如何使用 Tensorboard 在同一图上绘制不同的汇总指标？

我希望能够绘制每批次训练损失和average验证损失用于 Tensorboard 中同一图上的验证集。当我的验证集太大而无法放入内存时，我遇到了这个问题，因此需要批处理并使用tf.metrics更新操作。

这个问题可能适用于您想要显示在 Tensorboard 中同一图表上的任何 Tensorflow 指标。

我能够

• 分别绘制这两个图（参见here https://i.stack.imgur.com/o3fLM.png)
• 绘制每个验证批次的验证损失在同一张图上每个训练批次的训练损失（当验证集可以是单个批次并且我可以重用训练摘要操作时，这是可以的train_summ below)

在下面的示例代码中，我的问题源于我的验证摘要tf.summary.scalar with name=loss被重命名为loss_1因此被移动到 Tensorboard 中的单独图表中。据我所知，Tensorboard 需要“一样的名字”并将它们绘制在同一个图表上，无论它们位于哪个文件夹中。这令人沮丧，因为train_summ(name=loss) 只被写入train文件夹和valid_summ(name=loss) 只被写入valid文件夹 - 但仍重命名为loss_1.

示例代码：

# View graphs with (Linux): $ tensorboard --logdir=/tmp/my_tf_model

import tensorflow as tf
import numpy as np
import os
import tempfile

def train_data_gen():
    yield np.random.normal(size=[3]), np.array([0.5, 0.5, 0.5])

def valid_data_gen():
    yield np.random.normal(size=[3]), np.array([0.8, 0.8, 0.8])

batch_size = 25
n_training_batches = 4
n_valid_batches = 2
n_epochs = 5
summary_loc = os.path.join(tempfile.gettempdir(), 'my_tf_model')
print("Summaries written to" + summary_loc)

# Dummy data
train_data = tf.data.Dataset.from_generator(train_data_gen, (tf.float32, tf.float32)).repeat().batch(batch_size)
valid_data = tf.data.Dataset.from_generator(valid_data_gen, (tf.float32, tf.float32)).repeat().batch(batch_size)
handle = tf.placeholder(tf.string, shape=[])
iterator = tf.data.Iterator.from_string_handle(handle, 
train_data.output_types, train_data.output_shapes)
batch_x, batch_y = iterator.get_next()
train_iter = train_data.make_initializable_iterator()
valid_iter = valid_data.make_initializable_iterator()

# Some ops on the data
loss = tf.losses.mean_squared_error(batch_x, batch_y)
valid_loss, valid_loss_update = tf.metrics.mean(loss)

# Write to summaries
train_summ = tf.summary.scalar('loss', loss)
valid_summ = tf.summary.scalar('loss', valid_loss)  # <- will be renamed to "loss_1"

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    train_handle, valid_handle = sess.run([train_iter.string_handle(), valid_iter.string_handle()])
    sess.run([train_iter.initializer, valid_iter.initializer])

    # Summary writers
    writer_train = tf.summary.FileWriter(os.path.join(summary_loc, 'train'), sess.graph)
    writer_valid = tf.summary.FileWriter(os.path.join(summary_loc, 'valid'), sess.graph)

    global_step = 0  # implicit as no actual training
    for i in range(n_epochs):
        # "Training"
        for j in range(n_training_batches):
            global_step += 1
            summ = sess.run(train_summ, feed_dict={handle: train_handle})
            writer_train.add_summary(summary=summ, global_step=global_step)
        # "Validation"
        sess.run(tf.local_variables_initializer())
        for j in range(n_valid_batches):
             _, batch_summ = sess.run([valid_loss_update, train_summ], feed_dict={handle: valid_handle})
            # The following will plot the batch loss for the validation set on the loss plot with the training data:
            # writer_valid.add_summary(summary=batch_summ, global_step=global_step + j + 1)
        summ = sess.run(valid_summ)
        writer_valid.add_summary(summary=summ, global_step=global_step)  # <- I want this on the training loss graph

