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()
