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comparison deep/stacked_dae/v_sylvain/stacked_dae.py @ 251:02b141a466b4

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ajout de fonctionnalite pour different finetune dataset
author SylvainPL <sylvain.pannetier.lebeuf@umontreal.ca>
date Tue, 16 Mar 2010 21:24:30 -0400
parents ecb69e17950b
children c77ffb11f91d
comparison
equal deleted inserted replaced
250:6d49cf134a40 251:02b141a466b4
163 163
164 self.params = [ self.W, self.b, self.b_prime ] 164 self.params = [ self.W, self.b, self.b_prime ]
165 165
166 166
167 class SdA(object): 167 class SdA(object):
168 def __init__(self, train_set_x, train_set_y, batch_size, n_ins, 168 def __init__(self, batch_size, n_ins,
169 hidden_layers_sizes, n_outs, 169 hidden_layers_sizes, n_outs,
170 corruption_levels, rng, pretrain_lr, finetune_lr, input_divider=1.0): 170 corruption_levels, rng, pretrain_lr, finetune_lr):
171 # Just to make sure those are not modified somewhere else afterwards 171 # Just to make sure those are not modified somewhere else afterwards
172 hidden_layers_sizes = copy.deepcopy(hidden_layers_sizes) 172 hidden_layers_sizes = copy.deepcopy(hidden_layers_sizes)
173 corruption_levels = copy.deepcopy(corruption_levels) 173 corruption_levels = copy.deepcopy(corruption_levels)
174 174
175 update_locals(self, locals()) 175 update_locals(self, locals())
188 print "corruption_levels", corruption_levels 188 print "corruption_levels", corruption_levels
189 print "n_ins", n_ins 189 print "n_ins", n_ins
190 print "n_outs", n_outs 190 print "n_outs", n_outs
191 print "pretrain_lr", pretrain_lr 191 print "pretrain_lr", pretrain_lr
192 print "finetune_lr", finetune_lr 192 print "finetune_lr", finetune_lr
193 print "input_divider", input_divider
194 print "----" 193 print "----"
195
196 #self.shared_divider = theano.shared(numpy.asarray(input_divider, dtype=theano.config.floatX))
197 194
198 if len(hidden_layers_sizes) < 1 : 195 if len(hidden_layers_sizes) < 1 :
199 raiseException (' You must have at least one hidden layer ') 196 raiseException (' You must have at least one hidden layer ')
200 197
201 198
202 # allocate symbolic variables for the data 199 # allocate symbolic variables for the data
203 ##index = T.lscalar() # index to a [mini]batch 200 #index = T.lscalar() # index to a [mini]batch
204 self.x = T.matrix('x') # the data is presented as rasterized images 201 self.x = T.matrix('x') # the data is presented as rasterized images
205 self.y = T.ivector('y') # the labels are presented as 1D vector of 202 self.y = T.ivector('y') # the labels are presented as 1D vector of
206 # [int] labels 203 # [int] labels
207 ensemble = T.matrix('ensemble')
208 ensemble_x = T.matrix('ensemble_x')
209 ensemble_y = T.ivector('ensemble_y')
210 204
211 for i in xrange( self.n_layers ): 205 for i in xrange( self.n_layers ):
212 # construct the sigmoidal layer 206 # construct the sigmoidal layer
213 207
214 # the size of the input is either the number of hidden units of 208 # the size of the input is either the number of hidden units of
248 updates = {} 242 updates = {}
249 for param, gparam in zip(dA_layer.params, gparams): 243 for param, gparam in zip(dA_layer.params, gparams):
250 updates[param] = param - gparam * pretrain_lr 244 updates[param] = param - gparam * pretrain_lr
251 245
252 # create a function that trains the dA 246 # create a function that trains the dA
253 update_fn = theano.function([ensemble], dA_layer.cost, \ 247 update_fn = theano.function([self.x], dA_layer.cost, \
254 updates = updates, 248 updates = updates)#,
255 givens = { 249 # givens = {
256 self.x : ensemble}) 250 # self.x : ensemble})
251 # collect this function into a list
252 #update_fn = theano.function([index], dA_layer.cost, \
253 # updates = updates,
254 # givens = {
255 # self.x : train_set_x[index*batch_size:(index+1)*batch_size] / self.shared_divider})
257 # collect this function into a list 256 # collect this function into a list
258 self.pretrain_functions += [update_fn] 257 self.pretrain_functions += [update_fn]
259 258
260 259
261 # We now need to add a logistic layer on top of the MLP 260 # We now need to add a logistic layer on top of the MLP
274 # compute list of updates 273 # compute list of updates
275 updates = {} 274 updates = {}
276 for param,gparam in zip(self.params, gparams): 275 for param,gparam in zip(self.params, gparams):
277 updates[param] = param - gparam*finetune_lr 276 updates[param] = param - gparam*finetune_lr
278 277
279 self.finetune = theano.function([ensemble_x,ensemble_y], cost, 278 self.finetune = theano.function([self.x,self.y], cost,
280 updates = updates, 279 updates = updates)#,
281 givens = { 280 # givens = {
282 #self.x : train_set_x[index*batch_size:(index+1)*batch_size]/self.shared_divider, 281 # self.x : train_set_x[index*batch_size:(index+1)*batch_size]/self.shared_divider,
283 #self.y : train_set_y[index*batch_size:(index+1)*batch_size]} ) 282 # self.y : train_set_y[index*batch_size:(index+1)*batch_size]} )
284 self.x : ensemble_x,
285 self.y : ensemble_y} )
286 283
287 # symbolic variable that points to the number of errors made on the 284 # symbolic variable that points to the number of errors made on the
288 # minibatch given by self.x and self.y 285 # minibatch given by self.x and self.y
289 286
290 self.errors = self.logLayer.errors(self.y) 287 self.errors = self.logLayer.errors(self.y)
288
291 289
292 if __name__ == '__main__': 290 if __name__ == '__main__':
293 import sys 291 import sys
294 args = sys.argv[1:] 292 args = sys.argv[1:]
295 293