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annotate algorithms/aa.py @ 507:b8e6de17eaa6

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modifs to smallNorb
author James Bergstra <bergstrj@iro.umontreal.ca>
date Wed, 29 Oct 2008 18:06:49 -0400
parents 8fcd0f3d9a17
children
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 AutoEncoder(theano.FancyModule):
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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(AutoEncoder, 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 # HIDDEN LAYER
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34 self.hidden_activation = T.dot(input, self.w1) + self.b1
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35 self.hidden = self.build_hidden()
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36
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37 # RECONSTRUCTION LAYER
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38 self.output_activation = T.dot(self.hidden, self.w2) + self.b2
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39 self.output = self.build_output()
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40
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41 # RECONSTRUCTION COST
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42 self.reconstruction_cost = self.build_reconstruction_cost()
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43
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44 # REGULARIZATION COST
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45 self.regularization = self.build_regularization()
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46
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47 # TOTAL COST
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48 self.cost = self.reconstruction_cost
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49 if self.regularize:
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50 self.cost = self.cost + self.regularization
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51
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52 # GRADIENTS AND UPDATES
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53 if self.tie_weights:
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54 self.params = self.w1, self.b1, self.b2
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55 else:
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56 self.params = self.w1, self.w2, self.b1, self.b2
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57 gradients = T.grad(self.cost, self.params)
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58 updates = dict((p, p - self.lr * g) for p, g in zip(self.params, gradients))
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59
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60 # INTERFACE METHODS
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61 self.update = theano.Method(input, self.cost, updates)
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62 self.reconstruction = theano.Method(input, self.output)
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63 self.representation = theano.Method(input, self.hidden)
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64
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65 def _instance_initialize(self, obj, input_size = None, hidden_size = None, seed = None, **init):
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66 if (input_size is None) ^ (hidden_size is None):
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67 raise ValueError("Must specify hidden_size and target_size or neither.")
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68 super(AutoEncoder, self)._instance_initialize(obj, **init)
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69 if seed is not None:
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70 R = N.random.RandomState(seed)
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71 else:
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72 R = N.random
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73 if input_size is not None:
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74 sz = (input_size, hidden_size)
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75 range = 1/N.sqrt(input_size)
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76 obj.w1 = R.uniform(size = sz, low = -range, high = range)
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77 if not self.tie_weights:
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78 obj.w2 = R.uniform(size = list(reversed(sz)), low = -range, high = range)
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79 obj.b1 = N.zeros(hidden_size)
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80 obj.b2 = N.zeros(input_size)
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81
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82 def build_regularization(self):
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83 return T.zero() # no regularization!
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84
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85
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86 class SigmoidXEAutoEncoder(AutoEncoder):
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87
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88 def build_hidden(self):
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89 return NN.sigmoid(self.hidden_activation)
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90
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91 def build_output(self):
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92 return NN.sigmoid(self.output_activation)
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93
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94 def build_reconstruction_cost(self):
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95 self.reconstruction_cost_matrix = self.input * T.log(self.output) + (1.0 - self.input) * T.log(1.0 - self.output)
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96 self.reconstruction_costs = -T.sum(self.reconstruction_cost_matrix, axis=1)
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97 return T.sum(self.reconstruction_costs)
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98
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99 def build_regularization(self):
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100 self.l2_coef = theano.Member(T.scalar())
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101 if self.tie_weights:
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102 return self.l2_coef * T.sum(self.w1 * self.w1)
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103 else:
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104 return self.l2_coef * T.sum(self.w1 * self.w1) + T.sum(self.w2 * self.w2)
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105
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106 def _instance_initialize(self, obj, input_size = None, hidden_size = None, **init):
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107 init.setdefault('l2_coef', 0)
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108 super(SigmoidXEAutoEncoder, self)._instance_initialize(obj, input_size, hidden_size, **init)