Summary

In this chapter, we developed some TensorFlow models. We looked at TensorBoard, which is a great tool for visualizing and debugging deep learning models. We built a couple of models using TensorFlow, including a basic regression model and a Lenet model for computer vision models. From these examples, we saw that programming in TensorFlow was more complicated and error-prone than using the higher-level APIs (MXNet and Keras) that we used elsewhere in this book.

We then moved onto using TensorFlow estimators, which is a much easier interface than using TensorFlow. We then used that script in another package called tfruns, which stands for TensorFlow runs. This package allows us to call a TensorFlow estimators or Keras script with different flags each time. We used this for hyper-parameter selection, running, and evaluating multiple models. The TensorFlow runs have excellent integration with RStudio and we were able to view summaries for each run and compare runs to see the difference...