Mercurial > pylearn
annotate learner.py @ 129:4c2280edcaf5
Fixed typos in learner.py
author | Yoshua Bengio <bengioy@iro.umontreal.ca> |
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date | Wed, 07 May 2008 21:22:56 -0400 |
parents | ee5507af2c60 |
children | 57e6492644ec |
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2 from dataset import AttributesHolder,AbstractFunction |
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3 import compile |
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4 from theano import tensor as t |
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5 |
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6 class Learner(AttributesHolder): |
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7 """Base class for learning algorithms, provides an interface |
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8 that allows various algorithms to be applicable to generic learning |
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9 algorithms. |
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10 |
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11 A Learner can be seen as a learning algorithm, a function that when |
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12 applied to training data returns a learned function, an object that |
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13 can be applied to other data and return some output data. |
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14 """ |
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15 |
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16 def __init__(self): |
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17 pass |
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18 |
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19 def forget(self): |
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20 """ |
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21 Reset the state of the learner to a blank slate, before seeing |
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22 training data. The operation may be non-deterministic if the |
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23 learner has a random number generator that is set to use a |
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24 different seed each time it forget() is called. |
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25 """ |
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26 raise NotImplementedError |
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27 |
14 | 28 def update(self,training_set,train_stats_collector=None): |
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29 """ |
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30 Continue training a learner, with the evidence provided by the given training set. |
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31 Hence update can be called multiple times. This is particularly useful in the |
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32 on-line setting or the sequential (Bayesian or not) settings. |
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33 The result is a function that can be applied on data, with the same |
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34 semantics of the Learner.use method. |
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35 |
14 | 36 The user may optionally provide a training StatsCollector that is used to record |
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37 some statistics of the outputs computed during training. It is update(d) during |
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38 training. |
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39 """ |
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40 return self.use # default behavior is 'non-adaptive', i.e. update does not do anything |
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41 |
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42 |
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43 def __call__(self,training_set,train_stats_collector=None): |
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44 """ |
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45 Train a learner from scratch using the provided training set, |
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46 and return the learned function. |
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47 """ |
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48 self.forget() |
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49 return self.update(learning_task,train_stats_collector) |
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50 |
128 | 51 def use(self,input_dataset,output_fieldnames=None, |
52 test_stats_collector=None,copy_inputs=True, | |
53 put_stats_in_output_dataset=True, | |
54 output_attributes=[]): | |
55 """ | |
56 Once a Learner has been trained by one or more call to 'update', it can | |
57 be used with one or more calls to 'use'. The argument is an input DataSet (possibly | |
58 containing a single example) and the result is an output DataSet of the same length. | |
59 If output_fieldnames is specified, it may be use to indicate which fields should | |
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60 be constructed in the output DataSet (for example ['output','classification_error']). |
128 | 61 Otherwise, self.defaultOutputFields is called to choose the output fields. |
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62 Optionally, if copy_inputs, the input fields (of the input_dataset) can be made |
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63 visible in the output DataSet returned by this method. |
128 | 64 Optionally, attributes of the learner can be copied in the output dataset, |
65 and statistics computed by the stats collector also put in the output dataset. | |
66 Note the distinction between fields (which are example-wise quantities, e.g. 'input') | |
67 and attributes (which are not, e.g. 'regularization_term'). | |
68 | |
69 We provide here a default implementation that does all this using | |
70 a sub-class defined method: minibatchwiseUseFunction. | |
71 | |
72 @todo check if some of the learner attributes are actually SPECIFIED | |
73 as attributes of the input_dataset, and if so use their values instead | |
74 of the ones in the learner. | |
75 | |
76 The learner tries to compute in the output dataset the output fields specified. | |
77 If None is specified then self.defaultOutputFields(input_dataset.fieldNames()) | |
78 is called to determine the output fields. | |
79 | |
80 Attributes of the learner can also optionally be copied into the output dataset. | |
81 If output_attributes is None then all of the attributes in self.AttributeNames() | |
82 are copied in the output dataset, but if it is [] (the default), then none are copied. | |
83 If a test_stats_collector is provided, then its attributes (test_stats_collector.AttributeNames()) | |
84 are also copied into the output dataset attributes. | |
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85 """ |
128 | 86 minibatchwise_use_function = self.minibatchwiseUseFunction(input_dataset.fieldNames(), |
87 output_fieldnames, | |
88 test_stats_collector) | |
89 virtual_output_dataset = ApplyFunctionDataSet(input_dataset, | |
90 minibatchwise_use_function, | |
91 True,DataSet.numpy_vstack, | |
92 DataSet.numpy_hstack) | |
93 # actually force the computation | |
94 output_dataset = CachedDataSet(virtual_output_dataset,True) | |
95 if copy_inputs: | |
96 output_dataset = input_dataset | output_dataset | |
97 # copy the wanted attributes in the dataset | |
98 if output_attributes is None: | |
99 output_attributes = self.attributeNames() | |
100 if output_attributes: | |
101 assert set(attribute_names) <= set(self.attributeNames()) | |
102 output_dataset.setAttributes(output_attributes, | |
103 self.names2attributes(output_attributes,return_copy=True)) | |
104 if test_stats_collector: | |
105 test_stats_collector.update(output_dataset) | |
106 if put_stats_in_output_dataset: | |
107 output_dataset.setAttributes(test_stats_collector.attributeNames(), | |
108 test_stats_collector.attributes()) | |
109 return output_dataset | |
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110 |
