dkf = DKF(params, paramFile=pfile) displayTime('Building dkf', start_time, time.time()) # Set save prefix savef = os.path.join(params['savedir'], params['unique_id']) print 'Savefile: ', savef start_time = time.time() # Learn the model (see stinfmodel/learning.py) savedata = DKF_learn.learn(dkf, dataset['train'], dataset['mask_train'], epoch_start=0, epoch_end=params['epochs'], batch_size=params['batch_size'], savefreq=params['savefreq'], savefile=savef, dataset_eval=dataset['val'], mask_eval=dataset['mask_val'], replicate_K=params['replicate_K'], shuffle=False, cond_vals_train=train_cond_vals, cond_vals_eval=val_cond_vals) displayTime('Running DKF', start_time, time.time()) # Evaluate bound on test set (see stinfmodel/evaluate.py) savedata['bound_test'] \ = DKF_evaluate.evaluateBound(dkf, dataset['test'], dataset['mask_test'], batch_size=params['batch_size'], cond_vals=val_cond_vals) saveHDF5(savef + '-final.h5', savedata) print 'Test Bound: ', savedata['bound_test']
if os.path.exists(reloadFile): pfile=params.pop('paramFile') assert os.path.exists(pfile),pfile+' not found. Need paramfile' print 'Reloading trained model from : ',reloadFile print 'Assuming ',pfile,' corresponds to model' dkf = DKF(params, paramFile = pfile, reloadFile = reloadFile) else: pfile= params['savedir']+'/'+params['unique_id']+'-config.pkl' print 'Training model from scratch. Parameters in: ',pfile dkf = DKF(params, paramFile = pfile) displayTime('Building dkf',start_time, time.time()) savef = os.path.join(params['savedir'],params['unique_id']) print 'Savefile: ',savef start_time= time.time() savedata = DKF_learn.learn(dkf, dataset['train'], dataset['mask_train'], epoch_start =0 , epoch_end = params['epochs'], batch_size = params['batch_size'], savefreq = params['savefreq'], savefile = savef, dataset_eval=dataset['valid'], mask_eval = dataset['mask_valid'], replicate_K = 5 ) displayTime('Running DKF',start_time, time.time()) #Save file log file saveHDF5(savef+'-final.h5',savedata) #import ipdb;ipdb.set_trace()