Mercurial > ift6266
annotate code_tutoriel/deep.py @ 588:9a6abcf143e8
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author | fsavard |
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date | Thu, 30 Sep 2010 17:51:46 -0400 |
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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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476 self.x: test_set_x[index*batch_size:(index+1)*batch_size], |
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477 target: test_set_y[index*batch_size:(index+1)*batch_size]}) |
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478 |
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479 valid_score_i = theano.function([index], classifier.errors(target), |
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480 givens = { |
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481 self.x: valid_set_x[index*batch_size:(index+1)*batch_size], |
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482 target: valid_set_y[index*batch_size:(index+1)*batch_size]}) |
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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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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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489 |
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490 return train_fn, valid_scores, test_scores |
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491 |
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492 def load_mnist(filename): |
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493 f = gzip.open(filename,'rb') |
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494 train_set, valid_set, test_set = cPickle.load(f) |
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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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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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505 |
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506 return n_train_examples, datasets |
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507 |
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508 def dbn_main(finetune_lr = 0.01, |
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509 pretraining_epochs = 10, |
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510 pretrain_lr = 0.1, |
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511 training_epochs = 1000, |
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512 batch_size = 20, |
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513 mnist_file='mnist.pkl.gz'): |
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514 """ |
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515 Demonstrate stochastic gradient descent optimization for a multilayer perceptron |
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516 |
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517 This is demonstrated on MNIST. |
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518 |
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519 :param learning_rate: learning rate used in the finetune stage |
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520 (factor for the stochastic gradient) |
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521 |
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522 :param pretraining_epochs: number of epoch to do pretraining |
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523 |
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524 :param pretrain_lr: learning rate to be used during pre-training |
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525 |
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526 :param n_iter: maximal number of iterations ot run the optimizer |
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527 |
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528 :param mnist_file: path the the pickled mnist_file |
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529 |
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530 """ |
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531 |
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532 n_train_examples, train_valid_test = load_mnist(mnist_file) |
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533 |
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534 print "Creating a Deep Belief Network" |
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535 deep_model = DBN( |
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536 input_len=28*28, |
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537 hidden_layers_sizes = [500, 150, 100], |
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538 n_classes=10, |
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539 rng = numpy.random.RandomState()) |
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540 |
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541 #### |
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542 #### Phase 1: Pre-training |
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543 #### |
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544 print "Pretraining (unsupervised learning) ..." |
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|
545 |
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546 pretrain_functions = deep_model.pretraining_functions( |
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547 batch_size=batch_size, |
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548 train_set_x=train_valid_test[0][0], |
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549 learning_rate=pretrain_lr, |
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|
550 ) |
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|
551 |
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|
552 start_time = time.clock() |
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553 for layer_idx, pretrain_fn in enumerate(pretrain_functions): |
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554 # go through pretraining epochs |
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555 print 'Pre-training layer %i'% layer_idx |
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|
556 for i in xrange(pretraining_epochs * n_train_examples / batch_size): |
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parents:
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557 outstuff = pretrain_fn(i) |
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parents:
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558 xe, negsample = outstuff[:2] |
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559 print (layer_idx, i, |
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parents:
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560 n_train_examples / batch_size, |
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parents:
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|
561 float(xe), |
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562 'Wmin', deep_model.rbm_layers[0].W.value.min(), |
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parents:
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563 'Wmax', deep_model.rbm_layers[0].W.value.max(), |
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564 'vmin', deep_model.rbm_layers[0].vbias.value.min(), |
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565 'vmax', deep_model.rbm_layers[0].vbias.value.max(), |
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parents:
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|
566 #'x>0.3', (input_i>0.3).sum(), |
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parents:
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|
567 ) |
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parents:
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|
568 sys.stdout.flush() |
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|
569 if i % 1000 == 0: |
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parents:
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|
570 PIL.Image.fromarray( |
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parents:
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|
571 pylearn.io.image_tiling.tile_raster_images(negsample, (28,28), (10,10), |
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Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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572 tile_spacing=(1,1))).save('samples_%i_%i.png'%(layer_idx,i)) |
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parents:
diff
changeset
|
573 |
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parents:
diff
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|
574 PIL.Image.fromarray( |
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parents:
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|
575 pylearn.io.image_tiling.tile_raster_images( |
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parents:
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|
576 deep_model.rbm_layers[0].W.value.T, |
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parents:
diff
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|
577 (28,28), (10,10), |
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parents:
