athena.utils.metric_check

MetricChecker

Module Contents

Classes

MetricChecker

Hold and save best metric checkpoint

class athena.utils.metric_check.MetricChecker(optimizer)

Hold and save best metric checkpoint :param name: MetricChecker name :param maximum: more greater more better

__call__(self, loss, metrics, evaluate_epoch=-1)

summary the basic metrics like loss, lr :param loss: :param matrics: average loss of all previous steps in one epoch

if training is False, it must be provided

Parameters:evaluate_epoch – if evaluate_epoch >= 0: <evaluate mode> if evaluate_epoch == -1: <train mode> if evaluate_epoch < -1: <evaluate_log mode> (no tf.summary.write) Returns:return average and best(if improved) loss if training is False Return type:logging_str

summary_train(self, loss, metrics)

generate summary of learning_rate, loss, metrics, speed and write on Tensorboard

summary_evaluate(self, loss, metrics, epoch=-1)

If epoch > 0, return a summary of loss and metrics on dev set and write on Tensorboard Otherwise, just return evaluate loss and metrics