used to infer
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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302 a posterior (such as an unnormalized log-density or log-probability).
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303 Having the min and max and default limits us to the uniform distribution,
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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304 which may not always be appropriate. For example sometimes we'd like
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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305 Gaussian (-infty to infty) or Exponential (0 to infty) or Poisson (non-negative integers).
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306 For that reason, I think that "Type" is not a very good name.
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307 How about "Prior" or "Density" or something like that?
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308
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309 JB replies: I agree that being able to choose (and update) distributions over
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310 these values is important. I don't think the Type structure is the right place
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311 to handle it though. The challenge is to allow those distributions to change
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312 for a variety of reasons - e.g. the sampling distribution on the capacity
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313 variables is affected by the size of the dataset, it is also affected by
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314 previous experience in general as well as experiments on that particular
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315 dataset. I'm not sure that the 'Type' structure is right to deal with this.
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316 Also, even with a strategy for handling these distributions, I believe a simple
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317 mechanism for rejecting insane values might be useful.
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318
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319 So how should we handle it? Hmmm...
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320
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321
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322 Comments
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323 ~~~~~~~~
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324
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325 OD asks: (I hope it's ok to leave comments even though I'm not in committee... I'm
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326 interested to see how the learner interface is shaping up so I'll be keeping
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Olivier Delalleau <delallea@iro>
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327 an eye on this file)
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328 I'm wondering what's the benefit of such an API compared to simply defining a
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Olivier Delalleau <delallea@iro>
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329 new method for each instruction. It seems to me that typically, the 'execute'
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330 method would end up being something like
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331
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332 .. code-block:: python
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333
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334 if instruction == 'do_x':
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335 self.do_x(..)
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336 elif instruction == 'do_y':
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337 self.do_y(..)
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338 ...
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339
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Olivier Delalleau <delallea@iro>
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340 so why not directly call do_x / do_y instead?
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341
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342
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343 JB replies: I agree with you, and in the implementation of a Learner I suggest
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344 using Python decorators to get the best of both worlds:
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345
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346 .. code-block:: python
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347
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348 class NNet(Learner):
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349
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350 ...
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351
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352 @Instruction.new(arg_types=(Float(min=-8, max=-1, default=-4),))
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353 def set_log_lr(self, log_lr):
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354 self.lr.value = numpy.exp(log_lr)
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355
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356 ...
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357
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358 The Learner base class can implement a instruction_set() that walks through the
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359 methods of 'self' and pick out the ones that have corresponding instructions.
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360 But anyone can call the method normally. The NNet class can also have methods
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361 that are not instructions.
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362
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Olivier Delalleau <delallea@iro>
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363 OD replies: Ok thanks. I'm still unsure what is the end goal, but I'll keep
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Olivier Delalleau <delallea@iro>
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364 watching while you guys work on it, and hopefully it'll become clearer for me ;)
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365
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366 RP asks: James correct me if I'm wrong, but I think each instruction has a execute
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367 command. The job of the learner is to traverse the graph and for each edge
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368 that it decides to cross to call the execute of that edge. Maybe James has
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Razvan Pascanu <r.pascanu@gmail.com>
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369 something else in mind, but this was my understanding.
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370
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371 JB replies: close, but let me make a bit of a clarification. The job of a
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372 Learner is simply to implement the API of a Learner - to list what edges are
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373 available and to be able to cross them if asked. The code *using* the Learner
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374 (client) decides which edges to cross. The client may also be a Learner, but
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375 maybe not.
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376
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Razvan Pascanu <r.pascanu@gmail.com>
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377
f1732269bce8 comment on Olivier's comment
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378
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379 Just another view/spin on the same idea (Razvan)
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380 ================================================
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381
3b1fd599bafd my first draft of my own views which are close to be just a reformulation of what James proposes
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382
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383 My idea is probably just a spin off from what James wrote. It is an extension
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384 of what I send on the mailing list some time ago.
