コード例 #1
0
ファイル: gan_train.py プロジェクト: Bjornwolf/language-model
    def run(self):
        """Starts the main loop.

        The main loop ends when a training extension makes
        a `training_finish_requested` record in the log.

        """
        # This should do nothing if the user has already configured
        # logging, and will it least enable error messages otherwise.
        logging.basicConfig()

        # If this is resumption from a checkpoint, it is crucial to
        # reset `profile.current`. Otherwise, it simply does not hurt.
        self.profile.current = []

        with change_recursion_limit(config.recursion_limit):
            self.original_sigint_handler = signal.signal(
                signal.SIGINT, self._handle_epoch_interrupt)
            self.original_sigterm_handler = signal.signal(
                signal.SIGTERM, self._handle_batch_interrupt)
            try:
                logger.info("Entered the main loop")
                if not self.status['training_started']:
                    for extension in self.extensions:
                        extension.main_loop = self
                    self._run_extensions('before_training')
                    with Timer('initialization', self.profile):
                        self.algorithm_g.initialize()
                        self.algorithm_d.initialize()
                    self.status['training_started'] = True
                # We can not write "else:" here because extensions
                # called "before_training" could have changed the status
                # of the main loop.
                if self.log.status['iterations_done'] > 0:
                    self.log.resume()
                    self._run_extensions('on_resumption')
                    self.status['epoch_interrupt_received'] = False
                    self.status['batch_interrupt_received'] = False
                with Timer('training', self.profile):
                    while self._run_epoch():
                        pass
            except TrainingFinish:
                self.log.current_row['training_finished'] = True
            except Exception as e:
                self._restore_signal_handlers()
                self.log.current_row['got_exception'] = traceback.format_exc()
                logger.error("Error occured during training." + error_message)
                try:
                    self._run_extensions('on_error')
                except Exception:
                    logger.error(traceback.format_exc())
                    logger.error("Error occured when running extensions." +
                                 error_in_error_handling_message)
                reraise_as(e)
            finally:
                self._restore_signal_handlers()
                if self.log.current_row.get('training_finished', False):
                    self._run_extensions('after_training')
                if config.profile:
                    self.profile.report()
コード例 #2
0
ファイル: serialization.py プロジェクト: DoWhatILove/blocks
def continue_training(path):
    """Continues training using checkpoint.

    Parameters
    ----------
    path : str
        Path to checkpoint.

    Notes
    -----
    Python picklers can unpickle objects from global namespace only if
    they are present in namespace where unpickling happens. Often global
    functions are needed for mapping, filtering and other data stream
    operations. In a case if the main loop uses global objects and
    this function fails with a message like
    ```
    AttributeError: 'module' object has no attribute '...'
    ```
    it means that you need to import these objects.

    Examples
    --------
    This function can be used in two ways: in your script where a main
    loop defined or in a different script. For later options see Notes
    section.

    """
    with change_recursion_limit(config.recursion_limit):
        with open(path, "rb") as f:
            main_loop = load(f)
    main_loop.run()
コード例 #3
0
ファイル: main_loop.py プロジェクト: kelvinxu/blocks
    def run(self):
        """Starts the main loop.

        The main loop ends when a training extension makes
        a `training_finish_requested` record in the log.

        """
        # This should do nothing if the user has already configured
        # logging, and will it least enable error messages otherwise.
        logging.basicConfig()

        if self._model and isinstance(self.algorithm,
                                      DifferentiableCostMinimizer):
            # Sanity check: model and algorithm should be configured
            # similarly.
            if not self._model.get_objective() == self.algorithm.cost:
                logger.warning("different costs for model and algorithm")
            if not (set(self._model.get_params().values()) ==
                    set(self.algorithm.params)):
                logger.warning("different params for model and algorithm")

        with change_recursion_limit(config.recursion_limit):
            self.original_sigint_handler = signal.signal(
                signal.SIGINT, self._handle_epoch_interrupt)
            self.original_sigterm_handler = signal.signal(
                signal.SIGTERM, self._handle_batch_interrupt)
            try:
                logger.info("Entered the main loop")
                if not self.status._training_started:
                    for extension in self.extensions:
                        extension.main_loop = self
                    self._run_extensions('before_training')
                    self.algorithm.initialize()
                    self.status._training_started = True
                # We can not write "else:" here because extensions
                # called "before_training" could have changed the status
                # of the main loop.
                if self.log.status.iterations_done > 0:
                    self._run_extensions('on_resumption')
                while self._run_epoch():
                    pass
            except TrainingFinish:
                self.log.current_row.training_finished = True
            except Exception as e:
                self._restore_signal_handlers()
                self.log.current_row.got_exception = traceback.format_exc(e)
                logger.error("Error occured during training." + error_message)
                try:
                    self._run_extensions('on_error')
                except Exception as inner_e:
                    logger.error(traceback.format_exc(inner_e))
                    logger.error("Error occured when running extensions." +
                                 error_in_error_handling_message)
                reraise_as(e)
            finally:
                if self.log.current_row.training_finished:
                    self._run_extensions('after_training')
                self._restore_signal_handlers()
コード例 #4
0
ファイル: __init__.py プロジェクト: kelvinxu/blocks
def dump(pickle_path, dump_path):
    if not dump_path:
        root, ext = os.path.splitext(pickle_path)
        if not ext:
            raise ValueError
        dump_path = root
    with change_recursion_limit(config.recursion_limit):
        main_loop = cPickle.load(open(pickle_path, "rb"))
    MainLoopDumpManager(dump_path).dump(main_loop)
コード例 #5
0
    def run(self):
        logging.basicConfig()

