Mercurial > ift6266
annotate code_tutoriel/dA.py @ 361:b599886e3655
Ajout d'une fonctionnalite utile avec le programme voir_erreurs.py afin de voir les exemples ainsi que la prediction du modele donne dans le fichier config.py
author | SylvainPL <sylvain.pannetier.lebeuf@umontreal.ca> |
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date | Thu, 22 Apr 2010 13:17:19 -0400 |
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1 """ |
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2 This tutorial introduces denoising auto-encoders (dA) using Theano. |
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3 |
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4 Denoising autoencoders are the building blocks for SdA. |
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5 They are based on auto-encoders as the ones used in Bengio et al. 2007. |
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6 An autoencoder takes an input x and first maps it to a hidden representation |
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7 y = f_{\theta}(x) = s(Wx+b), parameterized by \theta={W,b}. The resulting |
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8 latent representation y is then mapped back to a "reconstructed" vector |
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9 z \in [0,1]^d in input space z = g_{\theta'}(y) = s(W'y + b'). The weight |
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10 matrix W' can optionally be constrained such that W' = W^T, in which case |
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11 the autoencoder is said to have tied weights. The network is trained such |
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12 that to minimize the reconstruction error (the error between x and z). |
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13 |
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14 For the denosing autoencoder, during training, first x is corrupted into |
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15 \tilde{x}, where \tilde{x} is a partially destroyed version of x by means |
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16 of a stochastic mapping. Afterwards y is computed as before (using |
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17 \tilde{x}), y = s(W\tilde{x} + b) and z as s(W'y + b'). The reconstruction |
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18 error is now measured between z and the uncorrupted input x, which is |
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19 computed as the cross-entropy : |
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20 - \sum_{k=1}^d[ x_k \log z_k + (1-x_k) \log( 1-z_k)] |
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21 |
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22 |
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23 References : |
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24 - P. Vincent, H. Larochelle, Y. Bengio, P.A. Manzagol: Extracting and |
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25 Composing Robust Features with Denoising Autoencoders, ICML'08, 1096-1103, |
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26 2008 |
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27 - Y. Bengio, P. Lamblin, D. Popovici, H. Larochelle: Greedy Layer-Wise |
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28 Training of Deep Networks, Advances in Neural Information Processing |
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29 Systems 19, 2007 |
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30 |
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31 """ |
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32 |
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33 import numpy, time, cPickle, gzip |
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34 |
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35 import theano |
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36 import theano.tensor as T |
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37 from theano.tensor.shared_randomstreams import RandomStreams |
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38 |
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39 from logistic_sgd import load_data |
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40 from utils import tile_raster_images |
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41 |
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42 import PIL.Image |
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43 |
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44 |
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45 class dA(object): |
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46 """Denoising Auto-Encoder class (dA) |
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47 |
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48 A denoising autoencoders tries to reconstruct the input from a corrupted |
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49 version of it by projecting it first in a latent space and reprojecting |
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50 it afterwards back in the input space. Please refer to Vincent et al.,2008 |
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51 for more details. If x is the input then equation (1) computes a partially |
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52 destroyed version of x by means of a stochastic mapping q_D. Equation (2) |
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53 computes the projection of the input into the latent space. Equation (3) |
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54 computes the reconstruction of the input, while equation (4) computes the |
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55 reconstruction error. |
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56 |
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57 .. math:: |
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58 |
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59 \tilde{x} ~ q_D(\tilde{x}|x) (1) |
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60 |
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61 y = s(W \tilde{x} + b) (2) |
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62 |
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63 x = s(W' y + b') (3) |
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64 |
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65 L(x,z) = -sum_{k=1}^d [x_k \log z_k + (1-x_k) \log( 1-z_k)] (4) |
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66 |
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67 """ |
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68 |
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69 def __init__(self, numpy_rng, theano_rng = None, input = None, n_visible= 784, n_hidden= 500, |
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70 W = None, bhid = None, bvis = None): |
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71 """ |
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72 Initialize the dA class by specifying the number of visible units (the |
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73 dimension d of the input ), the number of hidden units ( the dimension |
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74 d' of the latent or hidden space ) and the corruption level. The |
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75 constructor also receives symbolic variables for the input, weights and |
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76 bias. Such a symbolic variables are useful when, for example the input is |
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77 the result of some computations, or when weights are shared between the |
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78 dA and an MLP layer. When dealing with SdAs this always happens, |
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79 the dA on layer 2 gets as input the output of the dA on layer 1, |
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80 and the weights of the dA are used in the second stage of training |
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81 to construct an MLP. |
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82 |
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83 :type numpy_rng: numpy.random.RandomState |
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84 :param numpy_rng: number random generator used to generate weights |
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85 |
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86 :type theano_rng: theano.tensor.shared_randomstreams.RandomStreams |
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87 :param theano_rng: Theano random generator; if None is given one is generated |
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88 based on a seed drawn from `rng` |
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89 |
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90 :type input: theano.tensor.TensorType |
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91 :paran input: a symbolic description of the input or None for standalone |
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92 dA |
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93 |
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94 :type n_visible: int |
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95 :param n_visible: number of visible units |
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96 |
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97 :type n_hidden: int |
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98 :param n_hidden: number of hidden units |
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99 |
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100 :type W: theano.tensor.TensorType |
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101 :param W: Theano variable pointing to a set of weights that should be |
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102 shared belong the dA and another architecture; if dA should |
