Mercurial > pylearn
annotate learner.py @ 204:62c7527c9ec1
debugging mlp.py
author | Yoshua Bengio <bengioy@iro.umontreal.ca> |
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date | Wed, 14 May 2008 14:06:15 -0400 |
parents | 2698c0feeb54 |
children | cb6b945acf5a |
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2 from exceptions import * |
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3 from dataset import AttributesHolder,ApplyFunctionDataSet,DataSet,CachedDataSet |
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4 import theano |
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5 from theano import compile |
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6 from theano import tensor as t |
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7 from misc import Print |
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8 Print = lambda x: lambda y: y |
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9 |
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10 class Learner(AttributesHolder): |
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11 """ |
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12 Base class for learning algorithms, provides an interface |
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13 that allows various algorithms to be applicable to generic learning |
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14 algorithms. |
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15 |
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16 A L{Learner} can be seen as a learning algorithm, a function that when |
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17 applied to training data returns a learned function (which is an object that |
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18 can be applied to other data and return some output data). |
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19 """ |
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20 |
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21 def __init__(self): |
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22 pass |
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23 |
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24 def forget(self): |
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25 """ |
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26 Reset the state of the learner to a blank slate, before seeing |
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27 training data. The operation may be non-deterministic if the |
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28 learner has a random number generator that is set to use a |
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29 different seed each time it forget() is called. |
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30 """ |
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31 raise NotImplementedError |
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32 |
14 | 33 def update(self,training_set,train_stats_collector=None): |
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34 """ |
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35 Continue training a learner, with the evidence provided by the given training set. |
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36 Hence update can be called multiple times. This is particularly useful in the |
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37 on-line setting or the sequential (Bayesian or not) settings. |
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38 The result is a function that can be applied on data, with the same |
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39 semantics of the Learner.use method. |
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40 |
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41 The user may optionally provide a training L{StatsCollector} that is used to record |
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42 some statistics of the outputs computed during training. It is update(d) during |
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43 training. |
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44 """ |
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45 return self.use # default behavior is 'non-adaptive', i.e. update does not do anything |
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46 |
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47 |
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48 def __call__(self,training_set,train_stats_collector=None): |
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49 """ |
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50 Train a learner from scratch using the provided training set, |
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51 and return the learned function. |
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52 """ |
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53 self.forget() |
133 | 54 return self.update(training_set,train_stats_collector) |
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55 |
128 | 56 def use(self,input_dataset,output_fieldnames=None, |
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57 test_stats_collector=None,copy_inputs=False, |
128 | 58 put_stats_in_output_dataset=True, |
59 output_attributes=[]): | |
60 """ | |
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61 Once a L{Learner} has been trained by one or more call to 'update', it can |
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62 be used with one or more calls to 'use'. The argument is an input L{DataSet} (possibly |
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63 containing a single example) and the result is an output L{DataSet} of the same length. |
128 | 64 If output_fieldnames is specified, it may be use to indicate which fields should |
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65 be constructed in the output L{DataSet} (for example ['output','classification_error']). |
128 | 66 Otherwise, self.defaultOutputFields is called to choose the output fields. |
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67 Optionally, if copy_inputs, the input fields (of the input_dataset) can be made |
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68 visible in the output L{DataSet} returned by this method. |
128 | 69 Optionally, attributes of the learner can be copied in the output dataset, |
70 and statistics computed by the stats collector also put in the output dataset. | |
71 Note the distinction between fields (which are example-wise quantities, e.g. 'input') | |
72 and attributes (which are not, e.g. 'regularization_term'). | |
73 | |
74 We provide here a default implementation that does all this using | |
75 a sub-class defined method: minibatchwiseUseFunction. | |
76 | |
