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annotate algorithms/regressor.py @ 494:02a331ba833b

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merge
author Joseph Turian <turian@gmail.com>
date Tue, 28 Oct 2008 11:40:56 -0400
parents 8fcd0f3d9a17
children 2b0e10ac6929
rev   line source
476
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1
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2 import theano
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3 from theano import tensor as T
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4 from theano.tensor import nnet as NN
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5 import numpy as N
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6
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7 class Regressor(theano.FancyModule):
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8
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9 def __init__(self, input = None, target = None, regularize = True):
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10 super(Regressor, self).__init__()
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11
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12 # MODEL CONFIGURATION
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13 self.regularize = regularize
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14
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15 # ACQUIRE/MAKE INPUT AND TARGET
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16 self.input = theano.External(input) if input else T.matrix('input')
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17 self.target = theano.External(target) if target else T.matrix('target')
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18
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19 # HYPER-PARAMETERS
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20 self.lr = theano.Member(T.scalar())
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21
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22 # PARAMETERS
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23 self.w = theano.Member(T.matrix())
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24 self.b = theano.Member(T.vector())
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25
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26 # OUTPUT
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27 self.output_activation = T.dot(self.input, self.w) + self.b
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28 self.output = self.build_output()
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29
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30 # REGRESSION COST
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31 self.regression_cost = self.build_regression_cost()
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32
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33 # REGULARIZATION COST
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34 self.regularization = self.build_regularization()
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35
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36 # TOTAL COST
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37 self.cost = self.regression_cost
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38 if self.regularize:
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39 self.cost = self.cost + self.regularization
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40
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41 # GRADIENTS AND UPDATES
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42 self.params = self.w, self.b
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43 gradients = T.grad(self.cost, self.params)
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44 updates = dict((p, p - self.lr * g) for p, g in zip(self.params, gradients))
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45
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46 # INTERFACE METHODS
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47 self.update = theano.Method([self.input, self.target], self.cost, updates)
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48 self.predict = theano.Method(self.input, self.output)
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49
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50 self.build_extensions()
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51
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52 def _instance_initialize(self, obj, input_size = None, output_size = None, seed = None, **init):
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53 if seed is not None:
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54 R = N.random.RandomState(seed)
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55 else:
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56 R = N.random
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57 if (input_size is None) ^ (output_size is None):
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58 raise ValueError("Must specify input_size and output_size or neither.")
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59 super(Regressor, self)._instance_initialize(obj, **init)
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60 if input_size is not None:
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61 sz = (input_size, output_size)
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62 range = 1/N.sqrt(input_size)
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63 obj.w = R.uniform(size = sz, low = -range, high = range)
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64 obj.b = N.zeros(output_size)
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65 obj.__hide__ = ['params']
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66
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67 def _instance_flops_approx(self, obj):
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68 return obj.w.size
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69
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70 def build_extensions(self):
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71 pass
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72
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73 def build_output(self):
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74 raise NotImplementedError('override in subclass')
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75
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76 def build_regression_cost(self):
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77 raise NotImplementedError('override in subclass')
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78
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79 def build_regularization(self):
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80 return T.zero() # no regularization!
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81
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82
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83 class BinRegressor(Regressor):
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84
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85 def build_extensions(self):
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86 self.classes = T.iround(self.output)
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87 self.classify = theano.Method(self.input, self.classes)
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88
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89 def build_output(self):
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90 return NN.sigmoid(self.output_activation)
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91
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92 def build_regression_cost(self):
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93 self.regression_cost_matrix = self.target * T.log(self.output) + (1.0 - self.target) * T.log(1.0 - self.output)
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94 self.regression_costs = -T.sum(self.regression_cost_matrix, axis=1)
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95 return T.mean(self.regression_costs)
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96
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97 def build_regularization(self):
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98 self.l2_coef = theano.Member(T.scalar())
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99 return self.l2_coef * T.sum(self.w * self.w)
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100
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101 def _instance_initialize(self, obj, input_size = None, output_size = 1, seed = None, **init):
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102 init.setdefault('l2_coef', 0)
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103 super(BinRegressor, self)._instance_initialize(obj, input_size, output_size, seed, **init)