trainer.py 文件源码

def _initialize_metrics(self):
        """ Initialize the model metrics """
        self.metrics = {}
        self.metric_values = {}
        self.update_metrics = {}
        self.reset_metrics = {}
        for data_scope in (Data.TRAIN, Data.VALIDATE, Data.TEST):
            metrics = self.collect_metrics(data_scope)
            self.metrics[data_scope] = metrics

            self.metric_values[data_scope] = {
                name: metric['scalar']
                for name, metric in iteritems(metrics)}

            self.update_metrics[data_scope] = [
                metric['update_op']
                for metric in itervalues(metrics)]

            metric_variables = []
            with stats_utils.metric_scope(data_scope, graph=self.graph) as scope:
                for local in tf.get_collection(tf.GraphKeys.LOCAL_VARIABLES, scope):
                    metric_variables.append(local)
            self.reset_metrics[data_scope] = tf.variables_initializer(metric_variables)