我尝试过的

• 分离tf.summary.FileWriter对象（一个用于训练，一个用于验证），如推荐的这个问题 https://github.com/tensorflow/tensorflow/issues/7089 and 这个问题 https://stackoverflow.com/questions/48951136/plot-multiple-graphs-in-one-plot-using-tensorboard?rq=1（认为​​我所追求的内容在该问题的评论中有所提及）
• 指某东西的用途tf.summary.merge将我的所有训练和验证/测试指标合并到总体摘要操作中；做有用的簿记，但没有在同一张图表上绘制我想要的内容
• 使用tf.summary.scalar family属性 （loss仍然被重命名为loss_1)
• （完整的黑客解决方案） Use valid_loss, valid_loss_update = tf.metrics.mean(loss) on the training数据然后运行tf.local_variables_initializer()每个训练批次。这确实为您提供了相同的摘要操作，从而将事物放在同一个图表上，但肯定不是您的方式meant去做这个？它也不能推广到其他指标。

Context

• 张量流1.9.0
• 张量板1.9.0
• Python 3.5.2

The 张量板custom_scalar plugin https://github.com/tensorflow/tensorboard/tree/master/tensorboard/plugins/custom_scalar是解决这个问题的方法。

这是同样的例子custom_scalar在同一个图上绘制两个损失（每个训练批次+所有验证批次的平均值）：

# View graphs with (Linux): $ tensorboard --logdir=/tmp/my_tf_model

import os
import tempfile
import tensorflow as tf
import numpy as np
from tensorboard import summary as summary_lib
from tensorboard.plugins.custom_scalar import layout_pb2

def train_data_gen():
    yield np.random.normal(size=[3]), np.array([0.5, 0.5, 0.5])

def valid_data_gen():
    yield np.random.normal(size=[3]), np.array([0.8, 0.8, 0.8])

batch_size = 25
n_training_batches = 4
n_valid_batches = 2
n_epochs = 5
summary_loc = os.path.join(tempfile.gettempdir(), 'my_tf_model')
print("Summaries written to " + summary_loc)

# Dummy data
train_data = tf.data.Dataset.from_generator(
    train_data_gen, (tf.float32, tf.float32)).repeat().batch(batch_size)
valid_data = tf.data.Dataset.from_generator(
    valid_data_gen, (tf.float32, tf.float32)).repeat().batch(batch_size)
handle = tf.placeholder(tf.string, shape=[])
iterator = tf.data.Iterator.from_string_handle(handle, train_data.output_types,
                                               train_data.output_shapes)
batch_x, batch_y = iterator.get_next()
train_iter = train_data.make_initializable_iterator()
valid_iter = valid_data.make_initializable_iterator()

# Some ops on the data
loss = tf.losses.mean_squared_error(batch_x, batch_y)
valid_loss, valid_loss_update = tf.metrics.mean(loss)

with tf.name_scope('loss'):
    train_summ = summary_lib.scalar('training', loss)
    valid_summ = summary_lib.scalar('valid', valid_loss)

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    train_handle, valid_handle = sess.run([train_iter.string_handle(), valid_iter.string_handle()])
    sess.run([train_iter.initializer, valid_iter.initializer])

    writer_train = tf.summary.FileWriter(os.path.join(summary_loc, 'train'), sess.graph)
    writer_valid = tf.summary.FileWriter(os.path.join(summary_loc, 'valid'), sess.graph)

    layout_summary = summary_lib.custom_scalar_pb(
        layout_pb2.Layout(category=[
            layout_pb2.Category(
                title='losses',
                chart=[
                    layout_pb2.Chart(
                        title='losses',
                        multiline=layout_pb2.MultilineChartContent(tag=[
                            'loss/training', 'loss/valid'
                        ]))
                ])
        ]))
    writer_train.add_summary(layout_summary)

    global_step = 0
    for i in range(n_epochs):
        for j in range(n_training_batches): # "Training"
            global_step += 1
            summ = sess.run(train_summ, feed_dict={handle: train_handle})
            writer_train.add_summary(summary=summ, global_step=global_step)

        sess.run(tf.local_variables_initializer())
        for j in range(n_valid_batches):  # "Validation"
            _, batch_summ = sess.run([valid_loss_update, train_summ], feed_dict={handle: valid_handle})
        summ = sess.run(valid_summ)
        writer_valid.add_summary(summary=summ, global_step=global_step)

这是结果输出 https://i.stack.imgur.com/Ee1ky.png在张量板上。