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|
578 tile_spacing=(1,1))).save('filters_%i_%i.png'%(layer_idx,i)) |
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parents:
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|
579 end_time = time.clock() |
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parents:
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580 print 'Pretraining took %f minutes' %((end_time - start_time)/60.) |
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parents:
diff
changeset
|
581 |
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff
changeset
|
582 return |
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parents:
diff
changeset
|
583 |
4bc5eeec6394
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parents:
diff
changeset
|
584 print "Fine tuning (supervised learning) ..." |
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585 train_fn, valid_scores, test_scores =\ |
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586 deep_model.finetune_functions(train_valid_test[0][0], |
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587 learning_rate=finetune_lr, # the learning rate |
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588 batch_size = batch_size) # number of examples to use at once |
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589 |
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590 #### |
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591 #### Phase 2: Fine Tuning |
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592 #### |
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593 |
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594 patience = 10000 # look as this many examples regardless |
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595 patience_increase = 2. # wait this much longer when a new best is |
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596 # found |
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597 improvement_threshold = 0.995 # a relative improvement of this much is |
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598 # considered significant |
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599 validation_frequency = min(n_train_examples, patience/2) |
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600 # go through this many |
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601 # minibatche before checking the network |
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602 # on the validation set; in this case we |
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603 # check every epoch |
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604 |
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605 patience_max = n_train_examples * training_epochs |
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606 |
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607 best_epoch = None |
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608 best_epoch_test_score = None |
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609 best_epoch_valid_score = float('inf') |
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610 start_time = time.clock() |
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611 |
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612 for i in xrange(patience_max): |
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613 if i >= patience: |
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614 break |
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615 |
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616 cost_i = train_fn(i) |
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617 |
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618 if i % validation_frequency == 0: |
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619 validation_i = numpy.mean([score for score in valid_scores()]) |
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620 |
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621 # if we got the best validation score until now |
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622 if validation_i < best_epoch_valid_score: |
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623 |
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624 # improve patience if loss improvement is good enough |
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625 threshold_i = best_epoch_valid_score * improvement_threshold |
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626 if validation_i < threshold_i: |
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627 patience = max(patience, i * patience_increase) |
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628 |
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629 # save best validation score and iteration number |
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630 best_epoch_valid_score = validation_i |
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631 best_epoch = i/validation_i |
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632 best_epoch_test_score = numpy.mean( |
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633 [score for score in test_scores()]) |
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634 |
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635 print('epoch %i, validation error %f %%, test error %f %%'%( |
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636 i/validation_frequency, validation_i*100., |
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637 best_epoch_test_score*100.)) |
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638 else: |
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639 print('epoch %i, validation error %f %%' % ( |
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640 i/validation_frequency, validation_i*100.)) |
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641 end_time = time.clock() |
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642 |
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643 print(('Optimization complete with best validation score of %f %%,' |
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644 'with test performance %f %%') % |
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645 (finetune_status['best_validation_loss']*100., |
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646 finetune_status['test_score']*100.)) |
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647 print ('The code ran for %f minutes' % ((finetune_status['duration'])/60.)) |
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648 |
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649 def rbm_main(): |
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650 rbm = RBM(n_visible=20, n_hidden=30, |
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651 numpy_rng = numpy.random.RandomState(34)) |
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652 |
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653 cd_updates = rbm.cd_updates(lr=0.25) |
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654 |
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655 print cd_updates |
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656 |
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657 f = function([rbm.input], [], |
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658 updates={rbm.W:cd_updates[rbm.W]}) |
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659 |
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660 theano.printing.debugprint(f.maker.env.outputs[0], |
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661 file=sys.stdout) |
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662 |
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663 |
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664 if __name__ == '__main__': |
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665 dbn_main() |
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666 #rbm_main() |
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667 |
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668 |
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669 if 0: |
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670 class DAA(object): |
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671 def __init__(self, n_visible= 784, n_hidden= 500, corruption_level = 0.1,\ |
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672 input = None, shared_W = None, shared_b = None): |
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673 """ |
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674 Initialize the dA class by specifying the number of visible units (the |
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675 dimension d of the input ), the number of hidden units ( the dimension |
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676 d' of the latent or hidden space ) and the corruption level. The |
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677 constructor also receives symbolic variables for the input, weights and |
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678 bias. Such a symbolic variables are useful when, for example the input is |
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679 the result of some computations, or when weights are shared between the |
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680 dA and an MLP layer. When dealing with SdAs this always happens, |
