Mercurial > pylearn
annotate learner.py @ 134:3f4e5c9bdc5e
Fixes to ApplyFunctionDataSet and other things to make learner and mlp work
author | Yoshua Bengio <bengioy@iro.umontreal.ca> |
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date | Fri, 09 May 2008 17:38:57 -0400 |
parents | b4657441dd65 |
children | 0d8e721cc63c b7ca3545186b |
rev | line source |
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2 from dataset import AttributesHolder,AbstractFunction,ApplyFunctionDataSet,DataSet,CachedDataSet |
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3 import theano |
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4 from theano import compile |
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5 from theano import tensor as t |
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6 |
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7 class Learner(AttributesHolder): |
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8 """ |
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9 Base class for learning algorithms, provides an interface |
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10 that allows various algorithms to be applicable to generic learning |
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11 algorithms. |
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12 |
132
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13 A L{Learner} can be seen as a learning algorithm, a function that when |
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14 applied to training data returns a learned function, an object that |
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15 can be applied to other data and return some output data. |
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16 """ |
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17 |
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18 def __init__(self): |
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19 pass |
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20 |
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21 def forget(self): |
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22 """ |
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23 Reset the state of the learner to a blank slate, before seeing |
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24 training data. The operation may be non-deterministic if the |
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25 learner has a random number generator that is set to use a |
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26 different seed each time it forget() is called. |
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27 """ |
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28 raise NotImplementedError |
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29 |
14 | 30 def update(self,training_set,train_stats_collector=None): |
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31 """ |
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32 Continue training a learner, with the evidence provided by the given training set. |
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33 Hence update can be called multiple times. This is particularly useful in the |
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34 on-line setting or the sequential (Bayesian or not) settings. |
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35 The result is a function that can be applied on data, with the same |
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36 semantics of the Learner.use method. |
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37 |
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38 The user may optionally provide a training L{StatsCollector} that is used to record |
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39 some statistics of the outputs computed during training. It is update(d) during |
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40 training. |
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41 """ |
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42 return self.use # default behavior is 'non-adaptive', i.e. update does not do anything |
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43 |
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44 |
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45 def __call__(self,training_set,train_stats_collector=None): |
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46 """ |
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47 Train a learner from scratch using the provided training set, |
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48 and return the learned function. |
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49 """ |
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50 self.forget() |
133 | 51 return self.update(training_set,train_stats_collector) |
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52 |
128 | 53 def use(self,input_dataset,output_fieldnames=None, |
54 test_stats_collector=None,copy_inputs=True, | |
55 put_stats_in_output_dataset=True, | |
56 output_attributes=[]): | |
57 """ | |
132
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58 Once a L{Learner} has been trained by one or more call to 'update', it can |
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59 be used with one or more calls to 'use'. The argument is an input L{DataSet} (possibly |
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60 containing a single example) and the result is an output L{DataSet} of the same length. |
128 | 61 If output_fieldnames is specified, it may be use to indicate which fields should |
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62 be constructed in the output L{DataSet} (for example ['output','classification_error']). |
128 | 63 Otherwise, self.defaultOutputFields is called to choose the output fields. |
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64 Optionally, if copy_inputs, the input fields (of the input_dataset) can be made |
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65 visible in the output L{DataSet} returned by this method. |
128 | 66 Optionally, attributes of the learner can be copied in the output dataset, |
67 and statistics computed by the stats collector also put in the output dataset. | |
68 Note the distinction between fields (which are example-wise quantities, e.g. 'input') | |
69 and attributes (which are not, e.g. 'regularization_term'). | |
70 | |
71 We provide here a default implementation that does all this using | |
72 a sub-class defined method: minibatchwiseUseFunction. | |
73 | |
74 @todo check if some of the learner attributes are actually SPECIFIED | |
75 as attributes of the input_dataset, and if so use their values instead | |
76 of the ones in the learner. | |
77 | |