128 | 111 def minibatchwiseUseFunction(self, input_fields, output_fields, stats_collector): |
112 """ | |
113 Returns a function that can map the given input fields to the given output fields | |
114 and to the attributes that the stats collector needs for its computation. | |
115 That function is expected to operate on minibatches. | |
116 The function returned makes use of the self.useInputAttributes() and | |
117 sets the attributes specified by self.useOutputAttributes(). | |
118 """ | |
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119 def attributeNames(self): |
78 | 120 """ |
121 A Learner may have attributes that it wishes to export to other objects. To automate | |
122 such export, sub-classes should define here the names (list of strings) of these attributes. | |
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123 |
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124 @todo By default, attributeNames looks for all dictionary entries whose name does not start with _. |
78 | 125 """ |
126 return [] | |
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127 |
128 | 128 def attributes(self,return_copy=False): |
129 """ | |
130 Return a list with the values of the learner's attributes (or optionally, a deep copy). | |
131 """ | |
132 return self.names2attributes(self.attributeNames(),return_copy) | |
133 | |
134 def names2attributes(self,names,return_copy=False): | |
135 """ | |
136 Private helper function that maps a list of attribute names to a list | |
137 of (optionally copies) values of attributes. | |
138 """ | |
139 if return_copy: | |
129
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140 return [copy.deepcopy(self.__getattribute__(name).data) for name in names] |
128 | 141 else: |
129
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142 return [self.__getattribute__(name).data for name in names] |
128 | 143 |
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144 def updateInputAttributes(self): |
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145 """ |
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146 A subset of self.attributeNames() which are the names of attributes needed by update() in order |
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147 to do its work. |
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148 """ |
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149 raise AbstractFunction() |
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150 |
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151 def useInputAttributes(self): |
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152 """ |
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153 A subset of self.attributeNames() which are the names of attributes needed by use() in order |
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154 to do its work. |
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155 """ |
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156 raise AbstractFunction() |
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157 |
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158 def updateOutputAttributes(self): |
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159 """ |
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160 A subset of self.attributeNames() which are the names of attributes modified/created by update() in order |
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161 to do its work. |
111
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162 |
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163 By default these are inferred from the various update output attributes: |
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164 """ |
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165 return ["parameters"] + self.updateMinibatchOutputAttributes() + self.updateEndOutputAttributes() |
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166 |
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167 def useOutputAttributes(self): |
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168 """ |
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169 A subset of self.attributeNames() which are the names of attributes modified/created by use() in order |
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170 to do its work. |
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171 """ |
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172 raise AbstractFunction() |
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173 |
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174 |
78 | 175 class TLearner(Learner): |
176 """ | |
177 TLearner is a virtual class of Learners that attempts to factor out of the definition | |
178 of a learner the steps that are common to many implementations of learning algorithms, | |
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179 so as to leave only 'the equations' to define in particular sub-classes, using Theano. |
78 | 180 |
181 In the default implementations of use and update, it is assumed that the 'use' and 'update' methods | |
182 visit examples in the input dataset sequentially. In the 'use' method only one pass through the dataset is done, | |
183 whereas the sub-learner may wish to iterate over the examples multiple times. Subclasses where this | |
184 basic model is not appropriate can simply redefine update or use. | |
185 | |
186 Sub-classes must provide the following functions and functionalities: | |
187 - attributeNames(): defines all the names of attributes which can be used as fields or | |
188 attributes in input/output datasets or in stats collectors. | |
189 All these attributes are expected to be theano.Result objects | |
190 (with a .data property and recognized by theano.Function for compilation). | |
191 The sub-class constructor defines the relations between | |
192 the Theano variables that may be used by 'use' and 'update' | |
193 or by a stats collector. | |
194 - defaultOutputFields(input_fields): return a list of default dataset output fields when | |
195 None are provided by the caller of use. | |
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196 The following naming convention is assumed and important. |
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197 Attributes whose names are listed in attributeNames() can be of any type, |
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198 but those that can be referenced as input/output dataset fields or as |
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199 output attributes in 'use' or as input attributes in the stats collector |
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200 should be associated with a Theano Result variable. If the exported attribute |
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201 name is <name>, the corresponding Result name (an internal attribute of |
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202 the TLearner, created in the sub-class constructor) should be _<name>. |
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203 Typically <name> will be numpy ndarray and _<name> will be the corresponding |
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204 Theano Tensor (for symbolic manipulation). |
107
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205 |
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206 @todo pousser dans Learner toute la poutine qui peut l'etre sans etre |
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207 dependant de Theano |
78 | 208 """ |
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209 |
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210 def __init__(self): |
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211 Learner.__init__(self) |
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212 |
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213 def defaultOutputFields(self, input_fields): |
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214 """ |
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215 Return a default list of output field names (to put in the output dataset). |
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216 This will be used when None are provided (as output_fields) by the caller of the 'use' method. |
