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annotate learner.py @ 147:625d2b21ee48

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Automated merge with ssh://p-omega1@lgcm.iro.umontreal.ca/tlearn
author Yoshua Bengio <bengioy@iro.umontreal.ca>
date Mon, 12 May 2008 15:50:34 -0400
parents ceae4de18981
children 4803cb76e26b
rev   line source
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2 from dataset import AttributesHolder,AbstractFunction,ApplyFunctionDataSet,DataSet,CachedDataSet
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3 import theano
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4 from theano import compile
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5 from theano import tensor as t
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7 class Learner(AttributesHolder):
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8 """
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9 Base class for learning algorithms, provides an interface
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10 that allows various algorithms to be applicable to generic learning
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11 algorithms.
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13 A L{Learner} can be seen as a learning algorithm, a function that when
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14 applied to training data returns a learned function (which is an object that
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15 can be applied to other data and return some output data).
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17 """
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18
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19 def __init__(self):
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20 pass
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21
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22 def forget(self):
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23 """
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24 Reset the state of the learner to a blank slate, before seeing
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25 training data. The operation may be non-deterministic if the
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26 learner has a random number generator that is set to use a
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27 different seed each time it forget() is called.
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28 """
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29 raise NotImplementedError
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30
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31 def update(self,training_set,train_stats_collector=None):
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32 """
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33 Continue training a learner, with the evidence provided by the given training set.
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34 Hence update can be called multiple times. This is particularly useful in the
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35 on-line setting or the sequential (Bayesian or not) settings.
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36 The result is a function that can be applied on data, with the same
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37 semantics of the Learner.use method.
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38
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39 The user may optionally provide a training L{StatsCollector} that is used to record
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40 some statistics of the outputs computed during training. It is update(d) during
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41 training.
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42 """
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43 return self.use # default behavior is 'non-adaptive', i.e. update does not do anything
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45
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46 def __call__(self,training_set,train_stats_collector=None):
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47 """
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48 Train a learner from scratch using the provided training set,
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49 and return the learned function.
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50 """
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51 self.forget()
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52 return self.update(training_set,train_stats_collector)
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53
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54 def use(self,input_dataset,output_fieldnames=None,
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55 test_stats_collector=None,copy_inputs=False,
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56 put_stats_in_output_dataset=True,
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57 output_attributes=[]):
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58 """
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59 Once a L{Learner} has been trained by one or more call to 'update', it can
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60 be used with one or more calls to 'use'. The argument is an input L{DataSet} (possibly
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61 containing a single example) and the result is an output L{DataSet} of the same length.
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62 If output_fieldnames is specified, it may be use to indicate which fields should
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63 be constructed in the output L{DataSet} (for example ['output','classification_error']).
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64 Otherwise, self.defaultOutputFields is called to choose the output fields.
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65 Optionally, if copy_inputs, the input fields (of the input_dataset) can be made
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66 visible in the output L{DataSet} returned by this method.
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67 Optionally, attributes of the learner can be copied in the output dataset,
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68 and statistics computed by the stats collector also put in the output dataset.
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69 Note the distinction between fields (which are example-wise quantities, e.g. 'input')
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70 and attributes (which are not, e.g. 'regularization_term').
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71
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72 We provide here a default implementation that does all this using
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73 a sub-class defined method: minibatchwiseUseFunction.
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74
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75 @todo check if some of the learner attributes are actually SPECIFIED
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76 as attributes of the input_dataset, and if so use their values instead
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77 of the ones in the learner.
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78
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79 The learner tries to compute in the output dataset the output fields specified.
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80 If None is specified then self.defaultOutputFields(input_dataset.fieldNames())
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81 is called to determine the output fields.
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82
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83 Attributes of the learner can also optionally be copied into the output dataset.
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84 If output_attributes is None then all of the attributes in self.AttributeNames()
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85 are copied in the output dataset, but if it is [] (the default), then none are copied.
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86 If a test_stats_collector is provided, then its attributes (test_stats_collector.AttributeNames())
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87 are also copied into the output dataset attributes.