78 The learner tries to compute in the output dataset the output fields specified. | |
79 If None is specified then self.defaultOutputFields(input_dataset.fieldNames()) | |
80 is called to determine the output fields. | |
81 | |
82 Attributes of the learner can also optionally be copied into the output dataset. | |
83 If output_attributes is None then all of the attributes in self.AttributeNames() | |
84 are copied in the output dataset, but if it is [] (the default), then none are copied. | |
85 If a test_stats_collector is provided, then its attributes (test_stats_collector.AttributeNames()) | |
86 are also copied into the output dataset attributes. | |
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87 """ |
128 | 88 minibatchwise_use_function = self.minibatchwiseUseFunction(input_dataset.fieldNames(), |
89 output_fieldnames, | |
90 test_stats_collector) | |
91 virtual_output_dataset = ApplyFunctionDataSet(input_dataset, | |
92 minibatchwise_use_function, | |
93 True,DataSet.numpy_vstack, | |
94 DataSet.numpy_hstack) | |
95 # actually force the computation | |
96 output_dataset = CachedDataSet(virtual_output_dataset,True) | |
97 if copy_inputs: | |
98 output_dataset = input_dataset | output_dataset | |
99 # copy the wanted attributes in the dataset | |
100 if output_attributes is None: | |
101 output_attributes = self.attributeNames() | |
102 if output_attributes: | |
103 assert set(attribute_names) <= set(self.attributeNames()) | |
104 output_dataset.setAttributes(output_attributes, | |
105 self.names2attributes(output_attributes,return_copy=True)) | |
106 if test_stats_collector: | |
107 test_stats_collector.update(output_dataset) | |
108 if put_stats_in_output_dataset: | |
109 output_dataset.setAttributes(test_stats_collector.attributeNames(), | |
110 test_stats_collector.attributes()) | |
111 return output_dataset | |
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112 |
128 | 113 def minibatchwiseUseFunction(self, input_fields, output_fields, stats_collector): |
114 """ | |
115 Returns a function that can map the given input fields to the given output fields | |
116 and to the attributes that the stats collector needs for its computation. | |
117 That function is expected to operate on minibatches. | |
118 The function returned makes use of the self.useInputAttributes() and | |
119 sets the attributes specified by self.useOutputAttributes(). | |
120 """ | |
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121 def attributeNames(self): |
78 | 122 """ |
123 A Learner may have attributes that it wishes to export to other objects. To automate | |
124 such export, sub-classes should define here the names (list of strings) of these attributes. | |
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125 |
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126 @todo By default, attributeNames looks for all dictionary entries whose name does not start with _. |
78 | 127 """ |
128 return [] | |
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129 |
128 | 130 def attributes(self,return_copy=False): |
131 """ | |
132 Return a list with the values of the learner's attributes (or optionally, a deep copy). | |
133 """ | |
134 return self.names2attributes(self.attributeNames(),return_copy) | |
135 | |
134
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136 def names2attributes(self,names): |
128 | 137 """ |
138 Private helper function that maps a list of attribute names to a list | |
139 of (optionally copies) values of attributes. | |
140 """ | |
134
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141 res=[] |
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142 for name in names: |
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143 assert name in names |
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144 res.append(self.__getattribute__(name)) |
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145 return res |
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146 |
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147 def useInputAttributes(self): |
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148 """ |
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149 A subset of self.attributeNames() which are the names of attributes needed by use() in order |
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150 to do its work. |
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151 """ |
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152 raise AbstractFunction() |
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153 |
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154 def useOutputAttributes(self): |
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155 """ |
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156 A subset of self.attributeNames() which are the names of attributes modified/created by use() in order |
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157 to do its work. |
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158 """ |
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159 raise AbstractFunction() |
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160 |
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161 |
78 | 162 class TLearner(Learner): |
163 """ | |
164 TLearner is a virtual class of Learners that attempts to factor out of the definition | |
165 of a learner the steps that are common to many implementations of learning algorithms, | |
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166 so as to leave only 'the equations' to define in particular sub-classes, using Theano. |
78 | 167 |
168 In the default implementations of use and update, it is assumed that the 'use' and 'update' methods | |
169 visit examples in the input dataset sequentially. In the 'use' method only one pass through the dataset is done, | |
170 whereas the sub-learner may wish to iterate over the examples multiple times. Subclasses where this | |
171 basic model is not appropriate can simply redefine update or use. | |
172 | |
173 Sub-classes must provide the following functions and functionalities: | |
174 - attributeNames(): defines all the names of attributes which can be used as fields or | |
175 attributes in input/output datasets or in stats collectors. | |
176 All these attributes are expected to be theano.Result objects | |
177 (with a .data property and recognized by theano.Function for compilation). | |
178 The sub-class constructor defines the relations between | |
179 the Theano variables that may be used by 'use' and 'update' | |
180 or by a stats collector. | |
181 - defaultOutputFields(input_fields): return a list of default dataset output fields when | |
182 None are provided by the caller of use. | |
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183 The following naming convention is assumed and important. |
