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annotate deep/stacked_dae/v_guillaume/stacked_dae.py @ 436:0ca069550abd

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Added : single class version of SDA
author Guillaume Sicard <guitch21@gmail.com>
date Mon, 03 May 2010 06:14:05 -0400
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436
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1 #!/usr/bin/python
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2 # coding: utf-8
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3
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4 import numpy
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5 import theano
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6 import time
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7 import theano.tensor as T
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8 from theano.tensor.shared_randomstreams import RandomStreams
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9 import copy
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10
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11 from utils import update_locals
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12
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13 # taken from LeDeepNet/daa.py
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14 # has a special case when taking log(0) (defined =0)
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15 # modified to not take the mean anymore
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16 from theano.tensor.xlogx import xlogx, xlogy0
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17 # it's target*log(output)
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18 def binary_cross_entropy(target, output, sum_axis=1):
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19 XE = xlogy0(target, output) + xlogy0((1 - target), (1 - output))
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20 return -T.sum(XE, axis=sum_axis)
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21
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22 class LogisticRegression(object):
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23 def __init__(self, input, n_in, n_out):
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24 # initialize with 0 the weights W as a matrix of shape (n_in, n_out)
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25 self.W = theano.shared( value=numpy.zeros((n_in,n_out),
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26 dtype = theano.config.floatX) )
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27 # initialize the baises b as a vector of n_out 0s
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28 self.b = theano.shared( value=numpy.zeros((n_out,),
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29 dtype = theano.config.floatX) )
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30 # compute vector of class-membership. This is a sigmoid instead of
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31 #a softmax to be able later to classify as nothing
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32 self.p_y_given_x = T.nnet.softmax(T.dot(input, self.W)+self.b) #row-wise
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33 ## self.p_y_given_x = T.nnet.sigmoid(T.dot(input, self.W)+self.b)
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34
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35 # compute prediction as class whose probability is maximal in
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36 # symbolic form
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37 self.y_pred=T.argmax(self.p_y_given_x, axis=1)
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38
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39 # list of parameters for this layer
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40 self.params = [self.W, self.b]
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41
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42
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43 def negative_log_likelihood(self, y):
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44 return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]),y])
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45 ## return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]),y]+T.sum(T.log(1-self.p_y_given_x), axis=1)-T.log(1-self.p_y_given_x)[T.arange(y.shape[0]),y])
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46
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47
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48 ## def kullback_leibler(self,y):
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49 ## return -T.mean(T.log(1/float(self.p_y_given_x))[T.arange(y.shape[0]),y])
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50
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51
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52 def errors(self, y):
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53 # check if y has same dimension of y_pred
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54 if y.ndim != self.y_pred.ndim:
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55 raise TypeError('y should have the same shape as self.y_pred',
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56 ('y', target.type, 'y_pred', self.y_pred.type))
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57
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58 # check if y is of the correct datatype
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59 if y.dtype.startswith('int'):
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60 # the T.neq operator returns a vector of 0s and 1s, where 1
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61 # represents a mistake in prediction
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62 return T.mean(T.neq(self.y_pred, y))
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63 else:
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64 raise NotImplementedError()
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65
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66
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67 class SigmoidalLayer(object):
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68 def __init__(self, rng, input, n_in, n_out):
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69 self.input = input
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70
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71 W_values = numpy.asarray( rng.uniform( \
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72 low = -numpy.sqrt(6./(n_in+n_out)), \
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73 high = numpy.sqrt(6./(n_in+n_out)), \
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74 size = (n_in, n_out)), dtype = theano.config.floatX)
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75 self.W = theano.shared(value = W_values)
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76
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77 b_values = numpy.zeros((n_out,), dtype= theano.config.floatX)
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78 self.b = theano.shared(value= b_values)
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79
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80 self.output = T.nnet.sigmoid(T.dot(input, self.W) + self.b)
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81 self.params = [self.W, self.b]
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82
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83
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84 class TanhLayer(object):
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85 def __init__(self, rng, input, n_in, n_out):
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86 self.input = input
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87
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88 W_values = numpy.asarray( rng.uniform( \
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89 low = -numpy.sqrt(6./(n_in+n_out)), \
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90 high = numpy.sqrt(6./(n_in+n_out)), \
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91 size = (n_in, n_out)), dtype = theano.config.floatX)
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92 self.W = theano.shared(value = W_values)
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93
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94 b_values = numpy.zeros((n_out,), dtype= theano.config.floatX)
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95 self.b = theano.shared(value= b_values)
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96
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97 self.output = (T.tanh(T.dot(input, self.W) + self.b) + 1.0)/2.0
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98 # ( *+ 1) /2 is because tanh goes from -1 to 1 and sigmoid goes from 0 to 1
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99 # I want to use tanh, but the image has to stay the same. The correction is necessary.