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88 """
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89 input_fieldnames = input_dataset.fieldNames()
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90 if not output_fieldnames:
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91 output_fieldnames = self.defaultOutputFields(input_fieldnames)
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93 minibatchwise_use_function = self.minibatchwiseUseFunction(input_fieldnames,
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94 output_fieldnames,
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95 test_stats_collector)
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96 virtual_output_dataset = ApplyFunctionDataSet(input_dataset,
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97 minibatchwise_use_function,
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98 output_fieldnames,
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99 True,DataSet.numpy_vstack,
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100 DataSet.numpy_hstack)
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101 # actually force the computation
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102 output_dataset = CachedDataSet(virtual_output_dataset,True)
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103 if copy_inputs:
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104 output_dataset = input_dataset | output_dataset
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105 # copy the wanted attributes in the dataset
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106 if output_attributes is None:
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107 output_attributes = self.attributeNames()
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108 if output_attributes:
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109 assert set(attribute_names) <= set(self.attributeNames())
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110 output_dataset.setAttributes(output_attributes,
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111 self.names2attributes(output_attributes,return_copy=True))
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112 if test_stats_collector:
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113 test_stats_collector.update(output_dataset)
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114 if put_stats_in_output_dataset:
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115 output_dataset.setAttributes(test_stats_collector.attributeNames(),
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116 test_stats_collector.attributes())
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117 return output_dataset
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118
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119 def minibatchwiseUseFunction(self, input_fields, output_fields, stats_collector):
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120 """
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121 Returns a function that can map the given input fields to the given output fields
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122 and to the attributes that the stats collector needs for its computation.
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123 That function is expected to operate on minibatches.
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124 The function returned makes use of the self.useInputAttributes() and
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125 sets the attributes specified by self.useOutputAttributes().
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126 """
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127 raise AbstractFunction()
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129 def attributeNames(self):
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130 """
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131 A Learner may have attributes that it wishes to export to other objects. To automate
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132 such export, sub-classes should define here the names (list of strings) of these attributes.
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134 @todo By default, attributeNames looks for all dictionary entries whose name does not start with _.
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135 """
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136 return []
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137
128
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138 def attributes(self,return_copy=False):
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139 """
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140 Return a list with the values of the learner's attributes (or optionally, a deep copy).
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141 """
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142 return self.names2attributes(self.attributeNames(),return_copy)
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143
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144 def names2attributes(self,names):
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145 """
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146 Private helper function that maps a list of attribute names to a list
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147 of (optionally copies) values of attributes.
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148 """
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149 res=[]
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150 for name in names:
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151 assert name in names
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152 res.append(self.__getattribute__(name))
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153 return res
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154
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155 def useInputAttributes(self):
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156 """
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157 A subset of self.attributeNames() which are the names of attributes needed by use() in order
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158 to do its work.
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159 """
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160 raise AbstractFunction()
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161
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162 def useOutputAttributes(self):
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163 """
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164 A subset of self.attributeNames() which are the names of attributes modified/created by use() in order
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165 to do its work.
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166 """
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167 raise AbstractFunction()
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168
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169
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170 class TLearner(Learner):
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171 """
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172 TLearner is a virtual class of Learners that attempts to factor out of the definition
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173 of a learner the steps that are common to many implementations of learning algorithms,
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174 so as to leave only 'the equations' to define in particular sub-classes, using Theano.
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175
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176 In the default implementations of use and update, it is assumed that the 'use' and 'update' methods
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177 visit examples in the input dataset sequentially. In the 'use' method only one pass through the dataset is done,
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178 whereas the sub-learner may wish to iterate over the examples multiple times. Subclasses where this
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179 basic model is not appropriate can simply redefine update or use.
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180
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181 Sub-classes must provide the following functions and functionalities:
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182 - attributeNames(): defines all the names of attributes which can be used as fields or
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183 attributes in input/output datasets or in stats collectors.
