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comparison deep/stacked_dae/v2/stacked_dae.py @ 239:42005ec87747

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Mergé (manuellement) les changements de Sylvain pour utiliser le code de dataset d'Arnaud, à cette différence près que je n'utilse pas les givens. J'ai probablement une approche différente pour limiter la taille du dataset dans mon débuggage, aussi.
author fsavard
date Mon, 15 Mar 2010 18:30:21 -0400
parents 02eb98d051fe
children
comparison
equal deleted inserted replaced
238:9fc641d7adda 239:42005ec87747
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
207 204
208 for i in xrange( self.n_layers ): 205 for i in xrange( self.n_layers ):
245 updates = {} 242 updates = {}
246 for param, gparam in zip(dA_layer.params, gparams): 243 for param, gparam in zip(dA_layer.params, gparams):
247 updates[param] = param - gparam * pretrain_lr 244 updates[param] = param - gparam * pretrain_lr
248 245
249 # create a function that trains the dA 246 # create a function that trains the dA
250 update_fn = theano.function([index], dA_layer.cost, \ 247 update_fn = theano.function([self.x], dA_layer.cost, \
251 updates = updates, 248 updates = updates)#,
252 givens = { 249 # givens = {
253 self.x : train_set_x[index*batch_size:(index+1)*batch_size] / self.shared_divider}) 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})
254 # collect this function into a list 256 # collect this function into a list
255 self.pretrain_functions += [update_fn] 257 self.pretrain_functions += [update_fn]
256 258
257 259
258 # 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
271 # compute list of updates 273 # compute list of updates
272 updates = {} 274 updates = {}
273 for param,gparam in zip(self.params, gparams): 275 for param,gparam in zip(self.params, gparams):
274 updates[param] = param - gparam*finetune_lr 276 updates[param] = param - gparam*finetune_lr
275 277
276 self.finetune = theano.function([index], cost, 278 self.finetune = theano.function([self.x,self.y], cost,
277 updates = updates, 279 updates = updates)#,
278 givens = { 280 # givens = {
279 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,
280 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]} )
281 283
282 # 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
283 # minibatch given by self.x and self.y 285 # minibatch given by self.x and self.y
284 286
285 self.errors = self.logLayer.errors(self.y) 287 self.errors = self.logLayer.errors(self.y)