def reload(self, custom_objects={}): """ Load keras multitask DNN from disk. """ filename = Model.get_model_filename(self.model_dir) filename, _ = os.path.splitext(filename) json_filename = "%s.%s" % (filename, "json") h5_filename = "%s.%s" % (filename, "h5") with open(json_filename) as file_obj: model = model_from_json(file_obj.read(), custom_objects=custom_objects) model.load_weights(h5_filename) self.model_instance = model
def load(self, model_dir): """ Load keras multitask DNN from disk. """ filename = Model.get_model_filename(model_dir) filename, _ = os.path.splitext(filename) json_filename = "%s.%s" % (filename, "json") h5_filename = "%s.%s" % (filename, "h5") with open(json_filename) as file_obj: model = model_from_json(file_obj.read()) model.load_weights(h5_filename) self.raw_model = model
def reload(self): """ Load keras multitask DNN from disk. """ filename = Model.get_model_filename(self.model_dir) filename, _ = os.path.splitext(filename) json_filename = "%s.%s" % (filename, "json") h5_filename = "%s.%s" % (filename, "h5") with open(json_filename) as file_obj: model = model_from_json(file_obj.read()) model.load_weights(h5_filename) self.raw_model = model
def save(self, out_dir): """ Saves underlying keras model to disk. """ super(KerasModel, self).save(out_dir) model = self.get_raw_model() filename, _ = os.path.splitext(Model.get_model_filename(out_dir)) # Note that keras requires the model architecture and weights to be stored # separately. A json file is generated that specifies the model architecture. # The weights will be stored in an h5 file. The pkl.gz file with store the # target name. json_filename = "%s.%s" % (filename, "json") h5_filename = "%s.%s" % (filename, "h5") # Save architecture json_string = model.to_json() with open(json_filename, "wb") as file_obj: file_obj.write(json_string) model.save_weights(h5_filename, overwrite=True)
def reload(self): """Loads sklearn model from joblib file on disk.""" self.model_instance = load_from_disk( Model.get_model_filename(self.model_dir))
def load(self, model_dir): """Loads sklearn model from joblib file on disk.""" self.raw_model = load_from_disk(Model.get_model_filename(model_dir))
def reload(self): """Loads sklearn model from joblib file on disk.""" self.model_instance = load_from_disk(Model.get_model_filename(self.model_dir))