Module laplace.utils.utils
Functions
def get_nll(out_dist, targets)def validate(laplace, val_loader, loss, pred_type='glm', link_approx='probit', n_samples=100, loss_with_var=False) ‑> floatdef parameters_per_layer(model)-
Get number of parameters per layer.
Parameters
model:torch.nn.Module
Returns
params_per_layer:list[int]
def invsqrt_precision(M)-
Compute
M^{-0.5}as a tridiagonal matrix.Parameters
M:torch.Tensor
Returns
M_invsqrt:torch.Tensor
def kron(t1, t2)-
Computes the Kronecker product between two tensors.
Parameters
t1:torch.Tensort2:torch.Tensor
Returns
kron_product:torch.Tensor
def diagonal_add_scalar(X, value)-
Add scalar value
valueto diagonal ofX.Parameters
X:torch.Tensorvalue:torch.Tensororfloat
Returns
X_add_scalar:torch.Tensor
def symeig(M)-
Symetric eigendecomposition avoiding failure cases by adding and removing jitter to the diagonal.
Parameters
M:torch.Tensor
Returns
L:torch.Tensor- eigenvalues
W:torch.Tensor- eigenvectors
def block_diag(blocks)-
Compose block-diagonal matrix of individual blocks.
Parameters
blocks:list[torch.Tensor]
Returns
M:torch.Tensor
def expand_prior_precision(prior_prec, model)-
Expand prior precision to match the shape of the model parameters.
Parameters
prior_prec:torch.Tensor 1-dimensional- prior precision
model:torch.nn.Module- torch model with parameters that are regularized by prior_prec
Returns
expanded_prior_prec:torch.Tensor- expanded prior precision has the same shape as model parameters