grelu.lightning.losses

grelu.lightning.losses contains custom loss functions to train sequence-to-function models. These metrics are used in grelu.lightning.

All loss functions inherit from torch.nn.Module and define a forward function that takes input and target tensors. All loss functions produce a single value per task, which can be averaged across tasks by setting reduction=”mean”.

Classes

PoissonMultinomialLoss

Possion decomposition with multinomial specificity term.

Module Contents

class grelu.lightning.losses.PoissonMultinomialLoss(total_weight: float = 1, eps: float = 1e-07, log_input: bool = True, reduction: str = 'mean', multinomial_axis: str = 'length')

Bases: torch.nn.Module

Possion decomposition with multinomial specificity term.

Parameters:

• total_weight – Weight of the Poisson total term.

• eps – Added small value to avoid log(0). Only needed if log_input = False.

• log_input – If True, the input is transformed with torch.exp to produce predicted counts. Otherwise, the input is assumed to already represent predicted counts.

• multinomial_axis – Either “length” or “task”, representing the axis along which the multinomial distribution should be calculated.

• reduction – “mean” or “none”.

eps = 1e-07

total_weight = 1

log_input = True

reduction = 'mean'

forward(input: torch.Tensor, target: torch.Tensor) → torch.Tensor

Loss computation

Parameters:

• input – Tensor of shape (B, T, L)

• target – Tensor of shape (B, T, L)

Returns:

Loss value