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)