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100 self.params = [self.W, self.b]
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101
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102
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103 class dA(object):
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104 def __init__(self, n_visible= 784, n_hidden= 500, corruption_level = 0.1,\
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105 input = None, shared_W = None, shared_b = None):
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106 self.n_visible = n_visible
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107 self.n_hidden = n_hidden
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108
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109 # create a Theano random generator that gives symbolic random values
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110 theano_rng = RandomStreams()
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111
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112 if shared_W != None and shared_b != None :
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113 self.W = shared_W
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114 self.b = shared_b
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115 else:
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116 # initial values for weights and biases
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117 # note : W' was written as `W_prime` and b' as `b_prime`
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118
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119 # W is initialized with `initial_W` which is uniformely sampled
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120 # from -6./sqrt(n_visible+n_hidden) and 6./sqrt(n_hidden+n_visible)
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121 # the output of uniform if converted using asarray to dtype
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122 # theano.config.floatX so that the code is runable on GPU
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123 initial_W = numpy.asarray( numpy.random.uniform( \
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124 low = -numpy.sqrt(6./(n_hidden+n_visible)), \
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125 high = numpy.sqrt(6./(n_hidden+n_visible)), \
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126 size = (n_visible, n_hidden)), dtype = theano.config.floatX)
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127 initial_b = numpy.zeros(n_hidden, dtype = theano.config.floatX)
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128
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129
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130 # theano shared variables for weights and biases
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131 self.W = theano.shared(value = initial_W, name = "W")
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132 self.b = theano.shared(value = initial_b, name = "b")
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133
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134
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135 initial_b_prime= numpy.zeros(n_visible)
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136 # tied weights, therefore W_prime is W transpose
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137 self.W_prime = self.W.T
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138 self.b_prime = theano.shared(value = initial_b_prime, name = "b'")
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139
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140 # if no input is given, generate a variable representing the input
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141 if input == None :
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142 # we use a matrix because we expect a minibatch of several examples,
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143 # each example being a row
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144 self.x = T.matrix(name = 'input')
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145 else:
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146 self.x = input
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147 # Equation (1)
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148 # keep 90% of the inputs the same and zero-out randomly selected subset of 10% of the inputs
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149 # note : first argument of theano.rng.binomial is the shape(size) of
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150 # random numbers that it should produce
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151 # second argument is the number of trials
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152 # third argument is the probability of success of any trial
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153 #
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154 # this will produce an array of 0s and 1s where 1 has a
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155 # probability of 1 - ``corruption_level`` and 0 with
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156 # ``corruption_level``
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157 self.tilde_x = theano_rng.binomial( self.x.shape, 1, 1 - corruption_level, dtype=theano.config.floatX) * self.x
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158 # Equation (2)
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159 # note : y is stored as an attribute of the class so that it can be
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160 # used later when stacking dAs.
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161
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162 ## self.y = T.nnet.sigmoid(T.dot(self.tilde_x, self.W ) + self.b)
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163 ##
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164 ## # Equation (3)
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165 ## #self.z = T.nnet.sigmoid(T.dot(self.y, self.W_prime) + self.b_prime)
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166 ## # Equation (4)
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167 ## # note : we sum over the size of a datapoint; if we are using minibatches,
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168 ## # L will be a vector, with one entry per example in minibatch
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169 ## #self.L = - T.sum( self.x*T.log(self.z) + (1-self.x)*T.log(1-self.z), axis=1 )
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170 ## #self.L = binary_cross_entropy(target=self.x, output=self.z, sum_axis=1)
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171 ##
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172 ## # bypassing z to avoid running to log(0)
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173 ## z_a = T.dot(self.y, self.W_prime) + self.b_prime
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174 ## log_sigmoid = T.log(1.) - T.log(1.+T.exp(-z_a))
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175 ## # log(1-sigmoid(z_a))
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176 ## log_1_sigmoid = -z_a - T.log(1.+T.exp(-z_a))
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177 ## self.L = -T.sum( self.x * (log_sigmoid) \
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178 ## + (1.0-self.x) * (log_1_sigmoid), axis=1 )
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179
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180 # I added this epsilon to avoid getting log(0) and 1/0 in grad
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181 # This means conceptually that there'd be no probability of 0, but that
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182 # doesn't seem to me as important (maybe I'm wrong?).
