14 TensorBoard: Big brother of TensorFlow

Thus far we have focused on various models. We have talked about fully connected models (e.g., autoencoders), convolutional neural networks, and recurrent neural networks (e.g., LSTMs, GRUs). In chapter 13, we talked about Transformers, a powerful family of deep learning models that have paved the way to a new state-of-the-art performance in language understanding. Furthermore, inspired by the achievements in the field of natural language processing, Transformers are making waves in the computer vision field. We are past the modeling step, but we still have to plough through several more steps to reap the final harvest. One such step is making sure the data/features to the model are correct and the models are working as expected.

14.1 Visualize data with TensorBoard

14.2 Tracking and monitoring models with TensorBoard

14.3 Using tf.summary to write custom metrics during model training

14.4 Profiling models to detect performance bottlenecks

14.4.1 Optimizing the input pipeline

14.4.2 Mixed precision training

14.5 Visualizing word vectors with the TensorBoard

Summary

Answers to exercises