77 @todo check if some of the learner attributes are actually SPECIFIED | |
78 as attributes of the input_dataset, and if so use their values instead | |
79 of the ones in the learner. | |
80 | |
81 The learner tries to compute in the output dataset the output fields specified. | |
82 If None is specified then self.defaultOutputFields(input_dataset.fieldNames()) | |
83 is called to determine the output fields. | |
84 | |
85 Attributes of the learner can also optionally be copied into the output dataset. | |
86 If output_attributes is None then all of the attributes in self.AttributeNames() | |
87 are copied in the output dataset, but if it is [] (the default), then none are copied. | |
88 If a test_stats_collector is provided, then its attributes (test_stats_collector.AttributeNames()) | |
89 are also copied into the output dataset attributes. | |
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90 """ |
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91 input_fieldnames = input_dataset.fieldNames() |
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92 if not output_fieldnames: |
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93 output_fieldnames = self.defaultOutputFields(input_fieldnames) |
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94 |
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95 minibatchwise_use_function = self.minibatchwiseUseFunction(input_fieldnames, |
128 | 96 output_fieldnames, |
97 test_stats_collector) | |
98 virtual_output_dataset = ApplyFunctionDataSet(input_dataset, | |
99 minibatchwise_use_function, | |
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100 output_fieldnames, |
128 | 101 True,DataSet.numpy_vstack, |
102 DataSet.numpy_hstack) | |
103 # actually force the computation | |
104 output_dataset = CachedDataSet(virtual_output_dataset,True) | |
105 if copy_inputs: | |
106 output_dataset = input_dataset | output_dataset | |
107 # copy the wanted attributes in the dataset | |
108 if output_attributes is None: | |
109 output_attributes = self.attributeNames() | |
110 if output_attributes: | |
111 assert set(attribute_names) <= set(self.attributeNames()) | |
112 output_dataset.setAttributes(output_attributes, | |
113 self.names2attributes(output_attributes,return_copy=True)) | |
114 if test_stats_collector: | |
115 test_stats_collector.update(output_dataset) | |
116 if put_stats_in_output_dataset: | |
117 output_dataset.setAttributes(test_stats_collector.attributeNames(), | |
118 test_stats_collector.attributes()) | |
119 return output_dataset | |
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120 |
128 | 121 def minibatchwiseUseFunction(self, input_fields, output_fields, stats_collector): |
122 """ | |
123 Returns a function that can map the given input fields to the given output fields | |
124 and to the attributes that the stats collector needs for its computation. | |
125 That function is expected to operate on minibatches. | |
126 The function returned makes use of the self.useInputAttributes() and | |
127 sets the attributes specified by self.useOutputAttributes(). | |
128 """ | |
143
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129 raise AbstractFunction() |
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130 |
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131 def attributeNames(self): |
78 | 132 """ |
133 A Learner may have attributes that it wishes to export to other objects. To automate | |
134 such export, sub-classes should define here the names (list of strings) of these attributes. | |
107
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135 |
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136 @todo By default, attributeNames looks for all dictionary entries whose name does not start with _. |
78 | 137 """ |
138 return [] | |
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139 |
128 | 140 def attributes(self,return_copy=False): |
141 """ | |
142 Return a list with the values of the learner's attributes (or optionally, a deep copy). | |
143 """ | |
144 return self.names2attributes(self.attributeNames(),return_copy) | |
145 | |
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146 def names2attributes(self,names): |
128 | 147 """ |
148 Private helper function that maps a list of attribute names to a list | |
149 of (optionally copies) values of attributes. | |
150 """ | |
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151 res=[] |
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152 for name in names: |
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153 assert name in names |
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154 res.append(self.__getattribute__(name)) |
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155 return res |
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156 |
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157 def useInputAttributes(self): |
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158 """ |
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159 A subset of self.attributeNames() which are the names of attributes needed by use() in order |
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160 to do its work. |
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161 """ |
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162 raise AbstractFunction() |
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163 |
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164 def useOutputAttributes(self): |
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165 """ |
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166 A subset of self.attributeNames() which are the names of attributes modified/created by use() in order |
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167 to do its work. |
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168 """ |
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169 raise AbstractFunction() |
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170 |
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171 |
78 | 172 class TLearner(Learner): |
173 """ | |
167 | 174 TLearner is a virtual class of L{Learner}s that attempts to factor |
175 out of the definition of a learner the steps that are common to | |
176 many implementations of learning algorithms, so as to leave only | |
177 'the equations' to define in particular sub-classes, using Theano. | |
78 | 178 |
167 | 179 In the default implementations of use and update, it is assumed |
180 that the 'use' and 'update' methods visit examples in the input | |
181 dataset sequentially. In the 'use' method only one pass through the | |
182 dataset is done, whereas the sub-learner may wish to iterate over | |
183 the examples multiple times. Subclasses where this basic model is | |
184 not appropriate can simply redefine update or use. | |
185 | |
78 | 186 Sub-classes must provide the following functions and functionalities: |