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385
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386 Big Picture
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387 -----------
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388
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389 What do we care about ?
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390 ~~~~~~~~~~~~~~~~~~~~~~~
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391
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392 This is the list of the main points that I have in mind :
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393
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394 * Re-usability
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395 * Extensibility
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396 * Simplicity or easily readable code ( connected to re-usability )
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397 * Modularity ( connected to extensibility )
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398 * Fast to write code ( - sort of comes out of simplicity)
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399 * Efficient code
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400
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401
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402 Composition
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403 ~~~~~~~~~~~
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404
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405 To me this reads as code generated by composing pieces. Imagine this :
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406 you start of with something primitive that I will call a "variable", which
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407 probably is a very unsuitable name. And then you compose those intial
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408 "variables" or transform them through several "functions". Each such
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409 "function" hides some logic, that you as the user don't care about.
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410 You can have low-level or micro "functions" and high-level or macro
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411 "functions", where a high-level function is just a certain compositional
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412 pattern of low-level "functions". There are several classes of "functions"
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413 and "variables" that can be inter-changable. This is how modularity is
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parents: 1043
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414 obtained, by chainging between functions from a certain class.
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415
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>
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416 Now when you want to research something, what you do is first select
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Razvan Pascanu <r.pascanu@gmail.com>
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417 the step you want to look into. If you are lucky you can re-write this
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418 step as certain decomposition of low-level transformations ( there can be
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419 multiple such decompositions). If not you have to implement such a
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420 decompositions acording to your needs. Pick the low-level transformations you want
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421 to change and write new versions that implement your logic.
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422
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423 I think the code will be easy to read, because it is just applying a fixed
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Razvan Pascanu <r.pascanu@gmail.com>
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424 set of transformations, one after the other. The one who writes the code can
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425 decide how explicit he wants to write things by switching between high-level
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426 and low-level functions.
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427
3b1fd599bafd my first draft of my own views which are close to be just a reformulation of what James proposes
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428 I think the code this way is re-usable, because you can just take this chain
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Razvan Pascanu <r.pascanu@gmail.com>
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429 of transformation and replace the one you care about, without looking into
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430 the rest.
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431
3b1fd599bafd my first draft of my own views which are close to be just a reformulation of what James proposes
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432 You get this fractal property of the code. Zooming in, you always get just
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Razvan Pascanu <r.pascanu@gmail.com>
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433 a set of functions applied to a set of variables. In the begining those might
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434 not be there, and you would have to create new "low level" decompositions,
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Razvan Pascanu <r.pascanu@gmail.com>
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435 maybe even new "variables" that get data between those decompositions.
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436
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>
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437 The thing with variables here, is that I don't want this "functions" to have
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>
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438 a state. All the information is passed along through these variables. This
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Razvan Pascanu <r.pascanu@gmail.com>
parents: 1043
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439 way understanding the graph is easy, debugging it is also easier ( then having
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Razvan Pascanu <r.pascanu@gmail.com>
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440 all these hidden states ..)
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441
3b1fd599bafd my first draft of my own views which are close to be just a reformulation of what James proposes
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442 Note that while doing so we might ( and I strongly think we should) create
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443 a (symbolic) DAG of operations. ( this is where it becomes what James was saying).
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444 In such a DAG the "variables" will the nodes and the functions will be edges.
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445 I think having a DAG is useful in many ways (all this are things that one
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446 might think about implementing in a far future, I'm not proposing to implement
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447 them unless we want to use them - like the reconstruction ):
1189
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Frederic Bastien <nouiz@nouiz.org>
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448
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449 * there exist the posibility of writing optimizations ( theano style )
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Razvan Pascanu <r.pascanu@gmail.com>
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450 * there exist the posibility to add global view utility functions ( like
1189
0e12ea6ba661 fix many rst syntax error warning.
Frederic Bastien <nouiz@nouiz.org>
parents: 1167
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451 a reconstruction function for SdA - extremely low level here), or global
0e12ea6ba661 fix many rst syntax error warning.
