Mercurial > ift6266
annotate code_tutoriel/rbm.py @ 618:14ba0120baff
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author | Yoshua Bengio <bengioy@iro.umontreal.ca> |
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date | Sun, 09 Jan 2011 14:13:23 -0500 |
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rev | line source |
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1 """This tutorial introduces restricted boltzmann machines (RBM) using Theano. |
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2 |
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3 Boltzmann Machines (BMs) are a particular form of energy-based model which |
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4 contain hidden variables. Restricted Boltzmann Machines further restrict BMs |
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5 to those without visible-visible and hidden-hidden connections. |
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6 """ |
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7 |
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8 |
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9 import numpy, time, cPickle, gzip, PIL.Image |
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10 |
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11 import theano |
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12 import theano.tensor as T |
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13 import os |
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14 |
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15 from theano.tensor.shared_randomstreams import RandomStreams |
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16 |
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17 from utils import tile_raster_images |
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18 from logistic_sgd import load_data |
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19 |
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20 class RBM(object): |
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21 """Restricted Boltzmann Machine (RBM) """ |
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22 def __init__(self, input=None, n_visible=784, n_hidden=500, \ |
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23 W = None, hbias = None, vbias = None, numpy_rng = None, |
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24 theano_rng = None): |
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25 """ |
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26 RBM constructor. Defines the parameters of the model along with |
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27 basic operations for inferring hidden from visible (and vice-versa), |
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28 as well as for performing CD updates. |
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29 |
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30 :param input: None for standalone RBMs or symbolic variable if RBM is |
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31 part of a larger graph. |
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32 |
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33 :param n_visible: number of visible units |
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34 |
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35 :param n_hidden: number of hidden units |
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36 |
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37 :param W: None for standalone RBMs or symbolic variable pointing to a |
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38 shared weight matrix in case RBM is part of a DBN network; in a DBN, |
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39 the weights are shared between RBMs and layers of a MLP |
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40 |
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41 :param hbias: None for standalone RBMs or symbolic variable pointing |
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42 to a shared hidden units bias vector in case RBM is part of a |
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43 different network |
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44 |
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45 :param vbias: None for standalone RBMs or a symbolic variable |
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46 pointing to a shared visible units bias |
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47 """ |
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48 |
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49 self.n_visible = n_visible |
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50 self.n_hidden = n_hidden |
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51 |
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52 |
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53 if W is None : |
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54 # W is initialized with `initial_W` which is uniformely sampled |
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55 # from -6./sqrt(n_visible+n_hidden) and 6./sqrt(n_hidden+n_visible) |
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56 # the output of uniform if converted using asarray to dtype |
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57 # theano.config.floatX so that the code is runable on GPU |
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58 initial_W = numpy.asarray( numpy.random.uniform( |
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59 low = -numpy.sqrt(6./(n_hidden+n_visible)), |
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60 high = numpy.sqrt(6./(n_hidden+n_visible)), |
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61 size = (n_visible, n_hidden)), |
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62 dtype = theano.config.floatX) |
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63 # theano shared variables for weights and biases |
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64 W = theano.shared(value = initial_W, name = 'W') |
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65 |
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66 if hbias is None : |
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67 # create shared variable for hidden units bias |
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68 hbias = theano.shared(value = numpy.zeros(n_hidden, |
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69 dtype = theano.config.floatX), name='hbias') |
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70 |
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71 if vbias is None : |
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72 # create shared variable for visible units bias |
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73 vbias = theano.shared(value =numpy.zeros(n_visible, |
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74 dtype = theano.config.floatX),name='vbias') |
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75 |
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76 if numpy_rng is None: |
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77 # create a number generator |
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78 numpy_rng = numpy.random.RandomState(1234) |
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79 |
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80 if theano_rng is None : |
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81 theano_rng = RandomStreams(numpy_rng.randint(2**30)) |
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82 |
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83 |
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84 # initialize input layer for standalone RBM or layer0 of DBN |
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85 self.input = input if input else T.dmatrix('input') |
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86 |
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87 self.W = W |
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88 self.hbias = hbias |
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89 self.vbias = vbias |
