annotate code_tutoriel/deep.py @ 266:1e4e60ddadb1

Merge. Ah, et dans le dernier commit, j'avais oublié de mentionner que j'ai ajouté du code pour gérer l'isolation de différents clones pour rouler des expériences et modifier le code en même temps.
author fsavard
date Fri, 19 Mar 2010 10:56:16 -0400
parents 4bc5eeec6394
children
rev   line source
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1 """
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2 Draft of DBN, DAA, SDAA, RBM tutorial code
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3
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4 """
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5 import sys
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6 import numpy
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7 import theano
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8 import time
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9 import theano.tensor as T
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10 from theano.tensor.shared_randomstreams import RandomStreams
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11 from theano import shared, function
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12
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13 import gzip
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14 import cPickle
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15 import pylearn.io.image_tiling
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16 import PIL
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17
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18 # NNET STUFF
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19
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20 class LogisticRegression(object):
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21 """Multi-class Logistic Regression Class
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22
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23 The logistic regression is fully described by a weight matrix :math:`W`
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24 and bias vector :math:`b`. Classification is done by projecting data
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25 points onto a set of hyperplanes, the distance to which is used to
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26 determine a class membership probability.
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27 """
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28
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29 def __init__(self, input, n_in, n_out):
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30 """ Initialize the parameters of the logistic regression
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31 :param input: symbolic variable that describes the input of the
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32 architecture (one minibatch)
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33 :type n_in: int
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34 :param n_in: number of input units, the dimension of the space in
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35 which the datapoints lie
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36 :type n_out: int
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37 :param n_out: number of output units, the dimension of the space in
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38 which the labels lie
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39 """
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40
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41 # initialize with 0 the weights W as a matrix of shape (n_in, n_out)
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42 self.W = theano.shared( value=numpy.zeros((n_in,n_out),
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43 dtype = theano.config.floatX) )
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44 # initialize the baises b as a vector of n_out 0s
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45 self.b = theano.shared( value=numpy.zeros((n_out,),
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46 dtype = theano.config.floatX) )
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47 # compute vector of class-membership probabilities in symbolic form
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48 self.p_y_given_x = T.nnet.softmax(T.dot(input, self.W)+self.b)
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49
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50 # compute prediction as class whose probability is maximal in
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51 # symbolic form
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52 self.y_pred=T.argmax(self.p_y_given_x, axis=1)
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53
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54 # list of parameters for this layer
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55 self.params = [self.W, self.b]
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56
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57 def negative_log_likelihood(self, y):
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58 """Return the mean of the negative log-likelihood of the prediction
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59 of this model under a given target distribution.
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60 :param y: corresponds to a vector that gives for each example the
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61 correct label
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62 Note: we use the mean instead of the sum so that
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63 the learning rate is less dependent on the batch size
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64 """
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65 return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]),y])
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66
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67 def errors(self, y):
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68 """Return a float representing the number of errors in the minibatch
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69 over the total number of examples of the minibatch ; zero one
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70 loss over the size of the minibatch
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71 """
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72 # check if y has same dimension of y_pred
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73 if y.ndim != self.y_pred.ndim:
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74 raise TypeError('y should have the same shape as self.y_pred',
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75 ('y', target.type, 'y_pred', self.y_pred.type))
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76
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77 # check if y is of the correct datatype
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78 if y.dtype.startswith('int'):
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79 # the T.neq operator returns a vector of 0s and 1s, where 1
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80 # represents a mistake in prediction
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81 return T.mean(T.neq(self.y_pred, y))
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82 else:
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83 raise NotImplementedError()
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84
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85 class SigmoidalLayer(object):
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86 def __init__(self, rng, input, n_in, n_out):
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87 """
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88 Typical hidden layer of a MLP: units are fully-connected and have
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89 sigmoidal activation function. Weight matrix W is of shape (n_in,n_out)
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90 and the bias vector b is of shape (n_out,).
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91
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92 Hidden unit activation is given by: sigmoid(dot(input,W) + b)
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93
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94 :type rng: numpy.random.RandomState
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95 :param rng: a random number generator used to initialize weights
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96 :type input: theano.tensor.matrix
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97 :param input: a symbolic tensor of shape (n_examples, n_in)
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98 :type n_in: int
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99 :param n_in: dimensionality of input
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100 :type n_out: int
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101 :param n_out: number of hidden units
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102 """
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103 self.input = input
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104
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105 W_values = numpy.asarray( rng.uniform( \
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106 low = -numpy.sqrt(6./(n_in+n_out)), \
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107 high = numpy.sqrt(6./(n_in+n_out)), \
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108 size = (n_in, n_out)), dtype = theano.config.floatX)
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109 self.W = theano.shared(value = W_values)
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110
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111 b_values = numpy.zeros((n_out,), dtype= theano.config.floatX)
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112 self.b = theano.shared(value= b_values)
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113
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114 self.output = T.nnet.sigmoid(T.dot(input, self.W) + self.b)
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115 self.params = [self.W, self.b]
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116
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117 # PRETRAINING LAYERS
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118
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119 class RBM(object):
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120 """
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121 *** WRITE THE ENERGY FUNCTION USE SAME LETTERS AS VARIABLE NAMES IN CODE
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122 """
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123
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124 def __init__(self, input=None, n_visible=None, n_hidden=None,
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125 W=None, hbias=None, vbias=None,
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126 numpy_rng=None, theano_rng=None):
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127 """
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128 RBM constructor. Defines the parameters of the model along with
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129 basic operations for inferring hidden from visible (and vice-versa),
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130 as well as for performing CD updates.
