def save_checkpoint(self, path):
setup_directory(path)
check_point_path = path_join(path, 'check_point', 'instance.ckpt')
setup_file(check_point_path)
saver = tf.train.Saver(self.main_graph_var_list +
self.misc_ops_var_list)
saver.save(self.sess, check_point_path)
def save_meta(self, path):
setup_directory(path)
self.metadata.save(path_join(path, 'meta.pkl'))
self.metadata.save(path_join(path, 'meta.json'))
self.params_path = path_join(path, 'params.pkl')
self._save_params(self.params_path)
def __init__(self, logdir, name, device='/cpu:0', epoch=0, verbose=0):
super().__init__(verbose)
self.logdir = logdir
setup_directory(self.logdir)
self.name = name
self.device = device
self.writer = None
self.epoch = epoch
self.sess = None
self.is_build = False
self.x_shape = None
def __init__(self, path, max_best=True, name='BestSave', log=print):
self.path = path
self.max_best = max_best
self.name = name
self.log = log
if self.max_best:
self.best_metric = -np.Inf
else:
self.best_metric = np.Inf
setup_directory(self.path)
def __init__(self, path, k=5, max_best=True, save_model=True, name='top_k_save', log=print):
self.path = path
self.k = k
self.max_best = max_best
self.save_model = save_model
self.name = name
self.log = log
self.top_k_json_path = path_join(self.path, 'top_k.json')
if os.path.exists(self.top_k_json_path):
self.top_k = load_json(self.top_k_json_path)
else:
if self.max_best:
self.top_k = [np.Inf] + [-np.Inf for _ in range(self.k)]
else:
self.top_k = [-np.Inf] + [np.Inf for _ in range(self.k)]
if self.save_model:
for i in range(1, self.k + 1):
setup_directory(path_join(self.path, f'top_{i}'))