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90 self.theano_rng = theano_rng |
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91 # **** WARNING: It is not a good idea to put things in this list |
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92 # other than shared variables created in this function. |
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93 self.params = [self.W, self.hbias, self.vbias] |
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94 self.batch_size = self.input.shape[0] |
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95 |
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96 def free_energy(self, v_sample): |
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97 ''' Function to compute the free energy ''' |
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98 wx_b = T.dot(v_sample, self.W) + self.hbias |
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99 vbias_term = T.sum(T.dot(v_sample, self.vbias)) |
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100 hidden_term = T.sum(T.log(1+T.exp(wx_b))) |
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101 return -hidden_term - vbias_term |
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102 |
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103 def sample_h_given_v(self, v0_sample): |
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104 ''' This function infers state of hidden units given visible units ''' |
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105 # compute the activation of the hidden units given a sample of the visibles |
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106 h1_mean = T.nnet.sigmoid(T.dot(v0_sample, self.W) + self.hbias) |
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107 # get a sample of the hiddens given their activation |
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108 h1_sample = self.theano_rng.binomial(size = h1_mean.shape, n = 1, prob = h1_mean) |
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109 return [h1_mean, h1_sample] |
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110 |
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111 def sample_v_given_h(self, h0_sample): |
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112 ''' This function infers state of visible units given hidden units ''' |
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113 # compute the activation of the visible given the hidden sample |
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114 v1_mean = T.nnet.sigmoid(T.dot(h0_sample, self.W.T) + self.vbias) |
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115 # get a sample of the visible given their activation |
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116 v1_sample = self.theano_rng.binomial(size = v1_mean.shape,n = 1,prob = v1_mean) |
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117 return [v1_mean, v1_sample] |
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118 |
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119 def gibbs_hvh(self, h0_sample): |
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120 ''' This function implements one step of Gibbs sampling, |
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121 starting from the hidden state''' |
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122 v1_mean, v1_sample = self.sample_v_given_h(h0_sample) |
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123 h1_mean, h1_sample = self.sample_h_given_v(v1_sample) |
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124 return [v1_mean, v1_sample, h1_mean, h1_sample] |
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125 |
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126 def gibbs_vhv(self, v0_sample): |
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127 ''' This function implements one step of Gibbs sampling, |
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128 starting from the visible state''' |
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129 h1_mean, h1_sample = self.sample_h_given_v(v0_sample) |
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130 v1_mean, v1_sample = self.sample_v_given_h(h1_sample) |
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131 return [h1_mean, h1_sample, v1_mean, v1_sample] |
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132 |
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133 def cd(self, lr = 0.1, persistent=None): |
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134 """ |
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135 This functions implements one step of CD-1 or PCD-1 |
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136 |
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137 :param lr: learning rate used to train the RBM |
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138 :param persistent: None for CD. For PCD, shared variable containing old state |
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139 of Gibbs chain. This must be a shared variable of size (batch size, number of |
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140 hidden units). |
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141 |
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142 Returns the updates dictionary. The dictionary contains the update rules for weights |
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143 and biases but also an update of the shared variable used to store the persistent |
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144 chain, if one is used. |
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145 """ |
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146 |
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147 # compute positive phase |
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148 ph_mean, ph_sample = self.sample_h_given_v(self.input) |
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149 |
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150 # decide how to initialize persistent chain: |
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151 # for CD, we use the newly generate hidden sample |
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152 # for PCD, we initialize from the old state of the chain |
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153 if persistent is None: |
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154 chain_start = ph_sample |
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155 else: |
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156 chain_start = persistent |
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157 |
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158 # perform actual negative phase |
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159 [nv_mean, nv_sample, nh_mean, nh_sample] = self.gibbs_hvh(chain_start) |
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160 |
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161 # determine gradients on RBM parameters |
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162 g_vbias = T.sum( self.input - nv_mean, axis = 0)/self.batch_size |
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163 g_hbias = T.sum( ph_mean - nh_mean, axis = 0)/self.batch_size |
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164 g_W = T.dot(ph_mean.T, self.input )/ self.batch_size - \ |
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165 T.dot(nh_mean.T, nv_mean )/ self.batch_size |
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166 |
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167 gparams = [g_W.T, g_hbias, g_vbias] |
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168 |
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169 # constructs the update dictionary |
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170 updates = {} |
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171 for gparam, param in zip(gparams, self.params): |
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172 updates[param] = param + gparam * lr |
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173 |
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174 if persistent: |
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175 # Note that this works only if persistent is a shared variable |