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103 be standalone set this to None |
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104 |
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105 :type bhid: theano.tensor.TensorType |
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106 :param bhid: Theano variable pointing to a set of biases values (for |
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107 hidden units) that should be shared belong dA and another |
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108 architecture; if dA should be standalone set this to None |
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109 |
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110 :type bvis: theano.tensor.TensorType |
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111 :param bvis: Theano variable pointing to a set of biases values (for |
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112 visible units) that should be shared belong dA and another |
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113 architecture; if dA should be standalone set this to None |
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114 |
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115 |
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116 """ |
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117 self.n_visible = n_visible |
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118 self.n_hidden = n_hidden |
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119 |
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120 # create a Theano random generator that gives symbolic random values |
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121 if not theano_rng : |
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122 theano_rng = RandomStreams(rng.randint(2**30)) |
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123 |
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124 # note : W' was written as `W_prime` and b' as `b_prime` |
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125 if not W: |
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126 # W is initialized with `initial_W` which is uniformely sampled |
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127 # from -6./sqrt(n_visible+n_hidden) and 6./sqrt(n_hidden+n_visible) |
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128 # the output of uniform if converted using asarray to dtype |
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129 # theano.config.floatX so that the code is runable on GPU |
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130 initial_W = numpy.asarray( numpy_rng.uniform( |
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131 low = -numpy.sqrt(6./(n_hidden+n_visible)), |
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132 high = numpy.sqrt(6./(n_hidden+n_visible)), |
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133 size = (n_visible, n_hidden)), dtype = theano.config.floatX) |
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134 W = theano.shared(value = initial_W, name ='W') |
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135 |
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136 if not bvis: |
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137 bvis = theano.shared(value = numpy.zeros(n_visible, |
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138 dtype = theano.config.floatX)) |
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139 |
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140 if not bhid: |
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141 bhid = theano.shared(value = numpy.zeros(n_hidden, |
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142 dtype = theano.config.floatX)) |
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143 |
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144 |
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145 self.W = W |
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146 # b corresponds to the bias of the hidden |
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147 self.b = bhid |
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148 # b_prime corresponds to the bias of the visible |
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149 self.b_prime = bvis |
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150 # tied weights, therefore W_prime is W transpose |
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151 self.W_prime = self.W.T |
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152 self.theano_rng = theano_rng |
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153 # if no input is given, generate a variable representing the input |
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154 if input == None : |
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155 # we use a matrix because we expect a minibatch of several examples, |
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156 # each example being a row |
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157 self.x = T.dmatrix(name = 'input') |
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158 else: |
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159 self.x = input |
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160 |
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161 self.params = [self.W, self.b, self.b_prime] |
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162 |
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163 def get_corrupted_input(self, input, corruption_level): |
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164 """ This function keeps ``1-corruption_level`` entries of the inputs the same |
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165 and zero-out randomly selected subset of size ``coruption_level`` |
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166 Note : first argument of theano.rng.binomial is the shape(size) of |
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167 random numbers that it should produce |
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168 second argument is the number of trials |
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169 third argument is the probability of success of any trial |
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170 |
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171 this will produce an array of 0s and 1s where 1 has a probability of |
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172 1 - ``corruption_level`` and 0 with ``corruption_level`` |
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173 """ |
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174 return self.theano_rng.binomial( size = input.shape, n = 1, prob = 1 - corruption_level) * input |
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175 |
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176 |
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177 def get_hidden_values(self, input): |
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178 """ Computes the values of the hidden layer """ |
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179 return T.nnet.sigmoid(T.dot(input, self.W) + self.b) |
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180 |
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181 def get_reconstructed_input(self, hidden ): |
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182 """ Computes the reconstructed input given the values of the hidden layer """ |
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183 return T.nnet.sigmoid(T.dot(hidden, self.W_prime) + self.b_prime) |
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184 |
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185 def get_cost_updates(self, corruption_level, learning_rate): |
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186 """ This function computes the cost and the updates for one trainng |
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187 step of the dA """ |
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188 |
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189 tilde_x = self.get_corrupted_input(self.x, corruption_level) |
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190 y = self.get_hidden_values( tilde_x) |
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191 z = self.get_reconstructed_input(y) |
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192 # note : we sum over the size of a datapoint; if we are using minibatches, |
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193 # L will be a vector, with one entry per example in minibatch |
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194 L = - T.sum( self.x*T.log(z) + (1-self.x)*T.log(1-z), axis=1 ) |
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195 # note : L is now a vector, where each element is the cross-entropy cost |
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196 # of the reconstruction of the corresponding example of the |
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197 # minibatch. We need to compute the average of all these to get |
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198 # the cost of the minibatch |
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199 cost = T.mean(L) |
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200 |
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201 # compute the gradients of the cost of the `dA` with respect |
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202 # to its parameters |
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203 gparams = T.grad(cost, self.params) |
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204 # generate the list of updates |
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205 updates = {} |
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206 for param, gparam in zip(self.params, gparams): |
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207 updates[param] = param - learning_rate*gparam |
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208 |
