def test(files_):
global jobs
if use.drop: # dont use dropout when testing
drop.p_traj.set_value(float32(0.))
drop.p_vid.set_value(float32(0.))
drop.p_hidden.set_value(float32(0.))
ce = []
first_test_file = True
# use.aug=False
for file in files_:
if first_test_file:
augm = False
first_test_file = False
else:
augm = True
load_data(file, augm, istest=True)
ce.append(_batch(test_model, False))
if use.drop: # reset dropout
drop.p_traj.set_value(drop.p_traj_val)
drop.p_vid.set_value(drop.p_vid_val)
drop.p_hidden.set_value(drop.p_hidden_val)
if use.aug:
start_load(files.train, augm=use.aug)
return _avg(ce)
def test(files_):
global jobs
if use.drop: # dont use dropout when testing
drop.p_traj.set_value(float32(0.0))
drop.p_vid.set_value(float32(0.0))
drop.p_hidden.set_value(float32(0.0))
ce = []
first_test_file = True
# use.aug=False
for file in files_:
if first_test_file:
augm = False
first_test_file = False
else:
augm = True
load_data(file, augm, istest=True)
ce.append(_batch(test_model, False))
if use.drop: # reset dropout
drop.p_traj.set_value(drop.p_traj_val)
drop.p_vid.set_value(drop.p_vid_val)
drop.p_hidden.set_value(drop.p_hidden_val)
if use.aug:
start_load(files.train, augm=use.aug)
return _avg(ce)
# train = glob(src+'/train/batch_100_*.zip')#+glob(src+'/valid/batch_100_*.zip')
# valid = glob(src+'/valid/batch_100_*.zip')#[:1]
n_train = len(train)
n_valid = len(valid)
# if use.valid2: valid2 = data_files[n_train+n_valid:]
# valid2 = glob(src+'_valid/batch_100_*.p')
rng.shuffle(files.train)
# data augmentation
if use.aug:
jobs, queue = start_load(files.train, augm=use.aug, start=True)
# print data sizes
if use.valid2: files.n_test = len(files.valid2)
else: files.n_test = 0
write('data: total: %i train: %i valid: %i test: %i' % \
((files.n_test+files.n_train+files.n_valid)*batch_size,
files.n_train*batch_size,
files.n_valid*batch_size,
files.n_test*batch_size))
first_report2 = True
epoch = 0
def load_data(path, trans, istest=False):
# train = glob(src+'/train/batch_100_*.zip')#+glob(src+'/valid/batch_100_*.zip')
# valid = glob(src+'/valid/batch_100_*.zip')#[:1]
n_train = len(train)
n_valid = len(valid)
# if use.valid2: valid2 = data_files[n_train+n_valid:]
# valid2 = glob(src+'_valid/batch_100_*.p')
rng.shuffle(files.train)
# data augmentation
if use.aug:
jobs, queue = start_load(files.train, augm=use.aug, start=True)
# print data sizes
if use.valid2:
files.n_test = len(files.valid2)
else:
files.n_test = 0
write(
"data: total: %i train: %i valid: %i test: %i"
% (
(files.n_test + files.n_train + files.n_valid) * batch_size,
files.n_train * batch_size,
files.n_valid * batch_size,
files.n_test * batch_size,
)
)