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, ) )