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131
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132 :param input: None for standalone RBMs or symbolic variable if RBM is
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133 part of a larger graph.
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134
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135 :param n_visible: number of visible units (necessary when W or vbias is None)
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136
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137 :param n_hidden: number of hidden units (necessary when W or hbias is None)
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138
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139 :param W: weights to use for the RBM. None means that a shared variable will be
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140 created with a randomly chosen matrix of size (n_visible, n_hidden).
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141
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142 :param hbias: ***
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143
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144 :param vbias: ***
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145
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146 :param numpy_rng: random number generator (necessary when W is None)
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147
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148 """
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149
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150 params = []
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151 if W is None:
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152 # choose initial values for weight matrix of RBM
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153 initial_W = numpy.asarray(
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154 numpy_rng.uniform( \
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155 low=-numpy.sqrt(6./(n_hidden+n_visible)), \
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156 high=numpy.sqrt(6./(n_hidden+n_visible)), \
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157 size=(n_visible, n_hidden)), \
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158 dtype=theano.config.floatX)
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159 W = theano.shared(value=initial_W, name='W')
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160 params.append(W)
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161
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162 if hbias is None:
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163 # theano shared variables for hidden biases
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164 hbias = theano.shared(value=numpy.zeros(n_hidden,
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165 dtype=theano.config.floatX), name='hbias')
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166 params.append(hbias)
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167
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168 if vbias is None:
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169 # theano shared variables for visible biases
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170 vbias = theano.shared(value=numpy.zeros(n_visible,
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171 dtype=theano.config.floatX), name='vbias')
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172 params.append(vbias)
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173
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174 if input is None:
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175 # initialize input layer for standalone RBM or layer0 of DBN
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176 input = T.matrix('input')
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177
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178 # setup theano random number generator
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179 if theano_rng is None:
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180 theano_rng = RandomStreams(numpy_rng.randint(2**30))
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181
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182 self.visible = self.input = input
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183 self.W = W
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184 self.hbias = hbias
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185 self.vbias = vbias
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186 self.theano_rng = theano_rng
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187 self.params = params
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188 self.hidden_mean = T.nnet.sigmoid(T.dot(input, W)+hbias)
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189 self.hidden_sample = theano_rng.binomial(self.hidden_mean.shape, 1, self.hidden_mean)
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190
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191 def gibbs_k(self, v_sample, k):
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192 ''' This function implements k steps of Gibbs sampling '''
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193
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194 # We compute the visible after k steps of Gibbs by iterating
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195 # over ``gibs_1`` for k times; this can be done in Theano using
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196 # the `scan op`. For a more comprehensive description of scan see
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197 # http://deeplearning.net/software/theano/library/scan.html .
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198
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199 def gibbs_1(v0_sample, W, hbias, vbias):
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200 ''' This function implements one Gibbs step '''
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201
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202 # compute the activation of the hidden units given a sample of the
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203 # vissibles
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204 h0_mean = T.nnet.sigmoid(T.dot(v0_sample, W) + hbias)
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205 # get a sample of the hiddens given their activation
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206 h0_sample = self.theano_rng.binomial(h0_mean.shape, 1, h0_mean)
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207 # compute the activation of the visible given the hidden sample
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208 v1_mean = T.nnet.sigmoid(T.dot(h0_sample, W.T) + vbias)
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209 # get a sample of the visible given their activation
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210 v1_act = self.theano_rng.binomial(v1_mean.shape, 1, v1_mean)
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211 return [v1_mean, v1_act]
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212
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213
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214 # DEBUGGING TO DO ALL WITHOUT SCAN
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215 if k == 1:
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216 return gibbs_1(v_sample, self.W, self.hbias, self.vbias)
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217
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218
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219 # Because we require as output two values, namely the mean field
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220 # approximation of the visible and the sample obtained after k steps,
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221 # scan needs to know the shape of those two outputs. Scan takes
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222 # this information from the variables containing the initial state
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223 # of the outputs. Since we do not need a initial state of ``v_mean``
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224 # we provide a dummy one used only to get the correct shape
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225 v_mean = T.zeros_like(v_sample)
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226
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227 # ``outputs_taps`` is an argument of scan which describes at each
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228 # time step what past values of the outputs the function applied
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229 # recursively needs. This is given in the form of a dictionary,
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230 # where the keys are outputs indexes, and values are a list of
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231 # of the offsets used by the corresponding outputs