Frederic Bastien <nouiz@nouiz.org>
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452 view diagnostic tools
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453 * the posibility of creating a GUI ( where you just create the Graph by
1189
0e12ea6ba661 fix many rst syntax error warning.
Frederic Bastien <nouiz@nouiz.org>
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454 picking transforms and variables from a list ) or working interactively
0e12ea6ba661 fix many rst syntax error warning.
Frederic Bastien <nouiz@nouiz.org>
parents: 1167
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455 and then generating code that will reproduce the graph
1044
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456 * you can view the graph and different granularity levels to understand
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Frederic Bastien <nouiz@nouiz.org>
parents: 1167
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457 things ( global diagnostics)
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458
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Razvan Pascanu <r.pascanu@gmail.com>
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459 We should have a taxonomy of possible classes of functions and possible
3b1fd599bafd my first draft of my own views which are close to be just a reformulation of what James proposes
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460 classes of variables, but those should not be exclusive. We can work at a high
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parents: 1043
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461 level for now, and decompose those high level functions to lower level when
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462 we need to. We can introduce new classes of functions or intermediate
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Razvan Pascanu <r.pascanu@gmail.com>
parents: 1043
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463 variables between those low level functions.
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464
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Razvan Pascanu <r.pascanu@gmail.com>
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465
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Razvan Pascanu <r.pascanu@gmail.com>
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466 Similarities with James' idea
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467 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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468
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469 As I said before, this is I think just another view on what James proposed.
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Razvan Pascanu <r.pascanu@gmail.com>
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470 The learner in his case is the module that traverses the graph of this
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Razvan Pascanu <r.pascanu@gmail.com>
parents: 1043
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471 operations, which makes sense here as well.
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Razvan Pascanu <r.pascanu@gmail.com>
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472
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
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473 The 'execute' command in his api is just applying a function to some variables in
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>
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474 my case.
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475
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>
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476 The learner keeps track of the graph that is formed I think in both cases.
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Razvan Pascanu <r.pascanu@gmail.com>
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477
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
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478 His view is a bit more general. I see the graph as fully created by the user,
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Razvan Pascanu <r.pascanu@gmail.com>
parents: 1043
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479 and the learner just has to go from the start to the end. In his case the
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480 traversal is conditioned on some policies. I think these ideas can be mixed /
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parents: 1043
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481 united. What I would see in my case to have this functionality is something
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parents: 1043
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482 similar to the lazy linker for Theano.
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Razvan Pascanu <r.pascanu@gmail.com>
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483
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Razvan Pascanu <r.pascanu@gmail.com>
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484
3b1fd599bafd my first draft of my own views which are close to be just a reformulation of what James proposes
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485
1052
84f62533e7a8 v2planning learner - reply to comments
James Bergstra <bergstrj@iro.umontreal.ca>
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486 JB asks: There is definitely a strong theme of graphs in both suggestions,
84f62533e7a8 v2planning learner - reply to comments
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1046
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487 furthermore graphs that have heavy-duty nodes and light-weight edges. But I
84f62533e7a8 v2planning learner - reply to comments
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1046
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488 don't necessarily think that we're proposing the same thing. One difference is
84f62533e7a8 v2planning learner - reply to comments
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1046
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489 that the graph I talked about would be infinite in most cases of interest, so
84f62533e7a8 v2planning learner - reply to comments
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1046
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490 it's not going to be representable by Theano's data structures (even with lazy
84f62533e7a8 v2planning learner - reply to comments
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1046
diff changeset
491 if). Another difference is that the graph I had in mind doesn't feel fractal -
84f62533e7a8 v2planning learner - reply to comments
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1046
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492 it would be very common for a graph edge to be atomic. A proxy pattern, such as
84f62533e7a8 v2planning learner - reply to comments
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1046
diff changeset
493 in a hyper-learner would create a notion of being able to zoom in, but other
84f62533e7a8 v2planning learner - reply to comments
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1046
diff changeset
494 than that, i'm not sure what you mean.
