# HG changeset patch # User Arnaud Bergeron # Date 1270146588 14400 # Node ID df5273caad58a816874b31ebfad861e03273b558 # Parent 2937f2a421aa5eadd523d323cf916122af07508a# Parent e301a2f32665bcad171e421243f799494d357cb7 branch merge diff -r 2937f2a421aa -r df5273caad58 deep/stacked_dae/v_sylvain/nist_sda.py --- a/deep/stacked_dae/v_sylvain/nist_sda.py Thu Apr 01 14:28:50 2010 -0400 +++ b/deep/stacked_dae/v_sylvain/nist_sda.py Thu Apr 01 14:29:48 2010 -0400 @@ -106,44 +106,44 @@ 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) + optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),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) + optimizer.finetune(datasets.nist_P07(),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) + optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=21) + optimizer.finetune(datasets.nist_P07(),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) + optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1,special=1) if finetune_choice==-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) + optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),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) + optimizer.finetune(datasets.nist_P07(),datasets.nist_all(),max_finetune_epoch_P07,ind_test=0) channel.save() 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) + optimizer.finetune(datasets.nist_P07(),datasets.nist_all(),max_finetune_epoch_P07,ind_test=20) channel.save() 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) + optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1,special=1) channel.save() channel.save() diff -r 2937f2a421aa -r df5273caad58 deep/stacked_dae/v_sylvain/nist_sda_retrieve.py --- a/deep/stacked_dae/v_sylvain/nist_sda_retrieve.py Thu Apr 01 14:28:50 2010 -0400 +++ b/deep/stacked_dae/v_sylvain/nist_sda_retrieve.py Thu Apr 01 14:29:48 2010 -0400 @@ -118,24 +118,24 @@ if finetune_choice == 0: print('\n\n\tfinetune with NIST\n\n') optimizer.reload_parameters(PATH+'params_pretrain.txt') - optimizer.finetune(datasets.nist_all(),datasets.nist_P07(min_file=nb_file),max_finetune_epoch_NIST,ind_test=1) + optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1) channel.save() if finetune_choice == 1: print('\n\n\tfinetune with P07\n\n') optimizer.reload_parameters(PATH+'params_pretrain.txt') - optimizer.finetune(datasets.nist_P07(min_file=nb_file),datasets.nist_all(),max_finetune_epoch_P07,ind_test=0) + optimizer.finetune(datasets.nist_P07(),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(PATH+'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) + optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=21) + optimizer.finetune(datasets.nist_P07(),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(PATH+'params_pretrain.txt') - optimizer.finetune(datasets.nist_all(),datasets.nist_P07(min_file=nb_file),max_finetune_epoch_NIST,ind_test=1,special=1) + optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1,special=1) if finetune_choice==-1: @@ -143,23 +143,23 @@ print('\n\n\tfinetune with NIST\n\n') sys.stdout.flush() optimizer.reload_parameters(PATH+'params_pretrain.txt') - optimizer.finetune(datasets.nist_all(),datasets.nist_P07(min_file=nb_file),max_finetune_epoch_NIST,ind_test=1) + optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1) channel.save() print('\n\n\tfinetune with P07\n\n') sys.stdout.flush() optimizer.reload_parameters(PATH+'params_pretrain.txt') - optimizer.finetune(datasets.nist_P07(min_file=nb_file),datasets.nist_all(),max_finetune_epoch_P07,ind_test=0) + optimizer.finetune(datasets.nist_P07(),datasets.nist_all(),max_finetune_epoch_P07,ind_test=0) channel.save() print('\n\n\tfinetune with NIST (done earlier) followed by P07 (written here)\n\n') sys.stdout.flush() 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) + optimizer.finetune(datasets.nist_P07(),datasets.nist_all(),max_finetune_epoch_P07,ind_test=20) channel.save() 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') sys.stdout.flush() optimizer.reload_parameters(PATH+'params_pretrain.txt') - optimizer.finetune(datasets.nist_all(),datasets.nist_P07(min_file=nb_file),max_finetune_epoch_NIST,ind_test=1,special=1) + optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1,special=1) channel.save() channel.save()