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comparison deep/stacked_dae/sgd_optimization.py @ 186:d364a130b221

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Ajout du code de base pour scalar_series. Modifications à stacked_dae: réglé un problème avec les input_divider (empêchait une optimisation), et ajouté utilisation des séries. Si j'avais pas déjà commité, aussi, j'ai enlevé l'histoire de réutilisation du pretraining: c'était compliqué (error prone) et ça créait des jobs beaucoup trop longues.
author fsavard
date Mon, 01 Mar 2010 11:45:25 -0500
parents b9ea8e2d071a
children 3632e6258642
comparison
equal deleted inserted replaced
185:b9ea8e2d071a 186:d364a130b221
31 def __init__(self, dataset, hyperparameters, n_ins, n_outs, input_divider=1.0, series_mux=None): 31 def __init__(self, dataset, hyperparameters, n_ins, n_outs, input_divider=1.0, series_mux=None):
32 self.dataset = dataset 32 self.dataset = dataset
33 self.hp = hyperparameters 33 self.hp = hyperparameters
34 self.n_ins = n_ins 34 self.n_ins = n_ins
35 self.n_outs = n_outs 35 self.n_outs = n_outs
36 self.input_divider = numpy.asarray(input_divider, dtype=theano.config.floatX) 36 self.input_divider = input_divider
37 37
38 if not series_mux: 38 if not series_mux:
39 series_mux = DummyMux() 39 series_mux = DummyMux()
40 print "No series multiplexer set" 40 print "No series multiplexer set"
41 self.series_mux = series_mux 41 self.series_mux = series_mux
115 index = T.lscalar() # index to a [mini]batch 115 index = T.lscalar() # index to a [mini]batch
116 minibatch_size = self.hp.minibatch_size 116 minibatch_size = self.hp.minibatch_size
117 117
118 # create a function to compute the mistakes that are made by the model 118 # create a function to compute the mistakes that are made by the model
119 # on the validation set, or testing set 119 # on the validation set, or testing set
120 shared_divider = theano.shared(numpy.asarray(self.input_divider, dtype=theano.config.floatX))
120 test_model = theano.function([index], self.classifier.errors, 121 test_model = theano.function([index], self.classifier.errors,
121 givens = { 122 givens = {
122 self.classifier.x: self.test_set_x[index*minibatch_size:(index+1)*minibatch_size] / self.input_divider, 123 self.classifier.x: self.test_set_x[index*minibatch_size:(index+1)*minibatch_size] / shared_divider,
123 self.classifier.y: self.test_set_y[index*minibatch_size:(index+1)*minibatch_size]}) 124 self.classifier.y: self.test_set_y[index*minibatch_size:(index+1)*minibatch_size]})
124 125
125 validate_model = theano.function([index], self.classifier.errors, 126 validate_model = theano.function([index], self.classifier.errors,
126 givens = { 127 givens = {
127 self.classifier.x: self.valid_set_x[index*minibatch_size:(index+1)*minibatch_size] / self.input_divider, 128 self.classifier.x: self.valid_set_x[index*minibatch_size:(index+1)*minibatch_size] / shared_divider,
128 self.classifier.y: self.valid_set_y[index*minibatch_size:(index+1)*minibatch_size]}) 129 self.classifier.y: self.valid_set_y[index*minibatch_size:(index+1)*minibatch_size]})
129 130
130 131
131 # early-stopping parameters 132 # early-stopping parameters
132 patience = 10000 # look as this many examples regardless 133 patience = 10000 # look as this many examples regardless
159 160
160 if (iter+1) % validation_frequency == 0: 161 if (iter+1) % validation_frequency == 0:
161 162
162 validation_losses = [validate_model(i) for i in xrange(self.n_valid_batches)] 163 validation_losses = [validate_model(i) for i in xrange(self.n_valid_batches)]
163 this_validation_loss = numpy.mean(validation_losses) 164 this_validation_loss = numpy.mean(validation_losses)
165 self.series_mux.append("validation_error", this_validation_loss)
164 print('epoch %i, minibatch %i/%i, validation error %f %%' % \ 166 print('epoch %i, minibatch %i/%i, validation error %f %%' % \
165 (epoch, minibatch_index+1, self.n_train_batches, \ 167 (epoch, minibatch_index+1, self.n_train_batches, \
166 this_validation_loss*100.)) 168 this_validation_loss*100.))
167 169
168 170
179 best_iter = iter 181 best_iter = iter
180 182
181 # test it on the test set 183 # test it on the test set
182 test_losses = [test_model(i) for i in xrange(self.n_test_batches)] 184 test_losses = [test_model(i) for i in xrange(self.n_test_batches)]
183 test_score = numpy.mean(test_losses) 185 test_score = numpy.mean(test_losses)
186 self.series_mux.append("test_error", test_score)
184 print((' epoch %i, minibatch %i/%i, test error of best ' 187 print((' epoch %i, minibatch %i/%i, test error of best '
185 'model %f %%') % 188 'model %f %%') %
186 (epoch, minibatch_index+1, self.n_train_batches, 189 (epoch, minibatch_index+1, self.n_train_batches,
187 test_score*100.)) 190 test_score*100.))
188 191
189 sys.stdout.flush() 192 sys.stdout.flush()
190 193
191 self.series_mux.append("params", self.classifier.params) 194 self.series_mux.append("params", self.classifier.all_params)
192 195
193 if patience <= iter : 196 if patience <= iter :
194 done_looping = True 197 done_looping = True
195 break 198 break
196 199