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annotate gradient_learner.py @ 19:57f4015e2e09

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Iterators extend LookupList
author bergstrj@iro.umontreal.ca
date Thu, 27 Mar 2008 01:59:44 -0400
parents 5ede27026e05
children 266c68cb6136
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 from gradient_based_optimizer import *
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8 class GradientLearner(Learner):
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9 """
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10 Base class for gradient-based optimization of a training criterion
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11 that can consist in two parts, an additive part over examples, and
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12 an example-independent part (usually called the regularizer).
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13 The user provides a Theano formula that maps the fields of a training example
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14 and parameters to output fields (for the use function), one of which must be a cost
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15 that is the training criterion to be minimized. Subclasses implement
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16 a training strategy that uses the function to compute gradients and
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17 to compute outputs in the update method.
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18 The inputs, parameters, and outputs are lists of Theano tensors,
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19 while the example_wise_cost and regularization_term are Theano tensors.
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20 The user can specify a regularization coefficient that multiplies the regularization term.
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21 The training algorithm looks for parameters that minimize
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22 regularization_coefficienet * regularization_term(parameters) +
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23 sum_{inputs in training_set} example_wise_cost(inputs,parameters)
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24 i.e. the regularization_term should not depend on the inputs, only on the parameters.
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25 The learned function can map a subset of inputs to a subset of outputs (as long as the inputs subset
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26 includes all the inputs required in the Theano expression for the selected outputs).
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27 It is assumed that all the inputs are provided in the training set, but
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28 not necessarily when using the learned function.
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29 """
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30 def __init__(self, inputs, parameters, outputs, example_wise_cost, regularization_term,
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31 gradient_based_optimizer=StochasticGradientDescent(), regularization_coefficient = astensor(1.0)):
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32 self.inputs = inputs
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33 self.outputs = outputs
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34 self.parameters = parameters
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35 self.example_wise_cost = example_wise_cost
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36 self.regularization_term = regularization_term
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37 self.gradient_based_optimizer = gradient_based_optimizer
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38 self.regularization_coefficient = regularization_coefficient
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39 self.parameters_example_wise_gradient = gradient.grad(example_wise_cost, parameters)
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40 self.parameters_regularization_gradient = gradient.grad(self.regularization_coefficient * regularization, parameters)
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41 if example_wise_cost not in outputs:
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42 outputs.append(example_wise_cost)
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43 if regularization_term not in outputs:
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44 outputs.append(regularization_term)
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45 self.example_wise_gradient_fn = Function(inputs + parameters,
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46 [self.parameters_example_wise_gradient + self.parameters_regularization_gradient])
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47 self.use_functions = {frozenset([input.name for input in inputs]) : Function(inputs, outputs)}
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49 def update(self,training_set):
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