FIR module

class nnodely.layers.fir.Fir(*args, **kwargs)[source]

Represents a Finite Impulse Response (FIR) relation in the neural network model.

Notes

Note

The FIR relation works along the time dimension (second dimension) of the input tensor. You can find some initialization functions inside the initializer module.

Parameters:

output_dimension (int, optional) – The output dimension of the FIR relation.
W_init (Callable, str, optional) – A callable for initializing the parameters.
W_init_params (dict, optional) – A dictionary of parameters for the parameter initializer.
b_init (Callable, str, optional) – A callable for initializing the bias.
b_init_params (dict, optional) – A dictionary of parameters for the bias initializer.
W (Parameter or str, optional) – The parameter object or tag. The parameter can be defined using the relative class ‘Parameter’. If not given a new parameter will be auto-generated.
b (bool, str, or Parameter, optional) – The bias parameter object, tag, or a boolean indicating whether to use bias. If set to ‘True’ a new parameter will be auto-generated.
dropout (int or float, optional) – The dropout rate. Default is 0.

relation_name

The name of the relation.

Type:: str

W_init

The parameter initializer.

Type:: Callable

W_init_params

The parameters for the parameter initializer.

Type:: dict

W

The parameter object or name.

Type:: Parameter or str

b_init

The bias initializer.

Type:: Callable

b_init_params

The parameters for the bias initializer.

Type:: dict

b

The bias object, name, or a boolean indicating whether to use bias.

Type:: bool, str, or Parameter

pname

The name of the parameter.

Type:: str

bname

The name of the bias.

Type:: str

dropout

The dropout rate.

Type:: int or float

output_dimension

The output dimension of the FIR relation.

Type:: int

Examples

Basic usage:

input = Input('in')
relation = Fir(input.tw(0.05))

Passing a parameter:

input = Input('in')
par = Parameter('par', dimensions=3, sw=2, init='init_constant')
relation = Fir(W=par)(input.sw(2))

Parameters initialization:

x = Input('x')
F = Input('F')
fir_x = Fir(W_init='init_negexp')(x.tw(0.2))
fir_F = Fir(W_init='init_constant', W_init_params={'value':1})(F.last())

For more examples of how to use the FIR module, please refer to the FIR tutorial.