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232 # In our case the function ``gibbs_1`` applied recursively, requires
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233 # at time k the past value k-1 for the first output (index 0) and
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234 # no past value of the second output
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235 outputs_taps = { 0 : [-1], 1 : [] }
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236
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237 v_means, v_samples = theano.scan( fn = gibbs_1,
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238 sequences = [],
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239 initial_states = [v_sample, v_mean],
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240 non_sequences = [self.W, self.hbias, self.vbias],
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241 outputs_taps = outputs_taps,
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242 n_steps = k)
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243 return v_means[-1], v_samples[-1]
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244
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245 def free_energy(self, v_sample):
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246 wx_b = T.dot(v_sample, self.W) + self.hbias
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247 vbias_term = T.sum(T.dot(v_sample, self.vbias))
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248 hidden_term = T.sum(T.log(1+T.exp(wx_b)))
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249 return -hidden_term - vbias_term
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250
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251 def cd(self, visible = None, persistent = None, steps = 1):
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252 """
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253 Return a 5-tuple of values related to contrastive divergence: (cost,
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254 end-state of negative-phase chain, gradient on weights, gradient on
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255 hidden bias, gradient on visible bias)
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256
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257 If visible is None, it defaults to self.input
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258 If persistent is None, it defaults to self.input
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259
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260 CD aka CD1 - cd()
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261 CD-10 - cd(steps=10)
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262 PCD - cd(persistent=shared(numpy.asarray(initializer)))
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263 PCD-k - cd(persistent=shared(numpy.asarray(initializer)),
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264 steps=10)
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265 """
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266 if visible is None:
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267 visible = self.input
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268
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269 if visible is None:
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270 raise TypeError('visible argument is required when self.input is None')
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271
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272 if steps is None:
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273 steps = self.gibbs_1
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274
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275 if persistent is None:
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276 chain_start = visible
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277 else:
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278 chain_start = persistent
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279
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280 chain_end_mean, chain_end_sample = self.gibbs_k(chain_start, steps)
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281
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282 #print >> sys.stderr, "WARNING: DEBUGGING with wrong FREE ENERGY"
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283 #free_energy_delta = - self.free_energy(chain_end_sample)
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284 free_energy_delta = self.free_energy(visible) - self.free_energy(chain_end_sample)
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285
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286 # we will return all of these regardless of what is in self.params
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287 all_params = [self.W, self.hbias, self.vbias]
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288
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289 gparams = T.grad(free_energy_delta, all_params,
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290 consider_constant = [chain_end_sample])
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291
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292 cross_entropy = T.mean(T.sum(
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293 visible*T.log(chain_end_mean) + (1 - visible)*T.log(1-chain_end_mean),
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294 axis = 1))
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295
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296 return (cross_entropy, chain_end_sample,) + tuple(gparams)
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297
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298 def cd_updates(self, lr, visible = None, persistent = None, steps = 1):
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299 """
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300 Return the learning updates for the RBM parameters that are shared variables.
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301
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302 Also returns an update for the persistent if it is a shared variable.
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303
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304 These updates are returned as a dictionary.
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305
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306 :param lr: [scalar] learning rate for contrastive divergence learning
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307 :param visible: see `cd_grad`
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308 :param persistent: see `cd_grad`
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309 :param steps: see `cd_grad`
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310
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311 """
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312
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313 cross_entropy, chain_end, gW, ghbias, gvbias = self.cd(visible,
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314 persistent, steps)
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315
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316 updates = {}
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317 if hasattr(self.W, 'value'):
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318 updates[self.W] = self.W - lr * gW
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319 if hasattr(self.hbias, 'value'):
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320 updates[self.hbias] = self.hbias - lr * ghbias
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321 if hasattr(self.vbias, 'value'):
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322 updates[self.vbias] = self.vbias - lr * gvbias
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323 if persistent:
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324 #if persistent is a shared var, then it means we should use
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325 updates[persistent] = chain_end
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326
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327 return updates
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328
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329 # DEEP MODELS
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330
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331 class DBN(object):
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332 """
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333 *** WHAT IS A DBN?
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334 """
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335
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336 def __init__(self, input_len, hidden_layers_sizes, n_classes, rng):
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337 """ This class is made to support a variable number of layers.
