def after_epoch(self):
    for (n, v) in zip(self.learn.recorder.metric_names, self.learn.recorder.log):
        if (n not in ['epoch', 'time']):
            self.experiment.log_metric(f'epoch__{n}', v)
        if (n == 'time'):
            self.experiment.log_text(f'epoch__{n}', str(v))
    if (self.log_model_weights and hasattr(self.learn, 'save_model')):
        if self.learn.save_model.every_epoch:
            _file = join_path_file(f'{self.learn.save_model.fname}_{self.learn.save_model.epoch}', (self.learn.path / self.learn.model_dir), ext='.pth')
        else:
            _file = join_path_file(self.learn.save_model.fname, (self.learn.path / self.learn.model_dir), ext='.pth')
        self.experiment.log_artifact(_file)
def after_epoch(self):
    for (n, v) in zip(self.learn.recorder.metric_names, self.learn.recorder.log):
        if (n not in ['epoch', 'time']):
            self.experiment.log_metric(f'epoch__{n}', v)
        if (n == 'time'):
            self.experiment.log_text(f'epoch__{n}', str(v))
    if (self.log_model_weights and hasattr(self.learn, 'save_model')):
        if self.learn.save_model.every_epoch:
            _file = join_path_file(f'{self.learn.save_model.fname}_{self.learn.save_model.epoch}', (self.learn.path / self.learn.model_dir), ext='.pth')
        else:
            _file = join_path_file(self.learn.save_model.fname, (self.learn.path / self.learn.model_dir), ext='.pth')
        self.experiment.log_artifact(_file)