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681 the dA on layer 2 gets as input the output of the dA on layer 1, |
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682 and the weights of the dA are used in the second stage of training |
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683 to construct an MLP. |
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684 |
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685 :param n_visible: number of visible units |
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686 |
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687 :param n_hidden: number of hidden units |
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688 |
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689 :param input: a symbolic description of the input or None |
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690 |
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691 :param corruption_level: the corruption mechanism picks up randomly this |
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692 fraction of entries of the input and turns them to 0 |
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693 |
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694 |
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695 """ |
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696 self.n_visible = n_visible |
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697 self.n_hidden = n_hidden |
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698 |
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699 # create a Theano random generator that gives symbolic random values |
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700 theano_rng = RandomStreams() |
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701 |
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702 if shared_W != None and shared_b != None : |
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703 self.W = shared_W |
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704 self.b = shared_b |
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705 else: |
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706 # initial values for weights and biases |
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707 # note : W' was written as `W_prime` and b' as `b_prime` |
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708 |
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709 # W is initialized with `initial_W` which is uniformely sampled |
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710 # from -6./sqrt(n_visible+n_hidden) and 6./sqrt(n_hidden+n_visible) |
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711 # the output of uniform if converted using asarray to dtype |
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712 # theano.config.floatX so that the code is runable on GPU |
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713 initial_W = numpy.asarray( numpy.random.uniform( \ |
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714 low = -numpy.sqrt(6./(n_hidden+n_visible)), \ |
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715 high = numpy.sqrt(6./(n_hidden+n_visible)), \ |
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716 size = (n_visible, n_hidden)), dtype = theano.config.floatX) |
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717 initial_b = numpy.zeros(n_hidden, dtype = theano.config.floatX) |
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718 |
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719 |
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720 # theano shared variables for weights and biases |
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721 self.W = theano.shared(value = initial_W, name = "W") |
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722 self.b = theano.shared(value = initial_b, name = "b") |
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723 |
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724 |
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725 initial_b_prime= numpy.zeros(n_visible) |
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726 # tied weights, therefore W_prime is W transpose |
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727 self.W_prime = self.W.T |
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728 self.b_prime = theano.shared(value = initial_b_prime, name = "b'") |
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729 |
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730 # if no input is given, generate a variable representing the input |
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731 if input == None : |
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732 # we use a matrix because we expect a minibatch of several examples, |
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733 # each example being a row |
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734 self.x = T.matrix(name = 'input') |
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735 else: |
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736 self.x = input |
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737 # Equation (1) |
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738 # keep 90% of the inputs the same and zero-out randomly selected subset of 10% of the inputs |
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739 # note : first argument of theano.rng.binomial is the shape(size) of |
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740 # random numbers that it should produce |
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741 # second argument is the number of trials |
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742 # third argument is the probability of success of any trial |
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743 # |
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744 # this will produce an array of 0s and 1s where 1 has a |
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745 # probability of 1 - ``corruption_level`` and 0 with |
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746 # ``corruption_level`` |
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747 self.tilde_x = theano_rng.binomial( self.x.shape, 1, 1 - corruption_level) * self.x |
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748 # Equation (2) |
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749 # note : y is stored as an attribute of the class so that it can be |
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750 # used later when stacking dAs. |
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751 self.y = T.nnet.sigmoid(T.dot(self.tilde_x, self.W ) + self.b) |
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752 # Equation (3) |
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753 self.z = T.nnet.sigmoid(T.dot(self.y, self.W_prime) + self.b_prime) |
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754 # Equation (4) |
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755 # note : we sum over the size of a datapoint; if we are using minibatches, |
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756 # L will be a vector, with one entry per example in minibatch |
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757 self.L = - T.sum( self.x*T.log(self.z) + (1-self.x)*T.log(1-self.z), axis=1 ) |
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758 # note : L is now a vector, where each element is the cross-entropy cost |
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759 # of the reconstruction of the corresponding example of the |
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760 # minibatch. We need to compute the average of all these to get |
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761 # the cost of the minibatch |
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762 self.cost = T.mean(self.L) |
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763 |
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764 self.params = [ self.W, self.b, self.b_prime ] |
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765 |
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766 class StackedDAA(DeepLayerwiseModel): |
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767 """Stacked denoising auto-encoder class (SdA) |
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768 |
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769 A stacked denoising autoencoder model is obtained by stacking several |
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770 dAs. The hidden layer of the dA at layer `i` becomes the input of |
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771 the dA at layer `i+1`. The first layer dA gets as input the input of |
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772 the SdA, and the hidden layer of the last dA represents the output. |
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773 Note that after pretraining, the SdA is dealt with as a normal MLP, |
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774 the dAs are only used to initialize the weights. |
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775 """ |
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776 |
