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annotate gradient_learner.py @ 20:266c68cb6136

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Minor editions, plus adding untested ApplyFunctionDataset for GradientLearner in the works.
author bengioy@bengiomac.local
date Mon, 07 Apr 2008 09:48:39 -0400
parents 5ede27026e05
children 526e192b0699
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 training example
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13 and parameters to output fields (for the use function), one of which must be a cost
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14 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,
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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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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 if output_fields is None: output_fields = [output.name for output in outputs]
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52 # handle special case of inputs that are directly copied into outputs
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53
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54 use_function_key = input_fields+output_fields
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55 if not self.use_functions.has_key(use_function_key):
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56 self.use_function[use_function_key]=Function(input_fields,output_fields)
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57 use_function = self.use_functions[use_function_key]
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58 # return a virtual dataset that computes the outputs on demand
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59 return input_dataset.apply_function(use_function,input_fields,output_fields,copy_inputs,accept_minibatches=???)
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