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annotate algorithms/daa.py @ 482:b15dad843c8c

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bugfix
author Olivier Breuleux <breuleuo@iro.umontreal.ca>
date Tue, 28 Oct 2008 00:23:13 -0400
parents 8fcd0f3d9a17
children e8c37244b54f
rev   line source
476
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Olivier Breuleux <breuleuo@iro.umontreal.ca>
parents:
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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 DenoisingAA(T.RModule):
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8
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9 def __init__(self, input = None, regularize = True, tie_weights = True):
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10 super(DenoisingAA, 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 self.tie_weights = tie_weights
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15
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16 # ACQUIRE/MAKE INPUT
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17 if not input:
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18 input = T.matrix('input')
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19 self.input = theano.External(input)
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20
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21 # HYPER-PARAMETERS
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22 self.lr = theano.Member(T.scalar())
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23
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24 # PARAMETERS
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25 self.w1 = theano.Member(T.matrix())
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26 if not tie_weights:
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27 self.w2 = theano.Member(T.matrix())
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28 else:
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29 self.w2 = self.w1.T
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30 self.b1 = theano.Member(T.vector())
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31 self.b2 = theano.Member(T.vector())
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32
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33
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34 # REGULARIZATION COST
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35 self.regularization = self.build_regularization()
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36
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37
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38 ### NOISELESS ###
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39
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40 # HIDDEN LAYER
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41 self.hidden_activation = T.dot(self.input, self.w1) + self.b1
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42 self.hidden = self.hid_activation_function(self.hidden_activation)
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43
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44 # RECONSTRUCTION LAYER
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45 self.output_activation = T.dot(self.hidden, self.w2) + self.b2
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46 self.output = self.out_activation_function(self.output_activation)
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47
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48 # RECONSTRUCTION COST
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49 self.reconstruction_costs = self.build_reconstruction_costs(self.output)
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50 self.reconstruction_cost = T.mean(self.reconstruction_costs)
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51
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52 # TOTAL COST
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53 self.cost = self.reconstruction_cost
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54 if self.regularize:
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55 self.cost = self.cost + self.regularization
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56
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57
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58 ### WITH NOISE ###
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59 self.corrupted_input = self.build_corrupted_input()
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60
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61 # HIDDEN LAYER
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62 self.nhidden_activation = T.dot(self.corrupted_input, self.w1) + self.b1
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63 self.nhidden = self.hid_activation_function(self.nhidden_activation)
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64
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65 # RECONSTRUCTION LAYER
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66 self.noutput_activation = T.dot(self.nhidden, self.w2) + self.b2
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67 self.noutput = self.out_activation_function(self.noutput_activation)
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68
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69 # RECONSTRUCTION COST
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70 self.nreconstruction_costs = self.build_reconstruction_costs(self.noutput)
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71 self.nreconstruction_cost = T.mean(self.nreconstruction_costs)
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72
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73 # TOTAL COST
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74 self.ncost = self.nreconstruction_cost
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75 if self.regularize:
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76 self.ncost = self.ncost + self.regularization
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77
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78
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79 # GRADIENTS AND UPDATES
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80 if self.tie_weights:
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81 self.params = self.w1, self.b1, self.b2
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82 else:
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83 self.params = self.w1, self.w2, self.b1, self.b2
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84 gradients = T.grad(self.ncost, self.params)
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85 updates = dict((p, p - self.lr * g) for p, g in zip(self.params, gradients))
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86
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87 # INTERFACE METHODS
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88 self.update = theano.Method(self.input, self.ncost, updates)
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89 self.compute_cost = theano.Method(self.input, self.cost)
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90 self.noisify = theano.Method(self.input, self.corrupted_input)
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91 self.reconstruction = theano.Method(self.input, self.output)
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92 self.representation = theano.Method(self.input, self.hidden)
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93 self.reconstruction_through_noise = theano.Method(self.input, [self.corrupted_input, self.noutput])
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94
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95 def _instance_initialize(self, obj, input_size = None, hidden_size = None, seed = None, **init):
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96 if (input_size is None) ^ (hidden_size is None):
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97 raise ValueError("Must specify hidden_size and target_size or neither.")
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98 super(DenoisingAA, self)._instance_initialize(obj, **init)
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99 if seed is not None:
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100 R = N.random.RandomState(seed)
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101 else:
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102 R = N.random
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103 if input_size is not None:
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104 sz = (input_size, hidden_size)
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105 inf = 1/N.sqrt(input_size)
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106 hif = 1/N.sqrt(hidden_size)
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107 obj.w1 = R.uniform(size = sz, low = -inf, high = inf)
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108 if not self.tie_weights:
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109 obj.w2 = R.uniform(size = list(reversed(sz)), low = -inf, high = inf)
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110 obj.b1 = N.zeros(hidden_size)
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111 obj.b2 = N.zeros(input_size)
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112 if seed is not None:
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113 obj.seed(seed)
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114 obj.__hide__ = ['params']
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115
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116 def build_regularization(self):
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117 return T.zero() # no regularization!
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118
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119
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120 class SigmoidXEDenoisingAA(DenoisingAA):
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121
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122 def build_corrupted_input(self):
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123 self.noise_level = theano.Member(T.scalar())
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124 return self.random.binomial(T.shape(self.input), 1, 1 - self.noise_level) * self.input
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125
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126 def hid_activation_function(self, activation):
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127 return NN.sigmoid(activation)
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128
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129 def out_activation_function(self, activation):
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130 return NN.sigmoid(activation)
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131
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132 def build_reconstruction_costs(self, output):
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133 reconstruction_cost_matrix = -(self.input * T.log(output) + (1 - self.input) * T.log(1 - output))
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134 return T.sum(reconstruction_cost_matrix, axis=1)
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135
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136 def build_regularization(self):
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137 self.l2_coef = theano.Member(T.scalar())
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138 if self.tie_weights:
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139 return self.l2_coef * T.sum(self.w1 * self.w1)
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140 else:
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141 return self.l2_coef * T.sum(self.w1 * self.w1) + T.sum(self.w2 * self.w2)
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142
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143 def _instance_initialize(self, obj, input_size = None, hidden_size = None, seed = None, **init):
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144 init.setdefault('noise_level', 0)
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145 init.setdefault('l2_coef', 0)
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146 super(SigmoidXEDenoisingAA, self)._instance_initialize(obj, input_size, hidden_size, seed, **init)
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147