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...