167 | 187 - attributeNames(): defines all the names of attributes which can |
188 be used as fields or | |
189 attributes in input/output datasets or in | |
190 stats collectors. All these attributes | |
191 are expected to be theano.Result objects | |
192 (with a .data property and recognized by | |
180
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193 theano.function for compilation). The sub-class |
167 | 194 constructor defines the relations between the |
195 Theano variables that may be used by 'use' | |
196 and 'update' or by a stats collector. | |
197 - defaultOutputFields(input_fields): return a list of default | |
198 dataset output fields when | |
78 | 199 None are provided by the caller of use. |
167 | 200 The following naming convention is assumed and important. Attributes |
201 whose names are listed in attributeNames() can be of any type, | |
202 but those that can be referenced as input/output dataset fields or | |
203 as output attributes in 'use' or as input attributes in the stats | |
204 collector should be associated with a Theano Result variable. If the | |
205 exported attribute name is <name>, the corresponding Result name | |
206 (an internal attribute of the TLearner, created in the sub-class | |
207 constructor) should be _<name>. Typically <name> will be numpy | |
208 ndarray and _<name> will be the corresponding Theano Tensor (for | |
209 symbolic manipulation). | |
107
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210 |
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211 @todo pousser dans Learner toute la poutine qui peut l'etre sans etre |
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212 dependant de Theano |
78 | 213 """ |
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214 |
180
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215 def __init__(self,linker="c|py"): |
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216 Learner.__init__(self) |
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217 self.use_functions_dictionary={} |
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218 self.linker=linker |
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219 |
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220 def defaultOutputFields(self, input_fields): |
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221 """ |
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222 Return a default list of output field names (to put in the output dataset). |
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223 This will be used when None are provided (as output_fields) by the caller of the 'use' method. |
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224 This may involve looking at the input_fields (names) available in the |
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225 input_dataset. |
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226 """ |
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227 raise AbstractFunction() |
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228 |
128 | 229 def minibatchwiseUseFunction(self, input_fields, output_fields, stats_collector): |
110
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230 """ |
128 | 231 Implement minibatchwiseUseFunction by exploiting Theano compilation |
232 and the expression graph defined by a sub-class constructor. | |
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233 """ |
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234 if stats_collector: |
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235 stats_collector_inputs = stats_collector.input2UpdateAttributes() |
92
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236 for attribute in stats_collector_inputs: |
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237 if attribute not in input_fields: |
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238 output_fields.append(attribute) |
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239 key = (tuple(input_fields),tuple(output_fields)) |
92
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240 if key not in self.use_functions_dictionary: |
110
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241 use_input_attributes = self.useInputAttributes() |
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242 use_output_attributes = self.useOutputAttributes() |
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243 complete_f = compile.function(self.names2OpResults(input_fields+use_input_attributes), |
180
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244 self.names2OpResults(output_fields+use_output_attributes), |
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245 self.linker) |
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246 def f(*input_field_values): |
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247 input_attribute_values = self.names2attributes(use_input_attributes) |
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248 results = complete_f(*(list(input_field_values) + input_attribute_values)) |
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249 output_field_values = results[0:len(output_fields)] |
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250 output_attribute_values = results[len(output_fields):len(results)] |
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251 if use_output_attributes: |
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252 self.setAttributes(use_output_attributes,output_attribute_values) |
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253 return output_field_values |
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254 self.use_functions_dictionary[key]=f |
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255 return self.use_functions_dictionary[key] |
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256 |
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257 def names2OpResults(self,names): |
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258 """ |
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259 Private helper function that maps a list of attribute names to a list |
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260 of corresponding Op Results (with the same name but with a '_' prefix). |
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261 """ |
133 | 262 return [self.__getattribute__('_'+name) for name in names] |
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263 |
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264 |
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265 class MinibatchUpdatesTLearner(TLearner): |
107
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266 """ |
167 | 267 This adds the following functions to a L{TLearner}: |
110
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268 - updateStart(), updateEnd(), updateMinibatch(minibatch), isLastEpoch(): |
