NN-SVG | Publication-ready NN-architecture schematics | Machine Learning library
kandi X-RAY | NN-SVG Summary
kandi X-RAY | NN-SVG Summary
| Docs | Contributing. Illustrations of Neural Network architectures are often time-consuming to produce, and machine learning researchers all too often find themselves constructing these diagrams from scratch by hand. NN-SVG is a tool for creating Neural Network (NN) architecture drawings parametrically rather than manually. It also provides the ability to export those drawings to Scalable Vector Graphics (SVG) files, suitable for inclusion in academic papers or web pages. The tool provides the ability to generate figures of three kinds: classic Fully-Connected Neural Network (FCNN) figures, Convolutional Neural Network (CNN) figures of the sort introduced in the LeNet paper, and Deep Neural Network figures following the style introduced in the AlexNet paper. The former two are accomplished using the D3 javascript library and the latter with the javascript library Three.js. NN-SVG provides the ability to style the figure to the user's liking via many size, color, and layout parameters. I hope this tool will save machine learning researchers time, and I hope this software might also serve as a pedagogical tool in some contexts.
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Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of NN-SVG
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QUESTION
I would like to make a visualisation of a neural network and the line weight and the colour of the line between the nodes need to correspond to the values of the weights, (positive -> red , negative -> blue and the thickness of the line should change based on the absolute value) very similar to the NN-SVG illustration below.
I have been able to create the neural network using and show the weights value, shown in the code and result image below. How can I change the code to be to get the plot as above.
...ANSWER
Answered 2021-Mar-01 at 10:13The answer depends on how you want to normalize your weights. For example, if you want each layer of the network to be normalized you can do something that looks like this:
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