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comparison deep/stacked_dae/v_sylvain/stacked_dae.py @ 368:d391ad815d89

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Correction d'un bug avec la fonction de log-likelihood pour utilisation de non-linearite de sortie sigmoides
author SylvainPL <sylvain.pannetier.lebeuf@umontreal.ca>
date Fri, 23 Apr 2010 12:12:03 -0400
parents 14b28e43ce4e
children
comparison
equal deleted inserted replaced
367:f24b10e43a6f 368:d391ad815d89
26 dtype = theano.config.floatX) ) 26 dtype = theano.config.floatX) )
27 # initialize the baises b as a vector of n_out 0s 27 # initialize the baises b as a vector of n_out 0s
28 self.b = theano.shared( value=numpy.zeros((n_out,), 28 self.b = theano.shared( value=numpy.zeros((n_out,),
29 dtype = theano.config.floatX) ) 29 dtype = theano.config.floatX) )
30 # compute vector of class-membership. This is a sigmoid instead of 30 # compute vector of class-membership. This is a sigmoid instead of
31 #a softmax to be able to classify as nothing later 31 #a softmax to be able later to classify as nothing
32 ## self.p_y_given_x = T.nnet.softmax(T.dot(input, self.W)+self.b) 32 ## self.p_y_given_x = T.nnet.softmax(T.dot(input, self.W)+self.b) #row-wise
33 self.p_y_given_x = T.nnet.sigmoid(T.dot(input, self.W)+self.b) 33 self.p_y_given_x = T.nnet.sigmoid(T.dot(input, self.W)+self.b)
34 34
35 # compute prediction as class whose probability is maximal in 35 # compute prediction as class whose probability is maximal in
36 # symbolic form 36 # symbolic form
37 self.y_pred=T.argmax(self.p_y_given_x, axis=1) 37 self.y_pred=T.argmax(self.p_y_given_x, axis=1)
39 # list of parameters for this layer 39 # list of parameters for this layer
40 self.params = [self.W, self.b] 40 self.params = [self.W, self.b]
41 41
42 42
43 def negative_log_likelihood(self, y): 43 def negative_log_likelihood(self, y):
44 return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]),y]) 44 ## return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]),y])
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])
46
47
48 ## def kullback_leibler(self,y):
49 ## return -T.mean(T.log(1/float(self.p_y_given_x))[T.arange(y.shape[0]),y])
50
45 51
46 def errors(self, y): 52 def errors(self, y):
47 # check if y has same dimension of y_pred 53 # check if y has same dimension of y_pred
48 if y.ndim != self.y_pred.ndim: 54 if y.ndim != self.y_pred.ndim:
49 raise TypeError('y should have the same shape as self.y_pred', 55 raise TypeError('y should have the same shape as self.y_pred',
185 191
186 #Or use a Tanh everything is always between 0 and 1, the range is 192 #Or use a Tanh everything is always between 0 and 1, the range is
187 #changed so it remain the same as when sigmoid is used 193 #changed so it remain the same as when sigmoid is used
188 self.y = (T.tanh(T.dot(self.tilde_x, self.W ) + self.b)+1.0)/2.0 194 self.y = (T.tanh(T.dot(self.tilde_x, self.W ) + self.b)+1.0)/2.0
189 195
190 z_a = T.dot(self.y, self.W_prime) + self.b_prime 196 self.z = (T.tanh(T.dot(self.y, self.W_prime) + self.b_prime)+1.0) / 2.0
191 self.z = (T.tanh(z_a )+1.0) / 2.0
192 #To ensure to do not have a log(0) operation 197 #To ensure to do not have a log(0) operation
193 if self.z <= 0: 198 if self.z <= 0:
194 self.z = 0.000001 199 self.z = 0.000001
195 if self.z >= 1: 200 if self.z >= 1:
196 self.z = 0.999999 201 self.z = 0.999999
197 202
198 self.L = - T.sum( self.x*T.log(self.z) + (1-self.x)*T.log(1-self.z), axis=1 ) 203 self.L = - T.sum( self.x*T.log(self.z) + (1.0-self.x)*T.log(1.0-self.z), axis=1 )
199 204
200 self.cost = T.mean(self.L) 205 self.cost = T.mean(self.L)
201 206
202 self.params = [ self.W, self.b, self.b_prime ] 207 self.params = [ self.W, self.b, self.b_prime ]
203 208