def __init__(self,
verbose=False,
path=None,
resume=False,
searcher_args=None):
super().__init__(verbose)
if searcher_args is None:
searcher_args = {}
if path is None:
path = temp_folder_generator()
self.cnn = CnnModule(self.loss, self.metric, searcher_args, path,
verbose)
self.path = path
if has_file(os.path.join(self.path, 'text_classifier')) and resume:
classifier = pickle_from_file(
os.path.join(self.path, 'text_classifier'))
self.__dict__ = classifier.__dict__
else:
self.y_encoder = None
self.data_transformer = None
self.verbose = verbose
def __init__(self, verbose=False, path=constant.DEFAULT_SAVE_PATH, resume=False,
searcher_args=None):
"""Initialize the instance.
The classifier will be loaded from the files in 'path' if parameter 'resume' is True.
Otherwise it would create a new one.
Args:
verbose: An boolean of whether the search process will be printed to stdout.
path: A string. The path to a directory, where the intermediate results are saved.
resume: An boolean. If True, the classifier will continue to previous work saved in path.
Otherwise, the classifier will start a new search.
"""
if searcher_args is None:
searcher_args = {}
if has_file(os.path.join(path, 'classifier')) and resume:
classifier = pickle_from_file(os.path.join(path, 'classifier'))
self.__dict__ = classifier.__dict__
self.path = path
else:
self.y_encoder = None
self.data_transformer = None
self.verbose = verbose
self.searcher = False
self.path = path
self.searcher_args = searcher_args
ensure_dir(path)
def __init__(self, n_classes, input_shape, path, verbose):
"""Init Searcher class with n_classes, input_shape, path, verbose
The Searcher will be loaded from file if it has been saved before.
"""
if has_file(os.path.join(path, "searcher")):
searcher = pickle.load(open(os.path.join(path, 'searcher'), 'rb'))
self.__dict__ = searcher.__dict__
else:
self.n_classes = n_classes
self.input_shape = input_shape
self.verbose = verbose
self.history_configs = []
self.history = []
self.path = path
self.model_count = 0
def __init__(self,
verbose=False,
path=None,
resume=False,
searcher_args=None,
augment=None):
"""Initialize the instance.
The classifier will be loaded from the files in 'path' if parameter 'resume' is True.
Otherwise it would create a new one.
Args:
verbose: A boolean of whether the search process will be printed to stdout.
path: A string. The path to a directory, where the intermediate results are saved.
resume: A boolean. If True, the classifier will continue to previous work saved in path.
Otherwise, the classifier will start a new search.
augment: A boolean value indicating whether the data needs augmentation. If not define, then it
will use the value of Constant.DATA_AUGMENTATION which is True by default.
"""
super().__init__(verbose)
if searcher_args is None:
searcher_args = {}
if path is None:
path = temp_folder_generator()
self.cnn = CnnModule(self.loss, self.metric, searcher_args, path,
verbose)
if augment is None:
augment = Constant.DATA_AUGMENTATION
if has_file(os.path.join(path, 'classifier')) and resume:
classifier = pickle_from_file(os.path.join(path, 'classifier'))
self.__dict__ = classifier.__dict__
self.path = path
else:
self.y_encoder = None
self.data_transformer = None
self.verbose = verbose
self.searcher = False
self.path = path
self.searcher_args = searcher_args
self.augment = augment
ensure_dir(path)
def __init__(self,
verbose=False,
searcher_type=None,
path=constant.DEFAULT_SAVE_PATH,
resume=False):
"""Initialize the instance.
The classifier will be loaded from file if the directory in 'path' has a saved classifier.
Otherwise it would create a new one.
"""
if has_file(os.path.join(path, 'classifier')) and resume:
classifier = pickle.load(
open(os.path.join(path, 'classifier'), 'rb'))
self.__dict__ = classifier.__dict__
else:
self.y_encoder = None
self.verbose = verbose
self.searcher = False
self.searcher_type = searcher_type
self.path = path
ensure_dir(path)
def __init__(self, verbose=False, path=None, resume=False, searcher_args=None, augment=None):
"""Initialize the instance.
The classifier will be loaded from the files in 'path' if parameter 'resume' is True.
Otherwise it would create a new one.
Args:
verbose: A boolean of whether the search process will be printed to stdout.
path: A string. The path to a directory, where the intermediate results are saved.
resume: A boolean. If True, the classifier will continue to previous work saved in path.
Otherwise, the classifier will start a new search.
augment: A boolean value indicating whether the data needs augmentation. If not define, then it
will use the value of Constant.DATA_AUGMENTATION which is True by default.
"""
super().__init__(verbose)
if searcher_args is None:
searcher_args = {}
if path is None:
path = temp_folder_generator()
if augment is None:
augment = Constant.DATA_AUGMENTATION
if has_file(os.path.join(path, 'classifier')) and resume:
classifier = pickle_from_file(os.path.join(path, 'classifier'))
self.__dict__ = classifier.__dict__
self.path = path
else:
self.y_encoder = None
self.data_transformer = None
self.verbose = verbose
self.searcher = False
self.path = path
self.searcher_args = searcher_args
self.augment = augment
ensure_dir(path)
def __init__(self,
verbose=False,
path=None,
resume=False,
searcher_args=None):
"""Initialize the instance.
The classifier will be loaded from the files in 'path' if parameter 'resume' is True.
Otherwise it would create a new one.
Args:
verbose: A boolean of whether the search process will be printed to stdout.
path: A string. The path to a directory, where the intermediate results are saved.
resume: A boolean. If True, the classifier will continue to previous work saved in path.
Otherwise, the classifier will start a new search.
searcher_args: A dictionary containing the parameters for the searcher's __init__ function.
"""
super().__init__(verbose)
if searcher_args is None:
searcher_args = {}
if path is None:
path = temp_folder_generator()
self.cnn = CnnModule(self.loss, self.metric, searcher_args, path,
verbose)
self.path = path
if has_file(os.path.join(self.path, 'text_classifier')) and resume:
classifier = pickle_from_file(
os.path.join(self.path, 'text_classifier'))
self.__dict__ = classifier.__dict__
else:
self.y_encoder = None
self.data_transformer = None
self.verbose = verbose
