from torch import optim
from ..models.model import Model
from ..utils import tolist
from ..losses import ELBO
[docs]class VI(Model):
"""
Variational Inference (Amortized inference)
The ELBO for given distributions (p, approximate_dist) is set as the loss class of this model.
"""
[docs] def __init__(self, p, approximate_dist,
other_distributions=[],
optimizer=optim.Adam,
optimizer_params={},
clip_grad_norm=None,
clip_grad_value=None):
"""
Parameters
----------
p : torch.distributions.Distribution
Generative model (distribution).
approximate_dist : torch.distributions.Distribution
Approximate posterior distribution.
optimizer : torch.optim
Optimization algorithm.
optimizer_params : dict
Parameters of optimizer
clip_grad_norm : float or int
Maximum allowed norm of the gradients.
clip_grad_value : float or int
Maximum allowed value of the gradients.
"""
# set distributions (for training)
distributions = [p, approximate_dist] + tolist(other_distributions)
# set losses
elbo = ELBO(p, approximate_dist)
loss = -elbo.mean()
super().__init__(loss, test_loss=loss,
distributions=distributions,
optimizer=optimizer, optimizer_params=optimizer_params,
clip_grad_norm=clip_grad_norm, clip_grad_value=clip_grad_value)
[docs] def train(self, train_x_dict={}, **kwargs):
return super().train(train_x_dict, **kwargs)
[docs] def test(self, test_x_dict={}, **kwargs):
return super().test(test_x_dict, **kwargs)