def save(directory: str,
name: str,
active_model: keras.models.Model,
target_model: keras.models.Model,
memory=None):
if not os.path.isdir(directory):
os.mkdir(directory)
active_model.save("{}/{}_active.h5f".format(directory, name))
target_model.save("{}/{}_target.h5f".format(directory, name))
if memory is not None:
with open("{}/{}_memory.obj".format(directory, name), 'wb') as handler:
pickle.dump(memory, handler, pickle.HIGHEST_PROTOCOL)
def save_model(model: keras.models.Model, filepath: str) -> None:
"""Saves model to serialized file.
Args:
model: Keras model object.
filepath: Filepath to which model is saved.
Returns:
None.
"""
_logger.debug("Save model to {}".format(filepath))
model.save(filepath, overwrite=True)
def save_model(m: keras.models.Model, p: str = None, *args, **kwargs):
if p is None:
p = os.path.join(nb_dir, '.train_result')
os.makedirs(p, exist_ok=True)
window = kwargs.pop('window', None)
days = kwargs.pop('days', None)
stockcode = kwargs.pop('stockcode', None)
if stockcode is None or window is None or days is None:
raise ValueError()
p = _get_model_file_path(stockcode, window, days, p)
os.makedirs(os.path.dirname(p), exist_ok=True)
m.save(p)
return p
def save_model(model: keras.models.Model, model_type: int):
if model_type == ann_normalize.TYPE_FCN:
model.save(fcn_norm_fix_model_path)
elif model_type == ann_normalize.TYPE_CONV:
model.save(conv_norm_fix_model_path)
else:
model.save(conv_bn_norm_fix_model_path)
def save_normed_model(model: keras.models.Model, model_type: int):
if model_type == TYPE_FCN:
model.save(fcn_norm_model_path)
elif model_type == TYPE_CONV:
model.save(conv_norm_model_path)
else:
model.save(conv_bn_norm_model_path)
def save(self, path : str, model: keras.models.Model ):
dump(self, os.path.join(path, f"solution_{self.id:03d}.pkl"))
model.save(os.path.join(path, f"model_{self.id:03d}.h5"), include_optimizer=False)
