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annotate gradient_learner.py @ 99:a8da709eb6a9

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in ArrayDataSet.__init__ if a columns is an index, we change it to be a list that containt only this index. This way, we remove the special case where the columns is an index for all subsequent call. This was possing trouble with numpy.vstack() called by MinibatchWrapAroundIterator.next
author Frederic Bastien <bastienf@iro.umontreal.ca>
date Tue, 06 May 2008 13:57:36 -0400
parents 46c5c90019c2
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rev   line source
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2 from learner import *
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3 from tensor import *
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4 import gradient
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5 from compile import Function
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6
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7 class GradientLearner(Learner):
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8 """
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9 Base class for gradient-based optimization of a training criterion
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10 that can consist in two parts, an additive part over examples, and
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11 an example-independent part (usually called the regularizer).
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12 The user provides a Theano formula that maps the fields of a minibatch (each being a tensor with the
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13 same number of rows = minibatch size) and parameters to output fields (for the use function), one of which
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14 must be a cost that is the training criterion to be minimized. Subclasses implement
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15 a training strategy that uses the Theano formula to compute gradients and
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16 to compute outputs in the update method.
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17 The inputs, parameters, and outputs are lists of Theano tensors,
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18 while the example_wise_cost and regularization_term are Theano tensors.
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19 The user can specify a regularization coefficient that multiplies the regularization term.
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20 The training algorithm looks for parameters that minimize
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21 regularization_coefficient * regularization_term(parameters) +
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22 sum_{inputs in training_set} example_wise_cost(inputs,parameters)
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23 i.e. the regularization_term should not depend on the inputs, only on the parameters.
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24 The learned function can map a subset of inputs to a subset of outputs (as long as the inputs subset
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25 includes all the inputs required in the Theano expression for the selected outputs).
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26 It is assumed that all the inputs are provided in the training set (as dataset fields
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27 with the corresponding name), but not necessarily when using the learned function.
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28 """
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29 def __init__(self, inputs, parameters, outputs, example_wise_cost, regularization_term=astensor(0.0),
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30 regularization_coefficient = astensor(1.0)):
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31 self.inputs = inputs
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32 self.outputs = outputs
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33 self.parameters = parameters
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34 self.example_wise_cost = example_wise_cost
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35 self.regularization_term = regularization_term
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36 self.regularization_coefficient = regularization_coefficient
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37 self.parameters_example_wise_gradient = gradient.grad(example_wise_cost, parameters)
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38 self.parameters_regularization_gradient = gradient.grad(self.regularization_coefficient * regularization_term, parameters)
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39 if example_wise_cost not in outputs:
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40 outputs.append(example_wise_cost)
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41 if regularization_term not in outputs:
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42 outputs.append(regularization_term)
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43 self.example_wise_gradient_fn = Function(inputs + parameters,
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44 [self.parameters_example_wise_gradient + self.parameters_regularization_gradient])
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45 self.use_functions = {frozenset([input.name for input in inputs]+[output.name for output in outputs])
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46 : Function(inputs, outputs)}
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47
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48 def use(self,input_dataset,output_fields=None,copy_inputs=True):
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49 # obtain the function that maps the desired inputs to desired outputs
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50 input_fields = input_dataset.fieldNames()
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51 # map names of input fields to Theano tensors in self.inputs
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52 input_variables = ???
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53 if output_fields is None: output_fields = [output.name for output in outputs]
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54 # handle special case of inputs that are directly copied into outputs
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55 # map names of output fields to Theano tensors in self.outputs
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56 output_variables = ???
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57 use_function_key = input_fields+output_fields
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58 if not self.use_functions.has_key(use_function_key):
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59 self.use_function[use_function_key]=Function(input_variables,output_variables)
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60 use_function = self.use_functions[use_function_key]
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61 # return a dataset that computes the outputs
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62 return input_dataset.apply_function(use_function,input_fields,output_fields,copy_inputs,compute_now=True)
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63
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64
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65 class StochasticGradientDescent(object):
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66 def update_parameters(self):
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68 class StochasticGradientLearner(GradientLearner,StochasticGradientDescent):
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69 def __init__(self,inputs, parameters, outputs, example_wise_cost, regularization_term=astensor(0.0),
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70 regularization_coefficient = astensor(1.0),)
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71 def update()