(self, log_name, log_dict)
Write log to TensorBoard
            log_name  - <str> Name of tensorboard variable 
            log_value - <dict>/<array> Value of variable (e.g. dict of losses), passed if value = None
Write log to TensorBoard
            log_name  - <str> Name of tensorboard variable 
            log_value - <dict>/<array> Value of variable (e.g. dict of losses), passed if value = None
Write log to TensorBoard log_name - <str > Name of tensorboard variable log_value - <dict > / <array > Value of variable ( e . g . dict of losses ) passed if value = None
def write_log(self, log_name, log_dict):
        '''
        Write log to TensorBoard
            log_name  - <str> Name of tensorboard variable 
            log_value - <dict>/<array> Value of variable (e.g. dict of losses), passed if value = None
        '''
        if type(log_dict) is dict:
            log_dict = {key: val for key, val in log_dict.items() if (
                val is not None and not math.isnan(val))}
        if log_dict is None:
            pass
        elif len(log_dict) > 0:
            if 'align' in log_name or 'spec' in log_name:
                img, form = log_dict
                self.log.add_image(
                    log_name, img, global_step=self.step, dataformats=form)
            elif 'text' in log_name or 'hyp' in log_name:
                self.log.add_text(log_name, log_dict, self.step)
            else:
                self.log.add_scalars(log_name, log_dict, self.step)
def write_log ( self , log_name , log_dict ) : if type ( log_dict ) is dict : log_dict = { key : val for key , val in log_dict . items ( ) if ( val is not None and not math . isnan ( val ) ) } if log_dict is None : pass elif len ( log_dict ) > 0 : if 'align' in log_name or 'spec' in log_name : img , form = log_dict self . log . add_image ( log_name , img , global_step = self . step , dataformats = form ) elif 'text' in log_name or 'hyp' in log_name : self . log . add_text ( log_name , log_dict , self . step ) else : self . log . add_scalars ( log_name , log_dict , self . step )