Mercurial > ift6266
changeset 282:698313f8f6e6
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 | a8b92a4a708d |
| children | 28b628f331b2 |
| files | deep/stacked_dae/v_sylvain/nist_sda.py |
| diffstat | 1 files changed, 53 insertions(+), 26 deletions(-) [+] |
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--- a/deep/stacked_dae/v_sylvain/nist_sda.py Wed Mar 24 14:44:41 2010 -0400 +++ b/deep/stacked_dae/v_sylvain/nist_sda.py Wed Mar 24 14:45:02 2010 -0400 @@ -68,7 +68,21 @@ max_minibatches=rtt) parameters=[] - optimizer.pretrain(datasets.nist_P07()) + #Number of files of P07 used for pretraining + nb_file=0 + if state['pretrain_choice'] == 0: + print('\n\tpretraining with NIST\n') + optimizer.pretrain(datasets.nist_all()) + elif state['pretrain_choice'] == 1: + #To know how many file will be used during pretraining + nb_file = state['pretraining_epochs_per_layer'] + state['pretraining_epochs_per_layer'] = 1 #Only 1 time over the dataset + if nb_file >=100: + sys.exit("The code does not support this much pretraining epoch (99 max with P07).\n"+ + "You have to correct the code (and be patient, P07 is huge !!)\n"+ + "or reduce the number of pretraining epoch to run the code (better idea).\n") + print('\n\tpretraining with P07') + optimizer.pretrain(datasets.nist_P07(min_file=0,max_file=nb_file)) channel.save() #Set some of the parameters used for the finetuning @@ -89,34 +103,47 @@ #Decide how the finetune is done - if finetune_choice==0: - print('\n\n\tfinetune avec nist\n\n') - optimizer.reload_parameters() - optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1) - if finetune_choice==1: - print('\n\n\tfinetune avec P07\n\n') - optimizer.reload_parameters() - optimizer.finetune(datasets.nist_P07(),datasets.nist_all(),max_finetune_epoch_P07,ind_test=0) - if finetune_choice==2: - print('\n\n\tfinetune avec nist suivi de P07\n\n') - optimizer.reload_parameters() - optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1) - optimizer.finetune(datasets.nist_P07(),datasets.nist_all(),max_finetune_epoch_P07,ind_test=0) - + if finetune_choice == 0: + print('\n\n\tfinetune with NIST\n\n') + optimizer.reload_parameters('params_pretrain.txt') + optimizer.finetune(datasets.nist_all(),datasets.nist_P07(min_file=nb_file),max_finetune_epoch_NIST,ind_test=1) + channel.save() + if finetune_choice == 1: + print('\n\n\tfinetune with P07\n\n') + optimizer.reload_parameters('params_pretrain.txt') + optimizer.finetune(datasets.nist_P07(min_file=nb_file),datasets.nist_all(),max_finetune_epoch_P07,ind_test=0) + channel.save() + if finetune_choice == 2: + print('\n\n\tfinetune with NIST followed by P07\n\n') + optimizer.reload_parameters('params_pretrain.txt') + optimizer.finetune(datasets.nist_all(),datasets.nist_P07(min_file=nb_file),max_finetune_epoch_NIST,ind_test=21) + optimizer.finetune(datasets.nist_P07(min_file=nb_file),datasets.nist_all(),max_finetune_epoch_P07,ind_test=20) + channel.save() + if finetune_choice == 3: + print('\n\n\tfinetune with NIST only on the logistic regression on top (but validation on P07).\n\ + All hidden units output are input of the logistic regression\n\n') + optimizer.reload_parameters('params_pretrain.txt') + optimizer.finetune(datasets.nist_all(),datasets.nist_P07(min_file=nb_file),max_finetune_epoch_NIST,ind_test=1,special=1) + + if finetune_choice==-1: - print('\nSerie de 3 essais de fine-tuning') - print('\n\n\tfinetune avec nist\n\n') - optimizer.reload_parameters() - optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1) + print('\nSERIE OF 3 DIFFERENT FINETUNINGS') + print('\n\n\tfinetune with NIST\n\n') + optimizer.reload_parameters('params_pretrain.txt') + optimizer.finetune(datasets.nist_all(),datasets.nist_P07(min_file=nb_file),max_finetune_epoch_NIST,ind_test=1) + channel.save() + print('\n\n\tfinetune with P07\n\n') + optimizer.reload_parameters('params_pretrain.txt') + optimizer.finetune(datasets.nist_P07(min_file=nb_file),datasets.nist_all(),max_finetune_epoch_P07,ind_test=0) channel.save() - print('\n\n\tfinetune avec P07\n\n') - optimizer.reload_parameters() - optimizer.finetune(datasets.nist_P07(),datasets.nist_all(),max_finetune_epoch_P07,ind_test=0) + print('\n\n\tfinetune with NIST (done earlier) followed by P07 (written here)\n\n') + optimizer.reload_parameters('params_finetune_NIST.txt') + optimizer.finetune(datasets.nist_P07(min_file=nb_file),datasets.nist_all(),max_finetune_epoch_P07,ind_test=20) channel.save() - print('\n\n\tfinetune avec nist suivi de P07\n\n') - optimizer.reload_parameters() - optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1) - optimizer.finetune(datasets.nist_P07(),datasets.nist_all(),max_finetune_epoch_P07,ind_test=0) + print('\n\n\tfinetune with NIST only on the logistic regression on top.\n\ + All hidden units output are input of the logistic regression\n\n') + optimizer.reload_parameters('params_pretrain.txt') + optimizer.finetune(datasets.nist_all(),datasets.nist_P07(min_file=nb_file),max_finetune_epoch_NIST,ind_test=1,special=1) channel.save() channel.save()