Mercurial > ift6266

comparison deep/stacked_dae/v_sylvain/sgd_optimization.py @ 235:ecb69e17950b

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
correction de bugs
author SylvainPL <sylvain.pannetier.lebeuf@umontreal.ca>
date Sun, 14 Mar 2010 20:25:12 -0400
parents 02ed13244133
children 9fc641d7adda
comparison
equal deleted inserted replaced
234:c452e3a0a3b1 235:ecb69e17950b
116 ## Pre-train layer-wise 116 ## Pre-train layer-wise
117 for i in xrange(self.classifier.n_layers): 117 for i in xrange(self.classifier.n_layers):
118 # go through pretraining epochs 118 # go through pretraining epochs
119 for epoch in xrange(self.hp.pretraining_epochs_per_layer): 119 for epoch in xrange(self.hp.pretraining_epochs_per_layer):
120 # go through the training set 120 # go through the training set
121 batch_index=int(0)
121 for x,y in dataset.train(self.hp.minibatch_size): 122 for x,y in dataset.train(self.hp.minibatch_size):
122 c = self.classifier.pretrain_functions[i](x) 123 c = self.classifier.pretrain_functions[i](x)
123 124 batch_index+=1
125
124 self.series["reconstruction_error"].append((epoch, batch_index), c) 126 self.series["reconstruction_error"].append((epoch, batch_index), c)
125 127
126 print 'Pre-training layer %i, epoch %d, cost '%(i,epoch),c 128 print 'Pre-training layer %i, epoch %d, cost '%(i,epoch),c
127 sys.stdout.flush() 129 sys.stdout.flush()
128 130
138 def finetune(self,dataset): 140 def finetune(self,dataset):
139 print "STARTING FINETUNING, time = ", datetime.datetime.now() 141 print "STARTING FINETUNING, time = ", datetime.datetime.now()
140 142
141 #index = T.lscalar() # index to a [mini]batch 143 #index = T.lscalar() # index to a [mini]batch
142 minibatch_size = self.hp.minibatch_size 144 minibatch_size = self.hp.minibatch_size
145 ensemble_x = T.matrix('ensemble_x')
146 ensemble_y = T.ivector('ensemble_y')
143 147
144 # create a function to compute the mistakes that are made by the model 148 # create a function to compute the mistakes that are made by the model
145 # on the validation set, or testing set 149 # on the validation set, or testing set
146 shared_divider = theano.shared(numpy.asarray(self.input_divider, dtype=theano.config.floatX)) 150 shared_divider = theano.shared(numpy.asarray(self.input_divider, dtype=theano.config.floatX))
147 test_model = theano.function([ensemble_x,ensemble_y], self.classifier.errors, 151 test_model = theano.function([ensemble_x,ensemble_y], self.classifier.errors,