        with change_recursion_limit(cfg.recursion_limit):
            self.original_sigint_handler = signal.signal(
                signal.SIGINT, self._handle_epoch_interrupt)
            self.original_sigterm_handler = signal.signal(
                signal.SIGTERM, self._handle_batch_interrupt)
            try:
                logger.info("Entered the main loop")
                if not self.status['training_started']:
                    for extension in self.extensions:
                        extension.main_loop = self
                    self._run_extensions('before_training')
                    with Timer('initialization', self.profile):
                        self.algorithm.initialize()
                    self.status['training_started'] = True
                if self.log.status['iterations_done'] > 0:
                    self._run_extensions('on_resumption')
                    self.status['epoch_interrupt_received'] = False
                    self.status['batch_interrupt_received'] = False
                with Timer('training', self.profile):
                    while self._run_epoch():
                        pass
            except TrainingFinish:
                self.log.current_row['training_finished'] = True
            except Exception as e:
                self._restore_signal_handlers()
                self.log.current_row['got_exception'] = traceback.format_exc(e)
                logger.error("Error occured during training." + error_message)
                try:
                    self._run_extensions('on_error')
                except Exception as inner_e:
                    logger.error(traceback.format_exc(inner_e))
                    logger.error("Error occured when running extensions." +
                                 error_in_error_handling_message)
                reraise_as(e)
            finally:
                if self.log.current_row.get('training_finished', False):
                    self._run_extensions('after_training')
                if cfg.profile:
                    self.profile.report()
                self._restore_signal_handlers()
コード例 #6
0
ファイル: algorithm.py プロジェクト: Beronx86/dl4mt-multi
    def run(self):
        logging.basicConfig()

        with change_recursion_limit(cfg.recursion_limit):
            self.original_sigint_handler = signal.signal(
                signal.SIGINT, self._handle_epoch_interrupt)
            self.original_sigterm_handler = signal.signal(
                signal.SIGTERM, self._handle_batch_interrupt)
            try:
                logger.info("Entered the main loop")
                if not self.status['training_started']:
                    for extension in self.extensions:
                        extension.main_loop = self
                    self._run_extensions('before_training')
                    with Timer('initialization', self.profile):
                        self.algorithm.initialize()
                    self.status['training_started'] = True
                if self.log.status['iterations_done'] > 0:
                    self._run_extensions('on_resumption')
                    self.status['epoch_interrupt_received'] = False
                    self.status['batch_interrupt_received'] = False
                with Timer('training', self.profile):
                    while self._run_epoch():
                        pass
            except TrainingFinish:
                self.log.current_row['training_finished'] = True
            except Exception as e:
                self._restore_signal_handlers()
                self.log.current_row['got_exception'] = traceback.format_exc(e)
                logger.error("Error occured during training." + error_message)
                try:
                    self._run_extensions('on_error')
                except Exception as inner_e:
                    logger.error(traceback.format_exc(inner_e))
                    logger.error("Error occured when running extensions." +
                                 error_in_error_handling_message)
                reraise_as(e)
            finally:
                if self.log.current_row.get('training_finished', False):
                    self._run_extensions('after_training')
                if cfg.profile:
                    self.profile.report()
                self._restore_signal_handlers()
コード例 #7
0
ファイル: plot.py プロジェクト: zabin10/blocks-extras
def load_log(fname):
    """Load a :class:`TrainingLog` object from disk.

    This function automatically handles various file formats that contain
    an instance of an :class:`TrainingLog`. This includes a pickled
    Log object, a pickled :class:`MainLoop` or an experiment dump (TODO).

    """
    with change_recursion_limit(config.recursion_limit):
        with open(fname, 'rb') as f:
            from_disk = load(f)
        # TODO: Load "dumped" experiments

    if isinstance(from_disk, TrainingLog):
        log = from_disk
    elif isinstance(from_disk, MainLoop):
        log = from_disk.log
        del from_disk
    else:
        raise ValueError("Could not load '{}': Unrecognized content.")

    return log
コード例 #8
0
ファイル: main_loop.py プロジェクト: raphael-forks/blocks
    def run(self):
        """Starts the main loop.

