Mercurial > pylearn
annotate learner.py @ 130:3d8e40e7ed18
minor
author | Yoshua Bengio <bengioy@iro.umontreal.ca> |
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date | Wed, 07 May 2008 21:40:03 -0400 |
parents | ee5507af2c60 |
children | 57e6492644ec |
rev | line source |
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130 | 2 from dataset import AttributesHolder,AbstractFunction |
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3 import compile |
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4 |
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5 class Learner(AttributesHolder): |
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6 """Base class for learning algorithms, provides an interface |
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7 that allows various algorithms to be applicable to generic learning |
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8 algorithms. |
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9 |
10
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10 A Learner can be seen as a learning algorithm, a function that when |
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11 applied to training data returns a learned function, an object that |
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12 can be applied to other data and return some output data. |
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13 """ |
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14 |
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15 def __init__(self): |
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16 pass |
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17 |
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18 def forget(self): |
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19 """ |
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20 Reset the state of the learner to a blank slate, before seeing |
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21 training data. The operation may be non-deterministic if the |
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22 learner has a random number generator that is set to use a |
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23 different seed each time it forget() is called. |
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24 """ |
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25 raise NotImplementedError |
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26 |
14 | 27 def update(self,training_set,train_stats_collector=None): |
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28 """ |
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29 Continue training a learner, with the evidence provided by the given training set. |
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30 Hence update can be called multiple times. This is particularly useful in the |
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31 on-line setting or the sequential (Bayesian or not) settings. |
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32 The result is a function that can be applied on data, with the same |
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33 semantics of the Learner.use method. |
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34 |
14 | 35 The user may optionally provide a training StatsCollector that is used to record |
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36 some statistics of the outputs computed during training. It is update(d) during |
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37 training. |
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38 """ |
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39 return self.use # default behavior is 'non-adaptive', i.e. update does not do anything |
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40 |
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41 |
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42 def __call__(self,training_set,train_stats_collector=None): |
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43 """ |
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44 Train a learner from scratch using the provided training set, |
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45 and return the learned function. |
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46 """ |
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47 self.forget() |
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48 return self.update(learning_task,train_stats_collector) |
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49 |
128 | 50 def use(self,input_dataset,output_fieldnames=None, |
51 test_stats_collector=None,copy_inputs=True, | |
52 put_stats_in_output_dataset=True, | |
53 output_attributes=[]): | |
54 """ | |
55 Once a Learner has been trained by one or more call to 'update', it can | |
56 be used with one or more calls to 'use'. The argument is an input DataSet (possibly | |
57 containing a single example) and the result is an output DataSet of the same length. | |
58 If output_fieldnames is specified, it may be use to indicate which fields should | |
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59 be constructed in the output DataSet (for example ['output','classification_error']). |
128 | 60 Otherwise, self.defaultOutputFields is called to choose the output fields. |
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61 Optionally, if copy_inputs, the input fields (of the input_dataset) can be made |
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62 visible in the output DataSet returned by this method. |
128 | 63 Optionally, attributes of the learner can be copied in the output dataset, |
64 and statistics computed by the stats collector also put in the output dataset. | |
65 Note the distinction between fields (which are example-wise quantities, e.g. 'input') | |
66 and attributes (which are not, e.g. 'regularization_term'). | |
67 | |
68 We provide here a default implementation that does all this using | |
69 a sub-class defined method: minibatchwiseUseFunction. | |
70 | |
71 @todo check if some of the learner attributes are actually SPECIFIED | |
72 as attributes of the input_dataset, and if so use their values instead | |
73 of the ones in the learner. | |
74 | |
75 The learner tries to compute in the output dataset the output fields specified. | |
76 If None is specified then self.defaultOutputFields(input_dataset.fieldNames()) | |
77 is called to determine the output fields. | |
78 | |
79 Attributes of the learner can also optionally be copied into the output dataset. | |
80 If output_attributes is None then all of the attributes in self.AttributeNames() | |
81 are copied in the output dataset, but if it is [] (the default), then none are copied. | |
82 If a test_stats_collector is provided, then its attributes (test_stats_collector.AttributeNames()) | |
83 are also copied into the output dataset attributes. | |
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84 """ |
128 | 85 minibatchwise_use_function = self.minibatchwiseUseFunction(input_dataset.fieldNames(), |
86 output_fieldnames, | |
87 test_stats_collector) | |
88 virtual_output_dataset = ApplyFunctionDataSet(input_dataset, | |
89 minibatchwise_use_function, | |
90 True,DataSet.numpy_vstack, | |
91 DataSet.numpy_hstack) | |
92 # actually force the computation | |
93 output_dataset = CachedDataSet(virtual_output_dataset,True) | |
94 if copy_inputs: | |
95 output_dataset = input_dataset | output_dataset | |
96 # copy the wanted attributes in the dataset | |
97 if output_attributes is None: | |
98 output_attributes = self.attributeNames() | |
99 if output_attributes: | |
100 assert set(attribute_names) <= set(self.attributeNames()) | |
101 output_dataset.setAttributes(output_attributes, | |
102 self.names2attributes(output_attributes,return_copy=True)) | |
103 if test_stats_collector: | |
104 test_stats_collector.update(output_dataset) | |
105 if put_stats_in_output_dataset: | |
106 output_dataset.setAttributes(test_stats_collector.attributeNames(), | |
107 test_stats_collector.attributes()) | |
108 return output_dataset | |
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109 |
