def get_dataset(dataset_name, normalization):
# Load dataset
(train_images,
train_labels), (test_images,
test_labels) = datasets.get_dataset(dataset_name)
# Normalize data
if normalization == "-1to1":
train_images, min, max = preprocessing.normalize_minus_one_to_one(
train_images)
test_images = preprocessing.normalize_minus_one_to_one(
test_images, min, max)
elif normalization == 'gaussian':
train_images, mean, std = preprocessing.normalize_gaussian(
train_images)
test_images = preprocessing.normalize_gaussian(test_images, mean, std)
return (train_images, train_labels), (test_images, test_labels)
(train_images,
train_labels), (test_images,
test_labels) = datasets.get_dataset(DATASET_NAME)
if AUX_DATASET_NAME:
(aux_images, _), _ = datasets.get_dataset(AUX_DATASET_NAME)
train_images = np.concatenate((train_images, aux_images), axis=0)
# Normalize data
if NORMALIZATION == "-1to1":
train_images, min, max = preprocessing.normalize_minus_one_to_one(
train_images)
test_images = preprocessing.normalize_minus_one_to_one(
test_images, min, max)
elif NORMALIZATION == 'gaussian':
train_images, mean, std = preprocessing.normalize_gaussian(
train_images)
test_images = preprocessing.normalize_gaussian(test_images, mean, std)
# Load ensemble models
ensemble_model_names = saveload.get_ensemble_model_names()
model_names = ensemble_model_names[ENSEMBLE_LOAD_NAME][DATASET_NAME]
print(model_names)
measures = {'endd': defaultdict(list), 'ensm': defaultdict(list)}
for n_models in N_MODELS_LIST:
# Get model names
if SAMPLE_ENSEMBLE_MODELS:
model_name_subset = np.random.choice(model_names, n_models)
else:
model_name_subset = model_names[:n_models]
#model_name_subset = ['vgg_cifar10_cifar10_25']