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217 This may involve looking at the input_fields (names) available in the |
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218 input_dataset. |
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219 """ |
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220 raise AbstractFunction() |
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221 |
128 | 222 def minibatchwiseUseFunction(self, input_fields, output_fields, stats_collector): |
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223 """ |
128 | 224 Implement minibatchwiseUseFunction by exploiting Theano compilation |
225 and the expression graph defined by a sub-class constructor. | |
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226 """ |
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227 if not output_fields: |
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228 output_fields = self.defaultOutputFields(input_fields) |
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229 if stats_collector: |
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230 stats_collector_inputs = stats_collector.input2UpdateAttributes() |
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231 for attribute in stats_collector_inputs: |
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232 if attribute not in input_fields: |
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233 output_fields.append(attribute) |
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234 key = (input_fields,output_fields) |
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235 if key not in self.use_functions_dictionary: |
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236 use_input_attributes = self.useInputAttributes() |
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237 use_output_attributes = self.useOutputAttributes() |
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238 complete_f = compile.function(self.names2OpResults(input_fields+use_input_attributes), |
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239 self.names2OpResults(output_fields+use_output_attributes)) |
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240 def f(*input_field_values): |
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241 input_attribute_values = self.names2attributes(use_input_attributes) |
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242 results = complete_f(*(input_field_values + input_attribute_values)) |
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243 output_field_values = results[0:len(output_fields)] |
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244 output_attribute_values = results[len(output_fields):len(results)] |
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245 if use_output_attributes: |
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246 self.setAttributes(use_output_attributes,output_attribute_values) |
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247 return output_field_values |
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248 self.use_functions_dictionary[key]=f |
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249 return self.use_functions_dictionary[key] |
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250 |
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251 def names2OpResults(self,names): |
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252 """ |
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253 Private helper function that maps a list of attribute names to a list |
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254 of corresponding Op Results (with the same name but with a '_' prefix). |
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255 """ |
129
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256 return [self.__getattribute__('_'+name).data for name in names] |
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257 |
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258 |
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259 class MinibatchUpdatesTLearner(TLearner): |
107
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260 """ |
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261 This adds to TLearner a |
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262 - updateStart(), updateEnd(), updateMinibatch(minibatch), isLastEpoch(): |
107
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263 functions executed at the beginning, the end, in the middle |
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264 (for each minibatch) of the update method, and at the end |
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265 of each epoch. This model only |
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266 works for 'online' or one-shot learning that requires |
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267 going only once through the training data. For more complicated |
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268 models, more specialized subclasses of TLearner should be used |
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269 or a learning-algorithm specific update method should be defined. |
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270 |
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271 - a 'parameters' attribute which is a list of parameters (whose names are |
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272 specified by the user's subclass with the parameterAttributes() method) |
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273 |
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274 """ |
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275 |
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276 def __init__(self): |
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277 TLearner.__init__(self) |
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278 self.update_minibatch_function = compile.function |
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279 (self.names2OpResults(self.updateMinibatchOutputAttributes()+ |
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280 self.updateMinibatchInputFields()), |
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281 self.names2OpResults(self.updateMinibatchOutputAttributes())) |
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282 self.update_end_function = compile.function |
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283 (self.names2OpResults(self.updateEndInputAttributes()), |
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284 self.names2OpResults(self.updateEndOutputAttributes())) |
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285 |
128 | 286 def allocate(self, minibatch): |
287 """ | |
288 This function is called at the beginning of each updateMinibatch | |
289 and should be used to check that all required attributes have been | |
290 allocated and initialized (usually this function calls forget() | |
291 when it has to do an initialization). | |
292 """ | |
293 raise AbstractFunction() | |
294 | |
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295 def updateMinibatchInputFields(self): |
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296 raise AbstractFunction() |
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297 |
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298 def updateMinibatchInputAttributes(self): |
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299 raise AbstractFunction() |
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300 |
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301 def updateMinibatchOutputAttributes(self): |
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302 raise AbstractFunction() |
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303 |
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304 def updateEndInputAttributes(self): |
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305 raise AbstractFunction() |
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306 |
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307 def updateEndOutputAttributes(self): |
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308 raise AbstractFunction() |
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309 |
