src/proteingnn/model.py
def validation_epoch_end(self, outputs: Dict[str, Any]) -> None:
        """Aggregate validation step logging information and plot."""
        if self.global_step == 0:
            return

        outputs = _concat_step_output(outputs)
        if self._regression:
            self._log_scaler_reg(outputs['y'], outputs['pred'], stage='val')
        else:
            self._log_scaler_cls(outputs['y'], outputs['pred'], stage='val')

        if self.log_plot:  # overlay train and val visualization together
            self._log_plot_reg(outputs) if self._regression else self._log_plot_cls(outputs)
Aggregate validation step logging information and plot.
Aggregate validation step logging information and plot.
Aggregate validation step logging information and plot .
def validation_epoch_end(self, outputs: Dict[str, Any]) -> None:
        if self.global_step == 0:
            return
        outputs = _concat_step_output(outputs)
        if self._regression:
            self._log_scaler_reg(outputs['y'], outputs['pred'], stage='val')
        else:
            self._log_scaler_cls(outputs['y'], outputs['pred'], stage='val')
        if self.log_plot:  
            self._log_plot_reg(outputs) if self._regression else self._log_plot_cls(outputs)
def validation_epoch_end ( self , outputs : Dict [ str , Any ] ) -> None : if self . global_step == 0 : return outputs = _concat_step_output ( outputs ) if self . _regression : self . _log_scaler_reg ( outputs [ 'y' ] , outputs [ 'pred' ] , stage = 'val' ) else : self . _log_scaler_cls ( outputs [ 'y' ] , outputs [ 'pred' ] , stage = 'val' ) if self . log_plot : self . _log_plot_reg ( outputs ) if self . _regression else self . _log_plot_cls ( outputs )
Aggregate validation step logging information and plot.
Aggregate validation step logging information and plot .
"""Aggregate validation step logging information and plot.""" # overlay train and val visualization together
self
outputs
Dict[str, Any]