deepy/trainers/base.py
def load_params(self, path, exclude_free_params=False):
        """
        Load parameters for the training.
        This method can load free parameters and resume the training progress.
        """
        self.network.load_params(path, exclude_free_params=exclude_free_params)
        self.best_params = self.copy_params()
        # Resume the progress
        if self.network.train_logger.progress() > 0 or self.network.train_logger.epoch() > 0:
            self.skip(self.network.train_logger.progress(), self.network.train_logger.epoch() - 1)
def load_params(self, path, exclude_free_params=False):
        self.network.load_params(path, exclude_free_params=exclude_free_params)
        self.best_params = self.copy_params()
        # Resume the progress
        if self.network.train_logger.progress() > 0 or self.network.train_logger.epoch() > 0:
            self.skip(self.network.train_logger.progress(), self.network.train_logger.epoch() - 1)
def load_params ( self , path , exclude_free_params = False ) : self . network . load_params ( path , exclude_free_params = exclude_free_params ) self . best_params = self . copy_params ( ) # Resume the progress if self . network . train_logger . progress ( ) > 0 or self . network . train_logger . epoch ( ) > 0 : self . skip ( self . network . train_logger . progress ( ) , self . network . train_logger . epoch ( ) - 1 )
Load parameters for the training.
        This method can load free parameters and resume the training progress.
Load parameters for the training . This method can load free parameters and resume the training progress .