        The main loop ends when a training extension makes
        a `training_finish_requested` record in the log.

        """
        # This should do nothing if the user has already configured
        # logging, and will it least enable error messages otherwise.
        logging.basicConfig()

        if self._model and isinstance(self.algorithm,
                                      DifferentiableCostMinimizer):
            # Sanity check: model and algorithm should be configured
            # similarly.
            if not self._model.get_objective() == self.algorithm.cost:
                logger.warning("different costs for model and algorithm")
            if not (set(self._model.get_params().values()) == set(
                    self.algorithm.params)):
                logger.warning("different params for model and algorithm")

        with change_recursion_limit(config.recursion_limit):
            self.original_sigint_handler = signal.signal(
                signal.SIGINT, self._handle_epoch_interrupt)
            self.original_sigterm_handler = signal.signal(
                signal.SIGTERM, self._handle_batch_interrupt)
            try:
                logger.info("Entered the main loop")
                if not self.status['training_started']:
                    for extension in self.extensions:
                        extension.main_loop = self
                    self._run_extensions('before_training')
                    with Timer('initialization', self.profile):
                        self.algorithm.initialize()
                    self.status['training_started'] = True
                # We can not write "else:" here because extensions
                # called "before_training" could have changed the status
                # of the main loop.
                if self.log.status['iterations_done'] > 0:
                    self._run_extensions('on_resumption')
                    self.status['epoch_interrupt_received'] = False
                    self.status['batch_interrupt_received'] = False
                with Timer('training', self.profile):
                    while self._run_epoch():
                        pass
            except TrainingFinish:
                self.log.current_row['training_finished'] = True
            except Exception as e:
                self._restore_signal_handlers()
                self.log.current_row['got_exception'] = traceback.format_exc(e)
                logger.error("Error occured during training." + error_message)
                try:
                    self._run_extensions('on_error')
                except Exception as inner_e:
                    logger.error(traceback.format_exc(inner_e))
                    logger.error("Error occured when running extensions." +
                                 error_in_error_handling_message)
                reraise_as(e)
            finally:
                if self.log.current_row.get('training_finished', False):
                    self._run_extensions('after_training')
                if config.profile:
                    self.profile.report()
                self._restore_signal_handlers()
コード例 #9
0
def continue_training(path):
    with change_recursion_limit(config.recursion_limit):
        main_loop = cPickle.load(open(path, "rb"))
    main_loop.run()
コード例 #10
0
ファイル: __init__.py プロジェクト: saikrishnar/blocks
def dump(pickle_path, dump_path):
    with change_recursion_limit(config.recursion_limit):
        main_loop = cPickle.load(open(pickle_path, "rb"))
    MainLoopDumpManager(dump_path).dump(main_loop)
コード例 #11
0
    def run(self):
        """Starts the main loop.

        The main loop ends when a training extension makes
        a `training_finish_requested` record in the log.

        """
        # This should do nothing if the user has already configured
        # logging, and will it least enable error messages otherwise.
        logging.basicConfig()

        # If this is resumption from a checkpoint, it is crucial to
        # reset `profile.current`. Otherwise, it simply does not hurt.
        self.profile.current = []

        # check the model only if it wants to be checked
        if hasattr(self._model, 'check_sanity'):
            self._model.check_sanity(self.algorithm)

        with change_recursion_limit(config.recursion_limit):
            self.original_sigint_handler = signal.signal(
                signal.SIGINT, self._handle_epoch_interrupt)
            self.original_sigterm_handler = signal.signal(
                signal.SIGTERM, self._handle_batch_interrupt)
            try:
                logger.info("Entered the main loop")
                if not self.status['training_started']:
                    for extension in self.extensions:
                        extension.main_loop = self
                    self._run_extensions('before_training')
                    with Timer('initialization', self.profile):
                        self.algorithm.initialize()
                    self.status['training_started'] = True
                # We can not write "else:" here because extensions
                # called "before_training" could have changed the status
                # of the main loop.
                if self.log.status['iterations_done'] > 0:
                    self.log.resume()
                    self._run_extensions('on_resumption')
                    self.status['epoch_interrupt_received'] = False
                    self.status['batch_interrupt_received'] = False
                with Timer('training', self.profile):
                    while self._run_epoch():
                        pass
            except TrainingFinish:
                self.log.current_row['training_finished'] = True
            except Exception as e:
                self._restore_signal_handlers()
                self.log.current_row['got_exception'] = traceback.format_exc()
                logger.error("Error occured during training." + error_message)
                try:
                    self._run_extensions('on_error', e)
                except Exception:
                    logger.error(traceback.format_exc())
                    logger.error("Error occured when running extensions." +
                                 error_in_error_handling_message)
                reraise_as(e)
            finally:
                self._restore_signal_handlers()
                if self.log.current_row.get('training_finished', False):
                    self._run_extensions('after_training')
                if config.profile:
                    self.profile.report()