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183 #eps = 0.00000001
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184 #eps_1 = 1-eps
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185 #self.L = - T.sum( self.x * T.log(eps + eps_1*self.z) \
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186 # + (1-self.x)*T.log(eps + eps_1*(1-self.z)), axis=1 )
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187 # note : L is now a vector, where each element is the cross-entropy cost
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188 # of the reconstruction of the corresponding example of the
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189 # minibatch. We need to compute the average of all these to get
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190 # the cost of the minibatch
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191
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192 #Or use a Tanh everything is always between 0 and 1, the range is
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193 #changed so it remain the same as when sigmoid is used
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194 self.y = (T.tanh(T.dot(self.tilde_x, self.W ) + self.b)+1.0)/2.0
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195
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196 self.z = (T.tanh(T.dot(self.y, self.W_prime) + self.b_prime)+1.0) / 2.0
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197 #To ensure to do not have a log(0) operation
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198 if self.z <= 0:
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199 self.z = 0.000001
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200 if self.z >= 1:
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201 self.z = 0.999999
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202
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203 self.L = - T.sum( self.x*T.log(self.z) + (1.0-self.x)*T.log(1.0-self.z), axis=1 )
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204
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205 self.cost = T.mean(self.L)
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206
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207 self.params = [ self.W, self.b, self.b_prime ]
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208
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209
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210 class SdA(object):
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211 def __init__(self, batch_size, n_ins,
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212 hidden_layers_sizes, n_outs,
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213 corruption_levels, rng, pretrain_lr, finetune_lr):
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214 # Just to make sure those are not modified somewhere else afterwards
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215 hidden_layers_sizes = copy.deepcopy(hidden_layers_sizes)
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216 corruption_levels = copy.deepcopy(corruption_levels)
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217
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218 update_locals(self, locals())
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219
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220 self.layers = []
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221 self.pretrain_functions = []
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222 self.params = []
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223 # MODIF: added this so we also get the b_primes
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224 # (not used for finetuning... still using ".params")
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225 self.all_params = []
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226 self.n_layers = len(hidden_layers_sizes)
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227 self.logistic_params = []
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228
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229 print "Creating SdA with params:"
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230 print "batch_size", batch_size
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231 print "hidden_layers_sizes", hidden_layers_sizes
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232 print "corruption_levels", corruption_levels
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233 print "n_ins", n_ins
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234 print "n_outs", n_outs
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235 print "pretrain_lr", pretrain_lr
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236 print "finetune_lr", finetune_lr
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237 print "----"
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238
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239 if len(hidden_layers_sizes) < 1 :
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240 raiseException (' You must have at least one hidden layer ')
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241
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242
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243 # allocate symbolic variables for the data
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244 #index = T.lscalar() # index to a [mini]batch
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245 self.x = T.matrix('x') # the data is presented as rasterized images
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246 self.y = T.ivector('y') # the labels are presented as 1D vector of
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247 # [int] labels
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248 self.finetune_lr = T.fscalar('finetune_lr') #To get a dynamic finetune learning rate
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249 self.pretrain_lr = T.fscalar('pretrain_lr') #To get a dynamic pretrain learning rate
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250
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251 for i in xrange( self.n_layers ):
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252 # construct the sigmoidal layer
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253
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254 # the size of the input is either the number of hidden units of
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255 # the layer below or the input size if we are on the first layer
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256 if i == 0 :
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257 input_size = n_ins
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258 else:
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259 input_size = hidden_layers_sizes[i-1]
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260
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261 # the input to this layer is either the activation of the hidden
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262 # layer below or the input of the SdA if you are on the first
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263 # layer
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264 if i == 0 :
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265 layer_input = self.x
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266 else:
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267 layer_input = self.layers[-1].output
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268 #We have to choose between sigmoidal layer or tanh layer !
