#'unlab_loader_config':{'batch_size':2000}, 'net_config':{'k':256},'trainer':Classifier, 'trainer_config':{'log_dir':os.path.expanduser('~/tb-experiments/UCI/t3layer_baseline/'), 'log_args':{'minPeriod':.01, 'timeFrac':3/10}}#[1,.1,.3,3],}#'advEps':[10,3,1,.3]} } if __name__=='__main__': # thestudy = Study(PI_trial,uci_pi_spec2,study_name='uci_baseline2234_') # thestudy.run(num_trials=3,ordered=False) # #print(thestudy.covariates()) # covars = thestudy.covariates() # covars['test_Acc'] = thestudy.outcomes['test_Acc'].values # covars['dev_Acc'] = thestudy.outcomes['dev_Acc'].values # print(covars.drop(['log_suffix','saved_at'],axis=1)) # PI model baselines for AG-NEWS w/ best hyperparameters text_pi_cfg = {'dataset':AG_News,'num_epochs':50,'trainer':PiModel,'trainer_config':{'cons_weight':30},'opt_config':{'lr':1e-3}, 'loader_config': {'amnt_labeled':200+5000,'lab_BS':200}} text_classifier_cfg = {'dataset':AG_News,'num_epochs':500,'trainer':Classifier,'opt_config':{'lr':1e-3}, 'loader_config': {'amnt_labeled':200+5000,'lab_BS':200}} y_text_pi_cfg = {'dataset':YAHOO,'num_epochs':50,'trainer':PiModel,'trainer_config':{'cons_weight':[30]},'opt_config':{'lr':[1e-4]}, 'loader_config': {'amnt_labeled':800+5000,'lab_BS':800}} y_text_classifier_cfg = {'dataset':YAHOO,'num_epochs':500,'trainer':Classifier,'opt_config':{'lr':1e-3}, 'loader_config': {'amnt_labeled':800+5000,'lab_BS':800}} # Searched from # text_pi_cfg = {'num_epochs':50,'trainer':PiModel,'trainer_config':{'cons_weight':[10,30]},'opt_config':{'lr':[1e-3,3e-4,3e-3]}, # 'loader_config': {'amnt_labeled':200+5000,'lab_BS':200}} textstudy = Study(PI_trial,y_text_pi_cfg,study_name='Agnews') textstudy.run(3) print(textstudy.covariates()) print(textstudy.outcomes)
'opt_config': { 'lr': 3e-4 }, 'dataset': MINIBOONE, #[MINIBOONE,HEPMASS,AG_News], 'trainer_config': { 'log_dir': os.path.expanduser('~/tb-experiments/UCI/flowgmm/miniboone/'), 'unlab_weight': 1. }, 'loader_config': { 'amnt_labeled': 20 + 5000, 'amnt_dev': 5000, 'lab_BS': 20 }, 'net_config': { 'k': 256, 'coupling_layers': 7, 'nperlayer': 1 } } #trial(one_flowgmm_cfg) thestudy = Study(trial, text_flowgmm_cfg, study_name='text_hypers') thestudy.run(3) covars = thestudy.covariates() covars['test_Acc'] = thestudy.outcomes['test_Acc'].values covars['dev_Acc'] = thestudy.outcomes['dev_Acc'].values print(covars.drop(['log_suffix', 'saved_at'], axis=1)) # print(thestudy.covariates()) # print(thestudy.outcomes)