128 | 110 def minibatchwiseUseFunction(self, input_fields, output_fields, stats_collector): |
111 """ | |
112 Returns a function that can map the given input fields to the given output fields | |
113 and to the attributes that the stats collector needs for its computation. | |
114 That function is expected to operate on minibatches. | |
115 The function returned makes use of the self.useInputAttributes() and | |
116 sets the attributes specified by self.useOutputAttributes(). | |
117 """ | |
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118 def attributeNames(self): |
78 | 119 """ |
120 A Learner may have attributes that it wishes to export to other objects. To automate | |
121 such export, sub-classes should define here the names (list of strings) of these attributes. | |
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122 |
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123 @todo By default, attributeNames looks for all dictionary entries whose name does not start with _. |
78 | 124 """ |
125 return [] | |
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126 |
128 | 127 def attributes(self,return_copy=False): |
128 """ | |
129 Return a list with the values of the learner's attributes (or optionally, a deep copy). | |
130 """ | |
131 return self.names2attributes(self.attributeNames(),return_copy) | |
132 | |
133 def names2attributes(self,names,return_copy=False): | |
134 """ | |
135 Private helper function that maps a list of attribute names to a list | |
136 of (optionally copies) values of attributes. | |
137 """ | |
138 if return_copy: | |
139 return [copy.deepcopy(self.__getattr__(name).data) for name in names] | |
140 else: | |
141 return [self.__getattr__(name).data for name in names] | |
142 | |
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143 def updateInputAttributes(self): |
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144 """ |
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145 A subset of self.attributeNames() which are the names of attributes needed by update() in order |
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146 to do its work. |
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147 """ |
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148 raise AbstractFunction() |
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149 |
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150 def useInputAttributes(self): |
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151 """ |
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152 A subset of self.attributeNames() which are the names of attributes needed by use() in order |
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153 to do its work. |
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154 """ |
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155 raise AbstractFunction() |
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156 |
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157 def updateOutputAttributes(self): |
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158 """ |
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159 A subset of self.attributeNames() which are the names of attributes modified/created by update() in order |
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160 to do its work. |
111
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161 |
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162 By default these are inferred from the various update output attributes: |
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163 """ |
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164 return ["parameters"] + self.updateMinibatchOutputAttributes() + self.updateEndOutputAttributes() |
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165 |
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166 def useOutputAttributes(self): |
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167 """ |
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168 A subset of self.attributeNames() which are the names of attributes modified/created by use() in order |
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169 to do its work. |
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170 """ |
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171 raise AbstractFunction() |
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172 |
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173 |
78 | 174 class TLearner(Learner): |
175 """ | |
176 TLearner is a virtual class of Learners that attempts to factor out of the definition | |
177 of a learner the steps that are common to many implementations of learning algorithms, | |
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178 so as to leave only 'the equations' to define in particular sub-classes, using Theano. |
78 | 179 |
180 In the default implementations of use and update, it is assumed that the 'use' and 'update' methods | |
181 visit examples in the input dataset sequentially. In the 'use' method only one pass through the dataset is done, | |
182 whereas the sub-learner may wish to iterate over the examples multiple times. Subclasses where this | |
183 basic model is not appropriate can simply redefine update or use. | |
184 | |
185 Sub-classes must provide the following functions and functionalities: | |
186 - attributeNames(): defines all the names of attributes which can be used as fields or | |
187 attributes in input/output datasets or in stats collectors. | |
188 All these attributes are expected to be theano.Result objects | |
189 (with a .data property and recognized by theano.Function for compilation). | |
190 The sub-class constructor defines the relations between | |
191 the Theano variables that may be used by 'use' and 'update' | |
192 or by a stats collector. | |
193 - defaultOutputFields(input_fields): return a list of default dataset output fields when | |
194 None are provided by the caller of use. | |
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195 The following naming convention is assumed and important. |
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196 Attributes whose names are listed in attributeNames() can be of any type, |
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197 but those that can be referenced as input/output dataset fields or as |
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198 output attributes in 'use' or as input attributes in the stats collector |
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199 should be associated with a Theano Result variable. If the exported attribute |
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200 name is <name>, the corresponding Result name (an internal attribute of |
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201 the TLearner, created in the sub-class constructor) should be _<name>. |
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202 Typically <name> will be numpy ndarray and _<name> will be the corresponding |
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203 Theano Tensor (for symbolic manipulation). |
107
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204 |
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205 @todo pousser dans Learner toute la poutine qui peut l'etre sans etre |
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206 dependant de Theano |
78 | 207 """ |
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208 |
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209 def __init__(self): |
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210 Learner.__init__(self) |
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211 |
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212 def defaultOutputFields(self, input_fields): |
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213 """ |
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214 Return a default list of output field names (to put in the output dataset). |
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215 This will be used when None are provided (as output_fields) by the caller of the 'use' method. |
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216 This may involve looking at the input_fields (names) available in the |