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184 Attributes whose names are listed in attributeNames() can be of any type, |
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185 but those that can be referenced as input/output dataset fields or as |
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186 output attributes in 'use' or as input attributes in the stats collector |
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187 should be associated with a Theano Result variable. If the exported attribute |
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188 name is <name>, the corresponding Result name (an internal attribute of |
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189 the TLearner, created in the sub-class constructor) should be _<name>. |
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190 Typically <name> will be numpy ndarray and _<name> will be the corresponding |
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191 Theano Tensor (for symbolic manipulation). |
107
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192 |
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193 @todo pousser dans Learner toute la poutine qui peut l'etre sans etre |
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194 dependant de Theano |
78 | 195 """ |
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196 |
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197 def __init__(self): |
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198 Learner.__init__(self) |
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199 self.use_functions_dictionary={} |
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200 |
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201 def defaultOutputFields(self, input_fields): |
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202 """ |
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203 Return a default list of output field names (to put in the output dataset). |
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204 This will be used when None are provided (as output_fields) by the caller of the 'use' method. |
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205 This may involve looking at the input_fields (names) available in the |
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206 input_dataset. |
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207 """ |
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208 raise AbstractFunction() |
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209 |
128 | 210 def minibatchwiseUseFunction(self, input_fields, output_fields, stats_collector): |
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211 """ |
128 | 212 Implement minibatchwiseUseFunction by exploiting Theano compilation |
213 and the expression graph defined by a sub-class constructor. | |
92
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214 """ |
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215 if not output_fields: |
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216 output_fields = self.defaultOutputFields(input_fields) |
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217 if stats_collector: |
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218 stats_collector_inputs = stats_collector.input2UpdateAttributes() |
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219 for attribute in stats_collector_inputs: |
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220 if attribute not in input_fields: |
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221 output_fields.append(attribute) |
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222 key = (tuple(input_fields),tuple(output_fields)) |
92
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223 if key not in self.use_functions_dictionary: |
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224 use_input_attributes = self.useInputAttributes() |
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225 use_output_attributes = self.useOutputAttributes() |
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226 complete_f = compile.function(self.names2OpResults(input_fields+use_input_attributes), |
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227 self.names2OpResults(output_fields+use_output_attributes)) |
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228 def f(*input_field_values): |
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229 input_attribute_values = self.names2attributes(use_input_attributes) |
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230 results = complete_f(*(list(input_field_values) + input_attribute_values)) |
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231 output_field_values = results[0:len(output_fields)] |
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232 output_attribute_values = results[len(output_fields):len(results)] |
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233 if use_output_attributes: |
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234 self.setAttributes(use_output_attributes,output_attribute_values) |
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235 return output_field_values |
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236 self.use_functions_dictionary[key]=f |
92
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237 return self.use_functions_dictionary[key] |
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238 |
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239 def names2OpResults(self,names): |
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240 """ |
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241 Private helper function that maps a list of attribute names to a list |
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242 of corresponding Op Results (with the same name but with a '_' prefix). |
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243 """ |
133 | 244 return [self.__getattribute__('_'+name) for name in names] |
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245 |
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246 |
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247 class MinibatchUpdatesTLearner(TLearner): |
107
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248 """ |
132
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249 This adds to L{TLearner} a |
110
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250 - updateStart(), updateEnd(), updateMinibatch(minibatch), isLastEpoch(): |
107
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251 functions executed at the beginning, the end, in the middle |
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252 (for each minibatch) of the update method, and at the end |
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253 of each epoch. This model only |