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495
1056
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496 RP replies: I've been thinking about my idea a bit and yes, it might be
19033ef1636d some more details on my approach
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497 quite different from what James has in mind, though there are plently of common
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parents: 1055
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498 elements. I might have exagerated a bit with the zooming in, so in some cases
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parents: 1055
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499 you will end up with atomic edges, though my hope is that is not most of the
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Razvan Pascanu <r.pascanu@gmail.com>
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500 edges.
19033ef1636d some more details on my approach
Razvan Pascanu <r.pascanu@gmail.com>
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501
19033ef1636d some more details on my approach
Razvan Pascanu <r.pascanu@gmail.com>
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502 I think I should go into mode details when answering this question because
19033ef1636d some more details on my approach
Razvan Pascanu <r.pascanu@gmail.com>
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503 I feel I have not explained things sufficiently clear. Note, in many places
19033ef1636d some more details on my approach
Razvan Pascanu <r.pascanu@gmail.com>
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504 I replaced the word "function" by "transform".
19033ef1636d some more details on my approach
Razvan Pascanu <r.pascanu@gmail.com>
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505
19033ef1636d some more details on my approach
Razvan Pascanu <r.pascanu@gmail.com>
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506 Think of the learner as an object that traverses a DAG of steps created by the
19033ef1636d some more details on my approach
Razvan Pascanu <r.pascanu@gmail.com>
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507 user. On this DAG the learner can potentially do a lot of cool stuff, but we
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Razvan Pascanu <r.pascanu@gmail.com>
parents: 1055
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508 won't care about that for now. The DAG can be infinite in principle, and what
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509 the learner does is just to go on the path described by the user ( and here
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510 described is not through heuristics like in James case, but by giving the list
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511 of edges it needs to follow). A potential cool thing the learner can do is to
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512 regard the path given by the user as a suggestion ( or some form of heuristic)
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513 and try to improve it. This would be much closer to what James has in mind,
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514 and I definetely think is a cool way to go about it.
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515
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516 Now this path in the graph is given by the user by composing subgraphs or
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517 adding nodes to the graph. Or (expressing this in a more simple way) by applying
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518 functions to variables. Any such function will introduce an edge ( or a subgraph) that
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519 will connect the vertices corresponding to the input variables to the vertices
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520 corresponding to the output variables. The variables store the state of the
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521 learner. These functions are state-less, I think if you would give them states
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522 you will make this approach really ugly (I might be wrong).
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523 The variables would contain informations required by the function, like
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524 number of layers, on how many cores to run, cluster configurations, and so on.
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525
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526 Now about the zooming part, that James asked. I might have exagerated a bit,
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527 is not that you can zoom in on any part infinitely. You will end up with
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528 things that are atomic. The idea is that any such "transformation" or edge
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529 has the potential to be split up in several "transformations". This offers
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530 (in my view) a way of solving the time constraints of our project. We can
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531 start by difining a coarse division in segments. For now we can have
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532 a structure transform that makes a list of parameters into a deep
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533 network of some type, then a learner transform that adds SGD + pre-training
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534 on top of network, and then early stopper on top of that, and then a
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535 run_on_cluster on that.We would probably want something more finely grained
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536 even from the start .. this is just to prove my point. When any of us
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537 starts experimenting with a certain sub-step of this process ( like the
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538 structure) we will split that transform into several ( like ones that create
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539 a layer and so on) that make sense for that case, and then start working on
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540 the low level transform that we cares ( like the layer) introducing new
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541 versions of it. I think we can not find a universal split that will cover
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542 all of our cases, so I think we should allow different such splits. The one
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543 who researches should look at what low-level transforms are available and use
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544 those if they make sense, if not he would have to create a different split.
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545 Creating a different split might involve a lot of work and taking care of
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546 several issues so it should be done with care.
