annotate code_tutoriel/deep.py @ 399:99905d9bc9dd

Initial commit for calculating the test error of the AMT classifier
author humel
date Wed, 28 Apr 2010 00:38:31 -0400
parents 4bc5eeec6394
children
rev   line source
165
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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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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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diff changeset
477 target: test_set_y[index*batch_size:(index+1)*batch_size]})
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parents:
diff changeset
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:
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483
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484 def test_scores():
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485 return [test_score_i(i) for i in xrange(n_test_batches)]
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parents:
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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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parents:
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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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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500 shared_y = theano.shared(numpy.asarray(data_y, dtype=theano.config.floatX))
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501 return shared_x, T.cast(shared_y, 'int32')
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502
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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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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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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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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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parents:
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