luz_model_sequential: Build a 'luz' model composed of a linear stack of layers

In nn2poly: Neural Network Weights Transformation into Polynomial Coefficients

Build a luz model composed of a linear stack of layers

Description

Helper function to build luz models as a sequential model, by feeding it a stack of luz layers.

Usage

luz_model_sequential(...)

Arguments

...	Sequence of modules to be added.

Details

This step is needed so we can get the activation functions and layers and neurons architecture easily with nn2poly:::get_parameters(). Furthermore, this step is also needed to be able to impose the needed constraints when using the luz/torch framework.

Value

A nn_sequential module.

See Also

add_constraints()

Examples

## Not run: 
if (requireNamespace("luz", quietly=TRUE)) {
# Create a NN using luz/torch as a sequential model
# with 3 fully connected linear layers,
# the first one with input = 5 variables,
# 100 neurons and tanh activation function, the second
# one with 50 neurons and softplus activation function
# and the last one with 1 linear output.
nn <- luz_model_sequential(
  torch::nn_linear(5,100),
  torch::nn_tanh(),
  torch::nn_linear(100,50),
  torch::nn_softplus(),
  torch::nn_linear(50,1)
)

nn

# Check that the nn is of class nn_squential
class(nn)
}

## End(Not run)

nn2poly documentation built on May 29, 2024, 5:08 a.m.