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Neural networks have emerged as the backbone of modern machine learning and AI applications, revolutionising industries across the board. However, building a successful neural network requires more than just a good idea; it demands careful architecture design and visualization. In this Kaggle post, we will explore a curated list of tools and techniques that can help you design and visualize neural network architectures like a pro.
Net2Vis automatically generates abstract visualizations for convolutional neural networks from Keras code.
Visualkeras is a Python package to help visualize Keras & Tensorflow neural network architectures. It allows easy styling to fit most needs. This module supports layered-style architecture generation which is great for convolutional neural networks, and a graph-style architecture, which works great for most models including plain feed-forward networks.
Python script for illustrating convolutional neural networks.
Publication-ready NN-architecture schematics.
Latex code for drawing neural networks for reports and presentations.
A powerful tool for examining your TensorFlow model. You can quickly view a conceptual graph of your model’s structure and ensure it matches your intended design. You can also view top-level graphs to understand how TensorFlow understands your program.
A powerful tool for examining your Caffe model.
Netron is a viewer for neural networks, deep learning and machine learning models. Netron supports ONNX, TensorFlow Lite, Core ML, Keras, Caffe, Darknet, MXNet, PaddlePaddle, ncnn, MNN and TensorFlow.js.
A simple Python script to generate pictures of a feed-forward neural network using Python and Graphviz
Graphviz is open-source graph visualization software. Graph visualization is a way of representing structural information as diagrams of abstract graphs and networks. It has important applications in networking, bioinformatics, software engineering, database and web design, machine learning, and visual interfaces for other technical domains.
keras.utils.vis_utils module provides utility functions to plot a Keras model using Graphviz.
Implements Deep Learning neural network algorithms using a simple interface with easy visualizations and useful analytics. Built on top of Keras, which can use either TensorFlow, Theano, or CNTK.
Working on a drag-and-drop neural network visualizer (and more). Here’s an example of a visualization for a LeNet-like architecture.
These approaches are more oriented towards visualizing neural network operation, however, NN architecture is also somewhat visible in the resulting diagrams.
Neataptic offers flexible neural networks; neurons and synapses can be removed with a single line of code. No fixed architecture is required for neural networks to function at all. This flexibility allows networks to be shaped for your dataset through neuro-evolution, which is done using multiple threads.
TensorSpace is a neural network 3D visualization framework built using TensorFlow.js, Three.js and Tween.js. TensorSpace provides Keras-like APIs to build deep learning layers, load pre-trained models, and generate a 3D visualization in the browser. From TensorSpace, it is intuitive to learn what the model structure is, how the model is trained and how the model predicts the results based on the intermediate information. After preprocessing the model, TensorSpace supports to visualisation pre-trained model from TensorFlow, Keras and TensorFlow.js.
A web-based tool for visualizing and analyzing convolutional neural network architectures (or technically, any directed acyclic graph).
Human-friendly declarative dataflow notation for computational graphs.
Latex-based visualizer for any neural network
Quiver Interactive ConvNet features visualization for Keras
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