python - 你如何以编程方式读取 Tensorboard 文件？

Question

如何在不启动 GUI tensorboard --logdir=... 的情况下编写 Python 脚本来读取 Tensorboard 日志文件、提取损失和准确率以及其他数值数据？

Answer 1

您可以使用 TensorBoard 的 Python 类或脚本来提取数据:

How can I export data from TensorBoard?

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.

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.