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338
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339 :param train_set_x: symbolic variable pointing to the training dataset
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340
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341 :param train_set_y: symbolic variable pointing to the labels of the
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342 training dataset
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343
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344 :param input_len: dimension of the input to the sdA
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345
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346 :param n_layers_sizes: intermidiate layers size, must contain
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347 at least one value
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348
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349 :param n_classes: dimension of the output of the network
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350
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351 :param corruption_levels: amount of corruption to use for each
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352 layer
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353
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354 :param rng: numpy random number generator used to draw initial weights
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355
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356 :param pretrain_lr: learning rate used during pre-trainnig stage
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357
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358 :param finetune_lr: learning rate used during finetune stage
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359 """
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360
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361 self.sigmoid_layers = []
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362 self.rbm_layers = []
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363 self.pretrain_functions = []
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364 self.params = []
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365
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366 theano_rng = RandomStreams(rng.randint(2**30))
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367
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368 # allocate symbolic variables for the data
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369 index = T.lscalar() # index to a [mini]batch
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370 self.x = T.matrix('x') # the data is presented as rasterized images
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371 self.y = T.ivector('y') # the labels are presented as 1D vector of
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372 # [int] labels
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373 input = self.x
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374
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375 # The SdA is an MLP, for which all weights of intermidiate layers
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376 # are shared with a different denoising autoencoders
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377 # We will first construct the SdA as a deep multilayer perceptron,
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378 # and when constructing each sigmoidal layer we also construct a
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379 # denoising autoencoder that shares weights with that layer, and
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380 # compile a training function for that denoising autoencoder
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381
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382 for n_hid in hidden_layers_sizes:
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383 # construct the sigmoidal layer
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384
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385 sigmoid_layer = SigmoidalLayer(rng, input, input_len, n_hid)
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386 self.sigmoid_layers.append(sigmoid_layer)
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387
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388 self.rbm_layers.append(RBM(input=input,
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389 W=sigmoid_layer.W,
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390 hbias=sigmoid_layer.b,
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391 n_visible = input_len,
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392 n_hidden = n_hid,
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393 numpy_rng=rng,
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394 theano_rng=theano_rng))
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395
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396 # its arguably a philosophical question...
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397 # but we are going to only declare that the parameters of the
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398 # sigmoid_layers are parameters of the StackedDAA
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399 # the hidden-layer biases in the daa_layers are parameters of those
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400 # daa_layers, but not the StackedDAA
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401 self.params.extend(self.sigmoid_layers[-1].params)
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402
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403 # get ready for the next loop iteration
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404 input_len = n_hid
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405 input = self.sigmoid_layers[-1].output
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406
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407 # We now need to add a logistic layer on top of the MLP
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408 self.logistic_regressor = LogisticRegression(input = input,
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409 n_in = input_len, n_out = n_classes)
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410
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411 self.params.extend(self.logistic_regressor.params)
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412
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413 def pretraining_functions(self, train_set_x, batch_size, learning_rate, k=1):
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414 if k!=1:
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415 raise NotImplementedError()
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416 index = T.lscalar() # index to a [mini]batch
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417 n_train_batches = train_set_x.value.shape[0] / batch_size
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418 batch_begin = (index % n_train_batches) * batch_size
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419 batch_end = batch_begin+batch_size
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420
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421 print 'TRAIN_SET X', train_set_x.value.shape
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422 rval = []
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423 for rbm in self.rbm_layers:
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424 # N.B. these cd() samples are independent from the
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425 # samples used for learning
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426 outputs = list(rbm.cd())[0:2]
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427 rval.append(function([index], outputs,
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428 updates = rbm.cd_updates(lr=learning_rate),
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429 givens = {self.x: train_set_x[batch_begin:batch_end]}))
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430 if rbm is self.rbm_layers[0]:
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431 f = rval[-1]
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432 AA=len(outputs)
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433 for i, implicit_out in enumerate(f.maker.env.outputs): #[len(outputs):]:
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434 print 'OUTPUT ', i
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435 theano.printing.debugprint(implicit_out, file=sys.stdout)
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436
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437 return rval
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438
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439 def finetune(self, datasets, lr, batch_size):
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440
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441 # unpack the various datasets
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442 (train_set_x, train_set_y) = datasets[0]
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443 (valid_set_x, valid_set_y) = datasets[1]
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444 (test_set_x, test_set_y) = datasets[2]
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445
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446 # compute number of minibatches for training, validation and testing
