In chapter 14, we looked at a very versatile tool that comes with TensorFlow: the TensorBoard. TensorBoard is a visualization tool that helps you understand data and models better. Among other things, it facilitates
- Monitoring and tracking model performance
- Visualizing data inputs to models (e.g., images, audio)
- Profiling models to understand their performance or memory bottlenecks
We learned how we can use the TensorBoard to visualize high-dimensional data like images and word vectors. We looked at how we can incorporate Keras callbacks to send information to the TensorBoard to visualize model performance (accuracy and loss) and custom metrics. We then analyzed the execution of the model using the CUDA profiling tool kit to understand execution patterns and memory bottlenecks.