# CNF Layer Functions

The following layers are helper functions for easily building neural differential equation architectures specialized for the task of density estimation through Continuous Normalizing Flows (CNF).

`DiffEqFlux.DeterministicCNF`

— TypeConstructs a continuous-time recurrent neural network, also known as a neural ordinary differential equation (neural ODE), with fast gradient calculation via adjoints [1] and specialized for density estimation based on continuous normalizing flows (CNF) [2] with a direct computation of the trace of the dynamics' jacobian. At a high level this corresponds to the following steps:

- Parameterize the variable of interest x(t) as a function f(z, θ, t) of a base variable z(t) with known density p_z;
- Use the transformation of variables formula to predict the density p
*x as a function of the density p*z and the trace of the Jacobian of f; - Choose the parameter θ to minimize a loss function of p_x (usually the negative likelihood of the data);

!!!note This layer has been deprecated in favour of `FFJORD`

. Use FFJORD with `monte_carlo=false`

instead.

After these steps one may use the NN model and the learned θ to predict the density p_x for new values of x.

`DeterministicCNF(model, tspan, basedist=nothing, monte_carlo=false, args...; kwargs...)`

Arguments:

`model`

: A Chain neural network that defines the dynamics of the model.`basedist`

: Distribution of the base variable. Set to the unit normal by default.`tspan`

: The timespan to be solved on.`kwargs`

: Additional arguments splatted to the ODE solver. See the Common Solver Arguments documentation for more details.

References:

[1] Pontryagin, Lev Semenovich. Mathematical theory of optimal processes. CRC press, 1987.

[2] Chen, Ricky TQ, Yulia Rubanova, Jesse Bettencourt, and David Duvenaud. "Neural ordinary differential equations." In Proceedings of the 32nd International Conference on Neural Information Processing Systems, pp. 6572-6583. 2018.

[3] Grathwohl, Will, Ricky TQ Chen, Jesse Bettencourt, Ilya Sutskever, and David Duvenaud. "Ffjord: Free-form continuous dynamics for scalable reversible generative models." arXiv preprint arXiv:1810.01367 (2018).

`DiffEqFlux.FFJORD`

— TypeConstructs a continuous-time recurrent neural network, also known as a neural ordinary differential equation (neural ODE), with fast gradient calculation via adjoints [1] and specialized for density estimation based on continuous normalizing flows (CNF) [2] with a stochastic approach [2] for the computation of the trace of the dynamics' jacobian. At a high level this corresponds to the following steps:

- Parameterize the variable of interest x(t) as a function f(z, θ, t) of a base variable z(t) with known density p_z;
- Use the transformation of variables formula to predict the density p
*x as a function of the density p*z and the trace of the Jacobian of f; - Choose the parameter θ to minimize a loss function of p_x (usually the negative likelihood of the data);

After these steps one may use the NN model and the learned θ to predict the density p_x for new values of x.

`FFJORD(model, basedist=nothing, monte_carlo=false, tspan, args...; kwargs...)`

Arguments:

`model`

: A Chain neural network that defines the dynamics of the model.`basedist`

: Distribution of the base variable. Set to the unit normal by default.`tspan`

: The timespan to be solved on.`kwargs`

: Additional arguments splatted to the ODE solver. See the Common Solver Arguments documentation for more details.

References:

[1] Pontryagin, Lev Semenovich. Mathematical theory of optimal processes. CRC press, 1987.

[2] Chen, Ricky TQ, Yulia Rubanova, Jesse Bettencourt, and David Duvenaud. "Neural ordinary differential equations." In Proceedings of the 32nd International Conference on Neural Information Processing Systems, pp. 6572-6583. 2018.

[3] Grathwohl, Will, Ricky TQ Chen, Jesse Bettencourt, Ilya Sutskever, and David Duvenaud. "Ffjord: Free-form continuous dynamics for scalable reversible generative models." arXiv preprint arXiv:1810.01367 (2018).

`DiffEqFlux.FFJORDDistribution`

— TypeFFJORD can be used as a distribution to generate new samples by `rand`

or estimate densities by `pdf`

or `logpdf`

(from `Distributions.jl`

).

Arguments:

`model`

: A FFJORD instance`regularize`

: Whether we use regularization (default:`false`

)`monte_carlo`

: Whether we use monte carlo (default:`true`

)