Model Hubs & Serving

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.

  1. 01

    Limited documentation and examples for advanced use cases

    Official documentation lacks detailed guides on customizing RBMs beyond basic usage

  2. 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.

Open the setup guide →
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