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254 works for 'online' or one-shot learning that requires |
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255 going only once through the training data. For more complicated |
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256 models, more specialized subclasses of TLearner should be used |
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257 or a learning-algorithm specific update method should be defined. |
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258 |
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259 - a 'parameters' attribute which is a list of parameters (whose names are |
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260 specified by the user's subclass with the parameterAttributes() method) |
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261 |
107
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262 """ |
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263 |
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264 def __init__(self): |
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265 TLearner.__init__(self) |
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266 self.update_minibatch_function = compile.function(self.names2OpResults(self.updateMinibatchOutputAttributes()+ |
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267 self.updateMinibatchInputFields()), |
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268 self.names2OpResults(self.updateMinibatchOutputAttributes())) |
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269 self.update_end_function = compile.function(self.names2OpResults(self.updateEndInputAttributes()), |
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270 self.names2OpResults(self.updateEndOutputAttributes())) |
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271 |
128 | 272 def allocate(self, minibatch): |
273 """ | |
132
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274 This function is called at the beginning of each L{updateMinibatch} |
128 | 275 and should be used to check that all required attributes have been |
276 allocated and initialized (usually this function calls forget() | |
277 when it has to do an initialization). | |
278 """ | |
279 raise AbstractFunction() | |
280 | |
110
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281 def updateMinibatchInputFields(self): |
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282 raise AbstractFunction() |
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283 |
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284 def updateMinibatchInputAttributes(self): |
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285 raise AbstractFunction() |
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286 |
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287 def updateMinibatchOutputAttributes(self): |
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288 raise AbstractFunction() |
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289 |
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290 def updateEndInputAttributes(self): |
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291 raise AbstractFunction() |
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292 |
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293 def updateEndOutputAttributes(self): |
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294 raise AbstractFunction() |
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295 |
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296 def parameterAttributes(self): |
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297 raise AbstractFunction() |
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298 |
133 | 299 def updateStart(self,training_set): |
300 pass | |
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301 |
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302 def updateEnd(self): |
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303 self.setAttributes(self.updateEndOutputAttributes(), |
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304 self.update_end_function(*self.names2attributes(self.updateEndInputAttributes()))) |
111
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305 self.parameters = self.names2attributes(self.parameterAttributes()) |
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306 |
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307 def updateMinibatch(self,minibatch): |
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308 # make sure all required fields are allocated and initialized |
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309 self.allocate(minibatch) |
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310 input_attributes = self.names2attributes(self.updateMinibatchInputAttributes()) |
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311 input_fields = minibatch(*self.updateMinibatchInputFields()) |
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312 self.setAttributes(self.updateMinibatchOutputAttributes(), |
118
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313 # concatenate the attribute values and field values and then apply update fn |
134
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314 self.update_minibatch_function(*(input_attributes+input_fields))) |
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315 |
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316 def isLastEpoch(self): |
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317 """ |
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318 This method is called at the end of each epoch (cycling over the training set). |
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319 It returns a boolean to indicate if this is the last epoch. |
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320 By default just do one epoch. |
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321 """ |
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322 return True |
78 | 323 |
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324 def update(self,training_set,train_stats_collector=None): |
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325 """ |
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326 @todo check if some of the learner attributes are actually SPECIFIED |
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327 in as attributes of the training_set. |
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328 """ |
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329 self.updateStart(training_set) |
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330 stop=False |
133 | 331 if hasattr(self,'_minibatch_size') and self._minibatch_size: |
332 minibatch_size=self._minibatch_size | |
333 else: | |
334 minibatch_size=min(100,len(training_set)) | |
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335 while not stop: |
