Mercurial > ift6266

annotate deep/convolutional_dae/sgd_opt.py @ 282:698313f8f6e6

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rajout de methode reliant toutes les couches cachees a la logistic et changeant seulement les parametres de la logistic durant finetune
author SylvainPL <sylvain.pannetier.lebeuf@umontreal.ca>
date Wed, 24 Mar 2010 14:45:02 -0400
parents 727ed56fad12
children 80ee63c3e749
rev   line source
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727ed56fad12 Add reworked code for convolutional auto-encoder.
Arnaud Bergeron <abergeron@gmail.com>
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1 import time
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2 import sys
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3
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4 def sgd_opt(train, valid, test, training_epochs=10000, patience=10000,
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5 patience_increase=2., improvement_threshold=0.995,
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6 validation_frequency=None):
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7
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8 if validation_frequency is None:
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9 validation_frequency = patience/2
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10
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11 start_time = time.clock()
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12
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13 best_params = None
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14 best_validation_loss = float('inf')
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15 test_score = 0.
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17 start_time = time.clock()
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19 for epoch in xrange(1, training_epochs+1):
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20 train()
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21
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22 if epoch % validation_frequency == 0:
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Arnaud Bergeron <abergeron@gmail.com>
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23 this_validation_loss = valid()
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24 print('epoch %i, validation error %f %%' % \
727ed56fad12 Add reworked code for convolutional auto-encoder.
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25 (epoch, this_validation_loss*100.))
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26
727ed56fad12 Add reworked code for convolutional auto-encoder.
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27 # if we got the best validation score until now
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28 if this_validation_loss < best_validation_loss:
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29
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30 #improve patience if loss improvement is good enough
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31 if this_validation_loss < best_validation_loss * \
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32 improvement_threshold :
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33 patience = max(patience, epoch * patience_increase)
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34
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Arnaud Bergeron <abergeron@gmail.com>
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35 # save best validation score and epoch number
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Arnaud Bergeron <abergeron@gmail.com>
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36 best_validation_loss = this_validation_loss
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37 best_epoch = epoch
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38
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39 # test it on the test set
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40 test_score = test()
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41 print((' epoch %i, test error of best model %f %%') %
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42 (epoch, test_score*100.))
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43
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44 if patience <= epoch:
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45 break
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46
727ed56fad12 Add reworked code for convolutional auto-encoder.
Arnaud Bergeron <abergeron@gmail.com>
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47 end_time = time.clock()
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48 print(('Optimization complete with best validation score of %f %%,'
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49 'with test performance %f %%') %
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50 (best_validation_loss * 100., test_score*100.))
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51 print ('The code ran for %f minutes' % ((end_time-start_time)/60.))
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