Mercurial > pylearn
annotate algorithms/daa.py @ 512:da916044454c
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author | James Bergstra <bergstrj@iro.umontreal.ca> |
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date | Thu, 30 Oct 2008 19:39:10 -0400 |
parents | c7ce66b4e8f4 |
children | dc2d93590da0 |
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 | |
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9 class DenoisingAA(T.RModule): |
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10 """De-noising Auto-encoder |
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11 |
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12 WRITEME |
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13 |
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14 Abstract base class. Requires subclass with functions: |
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15 |
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16 - build_corrupted_input() |
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17 |
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18 Introductory article about this model WRITEME. |
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19 |
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20 |
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21 """ |
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22 |
487 | 23 def __init__(self, input = None, regularize = True, tie_weights = True, |
24 activation_function=NN.sigmoid, reconstruction_cost_function=cost.cross_entropy): | |
25 """ | |
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26 :param input: WRITEME |
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27 |
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28 :param regularize: WRITEME |
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29 |
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30 :param tie_weights: WRITEME |
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31 |
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32 :param activation_function: WRITEME |
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33 |
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34 :param reconstruction_cost: Should return one cost per example (row) |
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35 |
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36 :todo: Default noise level for all daa levels |
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37 |
487 | 38 """ |
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39 super(DenoisingAA, self).__init__() |
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40 |
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41 # MODEL CONFIGURATION |
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42 self.regularize = regularize |
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43 self.tie_weights = tie_weights |
487 | 44 self.activation_function = activation_function |
45 self.reconstruction_cost_function = reconstruction_cost_function | |
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46 |
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47 # ACQUIRE/MAKE INPUT |
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48 if not input: |
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49 input = T.matrix('input') |
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50 self.input = theano.External(input) |
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51 |
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52 # HYPER-PARAMETERS |
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53 self.lr = theano.Member(T.scalar()) |
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54 |
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55 # PARAMETERS |
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56 self.w1 = theano.Member(T.matrix()) |
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57 if not tie_weights: |
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58 self.w2 = theano.Member(T.matrix()) |
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59 else: |
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60 self.w2 = self.w1.T |
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61 self.b1 = theano.Member(T.vector()) |
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62 self.b2 = theano.Member(T.vector()) |
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63 |
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64 |
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65 # REGULARIZATION COST |
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66 self.regularization = self.build_regularization() |
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67 |
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68 |
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69 ### NOISELESS ### |
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70 |
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71 # HIDDEN LAYER |
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72 self.hidden_activation = T.dot(self.input, self.w1) + self.b1 |
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73 self.hidden = self.hid_activation_function(self.hidden_activation) |
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74 |
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75 # RECONSTRUCTION LAYER |
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76 self.output_activation = T.dot(self.hidden, self.w2) + self.b2 |
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77 self.output = self.out_activation_function(self.output_activation) |
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78 |
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79 # RECONSTRUCTION COST |
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80 self.reconstruction_costs = self.build_reconstruction_costs(self.output) |
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81 self.reconstruction_cost = T.mean(self.reconstruction_costs) |
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82 |
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83 # TOTAL COST |
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84 self.cost = self.reconstruction_cost |
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85 if self.regularize: |
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86 self.cost = self.cost + self.regularization |
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87 |
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88 |
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89 ### WITH NOISE ### |
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90 self.corrupted_input = self.build_corrupted_input() |
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91 |
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92 # HIDDEN LAYER |
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93 self.nhidden_activation = T.dot(self.corrupted_input, self.w1) + self.b1 |
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94 self.nhidden = self.hid_activation_function(self.nhidden_activation) |
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95 |
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96 # RECONSTRUCTION LAYER |
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97 self.noutput_activation = T.dot(self.nhidden, self.w2) + self.b2 |
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98 self.noutput = self.out_activation_function(self.noutput_activation) |
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99 |
