laplace.marglik_training
#
Functions:
-
marglik_training
–Marginal-likelihood based training (Algorithm 1 in [1]).
marglik_training
#
marglik_training(model: Module, train_loader: DataLoader, likelihood: Likelihood | str = CLASSIFICATION, hessian_structure: HessianStructure | str = KRON, backend: Type[CurvatureInterface] = AsdlGGN, optimizer_cls: Type[Optimizer] = Adam, optimizer_kwargs: dict | None = None, scheduler_cls: Type[LRScheduler] | None = None, scheduler_kwargs: dict | None = None, n_epochs: int = 300, lr_hyp: float = 0.1, prior_structure: PriorStructure | str = LAYERWISE, n_epochs_burnin: int = 0, n_hypersteps: int = 10, marglik_frequency: int = 1, prior_prec_init: float = 1.0, sigma_noise_init: float = 1.0, temperature: float = 1.0, fix_sigma_noise: bool = False, progress_bar: bool = False, enable_backprop: bool = False, dict_key_x: str = 'input_ids', dict_key_y: str = 'labels') -> tuple[BaseLaplace, Module, list[Number], list[Number]]
Marginal-likelihood based training (Algorithm 1 in [1]). Optimize model parameters and hyperparameters jointly. Model parameters are optimized to minimize negative log joint (train loss) while hyperparameters minimize negative log marginal likelihood.
This method replaces standard neural network training and adds hyperparameter optimization to the procedure.
The settings of standard training can be controlled by passing train_loader
,
optimizer_cls
, optimizer_kwargs
, scheduler_cls
, scheduler_kwargs
, and n_epochs
.
The model
should return logits, i.e., no softmax should be applied.
With likelihood=Likelihood.CLASSIFICATION
or Likelihood.REGRESSION
, one can choose between
categorical likelihood (CrossEntropyLoss) and Gaussian likelihood (MSELoss).
As in [1], we optimize prior precision and, for regression, observation noise
using the marginal likelihood. The prior precision structure can be chosen
as 'scalar'
, 'layerwise'
, or 'diagonal'
. 'layerwise'
is a good default
and available to all Laplace approximations. lr_hyp
is the step size of the
Adam hyperparameter optimizer, n_hypersteps
controls the number of steps
for each estimated marginal likelihood, n_epochs_burnin
controls how many
epochs to skip marginal likelihood estimation, marglik_frequency
controls
how often to estimate the marginal likelihood (default of 1 re-estimates
after every epoch, 5 would estimate every 5-th epoch).
References
[1] Immer, A., Bauer, M., Fortuin, V., Rätsch, G., Khan, EM. Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning. ICML 2021.
Parameters:
-
model
#Module
) –torch neural network model (needs to comply with Backend choice)
-
train_loader
#DataLoader
) –pytorch dataloader that implements
len(train_loader.dataset)
to obtain number of data points -
likelihood
#str
, default:Likelihood.CLASSIFICATION
) –Likelihood.CLASSIFICATION or Likelihood.REGRESSION
-
hessian_structure
#('diag', 'kron', 'full')
, default:'diag'
) –structure of the Hessian approximation
-
backend
#Backend
, default:AsdlGGN
) –Curvature subclass, e.g. AsdlGGN/AsdlEF or BackPackGGN/BackPackEF
-
optimizer_cls
#Optimizer
, default:Adam
) –optimizer to use for optimizing the neural network parameters togeth with
train_loader
-
optimizer_kwargs
#dict
, default:None
) –keyword arguments for
optimizer_cls
, for example to change learning rate or momentum -
scheduler_cls
#_LRScheduler
, default:None
) –optionally, a scheduler to use on the learning rate of the optimizer.
scheduler.step()
is called after every batch of the standard training. -
scheduler_kwargs
#dict
, default:None
) –keyword arguments for
scheduler_cls
, e.g.lr_min
for CosineAnnealingLR -
n_epochs
#int
, default:300
) –number of epochs to train for
-
lr_hyp
#float
, default:0.1
) –Adam learning rate for hyperparameters
-
prior_structure
#str
, default:'layerwise'
) –structure of the prior. one of
['scalar', 'layerwise', 'diag']
-
n_epochs_burnin
#int default=0
, default:0
) –how many epochs to train without estimating and differentiating marglik
-
n_hypersteps
#int
, default:10
) –how many steps to take on the hyperparameters when marglik is estimated
-
marglik_frequency
#int
, default:1
) –how often to estimate (and differentiate) the marginal likelihood
marglik_frequency=1
would be every epoch,marglik_frequency=5
would be every 5 epochs. -
prior_prec_init
#float
, default:1.0
) –initial prior precision
-
sigma_noise_init
#float
, default:1.0
) –initial observation noise (for regression only)
-
temperature
#float
, default:1.0
) –factor for the likelihood for 'overcounting' data. Might be required for data augmentation.
-
fix_sigma_noise
#bool
, default:False
) –if False, optimize observation noise via marglik otherwise use
sigma_noise_init
throughout. Only works for regression. -
progress_bar
#bool
, default:False
) –whether to show a progress bar (updated per epoch) or not
-
enable_backprop
#bool
, default:False
) –make the returned Laplace instance backpropable---useful for e.g. Bayesian optimization.
-
dict_key_x
#str
, default:'input_ids'
) –The dictionary key under which the input tensor
x
is stored. Only has effect when the model takes aMutableMapping
as the input. Useful for Huggingface LLM models. -
dict_key_y
#str
, default:'labels'
) –The dictionary key under which the target tensor
y
is stored. Only has effect when the model takes aMutableMapping
as the input. Useful for Huggingface LLM models.
Returns:
-
lap
(laplace
) –fit Laplace approximation with the best obtained marginal likelihood during training
-
model
(Module
) –corresponding model with the MAP parameters
-
margliks
(list
) –list of marginal likelihoods obtained during training (to monitor convergence)
-
losses
(list
) –list of losses (log joints) obtained during training (to monitor convergence)
Source code in laplace/marglik_training.py
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