Mercurial > ift6266
comparison deep/convolutional_dae/run_exp.py @ 294:8babd43235dd
Save best valid score and test score in the db.
author | Arnaud Bergeron <abergeron@gmail.com> |
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date | Sat, 27 Mar 2010 13:39:48 -0400 |
parents | d89820070ea0 |
children | a222af1d0598 |
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293:d89820070ea0 | 294:8babd43235dd |
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58 sys.stdout.flush() | 58 sys.stdout.flush() |
59 do_pretrain(pretrain_fs, state.pretrain_rounds, series['recons_error']) | 59 do_pretrain(pretrain_fs, state.pretrain_rounds, series['recons_error']) |
60 | 60 |
61 print "training ..." | 61 print "training ..." |
62 sys.stdout.flush() | 62 sys.stdout.flush() |
63 sgd_opt(train, valid, test, training_epochs=100000, patience=10000, | 63 best_valid, test_score = sgd_opt(train, valid, test, |
64 patience_increase=2., improvement_threshold=0.995, | 64 training_epochs=100000, patience=10000, |
65 validation_frequency=1000, series=series, net=net) | 65 patience_increase=2., |
66 improvement_threshold=0.995, | |
67 validation_frequency=1000, | |
68 series=series, net=net) | |
69 state.best_valid = best_valid | |
70 state.test_score = test_score | |
71 channel.save() | |
72 return channel.COMPLETE | |
66 | 73 |
67 if __name__ == '__main__': | 74 if __name__ == '__main__': |
68 st = dumb() | 75 st = dumb() |
69 st.bsize = 100 | 76 st.bsize = 100 |
70 st.pretrain_lr = 0.01 | 77 st.pretrain_lr = 0.01 |