segnet_model.py 文件源码

def _add_loss_summaries(self, total_loss):
        """Add summaries for losses in ip5wke model.
        Generates moving average for all losses and associated summaries for
        visualizing the performance of the network.
        Args:
          total_loss: Total loss from loss().
        Returns:
          loss_averages_op: op for generating moving averages of losses.
        """

        # Compute the moving average of all individual losses and the total
        # loss
        loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg')
        losses = tf.get_collection('losses')
        loss_averages_op = loss_averages.apply(losses + [total_loss])

        accuracies = tf.get_collection('accuracies')
        #for a in accuracies:
            #tf.summary.scalar('accuracy', a)

        # Attach a scalar summary to all individual losses and the total loss;
        # do the same for the averaged version of the losses.
        for l in losses + [total_loss]:
            #Name each loss as '(raw)' and name the moving average version of
            #the loss as the original loss name.
            tf.summary.scalar(l.op.name + ' (raw)',
                              tf.where(tf.is_nan(l), 0.0, l))
            tf.summary.scalar(l.op.name, loss_averages.average(l))

        return loss_averages_op