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184 All these attributes are expected to be theano.Result objects
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185 (with a .data property and recognized by theano.Function for compilation).
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186 The sub-class constructor defines the relations between
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187 the Theano variables that may be used by 'use' and 'update'
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188 or by a stats collector.
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189 - defaultOutputFields(input_fields): return a list of default dataset output fields when
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190 None are provided by the caller of use.
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191 The following naming convention is assumed and important.
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192 Attributes whose names are listed in attributeNames() can be of any type,
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193 but those that can be referenced as input/output dataset fields or as
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194 output attributes in 'use' or as input attributes in the stats collector
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195 should be associated with a Theano Result variable. If the exported attribute
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196 name is <name>, the corresponding Result name (an internal attribute of
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197 the TLearner, created in the sub-class constructor) should be _<name>.
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198 Typically <name> will be numpy ndarray and _<name> will be the corresponding
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199 Theano Tensor (for symbolic manipulation).
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200
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201 @todo pousser dans Learner toute la poutine qui peut l'etre sans etre
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202 dependant de Theano
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203 """
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204
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205 def __init__(self):
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206 Learner.__init__(self)
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207 self.use_functions_dictionary={}
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208
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209 def defaultOutputFields(self, input_fields):
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210 """
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211 Return a default list of output field names (to put in the output dataset).
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212 This will be used when None are provided (as output_fields) by the caller of the 'use' method.
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213 This may involve looking at the input_fields (names) available in the
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214 input_dataset.
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215 """
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216 raise AbstractFunction()
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217
128
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218 def minibatchwiseUseFunction(self, input_fields, output_fields, stats_collector):
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219 """
128
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220 Implement minibatchwiseUseFunction by exploiting Theano compilation
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221 and the expression graph defined by a sub-class constructor.
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222 """
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223 if stats_collector:
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224 stats_collector_inputs = stats_collector.input2UpdateAttributes()
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225 for attribute in stats_collector_inputs:
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226 if attribute not in input_fields:
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227 output_fields.append(attribute)
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228 key = (tuple(input_fields),tuple(output_fields))
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229 if key not in self.use_functions_dictionary:
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230 use_input_attributes = self.useInputAttributes()
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231 use_output_attributes = self.useOutputAttributes()
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232 complete_f = compile.function(self.names2OpResults(input_fields+use_input_attributes),
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233 self.names2OpResults(output_fields+use_output_attributes))
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234 def f(*input_field_values):
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235 input_attribute_values = self.names2attributes(use_input_attributes)
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236 results = complete_f(*(list(input_field_values) + input_attribute_values))
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237 output_field_values = results[0:len(output_fields)]
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238 output_attribute_values = results[len(output_fields):len(results)]
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239 if use_output_attributes:
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240 self.setAttributes(use_output_attributes,output_attribute_values)
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241 return output_field_values
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242 self.use_functions_dictionary[key]=f
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243 return self.use_functions_dictionary[key]
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244
110
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245 def names2OpResults(self,names):
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246 """
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247 Private helper function that maps a list of attribute names to a list
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248 of corresponding Op Results (with the same name but with a '_' prefix).
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249 """
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250 return [self.__getattribute__('_'+name) for name in names]
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251
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252
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253 class MinibatchUpdatesTLearner(TLearner):
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254 """
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255 This adds to L{TLearner} a
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256 - updateStart(), updateEnd(), updateMinibatch(minibatch), isLastEpoch():
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257 functions executed at the beginning, the end, in the middle
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258 (for each minibatch) of the update method, and at the end
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259 of each epoch. This model only
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260 works for 'online' or one-shot learning that requires
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261 going only once through the training data. For more complicated
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262 models, more specialized subclasses of TLearner should be used
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263 or a learning-algorithm specific update method should be defined.
