From Tinkering to Engineering: Measurements in Tensorflow Playground
Henrik Hoeiness, Axel Harstad, Gerald Friedland

TL;DR
This paper introduces TFMeter, an extension of Tensorflow Playground that visualizes neural network architectures along with information-theoretic measurements to enhance understanding and reproducibility in neural network design.
Contribution
The paper presents TFMeter, a novel interactive tool that integrates measurements into neural network visualization, aiding better engineering intuition and experimental reproducibility.
Findings
Provides real-time measurements during network construction
Enhances understanding of neural network learning dynamics
Available online for public use
Abstract
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…
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Anomaly Detection Techniques and Applications · Neural Networks and Applications
