#!/usr/bin/env python3
import torch
from linear_operator.operators import DiagLinearOperator
from ..distributions import MultivariateNormal
from ._variational_distribution import _VariationalDistribution
[docs]class MeanFieldVariationalDistribution(_VariationalDistribution):
"""
A :obj:`~gpytorch.variational._VariationalDistribution` that is defined to be a multivariate normal distribution
with a diagonal covariance matrix. This will not be as flexible/expressive as a
:obj:`~gpytorch.variational.CholeskyVariationalDistribution`.
:param int num_inducing_points: Size of the variational distribution. This implies that the variational mean
should be this size, and the variational covariance matrix should have this many rows and columns.
:param batch_shape: Specifies an optional batch size
for the variational parameters. This is useful for example when doing additive variational inference.
:type batch_shape: :obj:`torch.Size`, optional
:param float mean_init_std: (Default: 1e-3) Standard deviation of gaussian noise to add to the mean initialization.
"""
def __init__(self, num_inducing_points, batch_shape=torch.Size([]), mean_init_std=1e-3, **kwargs):
super().__init__(num_inducing_points=num_inducing_points, batch_shape=batch_shape, mean_init_std=mean_init_std)
mean_init = torch.zeros(num_inducing_points)
covar_init = torch.ones(num_inducing_points)
mean_init = mean_init.repeat(*batch_shape, 1)
covar_init = covar_init.repeat(*batch_shape, 1)
self.register_parameter(name="variational_mean", parameter=torch.nn.Parameter(mean_init))
self.register_parameter(name="_variational_stddev", parameter=torch.nn.Parameter(covar_init))
@property
def variational_stddev(self):
# TODO: if we don't multiply self._variational_stddev by a mask of one, Pyro models fail
# not sure where this bug is occuring (in Pyro or PyTorch)
# throwing this in as a hotfix for now - we should investigate later
mask = torch.ones_like(self._variational_stddev)
return self._variational_stddev.mul(mask).abs().clamp_min(1e-8)
def forward(self):
# TODO: if we don't multiply self._variational_stddev by a mask of one, Pyro models fail
# not sure where this bug is occuring (in Pyro or PyTorch)
# throwing this in as a hotfix for now - we should investigate later
mask = torch.ones_like(self._variational_stddev)
variational_covar = DiagLinearOperator(self._variational_stddev.mul(mask).pow(2))
return MultivariateNormal(self.variational_mean, variational_covar)
def initialize_variational_distribution(self, prior_dist):
self.variational_mean.data.copy_(prior_dist.mean)
self.variational_mean.data.add_(torch.randn_like(prior_dist.mean), alpha=self.mean_init_std)
self._variational_stddev.data.copy_(prior_dist.stddev)