Mercurial > pylearn
annotate algorithms/daa.py @ 494:02a331ba833b
merge
author | Joseph Turian <turian@gmail.com> |
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date | Tue, 28 Oct 2008 11:40:56 -0400 |
parents | e06666ac32d5 |
children | c7ce66b4e8f4 |
rev | line source |
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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 |
487 | 7 from pylearn import cost as cost |
8 | |
476
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9 class DenoisingAA(T.RModule): |
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10 |
487 | 11 def __init__(self, input = None, regularize = True, tie_weights = True, |
12 activation_function=NN.sigmoid, reconstruction_cost_function=cost.cross_entropy): | |
13 """ | |
14 @param reconstruction_cost: Should return one cost per example (row) | |
488 | 15 @todo: Default noise level for all daa levels |
487 | 16 """ |
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17 super(DenoisingAA, self).__init__() |
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18 |
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19 # MODEL CONFIGURATION |
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20 self.regularize = regularize |
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21 self.tie_weights = tie_weights |
487 | 22 self.activation_function = activation_function |
23 self.reconstruction_cost_function = reconstruction_cost_function | |
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24 |
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25 # ACQUIRE/MAKE INPUT |
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26 if not input: |
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27 input = T.matrix('input') |
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28 self.input = theano.External(input) |
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29 |
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30 # HYPER-PARAMETERS |
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31 self.lr = theano.Member(T.scalar()) |
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32 |
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33 # PARAMETERS |
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34 self.w1 = theano.Member(T.matrix()) |
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35 if not tie_weights: |
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36 self.w2 = theano.Member(T.matrix()) |
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37 else: |
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38 self.w2 = self.w1.T |
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39 self.b1 = theano.Member(T.vector()) |
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40 self.b2 = theano.Member(T.vector()) |
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41 |
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42 |
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43 # REGULARIZATION COST |
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44 self.regularization = self.build_regularization() |
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45 |
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46 |
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47 ### NOISELESS ### |
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48 |
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49 # HIDDEN LAYER |
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50 self.hidden_activation = T.dot(self.input, self.w1) + self.b1 |
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51 self.hidden = self.hid_activation_function(self.hidden_activation) |
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52 |
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53 # RECONSTRUCTION LAYER |
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54 self.output_activation = T.dot(self.hidden, self.w2) + self.b2 |
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55 self.output = self.out_activation_function(self.output_activation) |
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56 |
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57 # RECONSTRUCTION COST |
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58 self.reconstruction_costs = self.build_reconstruction_costs(self.output) |
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59 self.reconstruction_cost = T.mean(self.reconstruction_costs) |
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60 |
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61 # TOTAL COST |
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62 self.cost = self.reconstruction_cost |
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63 if self.regularize: |
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64 self.cost = self.cost + self.regularization |
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65 |
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66 |
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67 ### WITH NOISE ### |
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68 self.corrupted_input = self.build_corrupted_input() |
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69 |
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70 # HIDDEN LAYER |
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71 self.nhidden_activation = T.dot(self.corrupted_input, self.w1) + self.b1 |
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72 self.nhidden = self.hid_activation_function(self.nhidden_activation) |
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73 |
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74 # RECONSTRUCTION LAYER |
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75 self.noutput_activation = T.dot(self.nhidden, self.w2) + self.b2 |
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76 self.noutput = self.out_activation_function(self.noutput_activation) |
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77 |
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78 # RECONSTRUCTION COST |
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79 self.nreconstruction_costs = self.build_reconstruction_costs(self.noutput) |
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80 self.nreconstruction_cost = T.mean(self.nreconstruction_costs) |
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81 |
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82 # TOTAL COST |
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83 self.ncost = self.nreconstruction_cost |
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84 if self.regularize: |
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85 self.ncost = self.ncost + self.regularization |
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86 |
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87 |
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88 # GRADIENTS AND UPDATES |
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89 if self.tie_weights: |
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90 self.params = self.w1, self.b1, self.b2 |
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91 else: |
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92 self.params = self.w1, self.w2, self.b1, self.b2 |
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93 gradients = T.grad(self.ncost, self.params) |
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94 updates = dict((p, p - self.lr * g) for p, g in zip(self.params, gradients)) |
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95 |
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96 # INTERFACE METHODS |
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97 self.update = theano.Method(self.input, self.ncost, updates) |
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98 self.compute_cost = theano.Method(self.input, self.cost) |
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99 self.noisify = theano.Method(self.input, self.corrupted_input) |
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100 self.reconstruction = theano.Method(self.input, self.output) |
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101 self.representation = theano.Method(self.input, self.hidden) |
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102 self.reconstruction_through_noise = theano.Method(self.input, [self.corrupted_input, self.noutput]) |
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103 |
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104 def _instance_initialize(self, obj, input_size = None, hidden_size = None, seed = None, **init): |
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105 if (input_size is None) ^ (hidden_size is None): |
486
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106 raise ValueError("Must specify input_size and hidden_size or neither.") |
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107 super(DenoisingAA, self)._instance_initialize(obj, **init) |
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108 if seed is not None: |
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109 R = N.random.RandomState(seed) |
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110 else: |
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111 R = N.random |
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112 if input_size is not None: |
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113 sz = (input_size, hidden_size) |
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114 inf = 1/N.sqrt(input_size) |
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115 hif = 1/N.sqrt(hidden_size) |
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116 obj.w1 = R.uniform(size = sz, low = -inf, high = inf) |
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117 if not self.tie_weights: |
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118 obj.w2 = R.uniform(size = list(reversed(sz)), low = -inf, high = inf) |
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119 obj.b1 = N.zeros(hidden_size) |
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120 obj.b2 = N.zeros(input_size) |
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121 if seed is not None: |
482 | 122 obj.seed(seed) |
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123 obj.__hide__ = ['params'] |
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124 |
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125 def build_regularization(self): |
487 | 126 """ |
127 @todo: Why do we need this function? | |
128 """ | |
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129 return T.zero() # no regularization! |
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130 |
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131 |
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132 class SigmoidXEDenoisingAA(DenoisingAA): |
487 | 133 """ |
134 @todo: Merge this into the above. | |
488 | 135 @todo: Default noise level for all daa levels |
487 | 136 """ |
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137 |
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138 def build_corrupted_input(self): |
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139 self.noise_level = theano.Member(T.scalar()) |
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140 return self.random.binomial(T.shape(self.input), 1, 1 - self.noise_level) * self.input |
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141 |
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142 def hid_activation_function(self, activation): |
487 | 143 return self.activation_function(activation) |
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144 |
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145 def out_activation_function(self, activation): |
487 | 146 return self.activation_function(activation) |
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147 |
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148 def build_reconstruction_costs(self, output): |
487 | 149 return self.reconstruction_cost_function(self.input, output) |
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150 |
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151 def build_regularization(self): |
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152 self.l2_coef = theano.Member(T.scalar()) |
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153 if self.tie_weights: |
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154 return self.l2_coef * T.sum(self.w1 * self.w1) |
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155 else: |
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156 return self.l2_coef * (T.sum(self.w1 * self.w1) + T.sum(self.w2 * self.w2)) |
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157 |
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158 def _instance_initialize(self, obj, input_size = None, hidden_size = None, seed = None, **init): |
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159 init.setdefault('noise_level', 0) |
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160 init.setdefault('l2_coef', 0) |
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161 super(SigmoidXEDenoisingAA, self)._instance_initialize(obj, input_size, hidden_size, seed, **init) |
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162 |