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217 input_dataset. |
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218 """ |
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219 raise AbstractFunction() |
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220 |
128 | 221 def minibatchwiseUseFunction(self, input_fields, output_fields, stats_collector): |
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222 """ |
128 | 223 Implement minibatchwiseUseFunction by exploiting Theano compilation |
224 and the expression graph defined by a sub-class constructor. | |
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225 """ |
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226 if not output_fields: |
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227 output_fields = self.defaultOutputFields(input_fields) |
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228 if stats_collector: |
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229 stats_collector_inputs = stats_collector.input2UpdateAttributes() |
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230 for attribute in stats_collector_inputs: |
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231 if attribute not in input_fields: |
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232 output_fields.append(attribute) |
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233 key = (input_fields,output_fields) |
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234 if key not in self.use_functions_dictionary: |
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235 use_input_attributes = self.useInputAttributes() |
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236 use_output_attributes = self.useOutputAttributes() |
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237 complete_f = compile.function(self.names2OpResults(input_fields+use_input_attributes), |
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238 self.names2OpResults(output_fields+use_output_attributes)) |
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239 def f(*input_field_values): |
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240 input_attribute_values = self.names2attributes(use_input_attributes) |
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241 results = complete_f(*(input_field_values + input_attribute_values)) |
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242 output_field_values = results[0:len(output_fields)] |
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243 output_attribute_values = results[len(output_fields):len(results)] |
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244 if use_output_attributes: |
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245 self.setAttributes(use_output_attributes,output_attribute_values) |
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246 return output_field_values |
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247 self.use_functions_dictionary[key]=f |
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248 return self.use_functions_dictionary[key] |
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249 |
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250 def names2OpResults(self,names): |
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251 """ |
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252 Private helper function that maps a list of attribute names to a list |
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253 of corresponding Op Results (with the same name but with a '_' prefix). |
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254 """ |
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255 return [self.__getattr__('_'+name).data for name in names] |
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256 |
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257 |
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258 class MinibatchUpdatesTLearner(TLearner): |
107
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259 """ |
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260 This adds to TLearner a |
110
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261 - updateStart(), updateEnd(), updateMinibatch(minibatch), isLastEpoch(): |
107
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262 functions executed at the beginning, the end, in the middle |
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263 (for each minibatch) of the update method, and at the end |
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264 of each epoch. This model only |
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265 works for 'online' or one-shot learning that requires |
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266 going only once through the training data. For more complicated |
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267 models, more specialized subclasses of TLearner should be used |
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268 or a learning-algorithm specific update method should be defined. |
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269 |
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270 - a 'parameters' attribute which is a list of parameters (whose names are |
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271 specified by the user's subclass with the parameterAttributes() method) |
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272 |
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273 """ |
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274 |
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275 def __init__(self): |
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276 TLearner.__init__(self) |
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277 self.update_minibatch_function = compile.function |
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278 (self.names2OpResults(self.updateMinibatchOutputAttributes()+ |
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279 self.updateMinibatchInputFields()), |
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280 self.names2OpResults(self.updateMinibatchOutputAttributes())) |
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281 self.update_end_function = compile.function |
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282 (self.names2OpResults(self.updateEndInputAttributes()), |
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283 self.names2OpResults(self.updateEndOutputAttributes())) |
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284 |
128 | 285 def allocate(self, minibatch): |
286 """ | |
287 This function is called at the beginning of each updateMinibatch | |
288 and should be used to check that all required attributes have been | |
289 allocated and initialized (usually this function calls forget() | |
290 when it has to do an initialization). | |
291 """ | |
292 raise AbstractFunction() | |
293 | |
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294 def updateMinibatchInputFields(self): |
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295 raise AbstractFunction() |
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296 |
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297 def updateMinibatchInputAttributes(self): |
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298 raise AbstractFunction() |
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299 |
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300 def updateMinibatchOutputAttributes(self): |
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301 raise AbstractFunction() |
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302 |
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303 def updateEndInputAttributes(self): |
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304 raise AbstractFunction() |
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305 |
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306 def updateEndOutputAttributes(self): |
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307 raise AbstractFunction() |
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308 |
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309 def parameterAttributes(self): |
