您如何以编程方式读取Tensorboard文件? [英] How do you read Tensorboard files programmatically?

问题描述

如何编写Python脚本来读取Tensorboard日志文件，提取损失和准确性以及其他数字数据，而无需启动GUI tensorboard --logdir=...?

How can you write a python script to read Tensorboard log files, extracting the loss and accuracy and other numerical data, without launching the GUI tensorboard --logdir=...?

推荐答案

您可以使用TensorBoard的Python类或脚本来提取数据:

You can use TensorBoard's Python classes or script to extract the data:

如何从TensorBoard导出数据?

如果您想导出数据以在其他地方(例如iPython Notebook)进行可视化，那也是可能的.您可以直接依赖TensorBoard用于加载数据的基础类:python/summary/event_accumulator.py(用于从单个运行中加载数据)或python/summary/event_multiplexer.py(用于从多个运行中加载数据并保持数据有序).这些类加载事件文件组，丢弃由TensorFlow崩溃孤立"的数据，并按标记组织数据.

If you'd like to export data to visualize elsewhere (e.g. iPython Notebook), that's possible too. You can directly depend on the underlying classes that TensorBoard uses for loading data: python/summary/event_accumulator.py (for loading data from a single run) or python/summary/event_multiplexer.py (for loading data from multiple runs, and keeping it organized). These classes load groups of event files, discard data that was "orphaned" by TensorFlow crashes, and organize the data by tag.

另一种选择是，有一个脚本(tensorboard/scripts/serialize_tensorboard.py)可以像TensorBoard一样加载日志目录，但是将所有数据作为json写入磁盘，而不是启动服务器.设置该脚本是为了制作假TensorBoard后端"进行测试，因此边缘有些粗糙.

As another option, there is a script (tensorboard/scripts/serialize_tensorboard.py) which will load a logdir just like TensorBoard does, but write all of the data out to disk as json instead of starting a server. This script is setup to make "fake TensorBoard backends" for testing, so it is a bit rough around the edges.

使用 EventAccumulator :

# In [1]: from tensorflow.python.summary import event_accumulator  # deprecated
In [1]: from tensorboard.backend.event_processing import event_accumulator

In [2]: ea = event_accumulator.EventAccumulator('events.out.tfevents.x.ip-x-x-x-x',
   ...:  size_guidance={ # see below regarding this argument
   ...:      event_accumulator.COMPRESSED_HISTOGRAMS: 500,
   ...:      event_accumulator.IMAGES: 4,
   ...:      event_accumulator.AUDIO: 4,
   ...:      event_accumulator.SCALARS: 0,
   ...:      event_accumulator.HISTOGRAMS: 1,
   ...:  })

In [3]: ea.Reload() # loads events from file
Out[3]: <tensorflow.python.summary.event_accumulator.EventAccumulator at 0x7fdbe5ff59e8>

In [4]: ea.Tags()
Out[4]: 
{'audio': [],
 'compressedHistograms': [],
 'graph': True,
 'histograms': [],
 'images': [],
 'run_metadata': [],
 'scalars': ['Loss', 'Epsilon', 'Learning_rate']}

In [5]: ea.Scalars('Loss')
Out[5]: 
[ScalarEvent(wall_time=1481232633.080754, step=1, value=1.6365480422973633),
 ScalarEvent(wall_time=1481232633.2001867, step=2, value=1.2162202596664429),
 ScalarEvent(wall_time=1481232633.3877788, step=3, value=1.4660096168518066),
 ScalarEvent(wall_time=1481232633.5749283, step=4, value=1.2405034303665161),
 ScalarEvent(wall_time=1481232633.7419815, step=5, value=0.897326648235321),
 ...]

size_guidance :

size_guidance: Information on how much data the EventAccumulator should
  store in memory. The DEFAULT_SIZE_GUIDANCE tries not to store too much
  so as to avoid OOMing the client. The size_guidance should be a map
  from a `tagType` string to an integer representing the number of
  items to keep per tag for items of that `tagType`. If the size is 0,
  all events are stored.

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