This post is the second of a series; click here for the previous post, or here for a list of all posts in this series.
Naming and Scoping Naming Variables and Tensors As we discussed in Part 1, every time you call tf.get_variable(), you need to assign the variable a new, unique name. Actually, it goes deeper than that: every tensor in the graph gets a unique name too. The name can be accessed explicitly with the .
On August 5th, OpenAI successfully defeated top human players in a Dota 2 best-of-three series. Their AI Dota agent, called OpenAI Five, was a deep neural network trained using reinforcement learning. As a researcher studying deep reinforcement learning, as well as a long-time follower of competitive Dota 2, I found this face-off really interesting. The eventual OAI5 victory was both impressive and well-earned - congrats to the team at OpenAI, and props to the humans for a hard-fought battle!
In Tensorflow: The Confusing Parts (1), I described the abstractions underlying Tensorflow at a high level in an intuitive manner. In this follow-up post, I dig more deeply, and examine how these abstractions are actually implemented. Understanding these implementation details isn’t necessarily essential to writing and using Tensorflow, but it allows us to inspect and debug computational graphs.
Inspecting Graphs The computational graph is not just a nebulous, immaterial abstraction; it is a computational object that exists, and can be inspected.
This post is the first of a series; click here for the next post, or here for a list of all posts in this series.
Click here to skip the intro and dive right in!
Introduction What is this? Who are you? I’m Jacob, a Google AI Resident. When I started the residency program in the summer of 2017, I had a lot of experience programming, and a good understanding of machine learning, but I had never used Tensorflow before.