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547
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548 I'll give an example from where I started thinking this way. Let say we want
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549 to do the SdA with auxiliary inputs that encourages separation of the features
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550 in the hidden layer that Yoshua was saying ( I had an attempt
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551 at it some time ago for speech but I never eneded up finishing that project).
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552
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553 You start up with something like :
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554
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555 learner = Learner()
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556 # This will create the learner that will traverse our graph. We might
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557 # want it to be a function ``execute``, I just randomly picked this option.
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558 #I have no preference of this detail for now .. this is mostly work in progress
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559
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560 data = someSpeechData(path = 'some path')
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561 # This is such a transform that will generate from the string representing the
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562 # path a dataset variable ( that will contain all informations you need to
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563 # access data). This will probably be the object the datasets comittee will
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564 # provide. Note, you might need to provide more information then the path, but
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565 # you can easily see how to do that. All these stuff start from simple
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566 # variables like path, batch size and so on and return a complex heavy duty
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567 # variable (node).
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568
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569
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570 model = earlyStopping(pretrain(SdA(layers = [524, 500, 500,27], noise = [0.1,0.1]),data, epochs = 10), data)
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571 # This is a composition of two transforms. The SdA transform starts from the
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572 # info about layers and corruption /noise for each layer and construct a SdA.
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573 # This is a high level transform, so it will take care of defining all
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574 # details, like pre-training, defining the cost and so on. Note that maybe it will
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575 # require some more parameters .. you can assume that for anything else there
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576 # is a default value that the SdA will use. earlyStopping is yet another
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577 # transform that takes a model ( that we know how to train ) and some data,
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578 # and does early stoppign on it. For bravity I did not provide all the
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579 # information required like patience and so on. The SdA only knows how to do a
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580 # step of training. Same holds for pretrain. It will loop over the layers of
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581 # SdA and will train each one.
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582
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583 steps = cluster(model, getPropertiesAndRanges(model), n_jobs = 20, cluster_info = getClusterInfo())
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584 # This will lunch the wanted jobs. getPropertiesAndRanges will get from a
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585 # model all knobs that need to be turn, and their ranges and will uniformly
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586 # sample from them in each jobs. getCluterInfo will return a variable
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587 # containing informations about the cluster ( I added this for simplicity, it
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588 # should probably be replaced with something like username, password,
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589 # clusterpath or whatever).
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590
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591 learner.execute(steps)
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592 # As an option, each of this output variables could contain the entire graph
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593 # until that point. We could also have this in a different way .. this is
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594 # adhoc at the moment
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595
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596
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597 Now this is a coarse vanila SdA which is not what we wanted. We do not have a
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598 way of incorporating our auxiliary information in this. So what we have to do
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599 is split/change the SdA transform. We would re-write it as :
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600
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601
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602 arch = SdA(layers = [524, 500, 500, 27], noise = [0.1,0.1])
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603 model = earlyStopping(pretrain(arch,data,epochs = 10)
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604 ...
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605
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606 And then re-write things like :
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607
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608 arch = SGD( cross_entropy( logreg( DAAlayer( [DAAlayer([524,500],0.1),500],0.1))))
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609
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610
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611 We would re-write the DAAlayer as :
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612
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613 layer0 = DAAlayer([524,500],0.1)
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614 layer1 = cross_entropy(reconstruct( tanh(dotW_b( layer0,500)),noise = 0.1))
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615
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616 At this point of detail, we can start inserting our new stuff in as follows :
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617
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618
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619 input = empty_layer(600)
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620 # empty layer is a wrapper ; if I would to write dotW_b(200,500) which means
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621 # go from a layer of 200 units to a one of 500 by multiplying with a matrix
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622 # and adding a bias, what I would mean is dotW_b( empty_layer(200), 500).
