nessViewer

NessViewer

nessViewer is an open-source software for visualizing neural network architectures. It allows users to easily create, edit, and visualize neural network graphs with automatic layouts.
nessViewer image
neural-networks visualization graphing

nessViewer: Open-Source Neural Network Architecture Visualizer

nessViewer is an open-source software for visualizing neural network architectures. It allows users to easily create, edit, and visualize neural network graphs with automatic layouts.

What is NessViewer?

nessViewer is an open-source application for visualizing neural network architectures. It provides an intuitive graphical user interface that allows users to easily create, edit, and visualize neural network models.

Some key features of nessViewer include:

  • Drag-and-drop interface for building neural network graphs
  • Support for all common neural network layers including convolutional, recurrent, dense, etc.
  • Automatic graph layouts using dagre library for clean visualizations
  • Ability to customize nodes and connections with colors, labels, and more
  • Export images and share neural network diagrams
  • Cross-platform support for Windows, Mac, and Linux

nessViewer enables machine learning engineers, researchers, and students to prototype, understand, troubleshoot, and communicate complex neural network architectures with ease. Its simple yet flexible graphical interface accelerates neural network development and makes it easier to visualize and share innovations in deep learning.

NessViewer Features

Features

  1. Visualize neural network architectures
  2. Automatic layout of neural network graphs
  3. Create and edit neural network graphs
  4. Support for common neural network layers and architectures
  5. Export images and graphs

Pricing

  • Open Source

Pros

Open source and free to use

Intuitive and easy to use interface

Allows quick prototyping of neural network architectures

Platform independent

Cons

Limited customization options

Does not support training or running neural networks

Lacks some advanced features like hyperparameter tuning


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