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336 if train_stats_collector: |
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337 train_stats_collector.forget() # restart stats collectin at the beginning of each epoch |
133 | 338 for minibatch in training_set.minibatches(minibatch_size=minibatch_size): |
339 self.updateMinibatch(minibatch) | |
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340 if train_stats_collector: |
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341 minibatch_set = minibatch.examples() |
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342 minibatch_set.setAttributes(self.attributeNames(),self.attributes()) |
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343 train_stats_collector.update(minibatch_set) |
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344 stop = self.isLastEpoch() |
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345 self.updateEnd() |
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346 return self.use |
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347 |
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348 class OnlineGradientTLearner(MinibatchUpdatesTLearner): |
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349 """ |
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350 Specialization of L{MinibatchUpdatesTLearner} in which the minibatch updates |
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351 are obtained by performing an online (minibatch-based) gradient step. |
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352 |
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353 Sub-classes must define the following: |
132
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354 - self._learning_rate (may be changed by the sub-class between epochs or minibatches) |
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355 - self.lossAttribute() = name of the loss field |
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356 """ |
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357 def __init__(self,truly_online=False): |
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358 """ |
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359 If truly_online then only one pass is made through the training set passed to update(). |
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360 |
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361 SUBCLASSES SHOULD CALL THIS CONSTRUCTOR ONLY AFTER HAVING DEFINED ALL THEIR THEANO FORMULAS |
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362 """ |
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363 self.truly_online=truly_online |
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364 |
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365 # create the formulas for the gradient update |
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366 old_params = [self.__getattribute__("_"+name) for name in self.parameterAttributes()] |
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367 new_params_names = ["_new_"+name for name in self.parameterAttributes()] |
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368 loss = self.__getattribute__("_"+self.lossAttribute()) |
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369 self.setAttributes(new_params_names, |
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370 [t.add_inplace(param,self._learning_rate*t.grad(loss,param)) |
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371 for param in old_params]) |
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372 MinibatchUpdatesTLearner.__init__(self) |
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373 |
134
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374 |
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375 def namesOfAttributesToComputeOutputs(self,output_names): |
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376 """ |
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377 The output_names are attribute names (not the corresponding Result names, which have leading _). |
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378 Return the corresponding input names |
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379 """ |
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380 all_inputs = t.gof.graph.inputs(self.names2OpResults(output_names)) |
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381 # remove constants and leading '_' in name |
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382 |
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383 return [r.name for r in all_inputs if isinstance(r,theano.Result) and \ |
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384 not isinstance(r,theano.Constant) and not isinstance(r,theano.Value)] |
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385 #inputs = [] |
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386 #for r in all_inputs: |
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387 # if isinstance(r,theano.Result) and \ |
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388 # not isinstance(r,theano.Constant) and not isinstance(r,theano.Value): |
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389 # inputs.append(r.name) |
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390 #return inputs |
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391 |
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392 def isLastEpoch(self): |
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393 return self.truly_online |
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394 |
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395 def updateMinibatchInputAttributes(self): |
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396 return self.parameterAttributes() |
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397 |
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398 def updateMinibatchOutputAttributes(self): |
133 | 399 return ["new_"+name for name in self.parameterAttributes()] |
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400 |
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401 def updateEndInputAttributes(self): |
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402 return self.namesOfAttributesToComputeOutputs(self.updateEndOutputAttributes()) |
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403 |
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404 def useInputAttributes(self): |
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405 return self.parameterAttributes() |
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406 |
134
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407 def useOutputAttributes(self): |
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408 return [] |
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409 |