(self, nn, train_spec, eval_spec)
train and eval for one epoch
train and eval for one epoch
train and eval for one epoch
def train_and_eval_one_epoch(self, nn, train_spec, eval_spec):
    ''' train and eval for one epoch '''
    eval_result, export_result = tf.estimator.train_and_evaluate(  #pylint: disable=no-member
        nn, train_spec, eval_spec)
    logging.info("Export result:{}".format(export_result))
    self.log_eval_metrics(eval_result)
def train_and_eval_one_epoch ( self , nn , train_spec , eval_spec ) : eval_result , export_result = tf . estimator . train_and_evaluate ( #pylint: disable=no-member nn , train_spec , eval_spec ) logging . info ( "Export result:{}" . format ( export_result ) ) self . log_eval_metrics ( eval_result )