您可以使用TensorBoard的Python类或脚本来提取数据：
如何从TensorBoard导出数据？
如果您想导出数据以在其他地方可视化（例如iPython
Notebook），那也是可能的。您可以直接依赖TensorBoard用来加载数据的基础类：（用于python/summary/event_accumulator.py从一次运行python/summary/event_multiplexer.py中加载数据）或（用于从多个运行中加载数据并保持其组织化）。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。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，因此其边缘有些粗糙。
使用
EventAccumulator：
# In [1]: from tensorflow.python.summary import event_accumulator # deprecatedIn [1]: from tensorboard.backend.event_processing import event_accumulatorIn [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 fileOut[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.
