In situ TensorView: In situ Visualization of Convolutional Neural Networks

TL;DR

In situ TensorView offers real-time, in situ visualization of CNN training and operation, enabling better understanding and optimization of neural networks with minimal code changes.

Contribution

It introduces a loosely coupled, in situ visualization framework integrated with TensorFlow, inspired by scientific visualization tools like Paraview, for CNNs.

Findings

Visualized training of LeNet-5 and VGG16.

Reduced I/O overhead during visualization.

Provided insights for network architecture adjustments.

Abstract

Convolutional Neural Networks(CNNs) are complex systems. They are trained so they can adapt their internal connections to recognize images, texts and more. It is both interesting and helpful to visualize the dynamics within such deep artificial neural networks so that people can understand how these artificial networks are learning and making predictions. In the field of scientific simulations, visualization tools like Paraview have long been utilized to provide insights and understandings. We present in situ TensorView to visualize the training and functioning of CNNs as if they are systems of scientific simulations. In situ TensorView is a loosely coupled in situ visualization open framework that provides multiple viewers to help users to visualize and understand their networks. It leverages the capability of co-processing from Paraview to provide real-time visualization during training and predicting phases. This avoid heavy I/O overhead for visualizing large dynamic systems. Only a small number of lines of codes are injected in TensorFlow framework. The visualization can provide guidance to adjust the architecture of networks, or compress the pre-trained networks. We showcase visualizing the training of LeNet-5 and VGG16 using in situ TensorView.