times['setup'] = t2 - t1
#
if 1:
input_dim, tr_x, tr_y, va_x, va_y, te_x, te_y, y_mean, y_std = get_regression_dataset(
dataset, split, data_path=data_path)
if train_on_valid:
print tr_x.shape
tr_x = np.concatenate((tr_x, va_x), axis=0)
print tr_x.shape
tr_y = np.concatenate((tr_y, va_y), axis=0)
if model == 'MCD':
#lbda = get_lbda(tau, length_scale, drop_prob)
#print drop_prob, lbda
network = MCdropout_MLP(n_hiddens=n_hiddens,
n_units=n_units,
lbda=lbda,
input_dim=input_dim)
elif model == 'BHN':
#lbda = get_lbda(tau, length_scale)
prior = log_normal
#print lbda
if reinit:
init_batch_size = 64
init_batch = tr_x[-init_batch_size:]
else:
init_batch = None
network = MLPWeightNorm_BHN(lbda=lbda,
srng=RandomStreams(seed=seed + 2000),
prior=prior,
coupling=coupling,
if args.model == 'BHN':
model = MLPWeightNorm_BHN(lbda=lbda,
perdatapoint=perdatapoint,
srng=RandomStreams(seed=args.seed +
2000),
prior=prior,
coupling=coupling,
n_hiddens=n_hiddens,
n_units=n_units,
input_dim=input_dim,
flow=args.flow,
init_batch=init_batch)
elif args.model == 'MCDropout':
model = MCdropout_MLP(n_hiddens=n_hiddens,
n_units=n_units,
lbda=lbda,
input_dim=input_dim)
elif args.model == 'Backprop':
model = Backprop_MLP(n_hiddens=n_hiddens,
n_units=n_units,
lbda=lbda,
input_dim=input_dim)
else:
raise Exception('no model named `{}`'.format(args.model))
va_rec_name = name + '_recs'
save_path = name + '.params.npy'
e0 = 0
rec = 0
if args.totrain:
for trial, dataset in enumerate(datasets):
input_dim, train_x, train_y, valid_x, valid_y, test_x , test_y = dataset
for length_scale in length_scales:
for tau in taus:
for lr0 in lr0s:
# DROPOUT
if model == 'MCDropout':
for drop_prob in drop_probs:
t0 = time.time()
lbda = get_lbda(tau, length_scale, drop_prob)
print drop_prob, lbda
network = MCdropout_MLP(n_hiddens=n_hiddens,
n_units=n_units,
lbda=lbda,
#srng = RandomStreams(seed=seed+2000),
input_dim=input_dim)
path = save_dir
name = '{}/airfoil_regression_m{}p{}c{}lr0{}seed{}reinit{}flow{}trial{}tau{}l{}'.format(
path,
model,
drop_prob,
coupling,
lr0,
seed,
reinit,
flow,
trial,
tau,
else:
init_batch = None
if args.model == 'BHN_MLPWN':
model = MLPWeightNorm_BHN(lbda=lbda,
perdatapoint=perdatapoint,
srng=RandomStreams(seed=args.seed + 2000),
prior=prior,
coupling=coupling,
n_hiddens=n_hiddens,
n_units=n_units,
input_dim=input_dim,
flow=args.flow,
init_batch=init_batch)
elif args.model == 'MCdropout_MLP':
model = MCdropout_MLP(n_hiddens=n_hiddens, n_units=n_units)
else:
raise Exception('no model named `{}`'.format(args.model))
va_rec_name = name + '_recs'
tr_rec_name = name + '_recs_train' # TODO (we're already saving the valid_recs!)
save_path = name + '.params.npy'
if os.path.isfile(save_path) and not args.override:
print 'load best model'
e0 = model.load(save_path)
va_recs = open(va_rec_name, 'r').read().split('\n')[:e0]
#tr_recs = open(tr_rec_name,'r').read().split('\n')[:e0]
rec = max([float(r) for r in va_recs])
else:
e0 = 0