ott.solvers.nn.models.ICNN#

class ott.solvers.nn.models.ICNN(dim_data, dim_hidden, init_std=0.01, init_fn=<function normal>, act_fn=<jax._src.custom_derivatives.custom_jvp object>, pos_weights=True, gaussian_map=None, parent=<flax.linen.module._Sentinel object>, name=None)[source]#

Input convex neural network (ICNN) architecture with initialization.

Implementation of input convex neural networks as introduced in [Amos et al., 2017] with initialization schemes proposed by [Bunne et al., 2022].

Parameters:

  • dim_data (int) – data dimensionality.

  • dim_hidden (Sequence[int]) – sequence specifying size of hidden dimensions. The output dimension of the last layer is 1 by default.

  • init_std (float) – value of standard deviation of weight initialization method.

  • init_fn (Callable) – choice of initialization method for weight matrices (default: jax.nn.initializers.normal()).

  • act_fn (Callable[[Array], Array]) – choice of activation function used in network architecture (needs to be convex, default: jax.nn.relu).

  • pos_weights (bool) – Enforce positive weights with a projection. If False, the positive weights should be enforced with clipping or regularization in the loss.

  • gaussian_map (Optional[Tuple[Array, Array]]) – data inputs of source and target measures for initialization scheme based on Gaussian approximation of input and target measure (if None, identity initialization is used).

  • parent (Union[Type[Module], Type[Scope], Type[_Sentinel], None]) –

  • name (Optional[str]) –

Methods

apply(variables, *args[, rngs, method, ...])

Applies a module method to variables and returns output and modified variables.

clone(*[, parent])

Creates a clone of this Module, with optionally updated arguments.

create_train_state(rng, optimizer, input, ...)

Create initial training state.

get_variable(col, name[, default])

Retrieves the value of a Variable.

has_rng(name)

Returns true if a PRNGSequence with name name exists.

has_variable(col, name)

Checks if a variable of given collection and name exists in this Module.

init(rngs, *args[, method, mutable, ...])

Initializes a module method with variables and returns modified variables.

init_fn([dtype])

Builds an initializer that returns real normally-distributed random arrays.

init_with_output(rngs, *args[, method, ...])

Initializes a module method with variables and returns output and modified variables.

is_initializing()

Returns True if running under self.init(...) or nn.init(...)().

is_mutable_collection(col)

Returns true if the collection col is mutable.

lazy_init(rngs, *args[, method, mutable])

Initializes a module without computing on an actual input.

make_rng(name)

Returns a new RNG key from a given RNG sequence for this Module.

param(name, init_fn, *init_args[, unbox])

Declares and returns a parameter in this Module.

perturb(name, value[, collection])

Add an zero-value variable ('perturbation') to the intermediate value.

potential_gradient_fn(params)

Return a function giving the gradient of the potential.

potential_value_fn(params[, ...])

Return a function giving the value of the potential.

put_variable(col, name, value)

Updates the value of the given variable if it is mutable, or an error otherwise.

setup()

Initializes a Module lazily (similar to a lazy __init__).

sow(col, name, value[, reduce_fn, init_fn])

Stores a value in a collection.

tabulate(rngs, *args[, depth, ...])

Creates a summary of the Module represented as a table.

unbind()

Returns an unbound copy of a Module and its variables.

variable(col, name[, init_fn, unbox])

Declares and returns a variable in this Module.

Attributes

act_fn(x)

gaussian_map

init_std

is_potential

Indicates if the module defines the potential's value or the gradient.

name

parent

pos_weights

scope

variables

Returns the variables in this module.

dim_data

dim_hidden