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447 assert train_set_x.value.shape[0] % batch_size == 0
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448 assert valid_set_x.value.shape[0] % batch_size == 0
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449 assert test_set_x.value.shape[0] % batch_size == 0
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450 n_train_batches = train_set_x.value.shape[0] / batch_size
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451 n_valid_batches = valid_set_x.value.shape[0] / batch_size
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452 n_test_batches = test_set_x.value.shape[0] / batch_size
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453
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454 index = T.lscalar() # index to a [mini]batch
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455 target = self.y
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456
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457 train_index = index % n_train_batches
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458
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459 classifier = self.logistic_regressor
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460 cost = classifier.negative_log_likelihood(target)
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461 # compute the gradients with respect to the model parameters
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462 gparams = T.grad(cost, self.params)
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463
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464 # compute list of fine-tuning updates
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465 updates = [(param, param - gparam*finetune_lr)
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466 for param,gparam in zip(self.params, gparams)]
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467
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468 train_fn = theano.function([index], cost,
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469 updates = updates,
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470 givens = {
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471 self.x : train_set_x[train_index*batch_size:(train_index+1)*batch_size],
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472 target : train_set_y[train_index*batch_size:(train_index+1)*batch_size]})
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473
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474 test_score_i = theano.function([index], classifier.errors(target),
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diff changeset
475 givens = {
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diff changeset
476 self.x: test_set_x[index*batch_size:(index+1)*batch_size],
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Dumitru Erhan <dumitru.erhan@gmail.com>
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diff changeset
477 target: test_set_y[index*batch_size:(index+1)*batch_size]})
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parents:
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478
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diff changeset
479 valid_score_i = theano.function([index], classifier.errors(target),
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480 givens = {
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diff changeset
481 self.x: valid_set_x[index*batch_size:(index+1)*batch_size],
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diff changeset
482 target: valid_set_y[index*batch_size:(index+1)*batch_size]})
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parents:
diff changeset
483
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484 def test_scores():
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diff changeset
485 return [test_score_i(i) for i in xrange(n_test_batches)]
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parents:
diff changeset
486
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487 def valid_scores():
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488 return [valid_score_i(i) for i in xrange(n_valid_batches)]
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parents:
diff changeset
489
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490 return train_fn, valid_scores, test_scores
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diff changeset
491
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diff changeset
492 def load_mnist(filename):
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493 f = gzip.open(filename,'rb')
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diff changeset
494 train_set, valid_set, test_set = cPickle.load(f)
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diff changeset
495 f.close()
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496
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diff changeset
497 def shared_dataset(data_xy):
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498 data_x, data_y = data_xy
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diff changeset
499 shared_x = theano.shared(numpy.asarray(data_x, dtype=theano.config.floatX))
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diff changeset
500 shared_y = theano.shared(numpy.asarray(data_y, dtype=theano.config.floatX))
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diff changeset
501 return shared_x, T.cast(shared_y, 'int32')
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502
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diff changeset
503 n_train_examples = train_set[0].shape[0]
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504 datasets = shared_dataset(train_set), shared_dataset(valid_set), shared_dataset(test_set)
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parents:
diff changeset
505
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506 return n_train_examples, datasets
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parents:
diff changeset
507
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diff changeset
508 def dbn_main(finetune_lr = 0.01,
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diff changeset
509 pretraining_epochs = 10,
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parents:
diff changeset
510 pretrain_lr = 0.1,
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parents:
diff changeset
511 training_epochs = 1000,
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diff changeset
512 batch_size = 20,
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diff changeset
513 mnist_file='mnist.pkl.gz'):
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diff changeset
514 """
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diff changeset
515 Demonstrate stochastic gradient descent optimization for a multilayer perceptron
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diff changeset
516
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diff changeset
517 This is demonstrated on MNIST.
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Dumitru Erhan <dumitru.erhan@gmail.com>
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diff changeset
518
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diff changeset
519 :param learning_rate: learning rate used in the finetune stage
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Dumitru Erhan <dumitru.erhan@gmail.com>
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diff changeset
520 (factor for the stochastic gradient)
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Dumitru Erhan <dumitru.erhan@gmail.com>
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diff changeset
521
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diff changeset
522 :param pretraining_epochs: number of epoch to do pretraining
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diff changeset
523
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diff changeset
524 :param pretrain_lr: learning rate to be used during pre-training
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parents:
diff changeset
525
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diff changeset
526 :param n_iter: maximal number of iterations ot run the optimizer
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diff changeset
527
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
528 :param mnist_file: path the the pickled mnist_file
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
529
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
530 """
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
531
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
532 n_train_examples, train_valid_test = load_mnist(mnist_file)
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
533
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
534 print "Creating a Deep Belief Network"
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
535 deep_model = DBN(
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
536 input_len=28*28,
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
537 hidden_layers_sizes = [500, 150, 100],
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
538 n_classes=10,
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
539 rng = numpy.random.RandomState())
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
540
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
541 ####
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
542 #### Phase 1: Pre-training
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
543 ####
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
544 print "Pretraining (unsupervised learning) ..."
