Tools to Design or Visualize Architecture of Neural Network
draw_convnet : Python script for illustrating Convolutional Neural Network (ConvNet)
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:
keras-sequential-ascii - A library for Keras for investigating architectures and parameters of sequential models.
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.
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.
github地址：https://github.com/ashishpatel26/Tools-to-Design-or-Visualize-Architecture-of-Neural-Network