def predict(self, records_score: api.InputRecords) -> api.BatchRecordScores: """Score records. Args: records_score: InputRecords, scoring records. Returns: BatchRecordScores, scored data records. """ if not hasattr(self, "net"): self.load() ds_score = dataset.RecordDataset( artifact_dir=self.artifact_dir, cfg_dataset=self.cfg["dataset"], records=records_score, mode=api.RecordMode.SCORE, batch_size=self.cfg["solver"]["batch_size"], ) network_output = self.net.predict(ds_score, verbose=1) scores = [ ds_score.transformer.postprocess(score) for score in dataset.batchify_network_output( network_output, self.net.output_names ) ] return scores
def predict( self, records_score: Union[pd.DataFrame, api.Records], workers: int = 10, max_queue_size: int = 10, ) -> api.BatchRecordScores: """Score records. Args: records_score: Union[pd.DataFrame, Records], scoring records. workers: int (OPTIONAL = 10), number of process threads for the sequence. max_queue_size: int (OPTIONAL = 10), queue size for the sequence. Returns: BatchRecordScores, scored data records. """ if not self._is_loaded: self.load() ds_score = dataset.RecordDataset( artifact_dir=self._artifact_dir, cfg_dataset=self.cfg["dataset"], records=records_score, mode=api.RecordMode.SCORE, batch_size=self.cfg["solver"]["batch_size"], ) network_output = self.net.predict_generator( ds_score, use_multiprocessing=(workers > 1), max_queue_size=max_queue_size, workers=workers, verbose=1, ) scores = [ ds_score.transformer.postprocess(score) for score in dataset.batchify_network_output( network_output, self.net.output_names ) ] return scores
def train( self, cfg: dict, records_train: api.InputRecords, records_validation: api.InputRecords, ) -> tf.keras.Model: """Train the network. Args: cfg: dict, config. records_train: InputRecords, training records. records_validation: InputRecords, validation records. Returns: tf.keras.Model, trained network. """ logger.info("Starting training") tf_utils.reset() cfg = config.prepare_config(cfg) logger.info(f"Creating artifact directory: {self.artifact_dir}") services.make_artifact_dir(self.artifact_dir) io_utils.save_json(cfg, "config.json", self.artifact_dir) io_utils.save_pickle(cfg, "config.pkl", self.artifact_dir) logger.info("Creating datasets") ds_train = dataset.RecordDataset( artifact_dir=self.artifact_dir, cfg_dataset=cfg["dataset"], records=records_train, mode=api.RecordMode.TRAIN, batch_size=cfg["solver"]["batch_size"], ) ds_validation = dataset.RecordDataset( artifact_dir=self.artifact_dir, cfg_dataset=cfg["dataset"], records=records_validation, mode=api.RecordMode.VALIDATION, batch_size=cfg["solver"]["batch_size"], ) network_params = ds_train.transformer.network_params io_utils.save_json(network_params, "network_params.json", self.artifact_dir) io_utils.save_pickle(network_params, "network_params.pkl", self.artifact_dir) logger.info("Building network") net = model.build_network(cfg["model"], network_params) model.check_output_names(cfg["model"], net) logger.info("Compiling network") opt = solver.build_optimizer(cfg["solver"]) objective = model.build_objective(cfg["model"]) net.compile(optimizer=opt, **objective) logger.info("Creating services") callbacks = services.create_all_services(self.artifact_dir, cfg["services"]) if "learning_rate_reducer" in cfg["solver"]: logger.info("Creating learning rate reducer") callbacks.append(solver.create_learning_rate_reducer(cfg["solver"])) logger.info("Training network") net.summary() net.fit( ds_train, validation_data=ds_validation, epochs=cfg["solver"]["epochs"], steps_per_epoch=cfg["solver"].get("steps"), callbacks=callbacks, verbose=1, ) return net
def train( self, cfg: dict, records_train: Union[pd.DataFrame, api.Records], records_validation: Union[pd.DataFrame, api.Records], workers: int = 10, max_queue_size: int = 10, ) -> tf.keras.Model: """Train the network. Args: cfg: dict, config. records_train: Union[pd.DataFrame, Records], training records. records_validation: Union[pd.DataFrame, Records], validation records. workers: int (OPTIONAL = 10), number of process threads for the sequence. max_queue_size: int (OPTIONAL = 10), queue size for the sequence. Returns: tf.keras.Model, trained network. """ logger.info("Starting training") tf_utils.reset() logger.info("Validating config schema and applying defaults") cfg = config.prepare_config(cfg) logger.info(f"Making artifact directory: {self._artifact_dir}") services.make_artifact_dir(self._artifact_dir) logger.info("Saving config") io_utils.save_json(cfg, "config.json", self._artifact_dir) io_utils.save_pickle(cfg, "config.pkl", self._artifact_dir) logger.info("Building datasets") ds_train = dataset.RecordDataset( artifact_dir=self._artifact_dir, cfg_dataset=cfg["dataset"], records=records_train, mode=api.RecordMode.TRAIN, batch_size=cfg["solver"]["batch_size"], ) ds_validation = dataset.RecordDataset( artifact_dir=self._artifact_dir, cfg_dataset=cfg["dataset"], records=records_validation, mode=api.RecordMode.VALIDATION, batch_size=cfg["solver"]["batch_size"], ) network_params = ds_train.transformer.network_params io_utils.save_json(network_params, "network_params.json", self._artifact_dir) io_utils.save_pickle(network_params, "network_params.pkl", self._artifact_dir) logger.info("Building network") net = model.build_network(cfg["model"], network_params) logger.info("Checking network output names match config output names") model.check_output_names(cfg["model"], net) logger.info("Building optimizer") opt = solver.build_optimizer(cfg["solver"]) logger.info("Building objective") objective = model.build_objective(cfg["model"]) logger.info("Compiling network") net.compile(optimizer=opt, **objective) metrics_names = net.metrics_names logger.info("Creating services") callbacks = services.create_all_services( self._artifact_dir, cfg["services"], metrics_names ) if "learning_rate_reducer" in cfg["solver"]: logger.info("Creating learning rate reducer") callbacks.append( solver.create_learning_rate_reducer(cfg["solver"], metrics_names) ) logger.info("Training network") logger.info(net.summary()) net.fit_generator( ds_train, validation_data=ds_validation, epochs=cfg["solver"]["epochs"], steps_per_epoch=cfg["solver"].get("steps"), callbacks=callbacks, use_multiprocessing=(workers > 1), max_queue_size=max_queue_size, workers=workers, verbose=1, ) return net