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
545
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
546 pretrain_functions = deep_model.pretraining_functions(
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
547 batch_size=batch_size,
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
548 train_set_x=train_valid_test[0][0],
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
549 learning_rate=pretrain_lr,
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
550 )
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
551
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
552 start_time = time.clock()
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
553 for layer_idx, pretrain_fn in enumerate(pretrain_functions):
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
554 # go through pretraining epochs
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
555 print 'Pre-training layer %i'% layer_idx
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
556 for i in xrange(pretraining_epochs * n_train_examples / batch_size):
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
557 outstuff = pretrain_fn(i)
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
558 xe, negsample = outstuff[:2]
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
559 print (layer_idx, i,
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
560 n_train_examples / batch_size,
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
561 float(xe),
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
562 'Wmin', deep_model.rbm_layers[0].W.value.min(),
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
563 'Wmax', deep_model.rbm_layers[0].W.value.max(),
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
564 'vmin', deep_model.rbm_layers[0].vbias.value.min(),
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
565 'vmax', deep_model.rbm_layers[0].vbias.value.max(),
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
566 #'x>0.3', (input_i>0.3).sum(),
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
567 )
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
568 sys.stdout.flush()
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
569 if i % 1000 == 0:
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
570 PIL.Image.fromarray(
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
571 pylearn.io.image_tiling.tile_raster_images(negsample, (28,28), (10,10),
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
572 tile_spacing=(1,1))).save('samples_%i_%i.png'%(layer_idx,i))
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
573
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
574 PIL.Image.fromarray(
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
575 pylearn.io.image_tiling.tile_raster_images(
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
576 deep_model.rbm_layers[0].W.value.T,
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
577 (28,28), (10,10),
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
578 tile_spacing=(1,1))).save('filters_%i_%i.png'%(layer_idx,i))
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
579 end_time = time.clock()
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
580 print 'Pretraining took %f minutes' %((end_time - start_time)/60.)
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
581
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
582 return
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
583
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
584 print "Fine tuning (supervised learning) ..."
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
585 train_fn, valid_scores, test_scores =\
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
586 deep_model.finetune_functions(train_valid_test[0][0],
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
587 learning_rate=finetune_lr, # the learning rate
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
588 batch_size = batch_size) # number of examples to use at once
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
589
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
590 ####
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
591 #### Phase 2: Fine Tuning
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
592 ####
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
593
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
594 patience = 10000 # look as this many examples regardless
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
595 patience_increase = 2. # wait this much longer when a new best is
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
596 # found
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
597 improvement_threshold = 0.995 # a relative improvement of this much is
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
598 # considered significant
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
599 validation_frequency = min(n_train_examples, patience/2)
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
600 # go through this many
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
601 # minibatche before checking the network
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
602 # on the validation set; in this case we
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
603 # check every epoch
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
604
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
605 patience_max = n_train_examples * training_epochs
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
606
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
607 best_epoch = None
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
608 best_epoch_test_score = None
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
609 best_epoch_valid_score = float('inf')
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
610 start_time = time.clock()
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
611
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
612 for i in xrange(patience_max):
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
613 if i >= patience:
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
614 break
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
615
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
616 cost_i = train_fn(i)
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
617
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
618 if i % validation_frequency == 0:
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
619 validation_i = numpy.mean([score for score in valid_scores()])
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
620
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
621 # if we got the best validation score until now
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
622 if validation_i < best_epoch_valid_score:
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
623
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
624 # improve patience if loss improvement is good enough
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
625 threshold_i = best_epoch_valid_score * improvement_threshold
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
626 if validation_i < threshold_i:
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
627 patience = max(patience, i * patience_increase)
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
628
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
629 # save best validation score and iteration number
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
630 best_epoch_valid_score = validation_i
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
631 best_epoch = i/validation_i
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
632 best_epoch_test_score = numpy.mean(
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
633 [score for score in test_scores()])
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
634
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
635 print('epoch %i, validation error %f %%, test error %f %%'%(
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
636 i/validation_frequency, validation_i*100.,
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
637 best_epoch_test_score*100.))
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
638 else:
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
639 print('epoch %i, validation error %f %%' % (
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
640 i/validation_frequency, validation_i*100.))
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
641 end_time = time.clock()
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
642
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
643 print(('Optimization complete with best validation score of %f %%,'
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
644 'with test performance %f %%') %
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
645 (finetune_status['best_validation_loss']*100.,
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
646 finetune_status['test_score']*100.))
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
647 print ('The code ran for %f minutes' % ((finetune_status['duration'])/60.))
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
648
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
649 def rbm_main():
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
650 rbm = RBM(n_visible=20, n_hidden=30,
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
651 numpy_rng = numpy.random.RandomState(34))
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
652
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
653 cd_updates = rbm.cd_updates(lr=0.25)
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
654
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
655 print cd_updates
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
656
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
657 f = function([rbm.input], [],
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
658 updates={rbm.W:cd_updates[rbm.W]})
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
659
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
660 theano.printing.debugprint(f.maker.env.outputs[0],
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661 file=sys.stdout)
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diff changeset
662
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diff changeset
663
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Dumitru Erhan <dumitru.erhan@gmail.com>
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diff changeset
664 if __name__ == '__main__':
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parents:
diff changeset
665 dbn_main()
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parents:
diff changeset
666 #rbm_main()
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parents:
diff changeset
667
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parents:
diff changeset
668
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parents:
diff changeset
669 if 0:
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parents:
diff changeset
670 class DAA(object):
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diff changeset
671 def __init__(self, n_visible= 784, n_hidden= 500, corruption_level = 0.1,\
4bc5eeec6394 Updating the tutorial code to the latest revisions.