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310 def parameterAttributes(self): |
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311 raise AbstractFunction() |
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312 |
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313 def updateStart(self): pass |
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314 |
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315 def updateEnd(self): |
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316 self.setAttributes(self.updateEndOutputAttributes(), |
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317 self.update_end_function |
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318 (self.names2attributes(self.updateEndInputAttributes()))) |
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319 self.parameters = self.names2attributes(self.parameterAttributes()) |
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320 |
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321 def updateMinibatch(self,minibatch): |
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322 # make sure all required fields are allocated and initialized |
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323 self.allocate(minibatch) |
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324 self.setAttributes(self.updateMinibatchOutputAttributes(), |
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325 # concatenate the attribute values and field values and then apply update fn |
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326 self.update_minibatch_function(*(self.names2attributes |
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327 (self.updateMinibatchInputAttributes())) |
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328 + minibatch(self.updateMinibatchInputFields()))) |
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329 |
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330 def isLastEpoch(self): |
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331 """ |
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332 This method is called at the end of each epoch (cycling over the training set). |
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333 It returns a boolean to indicate if this is the last epoch. |
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334 By default just do one epoch. |
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335 """ |
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336 return True |
78 | 337 |
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338 def update(self,training_set,train_stats_collector=None): |
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339 """ |
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340 @todo check if some of the learner attributes are actually SPECIFIED |
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341 in as attributes of the training_set. |
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342 """ |
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343 self.updateStart(training_set) |
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344 stop=False |
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345 while not stop: |
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346 if train_stats_collector: |
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347 train_stats_collector.forget() # restart stats collectin at the beginning of each epoch |
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348 for minibatch in training_set.minibatches(self.training_set_input_fields, |
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349 minibatch_size=self.minibatch_size): |
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350 self.update_minibatch(minibatch) |
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351 if train_stats_collector: |
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352 minibatch_set = minibatch.examples() |
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353 minibatch_set.setAttributes(self.attributeNames(),self.attributes()) |
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354 train_stats_collector.update(minibatch_set) |
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355 stop = self.isLastEpoch() |
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356 self.updateEnd() |
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357 return self.use |
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358 |
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359 class OnlineGradientTLearner(MinibatchUpdatesTLearner): |
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360 """ |
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361 Specialization of MinibatchUpdatesTLearner in which the minibatch updates |
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362 are obtained by performing an online (minibatch-based) gradient step. |
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363 |
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364 Sub-classes must define the following: |
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365 |
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366 self._learning_rate (may be changed by the sub-class between epochs or minibatches) |
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367 |
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368 self.lossAttribute() = name of the loss field |
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369 |
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370 """ |
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371 def __init__(self,truly_online=False): |
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372 """ |
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373 If truly_online then only one pass is made through the training set passed to update(). |
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374 |
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375 SUBCLASSES SHOULD CALL THIS CONSTRUCTOR ONLY AFTER HAVING DEFINED ALL THEIR THEANO FORMULAS |
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376 """ |
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377 self.truly_online=truly_online |
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378 |
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379 # create the formulas for the gradient update |
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380 old_params = [self.__getattribute__("_"+name) for name in self.parameterAttributes()] |
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381 new_params_names = ["_new_"+name for name in self.parameterAttributes()] |
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382 loss = self.__getattribute__("_"+self.lossAttribute()) |
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383 self.setAttributes(new_params_names, |
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384 [t.add_inplace(param,self._learning_rate*t.grad(loss,param)) |
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385 for param in old_params]) |
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386 MinibatchUpdatesTLearner.__init__(self) |
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387 |
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388 def isLastEpoch(self): |
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389 return self.truly_online |
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390 |
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391 def updateMinibatchInputAttributes(self): |
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392 return self.parameterAttributes() |
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393 |
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394 def updateMinibatchOutputAttributes(self): |
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395 return ["_new"+name for name in self.parameterAttributes()] |
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396 |
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397 def updateEndInputAttributes(self): |
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398 return self.parameterAttributes() |
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399 |
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400 |