Question

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值数据，而无需启动GUI tensorboard --logdir=...？

Answer 1

您可以使用TensorBoard的Python类或脚本来提取数据：

如果你想将数据导出到其他地方可视化（例如iPython Notebook），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（用于从单次运行加载数据）或python/summary/event_multiplexer.py（用于从多次运行加载数据，并使其保持有序）。这些类加载事件文件组，丢弃TensorFlow崩溃“孤立”的数据，并按标签组织数据。

作为另一种选择，有一个脚本（tensorboard/scripts/serialize_tensorboard.py）将像TensorBoard一样加载logdir，但是将所有数据作为json写入磁盘而不是启动服务器。这个脚本被设置为制作“假TensorBoard后端”进行测试，所以它的边缘有点粗糙。

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.

Answer 2

要完成user1501961的回答，您只需使用pandas pd.DataFrame(ea.Scalars('Loss)).to_csv('Loss.csv')轻松地将标量列表导出到csv文件

你如何以编程方式阅读Tensorboard文件？

2 个答案: