Enables single machine or distributed training and evaluation of deep learning models.
Open source
24 alternativesLearnergy
Use Learnergy, a Python library for training and working with energy-based machine learning models.
About Learnergy
Learnergy is a Python library with PyTorch implementations of Restricted Boltzmann Machines and Deep Belief Networks for unsupervised feature learning, generative modeling, and classification. It includes Bernoulli, Gaussian, convolutional, deep, and recurrent temporal variants, plus dataset adapters, image-quality metrics, and visualization helpers.
It requires Python 3.11 or newer and installs with uv or pip, with an optional torchvision dependency for examples. Temporal models take floating-point tensors shaped (batch_size, sequence_length, n_visible), and enabled Gaussian normalization uses current batch statistics, so representations depend on batch composition.
Key features
- PyTorch RBM variants including Bernoulli, Gaussian, and convolutional types
- Deep belief networks including ConvDBN and ResidualDBN stacking
- Temporal RBM and DBN models for sequence data
- Dataset adapters for array-backed datasets
- Image-quality metrics including SSIM and scaling helpers
- Visualization helpers for convergence plots and image mosaics
Best for: Python developers working with RBMs, DBNs, and temporal energy-based models in PyTorch.
Good for
- Open-source Python library with PyTorch implementations
- Covers multiple RBM, DBN, and temporal model variants
- Includes example programs for training and classification
Watch out for
- Requires Python 3.11 or newer
- Examples require optional torchvision dependency
- Enabled Gaussian normalization uses current batch statistics, so representations depend on batch composition
Learnergy at a glance
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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Learnergy FAQ
How do I install Learnergy?
It requires Python 3.11 or newer. Use "uv add learnergy" or "pip install learnergy", and add the "examples" extra with torchvision to run the examples.
What input shape do temporal models expect?
Floating-point tensors shaped (batch_size, sequence_length, n_visible), with dataset items pairing a sequence with an ignored target.
Which model families are available?
Bernoulli, Gaussian, Sigmoid, deep DBN variants, and temporal variants including RTRBM and RTDBN.
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