algebra module

Full Documentation for hippynn.layers.algebra module. Click here for a summary page.

Layers for simple operations

class AtLeast2D(*args, **kwargs)

Bases: Module

forward(item)

Define 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.

class EnsembleTarget(*args, **kwargs)

Bases: Module

forward(*input_tensors)

Define 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.

class Idx(index, repr_info=None)

Bases: Module

extra_repr()

Return the extra representation of the module.

To print customized extra information, you should re-implement this method in your own modules. Both single-line and multi-line strings are acceptable.

forward(bundled_inputs)

Define 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.

class LambdaModule(fn)

Bases: Module

extra_repr()

Return the extra representation of the module.

To print customized extra information, you should re-implement this method in your own modules. Both single-line and multi-line strings are acceptable.

forward(*args, **kwargs)

Define 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.

class ListMod(*args, **kwargs)

Bases: Module

forward(*features)

Define 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.

class ValueMod(value, convert=True)

Bases: Module

extra_repr()

Return the extra representation of the module.

To print customized extra information, you should re-implement this method in your own modules. Both single-line and multi-line strings are acceptable.

forward()

Define 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.

class WeightedMAELoss(*args, **kwargs)

Bases: _WeightedLoss

static loss_func(input: Tensor, target: Tensor, size_average: bool | None = None, reduce: bool | None = None, reduction: str = 'mean', weight: Tensor | None = None) → Tensor

Compute the L1 loss, with optional weighting.

Function that takes the mean element-wise absolute value difference.

See L1Loss for details.

Args:

input (Tensor): Predicted values. target (Tensor): Ground truth values. size_average (bool, optional): Deprecated (see reduction). reduce (bool, optional): Deprecated (see reduction). reduction (str, optional): Specifies the reduction to apply to the output:

‘none’ | ‘mean’ | ‘sum’. ‘mean’: the mean of the output is taken. ‘sum’: the output will be summed. ‘none’: no reduction will be applied. Default: ‘mean’.

weight (Tensor, optional): Weights for each sample. Default: None.

Returns:

Tensor: L1 loss (optionally weighted).

class WeightedMSELoss(*args, **kwargs)

Bases: _WeightedLoss

static loss_func(input: Tensor, target: Tensor, size_average: bool | None = None, reduce: bool | None = None, reduction: str = 'mean', weight: Tensor | None = None) → Tensor

Compute the element-wise mean squared error, with optional weighting.

See MSELoss for details.

Args:

input (Tensor): Predicted values. target (Tensor): Ground truth values. size_average (bool, optional): Deprecated (see reduction). reduce (bool, optional): Deprecated (see reduction). reduction (str, optional): Specifies the reduction to apply to the output:

‘none’ | ‘mean’ | ‘sum’. ‘mean’: the mean of the output is taken. ‘sum’: the output will be summed. ‘none’: no reduction will be applied. Default: ‘mean’.

weight (Tensor, optional): Weights for each sample. Default: None.

Returns:

Tensor: Mean Squared Error loss (optionally weighted).