# RNNbow: Visualizing Learning via Backpropagation Gradients in Recurrent Neural Networks

## TL;DR

RNNbow is an interactive web tool that visualizes backpropagation gradients in RNNs, providing insights into learning dynamics, vanishing gradients, and training evolution during sequence processing.

## Contribution

It introduces a novel visualization method for backpropagation gradients in RNNs, enhancing understanding of training processes and gradient flow.

## Key findings

- Reveals vanishing gradient issues during training.
- Shows how gradients evolve as RNN processes sequences.
- Provides insights into RNN learning of code generation.

## Abstract

We present RNNbow, an interactive tool for visualizing the gradient flow during backpropagation training in recurrent neural networks. RNNbow is a web application that displays the relative gradient contributions from Recurrent Neural Network (RNN) cells in a neighborhood of an element of a sequence. We describe the calculation of backpropagation through time (BPTT) that keeps track of itemized gradients, or gradient contributions from one element of a sequence to previous elements of a sequence. By visualizing the gradient, as opposed to activations, RNNbow offers insight into how the network is learning. We use it to explore the learning of an RNN that is trained to generate code in the C programming language. We show how it uncovers insights into the vanishing gradient as well as the evolution of training as the RNN works its way through a corpus.