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A unified ensemble framework for PyTorch to improve the performance and robustness of your deep learning model.
| Date | Stars |
|---|---|
| 2026-07-24 | 1038 |
| 2026-07-25 | 1038 |
| 2026-07-28 | 1038 |
| 2026-07-30 | 1038 |
| 2026-07-31 | 1038 |
| 2026-08-06 | 1038 |
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.. image:: ./docs/_images/badge_small.png
|github|_ |readthedocs|_ |codecov|_ |license|_
.. |github| image:: https://github.com/TorchEnsemble-Community/Ensemble-Pytorch/workflows/torchensemble-CI/badge.svg
.. _github: https://github.com/TorchEnsemble-Community/Ensemble-Pytorch/actions
.. |readthedocs| image:: https://readthedocs.org/projects/ensemble-pytorch/badge/?version=latest
.. _readthedocs: https://ensemble-pytorch.readthedocs.io/en/latest/index.html
.. |codecov| image:: https://codecov.io/gh/TorchEnsemble-Community/Ensemble-Pytorch/branch/master/graph/badge.svg?token=2FXCFRIDTV
.. _codecov: https://codecov.io/gh/TorchEnsemble-Community/Ensemble-Pytorch
.. |license| image:: https://img.shields.io/github/license/TorchEnsemble-Community/Ensemble-Pytorch
.. _license: https://github.com/TorchEnsemble-Community/Ensemble-Pytorch/blob/master/LICENSE
Ensemble PyTorch
================
A unified ensemble framework for pytorch_ to easily improve the performance and robustness of your deep learning model. Ensemble-PyTorch is part of the `pytorch ecosystem <https://pytorch.org/ecosystem/>`__, which requires the project to be well maintained.
* `Document <https://ensemble-pytorch.readthedocs.io/>`__
* `Experiment <https://ensemble-pytorch.readthedocs.io/en/stable/experiment.html>`__
Installation
------------
.. code:: bash
pip install torchensemble
Example
-------
.. code:: python
from torchensemble import VotingClassifier # voting is a classic ensemble strategy
# Load data
train_loader = DataLoader(...)
test_loader = DataLoader(...)
# Define the ensemble
ensemble = VotingClassifier(
estimator=base_estimator, # estimator is your pytorch model
n_estimators=10, # number of base estimators
)
# Set the optimizer
ensemble.set_optimizer(
"Adam", # type of parameter optimizer
lr=learning_rate, # learning rate of parameter optimizer
weight_decay=weight_decay, # weight decay of parameter optimizer
)
# Set the learning rate scheduler
ensemble.set_scheduler(
"CosineAnnealingLR", # type of learning rate scheduler
T_max=epochs, # additional arguments on the scheduler
)
# Train the ensemble
ensemble.fit(
train_loader,
epochs=epochs, # number of training epochs
)
# Evaluate the ensemble
acc = ensemble.evaluate(test_loader) # testing accuracy
Supported Ensemble
------------------
+------------------------------+------------+---------------------------+-----------------------------+
| **Ensemble Name** | **Type** | **Source Code** | **Problem** |
+==============================+============+===========================+=============================+
| Fusion | Mixed | fusion.py | Classification / Regression |
+------------------------------+------------+---------------------------+-----------------------------+
| Voting [1]_ | Parallel | voting.py | Classification / Regression |
+------------------------------+------------+---------------------------+-----------------------------+
| Neural Forest | Parallel | voting.py | Classification / Regression |
+------------------------------+------------+---------------------------+-----------------------------+
| Bagging [2]_ | Parallel | bagging.py | Classification / Regression |
+------------------------------+------------+---------------------------+-----------------------------+
| Gradient Boosting [3]_ | Sequential | gradient_boosting.py | Classification / Regression |
+------------------------------+------------+---------------------------+-------------------------Excerpt of 6,006 characters
Read on GitHub
Yi-Xuan XU · Alibaba · China
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:c4aff3630ac592b9, topic:deep-learning, topic:pytorch