Mercurial > ift6266

annotate deep/convolutional_dae/sgd_opt.py @ 388:0d97fead004f

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Changement pour prendre en compte l'option finetune amelioree pour PNIST07
author SylvainPL <sylvain.pannetier.lebeuf@umontreal.ca>
date Tue, 27 Apr 2010 08:42:43 -0400
parents 8babd43235dd
children
rev   line source
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727ed56fad12 Add reworked code for convolutional auto-encoder.
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1 import time
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2 import sys, os
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4 from ift6266.utils.seriestables import *
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5
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6 default_series = {
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7 'train_error' : DummySeries(),
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8 'valid_error' : DummySeries(),
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9 'test_error' : DummySeries()
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10 }
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11
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12 def sgd_opt(train, valid, test, training_epochs=10000, patience=10000,
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13 patience_increase=2., improvement_threshold=0.995, net=None,
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14 validation_frequency=None, series=default_series):
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16 if validation_frequency is None:
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17 validation_frequency = patience/2
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18
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19 start_time = time.clock()
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20
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21 best_params = None
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22 best_validation_loss = float('inf')
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23 test_score = 0.
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24
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25 start_time = time.clock()
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27 for epoch in xrange(1, training_epochs+1):
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28 series['train_error'].append((epoch,), train())
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30 if epoch % validation_frequency == 0:
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31 this_validation_loss = valid()
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32 series['valid_error'].append((epoch,), this_validation_loss*100.)
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33 print('epoch %i, validation error %f %%' % \
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34 (epoch, this_validation_loss*100.))
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35
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36 # if we got the best validation score until now
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37 if this_validation_loss < best_validation_loss:
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38
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39 #improve patience if loss improvement is good enough
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40 if this_validation_loss < best_validation_loss * \
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41 improvement_threshold :
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42 patience = max(patience, epoch * patience_increase)
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43
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44 # save best validation score and epoch number
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45 best_validation_loss = this_validation_loss
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46 best_epoch = epoch
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47
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48 # test it on the test set
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49 test_score = test()
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50 series['test_error'].append((epoch,), test_score*100.)
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51 print((' epoch %i, test error of best model %f %%') %
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52 (epoch, test_score*100.))
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53 if net is not None:
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54 net.save('best.net.new')
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55 os.rename('best.net.new', 'best.net')
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57 if patience <= epoch:
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58 break
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60 end_time = time.clock()
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61 print(('Optimization complete with best validation score of %f %%,'
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62 'with test performance %f %%') %
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63 (best_validation_loss * 100., test_score*100.))
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64 print ('The code ran for %f minutes' % ((end_time-start_time)/60.))
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66 return best_validation_loss, test_score