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269
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270 ## layer = SigmoidalLayer(rng, layer_input, input_size,
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271 ## hidden_layers_sizes[i] )
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272
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273 layer = TanhLayer(rng, layer_input, input_size,
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274 hidden_layers_sizes[i] )
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275 # add the layer to the
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276 self.layers += [layer]
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277 self.params += layer.params
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278
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279 # Construct a denoising autoencoder that shared weights with this
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280 # layer
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281 dA_layer = dA(input_size, hidden_layers_sizes[i], \
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282 corruption_level = corruption_levels[0],\
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283 input = layer_input, \
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284 shared_W = layer.W, shared_b = layer.b)
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285
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286 self.all_params += dA_layer.params
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287
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288 # Construct a function that trains this dA
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289 # compute gradients of layer parameters
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290 gparams = T.grad(dA_layer.cost, dA_layer.params)
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291 # compute the list of updates
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292 updates = {}
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293 for param, gparam in zip(dA_layer.params, gparams):
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294 updates[param] = param - gparam * self.pretrain_lr
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295
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296 # create a function that trains the dA
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297 update_fn = theano.function([self.x, self.pretrain_lr], dA_layer.cost, \
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298 updates = updates)#,
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299 # givens = {
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300 # self.x : ensemble})
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301 # collect this function into a list
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302 #update_fn = theano.function([index], dA_layer.cost, \
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303 # updates = updates,
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304 # givens = {
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305 # self.x : train_set_x[index*batch_size:(index+1)*batch_size] / self.shared_divider})
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306 # collect this function into a list
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307 self.pretrain_functions += [update_fn]
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308
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309
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310 # We now need to add a logistic layer on top of the SDA
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311 self.logLayer = LogisticRegression(\
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312 input = self.layers[-1].output,\
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313 n_in = hidden_layers_sizes[-1], n_out = n_outs)
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314
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315 self.params += self.logLayer.params
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316 self.all_params += self.logLayer.params
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317 # construct a function that implements one step of finetunining
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318
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319 # compute the cost, defined as the negative log likelihood
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320 cost = self.logLayer.negative_log_likelihood(self.y)
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321 # compute the gradients with respect to the model parameters
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322 gparams = T.grad(cost, self.params)
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323 # compute list of updates
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324 updates = {}
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325 for param,gparam in zip(self.params, gparams):
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326 updates[param] = param - gparam*self.finetune_lr
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327
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328 self.finetune = theano.function([self.x,self.y,self.finetune_lr], cost,
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329 updates = updates)#,
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330
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331 # symbolic variable that points to the number of errors made on the
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332 # minibatch given by self.x and self.y
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333
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334 self.errors = self.logLayer.errors(self.y)
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335
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336
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337 #STRUCTURE FOR THE FINETUNING OF THE LOGISTIC REGRESSION ON THE TOP WITH
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338 #ALL HIDDEN LAYERS AS INPUT
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339
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340 all_h=[]
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341 for i in xrange(self.n_layers):
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342 all_h.append(self.layers[i].output)
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343 self.all_hidden=T.concatenate(all_h,axis=1)
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344
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345
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346 self.logLayer2 = LogisticRegression(\
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347 input = self.all_hidden,\
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348 n_in = sum(hidden_layers_sizes), n_out = n_outs)
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349 #n_in=hidden_layers_sizes[0],n_out=n_outs)
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350
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351 #self.logistic_params+= self.logLayer2.params
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352 # construct a function that implements one step of finetunining
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353
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354 self.logistic_params+=self.logLayer2.params
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355 # compute the cost, defined as the negative log likelihood
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356 cost2 = self.logLayer2.negative_log_likelihood(self.y)
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357 # compute the gradients with respect to the model parameters
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358 gparams2 = T.grad(cost2, self.logistic_params)
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359
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360 # compute list of updates
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361 updates2 = {}
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362 for param,gparam in zip(self.logistic_params, gparams2):
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363 updates2[param] = param - gparam*finetune_lr
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364
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365 self.finetune2 = theano.function([self.x,self.y], cost2,
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366 updates = updates2)
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367
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368 # symbolic variable that points to the number of errors made on the
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369 # minibatch given by self.x and self.y
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370
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371 self.errors2 = self.logLayer2.errors(self.y)
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372
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373
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374 if __name__ == '__main__':
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375 import sys
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376 args = sys.argv[1:]
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377