167 | 269 functions executed at the beginning, the end, in the middle (for |
270 each minibatch) of the update method, and at the end of each | |
271 epoch. This model only works for 'online' or one-shot learning | |
272 that requires going only once through the training data. For more | |
273 complicated models, more specialized subclasses of TLearner should | |
274 be used or a learning-algorithm specific update method should | |
275 be defined. | |
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276 |
167 | 277 - a 'parameters' attribute which is a list of parameters |
278 (whose names are specified by the user's subclass with the | |
279 parameterAttributes() method) | |
280 | |
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281 """ |
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282 |
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283 def __init__(self,linker="c|py"): |
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284 TLearner.__init__(self,linker) |
177 | 285 self.update_minibatch_function = compile.function(self.names2OpResults(self.updateMinibatchInputAttributes()+ |
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286 self.updateMinibatchInputFields()), |
180
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287 self.names2OpResults(self.updateMinibatchOutputAttributes()), |
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288 linker) |
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289 self.update_end_function = compile.function(self.names2OpResults(self.updateEndInputAttributes()), |
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290 self.names2OpResults(self.updateEndOutputAttributes()), |
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291 linker) |
110
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292 |
128 | 293 def allocate(self, minibatch): |
294 """ | |
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295 This function is called at the beginning of each L{updateMinibatch} |
128 | 296 and should be used to check that all required attributes have been |
297 allocated and initialized (usually this function calls forget() | |
298 when it has to do an initialization). | |
299 """ | |
300 raise AbstractFunction() | |
301 | |
110
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302 def updateMinibatchInputFields(self): |
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303 raise AbstractFunction() |
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304 |
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305 def updateMinibatchInputAttributes(self): |
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306 raise AbstractFunction() |
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307 |
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308 def updateMinibatchOutputAttributes(self): |
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309 raise AbstractFunction() |
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310 |
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311 def updateEndInputAttributes(self): |
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312 raise AbstractFunction() |
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313 |
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314 def updateEndOutputAttributes(self): |
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315 raise AbstractFunction() |
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316 |
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317 def parameterAttributes(self): |
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318 raise AbstractFunction() |
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319 |
133 | 320 def updateStart(self,training_set): |
321 pass | |
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322 |
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323 def updateEnd(self): |
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324 self.setAttributes(self.updateEndOutputAttributes(), |
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325 self.update_end_function(*self.names2attributes(self.updateEndInputAttributes()))) |
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326 self.parameters = self.names2attributes(self.parameterAttributes()) |
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327 |
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328 def updateMinibatch(self,minibatch): |
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329 # make sure all required fields are allocated and initialized |
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330 self.allocate(minibatch) |
134
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331 input_attributes = self.names2attributes(self.updateMinibatchInputAttributes()) |
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332 input_fields = minibatch(*self.updateMinibatchInputFields()) |
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333 self.setAttributes(self.updateMinibatchOutputAttributes(), |
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334 # concatenate the attribute values and field values and then apply update fn |
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335 self.update_minibatch_function(*(input_attributes+input_fields))) |
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336 |
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337 def isLastEpoch(self): |
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338 """ |
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339 This method is called at the end of each epoch (cycling over the training set). |
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340 It returns a boolean to indicate if this is the last epoch. |
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341 By default just do one epoch. |
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342 """ |
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343 return True |
78 | 344 |
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345 def update(self,training_set,train_stats_collector=None): |
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346 """ |
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347 @todo check if some of the learner attributes are actually SPECIFIED |
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348 in as attributes of the training_set. |
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349 """ |
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350 self.updateStart(training_set) |
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351 stop=False |
133 | 352 if hasattr(self,'_minibatch_size') and self._minibatch_size: |
353 minibatch_size=self._minibatch_size | |
354 else: | |
355 minibatch_size=min(100,len(training_set)) | |
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356 while not stop: |
