Here you will find links to TDNeuron and material on how to use the system to implement machine learning Natively in TouchDesigner

Why use TDNeuron?

There are several deep learning libraries and frameworks available today. Why yet another one, such as TDNeuron? Here are some of the reasons.


TDNeuron was created initially out of our desire to self-learn the rudiments of machine learning. We believe that the best way to fully understand a concept is to build it from scratch. By making our code open-source and enriching it with comments, tutorials and documention, we are trying to open the world of machine learning for the novice ‘regular’ user, though still keeping the doors open to allow for those curious to dive deep into the mathematics and core concepts.

Visual modelling

The modelling interface of TDNeuron is completely TouchDesigner-based. It uses the regular controls and the same patching mechanisms, like those one uses when creating a regular network. In addition, the ‘nodes’ in the model show extended realtime information about the data flowing through the layer. This gives great insights on what the machine is actually learning at any given time or for bug-fixing. Since TouchDesigner is a visually oriented programming environment, there is no need to code a single letter with TDNeuron to build a deep learning model.

GPU powered

Most calculations of the deep learning layers can be done in parallel. TDNeuron uses GLSL (pixel and compute) shaders, to achieve the best possible performance.

Extended built-in layers

The following deep learning layers are currently implemented:

  • Linear
  • Activation (sigmoid, tanh, relu, lrelu, swish, softplus)
  • Multiply, Add, Concat, Split
  • Convolution (1d and 2d)
  • Pool (maximum and average)
  • Flatten
  • Loss (MAE, MSE, Huber, CrossEntropy)
  • Softmax
  • Layer normalization

We have made sure that TDNeuron is as modular as possible, which makes it easy to implement new layers if needed.

TouchDesigner native

Since TDNeuron is fully build from scratch in TouchDesigner, with its own custom-made shaders and UI, everything is native to the program. There are no libraries to import and nothing new to learn outside the scope of TD. This allows you to quickly prototype models without leaving the comfort of your favourite software and to train them and share them with your colleagues, fit to function in your real-time projects.

Commercial licenses available

TDNeuron is released under a GNU Public License v3. If you want to use TDNeuron closed source in commercial projects, please contact, for specific licenses.

Limitations and disclaimer

Here we list some importation limitations of TDNeuron:

  • TDNeuron is Windows only (it uses compute shaders, which are not supported in MacOS
  • The platform state is highly experimental and subjected to many major changes still
  • TDNeuron is by no means state of the art (yet)
  • There are limitations on the quantity of input data (when too big, TouchDesigner cannot load it at once)
  • There are limitations on the lenght of input data (dependant on how big a texture your GPU can handle)
  • As with every ML platform, the quality of input data quality is responsibility of the user.


We look forward to any suggestions on how to improve the system as well as any feedback the community may have. For issues and bug reports please make use of the issue tracker in our Github repository:

Final words

We assume that you are already proficient with TouchDesigner. To get you started with TDNeuron, we offer a help module that you may consult at any point. Along with it, we offer a gentle Introduction to machine learning that you may want to follow as well. In there, we built a simplified version of a neural network with CHOPs, with step by step explanations.

To use TDNeuron you do not necessarily need to know GLSL or shaders, although to make the most out of it you need to have a fair understanding of the basics.

We strongly believe that knowledge should be shared and hope that TDNeuron can help others in their first steps in machine learning. We look forward to learn from you and see how far we can take this platform together.

Tim and Darien,