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After following this tutorial on summaries and TensorBoard, I've been able to successfully save and look at data with TensorBoard. Is it possible to open this data with something other than TensorBoard?
By the way, my application is to do off-policy learning. I'm currently saving each state-action-reward tuple using SummaryWriter. I know I could manually store/train on this data, but I thought it'd be nice to use TensorFlow's built in logging features to store/load this data.
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If you are using python logging in your project, one of the option will be to define the logger with name "tensorflow" in a logging config file. Then _logger = _logging. getLogger('tensorflow') will use the logger and specified handlers from your config file.
Overview. Using the TensorFlow Text Summary API, you can easily log arbitrary text and view it in TensorBoard. This can be extremely helpful to sample and examine your input data, or to record execution metadata or generated text.
Visualize the Computational Graph First, you will see what the computational graph of your model looks like. You can access this view by clicking on the Graphs view on in TensorBoard.
Just check the "Data download links" option on the upper-left in TensorBoard, and then click on the "CSV" button that will appear under your scalar summary.
As of March 2017, the EventAccumulator tool has been moved from Tensorflow core to the Tensorboard Backend. You can still use it to extract data from Tensorboard log files as follows:
from tensorboard.backend.event_processing.event_accumulator import EventAccumulator event_acc = EventAccumulator('/path/to/summary/folder') event_acc.Reload() # Show all tags in the log file print(event_acc.Tags()) # E. g. get wall clock, number of steps and value for a scalar 'Accuracy' w_times, step_nums, vals = zip(*event_acc.Scalars('Accuracy')) If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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