Tensorboard：如何累积摘要

Question

是否可以累积评估/测试集的tf.summary数据？ （以非黑客的方式）

非hacky我的意思是比我目前的解决方案更聪明：

# init writer
writer = tf.summary.FileWriter(path, graph)

# build model
...

# add stuff
for v in tf.trainable_variables():
    tf.summary.histogram(v.name, v)
tf.summary.scalar("loss", loss)
...

# merge summary
merged = tf.summary.merge_all()

# during training --- everything fine, since we operate per mini-batch only
summary, _ = session.run([merged, optimizer_op],
                         feed_dict={X: train_batch, Y: train_batch_labels}

writer.add_summary(summary, train_step)


# test eval
# here it does get ugly, because we need to buffer every mini-batch
# in the whole test set in order to get accu, loss, ... for the whole
# test set and not only per mini-batch
for batch in test_data:
    loss, accuracy = session.run([lossop, accuop],
                                 feed_dict={X: batch, Y: batch_labels})
    buffer_loss.append(loss)
    buffer_accuracy.append(accuracy)

# this includes a new filewriter for test evaluation
# plus a new operation that calcs the mean over both buffers
# plus a new summary for the calculated means
# plus writing that data

虽然这有效但它也是一个糟糕的解决方案，因为我必须为测试集评估重新创建摘要数据，并在Python本身中使用外部缓冲区迭代整个测试集的每个小批量，只是为了将完成的缓冲区传递回TensorFlow，最终获得所有测试批次的平均值。