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623 # an implementation of empty_layer could be just theano.tensor.vector()
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624 # where we add the size tag ( we will need it later)
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625
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626
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627 hidden0_mfcc = dotW_b(input[0:524],100)
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628 hidden0_noise = dotW_b(input[0:560],50)
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629 hidden0_speakerID = dotW_b(join(input[0:524], input[560:600]),50)
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630 hidden0 = tanh(join( layer0_mfcc, layer0_noise, layer0_speakerID))
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631 layer0 = cross_entropy( reconstruct( hidden0, noise = 0.1))
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632
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633 and so on. Hopefully you got what I mean by spliting a transform, or zooming
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634 in. When doing all this we did not change anything about the early stopping or
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635 lunching jobs on the cluster. In the same manner, if one would like to look
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636 into how jobs are send to the cluster, it could just expand that part. Note
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637 that if we wanted to do something else we might have split the DAA
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638 differently.
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639
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640 The key of this approach is to identify such low level units that can be
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641 shared by 90% of our architectures, and the splits that make most sense
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642 from a functional point of view that will cover the main points where people
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643 will like to change things. This will ensure that almost all the time we have
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644 the wanted low-level bits that we want to write our code into, and most of the
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645 time we will only work on one of that bit. There will definetely be cases when
19033ef1636d some more details on my approach
Razvan Pascanu <r.pascanu@gmail.com>
parents: 1055
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646 whatever we have will not be sufficient or convinient. In that case some
19033ef1636d some more details on my approach
Razvan Pascanu <r.pascanu@gmail.com>
parents: 1055
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647 effort has to be invested by the user to create a different decomposition of
19033ef1636d some more details on my approach
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648 the problem in the elements he need.
19033ef1636d some more details on my approach
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649
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650 I've been thinking about this a bit, and it definetely works in for deep
19033ef1636d some more details on my approach
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parents: 1055
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651 networks and theano ( the approach was inspired by theano). From what James
19033ef1636d some more details on my approach
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652 said, I think that other stuff might be possible to incorporate, at least as
19033ef1636d some more details on my approach
Razvan Pascanu <r.pascanu@gmail.com>
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653 atomic transforms if not in any other way.
19033ef1636d some more details on my approach
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654
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655 TODO: one has to give some thought of this low-level transform, to find a
19033ef1636d some more details on my approach
Razvan Pascanu <r.pascanu@gmail.com>
parents: 1055
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656 suitable set of them ( and variables) so that would end up most of the time
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657 re-using things and not creating new things.
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Razvan Pascanu <r.pascanu@gmail.com>
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658
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parents: 1055
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659 NOTES: there are some other implementation details missing of what this state
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parents: 1055
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660 variables should contain. I did not want to clutter this with what tricks
19033ef1636d some more details on my approach
Razvan Pascanu <r.pascanu@gmail.com>
parents: 1055
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661 could be used to get this transparent interface. I have a few of them in mind
19033ef1636d some more details on my approach
Razvan Pascanu <r.pascanu@gmail.com>
parents: 1055
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662 though..
19033ef1636d some more details on my approach
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parents: 1055
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663 there is a lot of hardcoded values in this example. Usually each transform
19033ef1636d some more details on my approach
Razvan Pascanu <r.pascanu@gmail.com>
parents: 1055
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664 that takes an input should "know" which of these inputs are tunable and mark
19033ef1636d some more details on my approach
Razvan Pascanu <r.pascanu@gmail.com>
parents: 1055
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665 them as such. The order of the input in this example is important as well.
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Razvan Pascanu <r.pascanu@gmail.com>
parents: 1055
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666 This can be easily solved at the expense of a few more lines of code that
19033ef1636d some more details on my approach
Razvan Pascanu <r.pascanu@gmail.com>
parents: 1055
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667 I did not want to write.
19033ef1636d some more details on my approach
Razvan Pascanu <r.pascanu@gmail.com>
parents: 1055
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668
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parents: 1055
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669
19033ef1636d some more details on my approach
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670
19033ef1636d some more details on my approach
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671
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672