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264
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265 - a 'parameters' attribute which is a list of parameters (whose names are
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266 specified by the user's subclass with the parameterAttributes() method)
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267
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268 """
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269
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270 def __init__(self):
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271 TLearner.__init__(self)
134
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272 self.update_minibatch_function = compile.function(self.names2OpResults(self.updateMinibatchOutputAttributes()+
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273 self.updateMinibatchInputFields()),
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274 self.names2OpResults(self.updateMinibatchOutputAttributes()))
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275 self.update_end_function = compile.function(self.names2OpResults(self.updateEndInputAttributes()),
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276 self.names2OpResults(self.updateEndOutputAttributes()))
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277
128
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278 def allocate(self, minibatch):
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279 """
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280 This function is called at the beginning of each L{updateMinibatch}
128
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281 and should be used to check that all required attributes have been
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282 allocated and initialized (usually this function calls forget()
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283 when it has to do an initialization).
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284 """
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285 raise AbstractFunction()
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286
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287 def updateMinibatchInputFields(self):
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288 raise AbstractFunction()
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289
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290 def updateMinibatchInputAttributes(self):
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291 raise AbstractFunction()
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292
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293 def updateMinibatchOutputAttributes(self):
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294 raise AbstractFunction()
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295
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296 def updateEndInputAttributes(self):
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297 raise AbstractFunction()
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298
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299 def updateEndOutputAttributes(self):
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300 raise AbstractFunction()
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301
111
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302 def parameterAttributes(self):
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303 raise AbstractFunction()
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304
133
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305 def updateStart(self,training_set):
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306 pass
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307
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308 def updateEnd(self):
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309 self.setAttributes(self.updateEndOutputAttributes(),
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310 self.update_end_function(*self.names2attributes(self.updateEndInputAttributes())))
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311 self.parameters = self.names2attributes(self.parameterAttributes())
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312
110
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313 def updateMinibatch(self,minibatch):
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314 # make sure all required fields are allocated and initialized
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315 self.allocate(minibatch)
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316 input_attributes = self.names2attributes(self.updateMinibatchInputAttributes())
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317 input_fields = minibatch(*self.updateMinibatchInputFields())
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318 self.setAttributes(self.updateMinibatchOutputAttributes(),
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319 # concatenate the attribute values and field values and then apply update fn
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320 self.update_minibatch_function(*(input_attributes+input_fields)))
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321
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322 def isLastEpoch(self):
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323 """
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324 This method is called at the end of each epoch (cycling over the training set).
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325 It returns a boolean to indicate if this is the last epoch.
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326 By default just do one epoch.
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327 """
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328 return True
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329
92
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330 def update(self,training_set,train_stats_collector=None):
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331 """
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332 @todo check if some of the learner attributes are actually SPECIFIED
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333 in as attributes of the training_set.
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334 """
110
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335 self.updateStart(training_set)
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336 stop=False
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337 if hasattr(self,'_minibatch_size') and self._minibatch_size:
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338 minibatch_size=self._minibatch_size
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339 else:
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340 minibatch_size=min(100,len(training_set))
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341 while not stop:
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342 if train_stats_collector:
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343 train_stats_collector.forget() # restart stats collectin at the beginning of each epoch
133
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344 for minibatch in training_set.minibatches(minibatch_size=minibatch_size):
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345 self.updateMinibatch(minibatch)
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346 if train_stats_collector:
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347 minibatch_set = minibatch.examples()
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348 minibatch_set.setAttributes(self.attributeNames(),self.attributes())
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349 train_stats_collector.update(minibatch_set)
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350 stop = self.isLastEpoch()
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351 self.updateEnd()
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352 return self.use
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353
129
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354 class OnlineGradientTLearner(MinibatchUpdatesTLearner):
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355 """
132
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356 Specialization of L{MinibatchUpdatesTLearner} in which the minibatch updates
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357 are obtained by performing an online (minibatch-based) gradient step.
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358
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359 Sub-classes must define the following:
132
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360 - self._learning_rate (may be changed by the sub-class between epochs or minibatches)
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361 - self.lossAttribute() = name of the loss field
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362 """
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363 def __init__(self,truly_online=False):
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364 """
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365 If truly_online then only one pass is made through the training set passed to update().