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diff changeset
672 input = None, shared_W = None, shared_b = None):
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parents:
diff changeset
673 """
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
674 Initialize the dA class by specifying the number of visible units (the
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
675 dimension d of the input ), the number of hidden units ( the dimension
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
676 d' of the latent or hidden space ) and the corruption level. The
4bc5eeec6394 Updating the tutorial code to the latest revisions.
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diff changeset
677 constructor also receives symbolic variables for the input, weights and
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
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diff changeset
678 bias. Such a symbolic variables are useful when, for example the input is
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
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diff changeset
679 the result of some computations, or when weights are shared between the
4bc5eeec6394 Updating the tutorial code to the latest revisions.
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parents:
diff changeset
680 dA and an MLP layer. When dealing with SdAs this always happens,
4bc5eeec6394 Updating the tutorial code to the latest revisions.
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parents:
diff changeset
681 the dA on layer 2 gets as input the output of the dA on layer 1,
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
682 and the weights of the dA are used in the second stage of training
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
683 to construct an MLP.
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parents:
diff changeset
684
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
685 :param n_visible: number of visible units
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parents:
diff changeset
686
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
687 :param n_hidden: number of hidden units
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parents:
diff changeset
688
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
689 :param input: a symbolic description of the input or None
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
690
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
691 :param corruption_level: the corruption mechanism picks up randomly this
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
692 fraction of entries of the input and turns them to 0
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
693
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
694
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Dumitru Erhan <dumitru.erhan@gmail.com>
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695 """
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
696 self.n_visible = n_visible
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
697 self.n_hidden = n_hidden
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
698
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
699 # create a Theano random generator that gives symbolic random values
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
700 theano_rng = RandomStreams()
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
701
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
702 if shared_W != None and shared_b != None :
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
703 self.W = shared_W
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
704 self.b = shared_b
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
705 else:
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
706 # initial values for weights and biases
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
707 # note : W' was written as `W_prime` and b' as `b_prime`
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
708
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
709 # W is initialized with `initial_W` which is uniformely sampled
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
710 # from -6./sqrt(n_visible+n_hidden) and 6./sqrt(n_hidden+n_visible)
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
711 # the output of uniform if converted using asarray to dtype
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
712 # theano.config.floatX so that the code is runable on GPU
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
713 initial_W = numpy.asarray( numpy.random.uniform( \
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
714 low = -numpy.sqrt(6./(n_hidden+n_visible)), \
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
715 high = numpy.sqrt(6./(n_hidden+n_visible)), \
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
716 size = (n_visible, n_hidden)), dtype = theano.config.floatX)
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
717 initial_b = numpy.zeros(n_hidden, dtype = theano.config.floatX)
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
718
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
719
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
720 # theano shared variables for weights and biases
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
721 self.W = theano.shared(value = initial_W, name = "W")
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
722 self.b = theano.shared(value = initial_b, name = "b")
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
723
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
724
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
725 initial_b_prime= numpy.zeros(n_visible)
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
726 # tied weights, therefore W_prime is W transpose
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
727 self.W_prime = self.W.T
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
728 self.b_prime = theano.shared(value = initial_b_prime, name = "b'")
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
729
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
730 # if no input is given, generate a variable representing the input
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
731 if input == None :
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
732 # we use a matrix because we expect a minibatch of several examples,
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
733 # each example being a row
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
734 self.x = T.matrix(name = 'input')
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
735 else:
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
736 self.x = input
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
737 # Equation (1)
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
738 # keep 90% of the inputs the same and zero-out randomly selected subset of 10% of the inputs
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
739 # note : first argument of theano.rng.binomial is the shape(size) of
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
740 # random numbers that it should produce
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
741 # second argument is the number of trials
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
742 # third argument is the probability of success of any trial
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
743 #
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
744 # this will produce an array of 0s and 1s where 1 has a
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
745 # probability of 1 - ``corruption_level`` and 0 with
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
746 # ``corruption_level``
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
747 self.tilde_x = theano_rng.binomial( self.x.shape, 1, 1 - corruption_level) * self.x
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
748 # Equation (2)
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
749 # note : y is stored as an attribute of the class so that it can be
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
750 # used later when stacking dAs.
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
751 self.y = T.nnet.sigmoid(T.dot(self.tilde_x, self.W ) + self.b)
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
752 # Equation (3)
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
753 self.z = T.nnet.sigmoid(T.dot(self.y, self.W_prime) + self.b_prime)
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
754 # Equation (4)
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
755 # note : we sum over the size of a datapoint; if we are using minibatches,
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
756 # L will be a vector, with one entry per example in minibatch
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
757 self.L = - T.sum( self.x*T.log(self.z) + (1-self.x)*T.log(1-self.z), axis=1 )
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
758 # note : L is now a vector, where each element is the cross-entropy cost
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
759 # of the reconstruction of the corresponding example of the
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
760 # minibatch. We need to compute the average of all these to get
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
761 # the cost of the minibatch
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
762 self.cost = T.mean(self.L)
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
763
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
764 self.params = [ self.W, self.b, self.b_prime ]
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
765
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
766 class StackedDAA(DeepLayerwiseModel):
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
767 """Stacked denoising auto-encoder class (SdA)
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
768
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
769 A stacked denoising autoencoder model is obtained by stacking several
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
770 dAs. The hidden layer of the dA at layer `i` becomes the input of
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
771 the dA at layer `i+1`. The first layer dA gets as input the input of
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
772 the SdA, and the hidden layer of the last dA represents the output.
