A unified ensemble framework for PyTorch to improve the performance and robustness of your deep learning model.
BSD-3-CLAUSE License
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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>
__.. code:: bash
pip install torchensemble
.. 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
+------------------------------+------------+---------------------------+-----------------------------+ | 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 | +------------------------------+------------+---------------------------+-----------------------------+ | Snapshot Ensemble [4]_ | Sequential | snapshot_ensemble.py | Classification / Regression | +------------------------------+------------+---------------------------+-----------------------------+ | Adversarial Training [5]_ | Parallel | adversarial_training.py | Classification / Regression | +------------------------------+------------+---------------------------+-----------------------------+ | Fast Geometric Ensemble [6]_ | Sequential | fast_geometric.py | Classification / Regression | +------------------------------+------------+---------------------------+-----------------------------+ | Soft Gradient Boosting [7]_ | Parallel | soft_gradient_boosting.py | Classification / Regression | +------------------------------+------------+---------------------------+-----------------------------+
.. [1] Zhou, Zhi-Hua. Ensemble Methods: Foundations and Algorithms. CRC press, 2012.
.. [2] Breiman, Leo. Bagging Predictors. Machine Learning (1996): 123-140.
.. [3] Friedman, Jerome H. Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics (2001): 1189-1232.
.. [4] Huang, Gao, et al. Snapshot Ensembles: Train 1, Get M For Free. ICLR, 2017.
.. [5] Lakshminarayanan, Balaji, et al. Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles. NIPS, 2017.
.. [6] Garipov, Timur, et al. Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs. NeurIPS, 2018.
.. [7] Feng, Ji, et al. Soft Gradient Boosting Machine. ArXiv, 2020.
.. _pytorch: https://pytorch.org/
.. _pypi: https://pypi.org/project/torchensemble/
|contributors|
.. |contributors| image:: https://contributors-img.web.app/image?repo=TorchEnsemble-Community/Ensemble-Pytorch .. _contributors: https://github.com/TorchEnsemble-Community/Ensemble-Pytorch/graphs/contributors