add_constraints: Add constraints to a neural network

Description

This function sets up a neural network object with the constraints required by the nn2poly algorithm. Currently supported neural network frameworks are keras/tensorflow and luz/torch.

Usage

add_constraints(object, type = c("l1_norm", "l2_norm"), ...)

Value

A nn2poly neural network object.

Arguments

object: A neural network object in sequential form from one of the supported frameworks.
type: Constraint type. Currently, l1_norm and l2_norm are supported.
...: Additional arguments (unused).

Details

Constraints are added to the model object using callbacks in their specific framework. These callbacks are used during training when calling fit on the model. Specifically we are using callbacks that are applied at the end of each train batch.

Models in luz/torch need to use the luz_model_sequential helper in order to have a sequential model in the appropriate form.

Examples

Run this code

if (FALSE) {
if (requireNamespace("keras", quietly=TRUE)) {
  # ---- Example with a keras/tensorflow network ----
  # Build a small nn:
  nn <- keras::keras_model_sequential()
  nn <- keras::layer_dense(nn, units = 10, activation = "tanh", input_shape = 2)
  nn <- keras::layer_dense(nn, units = 1, activation = "linear")

  # Add constraints
  nn_constrained <- add_constraints(nn, constraint_type = "l1_norm")

  # Check that class of the constrained nn is "nn2poly"
  class(nn_constrained)[1]
}

if (requireNamespace("luz", quietly=TRUE)) {
  # ---- Example with a luz/torch network ----

  # Build a small nn
  nn <- luz_model_sequential(
    torch::nn_linear(2,10),
    torch::nn_tanh(),
    torch::nn_linear(10,1)
  )

  # With luz/torch we need to setup the nn before adding the constraints
  nn <- luz::setup(module = nn,
    loss = torch::nn_mse_loss(),
    optimizer = torch::optim_adam,
  )

  # Add constraints
  nn <- add_constraints(nn)

  # Check that class of the constrained nn is "nn2poly"
  class(nn)[1]
}
}