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
773 Note that after pretraining, the SdA is dealt with as a normal MLP,
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
774 the dAs are only used to initialize the weights.
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
775 """
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
776
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
777 def __init__(self, n_ins, hidden_layers_sizes, n_outs,
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
778 corruption_levels, rng, ):
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
779 """ This class is made to support a variable number of layers.
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
780
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
781 :param train_set_x: symbolic variable pointing to the training dataset
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
782
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
783 :param train_set_y: symbolic variable pointing to the labels of the
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
784 training dataset
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
785
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
786 :param n_ins: dimension of the input to the sdA
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
787
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
788 :param n_layers_sizes: intermidiate layers size, must contain
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
789 at least one value
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
790
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
791 :param n_outs: dimension of the output of the network
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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792
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
793 :param corruption_levels: amount of corruption to use for each
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
794 layer
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
795
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
796 :param rng: numpy random number generator used to draw initial weights
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
797
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
798 :param pretrain_lr: learning rate used during pre-trainnig stage
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
799
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
800 :param finetune_lr: learning rate used during finetune stage
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
801 """
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
802
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
803 self.sigmoid_layers = []
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
804 self.daa_layers = []
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
805 self.pretrain_functions = []
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
806 self.params = []
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
807 self.n_layers = len(hidden_layers_sizes)
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
808
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
809 if len(hidden_layers_sizes) < 1 :
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
810 raiseException (' You must have at least one hidden layer ')
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
811
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
812 theano_rng = RandomStreams(rng.randint(2**30))
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
813
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
814 # allocate symbolic variables for the data
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
815 index = T.lscalar() # index to a [mini]batch
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
816 self.x = T.matrix('x') # the data is presented as rasterized images
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
817 self.y = T.ivector('y') # the labels are presented as 1D vector of
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
818 # [int] labels
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
819
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
820 # The SdA is an MLP, for which all weights of intermidiate layers
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
821 # are shared with a different denoising autoencoders
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
822 # We will first construct the SdA as a deep multilayer perceptron,
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
823 # and when constructing each sigmoidal layer we also construct a
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
824 # denoising autoencoder that shares weights with that layer, and
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
825 # compile a training function for that denoising autoencoder
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
826
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
827 for i in xrange( self.n_layers ):
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
828 # construct the sigmoidal layer
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
829
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
830 sigmoid_layer = SigmoidalLayer(rng,
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
831 self.layers[-1].output if i else self.x,
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
832 hidden_layers_sizes[i-1] if i else n_ins,
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
833 hidden_layers_sizes[i])
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
834
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
835 daa_layer = DAA(corruption_level = corruption_levels[i],
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
836 input = sigmoid_layer.input,
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
837 W = sigmoid_layer.W,
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
838 b = sigmoid_layer.b)
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
839
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
840 # add the layer to the
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
841 self.sigmoid_layers.append(sigmoid_layer)
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
842 self.daa_layers.append(daa_layer)
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
843
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
844 # its arguably a philosophical question...
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
845 # but we are going to only declare that the parameters of the
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
846 # sigmoid_layers are parameters of the StackedDAA
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
847 # the hidden-layer biases in the daa_layers are parameters of those
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
848 # daa_layers, but not the StackedDAA
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
849 self.params.extend(sigmoid_layer.params)
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
850
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
851 # We now need to add a logistic layer on top of the MLP
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
852 self.logistic_regressor = LogisticRegression(
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
853 input = self.sigmoid_layers[-1].output,
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
854 n_in = hidden_layers_sizes[-1],
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
855 n_out = n_outs)
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
856
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
857 self.params.extend(self.logLayer.params)
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
858
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
859 def pretraining_functions(self, train_set_x, batch_size):
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
860
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
861 # compiles update functions for each layer, and
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
862 # returns them as a list
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
863 #
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
864 # Construct a function that trains this dA
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
865 # compute gradients of layer parameters
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
866 gparams = T.grad(dA_layer.cost, dA_layer.params)
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
867 # compute the list of updates
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
868 updates = {}
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
869 for param, gparam in zip(dA_layer.params, gparams):
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
870 updates[param] = param - gparam * pretrain_lr
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
871
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
872 # create a function that trains the dA
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
873 update_fn = theano.function([index], dA_layer.cost, \
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
874 updates = updates,
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
875 givens = {
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
876 self.x : train_set_x[index*batch_size:(index+1)*batch_size]})
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
877 # collect this function into a list
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
878 self.pretrain_functions += [update_fn]
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
879
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
880