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209 return (cost, updates) |
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210 |
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211 |
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212 |
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213 |
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214 def test_dA( learning_rate = 0.1, training_epochs = 15, dataset ='mnist.pkl.gz' ): |
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215 |
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216 """ |
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217 This demo is tested on MNIST |
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218 |
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219 :type learning_rate: float |
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220 :param learning_rate: learning rate used for training the DeNosing AutoEncoder |
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221 |
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222 :type training_epochs: int |
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223 :param training_epochs: number of epochs used for training |
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224 |
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225 :type dataset: string |
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226 :param dataset: path to the picked dataset |
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227 |
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228 """ |
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229 datasets = load_data(dataset) |
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230 train_set_x, train_set_y = datasets[0] |
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231 |
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232 batch_size = 20 # size of the minibatch |
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233 |
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234 # compute number of minibatches for training, validation and testing |
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235 n_train_batches = train_set_x.value.shape[0] / batch_size |
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236 |
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237 # allocate symbolic variables for the data |
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238 index = T.lscalar() # index to a [mini]batch |
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239 x = T.matrix('x') # the data is presented as rasterized images |
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240 |
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241 #################################### |
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242 # BUILDING THE MODEL NO CORRUPTION # |
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243 #################################### |
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244 |
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245 rng = numpy.random.RandomState(123) |
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246 theano_rng = RandomStreams( rng.randint(2**30)) |
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247 |
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248 da = dA(numpy_rng = rng, theano_rng = theano_rng, input = x, |
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249 n_visible = 28*28, n_hidden = 500) |
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250 |
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251 cost, updates = da.get_cost_updates(corruption_level = 0., |
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252 learning_rate = learning_rate) |
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253 |
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254 |
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255 train_da = theano.function([index], cost, updates = updates, |
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256 givens = {x:train_set_x[index*batch_size:(index+1)*batch_size]}) |
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257 |
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258 start_time = time.clock() |
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259 |
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260 ############ |
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261 # TRAINING # |
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262 ############ |
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263 |
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264 # go through training epochs |
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265 for epoch in xrange(training_epochs): |
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266 # go through trainng set |
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267 c = [] |
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268 for batch_index in xrange(n_train_batches): |
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269 c.append(train_da(batch_index)) |
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270 |
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271 print 'Training epoch %d, cost '%epoch, numpy.mean(c) |
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272 |
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273 end_time = time.clock() |
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274 |
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275 training_time = (end_time - start_time) |
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276 |
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277 print ('Training took %f minutes' %(training_time/60.)) |
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278 |
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279 image = PIL.Image.fromarray(tile_raster_images( X = da.W.value.T, |
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280 img_shape = (28,28),tile_shape = (10,10), |
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281 tile_spacing=(1,1))) |
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282 image.save('filters_corruption_0.png') |
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283 |
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284 ##################################### |
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285 # BUILDING THE MODEL CORRUPTION 30% # |
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286 ##################################### |
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287 |
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288 rng = numpy.random.RandomState(123) |
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289 theano_rng = RandomStreams( rng.randint(2**30)) |
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290 |
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291 da = dA(numpy_rng = rng, theano_rng = theano_rng, input = x, |
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292 n_visible = 28*28, n_hidden = 500) |
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293 |
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294 cost, updates = da.get_cost_updates(corruption_level = 0.3, |
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295 learning_rate = learning_rate) |
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296 |
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297 |
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298 train_da = theano.function([index], cost, updates = updates, |
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299 givens = {x:train_set_x[index*batch_size:(index+1)*batch_size]}) |
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300 |
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301 start_time = time.clock() |
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302 |
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303 ############ |
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304 # TRAINING # |
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305 ############ |
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306 |
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307 # go through training epochs |
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308 for epoch in xrange(training_epochs): |
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309 # go through trainng set |
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310 c = [] |
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311 for batch_index in xrange(n_train_batches): |
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312 c.append(train_da(batch_index)) |
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313 |
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314 print 'Training epoch %d, cost '%epoch, numpy.mean(c) |
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315 |
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316 end_time = time.clock() |
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317 |
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318 training_time = (end_time - start_time) |
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319 |
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320 print ('Training took %f minutes' %(training_time/60.)) |
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321 |
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322 image = PIL.Image.fromarray(tile_raster_images( X = da.W.value.T, |
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323 img_shape = (28,28),tile_shape = (10,10), |
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324 tile_spacing=(1,1))) |
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325 image.save('filters_corruption_30.png') |
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326 |
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327 |
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328 |
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329 if __name__ == '__main__': |
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330 test_dA() |