Tools to Design or Visualize Architecture of Neural Network draw_convnet : Python script for illustrating Convolutional Neural Network (ConvNet) NNSVG PlotNeuralNet : Latex code for drawing neural networks for reports and presentation. Have a look into examples to see how they are made. Additionally, lets consolidate any improvements that you make and fix any bugs to help more people with this code. Tensorboard - TensorBoard’s Graphs dashboard is a powerful tool for examining your TensorFlow model. Caffe - In Caffe you can use caffe/draw.py to draw the NetParameter protobuffer: Matlab Keras.js keras-sequential-ascii - A library for Keras for investigating architectures and parameters of sequential models. Netron DotNet Graphviz : Tutorial Keras Visualization - The keras.utils.vis_utils module provides utility functions to plot a Keras model (using graphviz) Conx - The Python package conx can visualize networks with activations with the function net.picture() to produce SVG, PNG, or PIL Images like this: ENNUI - Working on a drag-and-drop neural network visualizer (and more). Here's an example of a visualization for a LeNet-like architecture. NNet - R Package - Tutorial GraphCore - These approaches are more oriented towards visualizing neural network operation, however, NN architecture is also somewhat visible on the resulting diagrams. Neataptic TensorSpace : TensorSpace is a neural network 3D visualization framework built by TensorFlow.js, Three.js and Tween.js. TensorSpace provides Layer APIs to build deep learning layers, load pre-trained models, and generate a 3D visualization in the browser. By applying TensorSpace API, it is more intuitive to visualize and understand any pre-trained models built by TensorFlow, Keras, TensorFlow.js, etc. Netscope CNN Analyzer Monial Texample Quiver github地址： https://github.com/ashishpatel26/Tools-to-Design-or-Visualize-Architecture-of-Neural-Network