def __init__(self, teacher, dataset_path, store_path, dataset_config,
best_trade_off):
self.dataset_path = dataset_path
self.store_path = store_path
self.teacher = teacher
self.dataset_config = dataset_config
self.rotate = dataset_config.use_rotation
self.trade_off = best_trade_off
if os.path.exists(self.store_path):
raise Exception("Store path already exists")
else:
os.makedirs(self.store_path)
os.makedirs(os.path.join(self.store_path, "train"))
os.makedirs(os.path.join(self.store_path, "valid"))
os.makedirs(os.path.join(self.store_path, "test"))
self.evaluate = util.create_simple_predictor(teacher['model'],
teacher['params'])
self.creator = Creator(
self.dataset_path,
dim=(self.dataset_config.input_dim,
self.dataset_config.output_dim),
preproccessing=self.dataset_config.use_preprocessing,
std=self.dataset_config.dataset_std,
reduce_training=self.dataset_config.reduce_training,
reduce_testing=self.dataset_config.reduce_testing,
reduce_validation=self.dataset_config.reduce_validation,
only_mixed=self.dataset_config.only_mixed_labels,
mix_ratio=self.dataset_config.mix_ratio)
self.creator.load_dataset()
def __init__(self, teacher, dataset_path, store_path, dataset_config, best_trade_off):
self.dataset_path = dataset_path
self.store_path = store_path
self.teacher = teacher
self.dataset_config = dataset_config
self.rotate = dataset_config.use_rotation
self.trade_off = best_trade_off
if os.path.exists(self.store_path):
raise Exception("Store path already exists")
else:
os.makedirs(self.store_path)
os.makedirs(os.path.join(self.store_path, "train"))
os.makedirs(os.path.join(self.store_path, "valid"))
os.makedirs(os.path.join(self.store_path, "test"))
self.evaluate = util.create_simple_predictor(teacher['model'], teacher['params'])
self.creator = Creator(
self.dataset_path,
dim=(self.dataset_config.input_dim, self.dataset_config.output_dim),
preproccessing=self.dataset_config.use_preprocessing,
std=self.dataset_config.dataset_std,
reduce_training=self.dataset_config.reduce_training,
reduce_testing=self.dataset_config.reduce_testing,
reduce_validation=self.dataset_config.reduce_validation,
only_mixed=self.dataset_config.only_mixed_labels,
mix_ratio=self.dataset_config.mix_ratio
)
self.creator.load_dataset()
verify, stage = get_command('-verify', default="0")
stage = "stage" + stage
is_tradeoff, tradeoff = get_command('-tradeoff', default="0.5")
tradeoff = float(tradeoff)
#Dataset path. Config used if not supplied
is_alt_dataset, alt_dataset = get_command('-dataset')
if is_alt_dataset:
dataset_path = alt_dataset
#==============================================================
store = ParamStorage()
teacher = store.load_params(path=teacher_location)
evaluate = util.create_simple_predictor(teacher['model'], teacher['params'])
if not verify:
creator = Creator(pr_path,
dim=(dataset_params.input_dim,
dataset_params.output_dim),
preproccessing=dataset_params.use_preprocessing,
std=dataset_params.dataset_std,
reduce_training=dataset_params.reduce_training,
reduce_testing=dataset_params.reduce_testing,
reduce_validation=dataset_params.reduce_validation)
creator.load_dataset()
data, labels = creator.sample_data(creator.train,
samples,
rotation=dataset_params.use_rotation)
stage = "stage" + stage
is_tradeoff, tradeoff = get_command('-tradeoff', default="0.5")
tradeoff = float(tradeoff)
#Dataset path. Config used if not supplied
is_alt_dataset, alt_dataset = get_command('-dataset')
if is_alt_dataset:
dataset_path = alt_dataset
#==============================================================
store = ParamStorage()
teacher = store.load_params(path=teacher_location)
evaluate = util.create_simple_predictor(teacher['model'], teacher['params'])
if not verify:
creator = Creator(
pr_path,
dim=(dataset_params.input_dim, dataset_params.output_dim),
preproccessing=dataset_params.use_preprocessing,
std=dataset_params.dataset_std,
reduce_training=dataset_params.reduce_training,
reduce_testing=dataset_params.reduce_testing,
reduce_validation=dataset_params.reduce_validation
)
creator.load_dataset()
data, labels = creator.sample_data(
creator.train,