extorch.nn.modules.loss

CrossEntropyLabelSmooth
CrossEntropyMixupLoss	CrossEntropyLoss with mixup technique.
DECLoss	Loss used by Deep Embedded Clustering (DEC, `Link`_).

class extorch.nn.modules.loss.CrossEntropyLabelSmooth(epsilon: float)

Bases: torch.nn.modules.module.Module

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

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class extorch.nn.modules.loss.CrossEntropyMixupLoss(alpha: float, **kwargs)

Bases: torch.nn.modules.module.Module

CrossEntropyLoss with mixup technique.

Parameters

• alpha (float) – Parameter of the beta distribution. Default: 1.0.

• kwargs – Other arguments of torch.nn.CrossEntropyLoss (`Link`_).

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

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class extorch.nn.modules.loss.DECLoss(alpha: float = 1.0, **kwargs)

Bases: torch.nn.modules.module.Module

Loss used by Deep Embedded Clustering (DEC, `Link`_).

Parameters

alpha (float) – The degrees of freedom of the Student’s tdistribution. Default: 1.0.

Examples::

>>> criterion = DECLoss(alpha = 1.)
>>> embeddings = torch.randn((2, 10))
>>> centers = torch.randn((3, 10))
>>> loss = criterion(embeddings, centers)

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

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

static target_distribution(input: torch.Tensor) → torch.Tensor

training: bool