  In this article, we present an extension of the Tensorflow Playground, called
Tensorflow Meter (short TFMeter). TFMeter is an interactive neural network
architecting tool that allows the visual creation of different architectures of
neural networks. In addition to its ancestor, the playground, our tool shows
information-theoretic measurements while constructing, training, and testing
the network. As a result, each change results in a change in at least one of
the measurements, providing for a better engineering intuition of what
different architectures are able to learn. The measurements are derived from
various places in the literature. In this demo, we describe our web application
that is available online at http://tfmeter.icsi.berkeley.edu/ and argue that in
the same way that the original Playground is meant to build an intuition about
neural networks, our extension educates users on available measurements, which
we hope will ultimately improve experimental design and reproducibility in the
field.