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176 updates[persistent] = T.cast(nh_sample, dtype=theano.config.floatX) |
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177 # pseudo-likelihood is a better proxy for PCD |
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178 cost = self.get_pseudo_likelihood_cost(updates) |
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179 else: |
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180 # reconstruction cross-entropy is a better proxy for CD |
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181 cost = self.get_reconstruction_cost(updates, nv_mean) |
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182 |
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183 return cost, updates |
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184 |
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185 def get_pseudo_likelihood_cost(self, updates): |
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186 """Stochastic approximation to the pseudo-likelihood""" |
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187 |
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188 # index of bit i in expression p(x_i | x_{\i}) |
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189 bit_i_idx = theano.shared(value=0, name = 'bit_i_idx') |
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190 |
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191 # binarize the input image by rounding to nearest integer |
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192 xi = T.iround(self.input) |
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193 |
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194 # calculate free energy for the given bit configuration |
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195 fe_xi = self.free_energy(xi) |
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196 |
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197 # flip bit x_i of matrix xi and preserve all other bits x_{\i} |
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198 # Equivalent to xi[:,bit_i_idx] = 1-xi[:, bit_i_idx] |
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199 # NB: slice(start,stop,step) is the python object used for |
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200 # slicing, e.g. to index matrix x as follows: x[start:stop:step] |
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201 xi_flip = T.setsubtensor(xi, 1-xi[:, bit_i_idx], |
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202 idx_list=(slice(None,None,None),bit_i_idx)) |
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203 |
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204 # calculate free energy with bit flipped |
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205 fe_xi_flip = self.free_energy(xi_flip) |
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206 |
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207 # equivalent to e^(-FE(x_i)) / (e^(-FE(x_i)) + e^(-FE(x_{\i}))) |
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208 cost = self.n_visible * T.log(T.nnet.sigmoid(fe_xi_flip - fe_xi)) |
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209 |
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210 # increment bit_i_idx % number as part of updates |
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211 updates[bit_i_idx] = (bit_i_idx + 1) % self.n_visible |
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212 |
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213 return cost |
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214 |
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215 def get_reconstruction_cost(self, updates, nv_mean): |
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216 """Approximation to the reconstruction error""" |
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217 |
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218 cross_entropy = T.mean( |
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219 T.sum(self.input*T.log(nv_mean) + |
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220 (1 - self.input)*T.log(1-nv_mean), axis = 1)) |
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221 |
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222 return cross_entropy |
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223 |
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224 |
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225 |
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226 def test_rbm(learning_rate=0.1, training_epochs = 15, |
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227 dataset='mnist.pkl.gz'): |
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228 """ |
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229 Demonstrate *** |
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230 |
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231 This is demonstrated on MNIST. |
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232 |
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233 :param learning_rate: learning rate used for training the RBM |
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234 |
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235 :param training_epochs: number of epochs used for training |
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236 |
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237 :param dataset: path the the pickled dataset |
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238 |
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239 """ |
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240 datasets = load_data(dataset) |
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241 |
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242 train_set_x, train_set_y = datasets[0] |
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243 test_set_x , test_set_y = datasets[2] |
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244 |
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245 |
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246 batch_size = 20 # size of the minibatch |
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247 |
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248 # compute number of minibatches for training, validation and testing |
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249 n_train_batches = train_set_x.value.shape[0] / batch_size |
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250 |
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251 # allocate symbolic variables for the data |
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252 index = T.lscalar() # index to a [mini]batch |
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253 x = T.matrix('x') # the data is presented as rasterized images |
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254 |
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255 rng = numpy.random.RandomState(123) |
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256 theano_rng = RandomStreams( rng.randint(2**30)) |
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257 |
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258 # initialize storage fot the persistent chain (state = hidden layer of chain) |
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259 persistent_chain = theano.shared(numpy.zeros((batch_size, 500))) |
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260 |
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261 # construct the RBM class |
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262 rbm = RBM( input = x, n_visible=28*28, \ |
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263 n_hidden = 500,numpy_rng = rng, theano_rng = theano_rng) |
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264 |
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265 # get the cost and the gradient corresponding to one step of CD |
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266 cost, updates = rbm.cd(lr=learning_rate, persistent=persistent_chain) |
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267 |
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268 |
