enerzyme.models.layers.denormalize.AtomicAffineLayer#

class enerzyme.models.layers.denormalize.AtomicAffineLayer(max_Za: int, shifts: Dict[Literal['Ea', 'Qa'], Dict[Literal['values', 'learnable'], Dict[str, float] | float | bool]] = {'Ea': {'learnable': True, 'values': 0}, 'Qa': {'learnable': True, 'values': 0}}, scales: Dict[Literal['Ea', 'Qa'], Dict[Literal['values', 'learnable'], Dict[str, float] | float | bool]] = {'Ea': {'learnable': True, 'values': 1}, 'Qa': {'learnable': True, 'values': 1}})[source]#

Bases: BaseFFLayer

__init__(max_Za: int, shifts: Dict[Literal['Ea', 'Qa'], Dict[Literal['values', 'learnable'], Dict[str, float] | float | bool]] = {'Ea': {'learnable': True, 'values': 0}, 'Qa': {'learnable': True, 'values': 0}}, scales: Dict[Literal['Ea', 'Qa'], Dict[Literal['values', 'learnable'], Dict[str, float] | float | bool]] = {'Ea': {'learnable': True, 'values': 1}, 'Qa': {'learnable': True, 'values': 1}}) → None[source]#
build_affine(params: Dict[Literal['Ea', 'Qa'], Dict[Literal['values', 'learnable'], Dict[str, float] | float | bool]], default_value: float) → ParameterDict[source]#
get_Ea(Ea: Tensor, Qa: Tensor, Za: Tensor) → Tensor[source]#
get_Qa(Ea: Tensor, Qa: Tensor, Za: Tensor) → Tensor[source]#