annotate algorithms/daa.py @ 520:82bafb80ba65

merge
author Joseph Turian <turian@iro.umontreal.ca>
date Fri, 14 Nov 2008 02:09:23 -0500
parents dc2d93590da0
children
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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 from pylearn import cost as cost
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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
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23 def __init__(self, input = None, regularize = True, tie_weights = True,
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24 activation_function=NN.sigmoid, reconstruction_cost_function=cost.cross_entropy):
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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
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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
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44 self.activation_function = activation_function
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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 = -hif, high = hif)
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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:
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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):
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150 """
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151 @todo: Why do we need this function?
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152 """
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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):
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157 """
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158 @todo: Merge this into the above.
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159 @todo: Default noise level for all daa levels
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160 """
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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):
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167 return self.activation_function(activation)
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168
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169 def out_activation_function(self, activation):
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170 return self.activation_function(activation)
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171
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172 def build_reconstruction_costs(self, output):
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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