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 which requires the project to be well maintained.

• Document
• Source Code
• Experiment

Installation

Stable version:

pip install torchensemble

Latest version (under development):

pip install git+https://github.com/TorchEnsemble-Community/Ensemble-Pytorch.git

Example

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,               # here is your deep learning 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.predict(test_loader)         # testing accuracy

Supported Ensemble

Ensemble Name	Type	Source Code
Fusion	Mixed	fusion.py
Voting [1]	Parallel	voting.py
Bagging [2]	Parallel	bagging.py
Gradient Boosting [3]	Sequential	gradient_boosting.py
Snapshot Ensemble [4]	Sequential	snapshot_ensemble.py
Adversarial Training [5]	Parallel	adversarial_training.py
Fast Geometric Ensemble [6]	Sequential	fast_geometric.py

Dependencies

• scikit-learn>=0.23.0
• torch>=1.4.0
• torchvision>=0.2.2

Reference

[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.