xRBM
Library for Restricted Boltzmann Machines in TensorFlow
- Status
- UnmaintainedNo commits in 3297 days.
- Last checked
- Checked · Oct 12, 2026
- License
- Open source
- Pricing
- Free tier · from $0
What it is
A library for implementing Restricted Boltzmann Machine and its conditional variants using TensorFlow. It is useful for deep learning tasks requiring generative models.
xRBM offers a specialized library for implementing Restricted Boltzmann Machines in TensorFlow, providing flexibility and customization options that are not readily available in more general-purpose deep learning frameworks.
When it fits — and when it doesn’t
A good fit for
Researchers working on generative modeling who need a flexible RBM implementation in TensorFlow
Developers building custom deep learning architectures that require RBMs as components
Look elsewhere if
Teams looking for out-of-the-box solutions without the need to customize models
Projects requiring real-time inference with minimal latency
Known weaknesses
Each one links to the public issue, changelog or docs page it came from, where we have one.
- 01
Limited documentation and examples for advanced use cases
Official documentation lacks detailed guides on customizing RBMs beyond basic usage
- 02
Performance issues with large datasets
Training times significantly increase and can be prohibitive for very large datasets due to TensorFlow's overhead
What it does well
Implementation of Restricted Boltzmann Machines in TensorFlow
Support for conditional variants of RBMs
Flexibility to customize and extend models
Pricing
- Free tier
- YesOpen source — free to use
- Paid from
- $0
- Model
- Flat rate
- Enterprise plan
- No
Pricing not independently confirmed — check the vendor’s page.
Performance benchmarks
How Fast Is It?
Set up xRBM
Setup steps, common gotchas and what to check after 30 days.
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