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269 ################################# |
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270 # Training the RBM # |
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271 ################################# |
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272 dirname = 'lr=%.5f'%learning_rate |
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273 os.makedirs(dirname) |
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274 os.chdir(dirname) |
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275 |
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276 # it is ok for a theano function to have no output |
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277 # the purpose of train_rbm is solely to update the RBM parameters |
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278 train_rbm = theano.function([index], cost, |
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279 updates = updates, |
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280 givens = { x: train_set_x[index*batch_size:(index+1)*batch_size]}) |
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281 |
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282 plotting_time = 0. |
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283 start_time = time.clock() |
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284 |
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285 |
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286 # go through training epochs |
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287 for epoch in xrange(training_epochs): |
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288 |
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289 # go through the training set |
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290 mean_cost = [] |
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291 for batch_index in xrange(n_train_batches): |
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292 mean_cost += [train_rbm(batch_index)] |
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293 |
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294 print 'Training epoch %d, cost is '%epoch, numpy.mean(mean_cost) |
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295 |
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296 # Plot filters after each training epoch |
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297 plotting_start = time.clock() |
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298 # Construct image from the weight matrix |
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299 image = PIL.Image.fromarray(tile_raster_images( X = rbm.W.value.T, |
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300 img_shape = (28,28),tile_shape = (10,10), |
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301 tile_spacing=(1,1))) |
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302 image.save('filters_at_epoch_%i.png'%epoch) |
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303 plotting_stop = time.clock() |
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304 plotting_time += (plotting_stop - plotting_start) |
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305 |
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306 end_time = time.clock() |
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307 |
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308 pretraining_time = (end_time - start_time) - plotting_time |
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309 |
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310 print ('Training took %f minutes' %(pretraining_time/60.)) |
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311 |
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312 |
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313 ################################# |
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314 # Sampling from the RBM # |
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315 ################################# |
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316 |
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317 # find out the number of test samples |
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318 number_of_test_samples = test_set_x.value.shape[0] |
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319 |
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320 # pick random test examples, with which to initialize the persistent chain |
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321 test_idx = rng.randint(number_of_test_samples-20) |
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322 persistent_vis_chain = theano.shared(test_set_x.value[test_idx:test_idx+20]) |
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323 |
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324 # define one step of Gibbs sampling (mf = mean-field) |
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325 [hid_mf, hid_sample, vis_mf, vis_sample] = rbm.gibbs_vhv(persistent_vis_chain) |
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326 |
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327 # the sample at the end of the channel is returned by ``gibbs_1`` as |
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328 # its second output; note that this is computed as a binomial draw, |
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329 # therefore it is formed of ints (0 and 1) and therefore needs to |
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330 # be converted to the same dtype as ``persistent_vis_chain`` |
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331 vis_sample = T.cast(vis_sample, dtype=theano.config.floatX) |
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332 |
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333 # construct the function that implements our persistent chain |
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334 # we generate the "mean field" activations for plotting and the actual samples for |
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335 # reinitializing the state of our persistent chain |
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336 sample_fn = theano.function([], [vis_mf, vis_sample], |
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337 updates = { persistent_vis_chain:vis_sample}) |
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338 |
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339 # sample the RBM, plotting every `plot_every`-th sample; do this |
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340 # until you plot at least `n_samples` |
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341 n_samples = 10 |
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342 plot_every = 1000 |
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343 |
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344 for idx in xrange(n_samples): |
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345 |
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346 # do `plot_every` intermediate samplings of which we do not care |
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347 for jdx in xrange(plot_every): |
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348 vis_mf, vis_sample = sample_fn() |
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349 |
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350 # construct image |
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351 image = PIL.Image.fromarray(tile_raster_images( |
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352 X = vis_mf, |
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353 img_shape = (28,28), |
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354 tile_shape = (10,10), |
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355 tile_spacing = (1,1) ) ) |
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356 print ' ... plotting sample ', idx |
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357 image.save('sample_%i_step_%i.png'%(idx,idx*jdx)) |
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358 |
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359 if __name__ == '__main__': |
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360 test_rbm() |