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310 raise AbstractFunction() |
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311 |
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312 def updateStart(self): pass |
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313 |
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314 def updateEnd(self): |
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315 self.setAttributes(self.updateEndOutputAttributes(), |
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316 self.update_end_function |
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317 (self.names2attributes(self.updateEndInputAttributes()))) |
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318 self.parameters = self.names2attributes(self.parameterAttributes()) |
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319 |
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320 def updateMinibatch(self,minibatch): |
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321 # make sure all required fields are allocated and initialized |
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322 self.allocate(minibatch) |
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323 self.setAttributes(self.updateMinibatchOutputAttributes(), |
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324 # concatenate the attribute values and field values and then apply update fn |
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325 self.update_minibatch_function(*(self.names2attributes |
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326 (self.updateMinibatchInputAttributes())) |
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327 + minibatch(self.updateMinibatchInputFields()))) |
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328 |
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329 def isLastEpoch(self): |
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330 """ |
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331 This method is called at the end of each epoch (cycling over the training set). |
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332 It returns a boolean to indicate if this is the last epoch. |
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333 By default just do one epoch. |
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334 """ |
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335 return True |
78 | 336 |
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337 def update(self,training_set,train_stats_collector=None): |
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338 """ |
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339 @todo check if some of the learner attributes are actually SPECIFIED |
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340 in as attributes of the training_set. |
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341 """ |
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342 self.updateStart(training_set) |
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343 stop=False |
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344 while not stop: |
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345 if train_stats_collector: |
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346 train_stats_collector.forget() # restart stats collectin at the beginning of each epoch |
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347 for minibatch in training_set.minibatches(self.training_set_input_fields, |
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348 minibatch_size=self.minibatch_size): |
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349 self.update_minibatch(minibatch) |
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350 if train_stats_collector: |
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351 minibatch_set = minibatch.examples() |
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352 minibatch_set.setAttributes(self.attributeNames(),self.attributes()) |
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353 train_stats_collector.update(minibatch_set) |
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354 stop = self.isLastEpoch() |
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355 self.updateEnd() |
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356 return self.use |
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357 |
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358 class OnlineGradientBasedTLearner(MinibatchUpdatesTLearner): |
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359 """ |
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360 Specialization of MinibatchUpdatesTLearner in which the minibatch updates |
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361 are obtained by performing an online (minibatch-based) gradient step. |
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362 |
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363 Sub-classes must define the following: |
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364 |
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365 self._learning_rate (may be changed by the sub-class between epochs or minibatches) |
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366 |
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367 self.lossAttribute() = name of the loss field |
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368 |
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369 """ |
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370 def __init__(self,truly_online=False): |
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371 """ |
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372 If truly_online then only one pass is made through the training set passed to update(). |
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373 |
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374 SUBCLASSES SHOULD CALL THIS CONSTRUCTOR ONLY AFTER HAVING DEFINED ALL THEIR THEANO FORMULAS |
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375 """ |
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376 self.truly_online=truly_online |
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377 |
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378 # create the formulas for the gradient update |
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379 old_params = [self.__getattr__("_"+name) for name in self.parameterAttributes()] |
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380 new_params_names = ["_new_"+name for name in self.parameterAttributes()] |
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381 loss = self.__getattr__(self.lossAttribute()) |
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382 self.setAttributes(new_params_names, |
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383 [t.add_inplace(self.param, |
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384 self._learning_rate*t.grad(loss,param)) |
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385 for param in old_params]) |
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386 |
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387 def isLastEpoch(self): |
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388 return self.truly_online |
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389 |
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390 def updateMinibatchInputAttributes(self): |
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391 return self.parameterAttributes() |
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392 |
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393 def updateMinibatchOutputAttributes(self): |
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394 return ["_new"+name for name in self.parameterAttributes()] |
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395 |
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396 def updateEndInputAttributes(self): |
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397 return self.parameterAttributes() |
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398 |
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399 |