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100 # RECONSTRUCTION COST |
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101 self.nreconstruction_costs = self.build_reconstruction_costs(self.noutput) |
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102 self.nreconstruction_cost = T.mean(self.nreconstruction_costs) |
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103 |
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104 # TOTAL COST |
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105 self.ncost = self.nreconstruction_cost |
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106 if self.regularize: |
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107 self.ncost = self.ncost + self.regularization |
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108 |
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109 |
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110 # GRADIENTS AND UPDATES |
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111 if self.tie_weights: |
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112 self.params = self.w1, self.b1, self.b2 |
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113 else: |
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114 self.params = self.w1, self.w2, self.b1, self.b2 |
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115 gradients = T.grad(self.ncost, self.params) |
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116 updates = dict((p, p - self.lr * g) for p, g in zip(self.params, gradients)) |
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117 |
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118 # INTERFACE METHODS |
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119 self.update = theano.Method(self.input, self.ncost, updates) |
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120 self.compute_cost = theano.Method(self.input, self.cost) |
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121 self.noisify = theano.Method(self.input, self.corrupted_input) |
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122 self.reconstruction = theano.Method(self.input, self.output) |
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123 self.representation = theano.Method(self.input, self.hidden) |
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124 self.reconstruction_through_noise = theano.Method(self.input, [self.corrupted_input, self.noutput]) |
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125 |
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126 self.validate = theano.Method(self.input, [self.cost, self.output]) |
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127 |
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128 def _instance_initialize(self, obj, input_size = None, hidden_size = None, seed = None, **init): |
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129 if (input_size is None) ^ (hidden_size is None): |
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130 raise ValueError("Must specify input_size and hidden_size or neither.") |
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131 super(DenoisingAA, self)._instance_initialize(obj, **init) |
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132 if seed is not None: |
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133 R = N.random.RandomState(seed) |
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134 else: |
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135 R = N.random |
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136 if input_size is not None: |
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137 sz = (input_size, hidden_size) |
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138 inf = 1/N.sqrt(input_size) |
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139 hif = 1/N.sqrt(hidden_size) |
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140 obj.w1 = R.uniform(size = sz, low = -inf, high = inf) |
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141 if not self.tie_weights: |
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142 obj.w2 = R.uniform(size = list(reversed(sz)), low = -inf, high = inf) |
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143 obj.b1 = N.zeros(hidden_size) |
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144 obj.b2 = N.zeros(input_size) |
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145 if seed is not None: |
482 | 146 obj.seed(seed) |
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147 obj.__hide__ = ['params'] |
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148 |
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149 def build_regularization(self): |
487 | 150 """ |
151 @todo: Why do we need this function? | |
152 """ | |
476
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153 return T.zero() # no regularization! |
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154 |
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155 |
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156 class SigmoidXEDenoisingAA(DenoisingAA): |
487 | 157 """ |
158 @todo: Merge this into the above. | |
488 | 159 @todo: Default noise level for all daa levels |
487 | 160 """ |
476
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161 |
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162 def build_corrupted_input(self): |
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163 self.noise_level = theano.Member(T.scalar()) |
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164 return self.random.binomial(T.shape(self.input), 1, 1 - self.noise_level) * self.input |
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165 |
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166 def hid_activation_function(self, activation): |
487 | 167 return self.activation_function(activation) |
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168 |
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169 def out_activation_function(self, activation): |
487 | 170 return self.activation_function(activation) |
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171 |
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172 def build_reconstruction_costs(self, output): |
487 | 173 return self.reconstruction_cost_function(self.input, output) |
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174 |
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175 def build_regularization(self): |
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176 self.l2_coef = theano.Member(T.scalar()) |
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177 if self.tie_weights: |
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178 return self.l2_coef * T.sum(self.w1 * self.w1) |
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179 else: |
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180 return self.l2_coef * (T.sum(self.w1 * self.w1) + T.sum(self.w2 * self.w2)) |
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181 |
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182 def _instance_initialize(self, obj, input_size = None, hidden_size = None, seed = None, **init): |
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183 init.setdefault('noise_level', 0) |
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184 init.setdefault('l2_coef', 0) |
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185 super(SigmoidXEDenoisingAA, self)._instance_initialize(obj, input_size, hidden_size, seed, **init) |
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186 |