net_model.py 文件源码

def add_loss_summaries(total_loss):
    """
    Generates moving average for all losses and associated summaries for
    visualizing the performance of the network.
    Args:
        total_loss       -> total loss from loss()
    Rtns:
        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])

    # 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)', l)
        tf.summary.scalar(l.op.name,loss_averages.average(l))
    return loss_averages_op