# Source code for gpytorch.variational.mean_field_variational_distribution

#!/usr/bin/env python3

import torch

from ..distributions import MultivariateNormal
from ..lazy import DiagLazyTensor
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 = DiagLazyTensor(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)