Learnergy: Energy-based Machine Learners

Learnergy provides PyTorch implementations of Restricted Boltzmann Machines (RBMs) and Deep Belief Networks (DBNs) for unsupervised feature learning, generative modeling, and classification. It also includes dataset adapters, image-quality metrics, and visualization helpers.

Installation

Learnergy requires Python 3.11 or newer.

pip install learnergy

Install the optional torchvision dependency to run the examples:

pip install "learnergy[examples]"

Quick start

import torch
from torch.utils.data import TensorDataset

from learnergy.models.bernoulli import RBM

samples = torch.bernoulli(torch.rand(1_024, 784))
targets = torch.zeros(1_024)
dataset = TensorDataset(samples, targets)

model = RBM(n_visible=784, n_hidden=128, learning_rate=0.1)
mse, pseudo_likelihood = model.fit(dataset, batch_size=128, epochs=5)
reconstruction_mse, reconstructed = model.reconstruct(dataset)

Stack RBMs into a DBN:

from learnergy.models.deep import DBN

model = DBN(
    model=("gaussian", "sigmoid"),
    n_visible=784,
    n_hidden=(256, 128),
    steps=(1, 1),
    learning_rate=(0.01, 0.01),
    momentum=(0, 0),
    decay=(0, 0),
    temperature=(1, 1),
)
model.fit(dataset, batch_size=128, epochs=(5, 5))

Available models

The learnergy.core.Dataset, learnergy.math, and learnergy.visual modules remain available for array-backed datasets, SSIM/scaling helpers, convergence plots, image mosaics, and tensor rendering.

See examples/applications for complete training and classification programs.

Development

The repository uses uv for reproducible environments and packaging:

uv sync --locked
uv run pytest
uv build

Citation

@misc{roder2020learnergy,
    title={Learnergy: Energy-based Machine Learners},
    author={Mateus Roder and Gustavo Henrique de Rosa and João Paulo Papa},
    year={2020},
    eprint={2003.07443},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

Support

Open an issue for bug reports and questions.