Parametric Function module

class nnodely.layers.parametricfunction.ParamFun(param_fun: Callable, parameters_and_constants: list | dict | None = None, *, map_over_batch: bool = False)[source]

Represents a parametric function in the neural network model.

Parameters:

param_fun (Callable) – The parametric function to be used.
constants (list or dict or None, optional) – A list or dictionary of constants to be used in the function. Default is None.
parameters_dimensions (list or dict or None, optional) – A list or dictionary specifying the dimensions of the parameters. Default is None.
parameters (list or dict or None, optional) – A list or dictionary of parameters to be used in the function. Default is None.
map_over_batch (bool, optional) – A boolean indicating whether to map the function over the batch dimension. Default is False.

relation_name

The name of the relation.

Type:: str

param_fun

The parametric function to be used.

Type:: Callable

constants

A list or dictionary of constants to be used in the function.

Type:: list or dict or None

parameters_dimensions

A list or dictionary specifying the dimensions of the parameters.

Type:: list or dict or None

parameters

A list or dictionary of parameters to be used in the function.

Type:: list or dict or None

map_over_batch

A boolean indicating whether to map the function over the batch dimension.

Type:: bool

output_dimension

A dictionary containing the output dimensions of the function.

Type:: dict

json

A dictionary containing the configuration of the function.

Type:: dict

Examples

input1 = Input('input1')
input2 = Input('input2')

def my_function(x, y, param1, const1):
    return param1 * x + const1 * y

param_fun = ParamFun(
    my_function,
    constants={'const1': 1.0},
    parameters_dimensions={'param1': 1}
)
result = param_fun(input1, input2)

For more examples of how to use the parametric function module, please refer to the Parametric Function tutorial.