# 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 favor 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.

Ref [1]L. S. Pontryagin, Mathematical Theory of Optimal Processes. CRC Press, 1987. [2]R. T. Q. Chen, Y. Rubanova, J. Bettencourt, D. Duvenaud. Neural Ordinary Differential Equations. arXiv preprint at arXiv1806.07366, 2019. [3]W. Grathwohl, R. T. Q. Chen, J. Bettencourt, I. Sutskever, D. Duvenaud. FFJORD: Free-Form Continuous Dynamic For Scalable Reversible Generative Models. arXiv preprint at arXiv1810.01367, 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.

Ref [1]L. S. Pontryagin, Mathematical Theory of Optimal Processes. CRC Press, 1987. [2]R. T. Q. Chen, Y. Rubanova, J. Bettencourt, D. Duvenaud. Neural Ordinary Differential Equations. arXiv preprint at arXiv1806.07366, 2019. [3]W. Grathwohl, R. T. Q. Chen, J. Bettencourt, I. Sutskever, D. Duvenaud. FFJORD: Free-Form Continuous Dynamic For Scalable Reversible Generative Models. arXiv preprint at arXiv1810.01367, 2018.