TensorBoard PR Curve
Trains is now ClearML
This documentation applies to the legacy Trains versions. For the latest documentation, see ClearML.
The tensorboard_pr_curve.py example demonstrates the integration of Trains into code which uses TensorFlow and TensorBoard. It creates three classes, R, G, and B, and generates colors within the RGB space from normal distributions. The true label of each random color is associated with the normal distribution that generated it. Using three other normal distributions, the example code computes the probability that each color belongs to the class. The example uses those probabilities to generate PR curves. Using tensorboard.plugins.pr_curve.summary, the example code create a summary per class. Trains automatically logs the TensorBoard output, as well as the TensorFlow DEFINES and output to the console. When the script runs, it creates an experiment named
tensorboard pr_curve, which is associated with the
In the Trains Web (UI), the PR Curve summaries appears in RESULTS > PLOTS.
- Blue PR curves
- Green PR curves
- Red PR curves
Trains automatically logs TensorFlow DEFINEs. They appear in CONFIGURATIONS > HYPER PARAMETERS > TF_DEFINE.
All other console output appears in RESULTS > LOG.