The Tensorboard interface is used to visualize data using graphs and other tools to understand, debug, and optimize machine learning models. Tensorboard helps track metrics like loss and accuracy, visualize model graphs, and project embedding to lower-dimensional spaces, among other things.
For machine learning experiments, TensorBoard provides tools such as:
The graphic below shows around the Tensorboard dashboard
The picture below shows the TensorBoard graph visualization panel. There are different tabs in the panel depending on the amount of information you add to the model.
Tensorboard is supported by all algorithms on the AIEX platform. Using Tensorboard, Metrics like loss and accuracy can be tracked and model graphs can be visualized. The figure below demostrates how you can access the Tensorboard on the AIEX platform, which is through “TRAIN RESULT” in “Step 2: train”.
When you open the Tensorboard,
After entering the Tensorboard section, you will see the algorithm’s Tensorboard once they load, as shown in the picture below.
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