Ensemble PyTorch Documentation¶
Ensemble PyTorch is a unified ensemble framework for PyTorch to easily improve the performance and robustness of your deep learning model. It provides:
Easy ways to improve the performance and robustness of your deep learning model.
Easy-to-use APIs on training and evaluating the ensemble.
High training efficiency with parallelization.
Guidepost¶
To get started, please refer to Quick Start;
To learn more about ensemble methods supported, please refer to Introduction;
If you are confused on which ensemble method to use, our experiments and the instructions in guidance may be helpful.
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 criterion
criterion = nn.CrossEntropyLoss() # training objective
ensemble.set_criterion(criterion)
# 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