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366
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367 SUBCLASSES SHOULD CALL THIS CONSTRUCTOR ONLY AFTER HAVING DEFINED ALL THEIR THEANO FORMULAS
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368 """
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369 self.truly_online=truly_online
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370
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371 # create the formulas for the gradient update
129
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372 old_params = [self.__getattribute__("_"+name) for name in self.parameterAttributes()]
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373 new_params_names = ["_new_"+name for name in self.parameterAttributes()]
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374 loss = self.__getattribute__("_"+self.lossAttribute())
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375 self.setAttributes(new_params_names,
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376 [t.add_inplace(param,self._learning_rate*t.grad(loss,param))
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377 for param in old_params])
129
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378 MinibatchUpdatesTLearner.__init__(self)
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379
134
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380
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381 def namesOfAttributesToComputeOutputs(self,output_names):
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382 """
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383 The output_names are attribute names (not the corresponding Result names, which have leading _).
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384 Return the corresponding input names
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385 """
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386 all_inputs = t.gof.graph.inputs(self.names2OpResults(output_names))
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387 # remove constants and leading '_' in name
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388
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389 return [r.name for r in all_inputs if isinstance(r,theano.Result) and \
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390 not isinstance(r,theano.Constant) and not isinstance(r,theano.Value)]
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391 #inputs = []
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392 #for r in all_inputs:
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393 # if isinstance(r,theano.Result) and \
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394 # not isinstance(r,theano.Constant) and not isinstance(r,theano.Value):
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395 # inputs.append(r.name)
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396 #return inputs
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397
111
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398 def isLastEpoch(self):
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399 return self.truly_online
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400
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d0a1bd0378c6 Finished draft of OneHiddenLayerNNetClassifier to debut learner.py
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 111
diff changeset
401 def updateMinibatchInputAttributes(self):
d0a1bd0378c6 Finished draft of OneHiddenLayerNNetClassifier to debut learner.py
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 111
diff changeset
402 return self.parameterAttributes()
d0a1bd0378c6 Finished draft of OneHiddenLayerNNetClassifier to debut learner.py
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 111
diff changeset
403
d0a1bd0378c6 Finished draft of OneHiddenLayerNNetClassifier to debut learner.py
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 111
diff changeset
404 def updateMinibatchOutputAttributes(self):
133
b4657441dd65 Corrected typos
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 132
diff changeset
405 return ["new_"+name for name in self.parameterAttributes()]
118
d0a1bd0378c6 Finished draft of OneHiddenLayerNNetClassifier to debut learner.py
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 111
diff changeset
406
d0a1bd0378c6 Finished draft of OneHiddenLayerNNetClassifier to debut learner.py
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 111
diff changeset
407 def updateEndInputAttributes(self):
134
3f4e5c9bdc5e Fixes to ApplyFunctionDataSet and other things to make learner and mlp work
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 133
diff changeset
408 return self.namesOfAttributesToComputeOutputs(self.updateEndOutputAttributes())
3f4e5c9bdc5e Fixes to ApplyFunctionDataSet and other things to make learner and mlp work
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 133
diff changeset
409
3f4e5c9bdc5e Fixes to ApplyFunctionDataSet and other things to make learner and mlp work
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 133
diff changeset
410 def useInputAttributes(self):
118
d0a1bd0378c6 Finished draft of OneHiddenLayerNNetClassifier to debut learner.py
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 111
diff changeset
411 return self.parameterAttributes()
111
88257dfedf8c Added another work in progress, for mlp's
bengioy@bengiomac.local
parents: 110
diff changeset
412
134
3f4e5c9bdc5e Fixes to ApplyFunctionDataSet and other things to make learner and mlp work
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 133
diff changeset
413 def useOutputAttributes(self):
3f4e5c9bdc5e Fixes to ApplyFunctionDataSet and other things to make learner and mlp work
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 133
diff changeset
414 return []
118
d0a1bd0378c6 Finished draft of OneHiddenLayerNNetClassifier to debut learner.py
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 111
diff changeset
415