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777 def __init__(self, n_ins, hidden_layers_sizes, n_outs, |
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778 corruption_levels, rng, ): |
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779 """ This class is made to support a variable number of layers. |
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780 |
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781 :param train_set_x: symbolic variable pointing to the training dataset |
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782 |
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783 :param train_set_y: symbolic variable pointing to the labels of the |
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784 training dataset |
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|
785 |
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786 :param n_ins: dimension of the input to the sdA |
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787 |
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788 :param n_layers_sizes: intermidiate layers size, must contain |
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789 at least one value |
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790 |
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791 :param n_outs: dimension of the output of the network |
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792 |
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793 :param corruption_levels: amount of corruption to use for each |
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794 layer |
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795 |
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796 :param rng: numpy random number generator used to draw initial weights |
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797 |
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798 :param pretrain_lr: learning rate used during pre-trainnig stage |
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799 |
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800 :param finetune_lr: learning rate used during finetune stage |
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801 """ |
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802 |
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803 self.sigmoid_layers = [] |
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804 self.daa_layers = [] |
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805 self.pretrain_functions = [] |
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806 self.params = [] |
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807 self.n_layers = len(hidden_layers_sizes) |
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808 |
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809 if len(hidden_layers_sizes) < 1 : |
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810 raiseException (' You must have at least one hidden layer ') |
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811 |
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812 theano_rng = RandomStreams(rng.randint(2**30)) |
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813 |
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814 # allocate symbolic variables for the data |
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815 index = T.lscalar() # index to a [mini]batch |
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816 self.x = T.matrix('x') # the data is presented as rasterized images |
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817 self.y = T.ivector('y') # the labels are presented as 1D vector of |
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818 # [int] labels |
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819 |
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820 # The SdA is an MLP, for which all weights of intermidiate layers |
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821 # are shared with a different denoising autoencoders |
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822 # We will first construct the SdA as a deep multilayer perceptron, |
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823 # and when constructing each sigmoidal layer we also construct a |
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824 # denoising autoencoder that shares weights with that layer, and |
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825 # compile a training function for that denoising autoencoder |
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826 |
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827 for i in xrange( self.n_layers ): |
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828 # construct the sigmoidal layer |
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829 |
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830 sigmoid_layer = SigmoidalLayer(rng, |
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831 self.layers[-1].output if i else self.x, |
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832 hidden_layers_sizes[i-1] if i else n_ins, |
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833 hidden_layers_sizes[i]) |
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834 |
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835 daa_layer = DAA(corruption_level = corruption_levels[i], |
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836 input = sigmoid_layer.input, |
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837 W = sigmoid_layer.W, |
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838 b = sigmoid_layer.b) |
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839 |
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840 # add the layer to the |
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841 self.sigmoid_layers.append(sigmoid_layer) |
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842 self.daa_layers.append(daa_layer) |
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843 |
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844 # its arguably a philosophical question... |
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845 # but we are going to only declare that the parameters of the |
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846 # sigmoid_layers are parameters of the StackedDAA |
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847 # the hidden-layer biases in the daa_layers are parameters of those |
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848 # daa_layers, but not the StackedDAA |
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849 self.params.extend(sigmoid_layer.params) |
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850 |
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851 # We now need to add a logistic layer on top of the MLP |
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852 self.logistic_regressor = LogisticRegression( |
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853 input = self.sigmoid_layers[-1].output, |
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854 n_in = hidden_layers_sizes[-1], |
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855 n_out = n_outs) |
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856 |
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857 self.params.extend(self.logLayer.params) |
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858 |
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859 def pretraining_functions(self, train_set_x, batch_size): |
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860 |
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861 # compiles update functions for each layer, and |
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862 # returns them as a list |
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863 # |
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864 # Construct a function that trains this dA |
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865 # compute gradients of layer parameters |
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866 gparams = T.grad(dA_layer.cost, dA_layer.params) |
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867 # compute the list of updates |
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868 updates = {} |
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869 for param, gparam in zip(dA_layer.params, gparams): |
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870 updates[param] = param - gparam * pretrain_lr |
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871 |
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872 # create a function that trains the dA |
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873 update_fn = theano.function([index], dA_layer.cost, \ |
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874 updates = updates, |
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875 givens = { |
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876 self.x : train_set_x[index*batch_size:(index+1)*batch_size]}) |
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877 # collect this function into a list |
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878 self.pretrain_functions += [update_fn] |
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879 |
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880 |