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357 if train_stats_collector: |
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358 train_stats_collector.forget() # restart stats collectin at the beginning of each epoch |
133 | 359 for minibatch in training_set.minibatches(minibatch_size=minibatch_size): |
360 self.updateMinibatch(minibatch) | |
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361 if train_stats_collector: |
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362 minibatch_set = minibatch.examples() |
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363 minibatch_set.setAttributes(self.attributeNames(),self.attributes()) |
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364 train_stats_collector.update(minibatch_set) |
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365 stop = self.isLastEpoch() |
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366 self.updateEnd() |
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367 return self.use |
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368 |
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369 class OnlineGradientTLearner(MinibatchUpdatesTLearner): |
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370 """ |
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371 Specialization of L{MinibatchUpdatesTLearner} in which the minibatch updates |
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372 are obtained by performing an online (minibatch-based) gradient step. |
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373 |
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374 Sub-classes must define the following: |
132
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375 - self._learning_rate (may be changed by the sub-class between epochs or minibatches) |
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376 - self.lossAttribute() = name of the loss field |
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377 """ |
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378 def __init__(self,truly_online=False,linker="c|py"): |
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379 """ |
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380 If truly_online then only one pass is made through the training set passed to update(). |
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381 |
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382 SUBCLASSES SHOULD CALL THIS CONSTRUCTOR ONLY AFTER HAVING DEFINED ALL THEIR THEANO FORMULAS |
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383 """ |
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384 self.truly_online=truly_online |
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385 |
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386 # create the formulas for the gradient update |
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387 old_params = [self.__getattribute__("_"+name) for name in self.parameterAttributes()] |
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388 new_params_names = ["_new_"+name for name in self.parameterAttributes()] |
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389 loss = self.__getattribute__("_"+self.lossAttribute()) |
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390 self.setAttributes(new_params_names, |
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391 [t.add_inplace(param,-self._learning_rate*Print("grad("+param.name+")")(t.grad(loss,param))) |
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392 for param in old_params]) |
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393 MinibatchUpdatesTLearner.__init__(self,linker) |
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394 |
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395 |
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396 def namesOfAttributesToComputeOutputs(self,output_names): |
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397 """ |
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398 The output_names are attribute names (not the corresponding Result names, which have leading _). |
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399 Return the corresponding input names |
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400 """ |
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401 all_inputs = t.gof.graph.inputs(self.names2OpResults(output_names)) |
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402 # remove constants and leading '_' in name |
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403 |
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404 return [r.name for r in all_inputs if isinstance(r,theano.Result) and \ |
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405 not isinstance(r,theano.Constant) and not isinstance(r,theano.Value)] |
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406 #inputs = [] |
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407 #for r in all_inputs: |
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408 # if isinstance(r,theano.Result) and \ |
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409 # not isinstance(r,theano.Constant) and not isinstance(r,theano.Value): |
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410 # inputs.append(r.name) |
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411 #return inputs |
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412 |
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413 def isLastEpoch(self): |
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414 return self.truly_online |
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415 |
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416 def updateMinibatchInputAttributes(self): |
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417 return self.parameterAttributes()+["learning_rate"] |
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418 |
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419 def updateMinibatchOutputAttributes(self): |
133 | 420 return ["new_"+name for name in self.parameterAttributes()] |
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421 |
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422 def updateEndInputAttributes(self): |
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423 return self.namesOfAttributesToComputeOutputs(self.updateEndOutputAttributes()) |
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424 |
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425 def useInputAttributes(self): |
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426 return self.parameterAttributes() |
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427 |
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428 def useOutputAttributes(self): |
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429 return [] |
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430 |