Now, we study a single layer neural network that can estimate the density
p_x of a variable of interest
x by re-parameterizing a base variable
z with known density
p_z through the Neural Network model passed to the layer.
Before getting to the explanation, here's some code to start with. We will follow a full explanation of the definition and training process:
using DiffEqFlux, DifferentialEquations, Distributions, GalacticOptim nn = Chain(Dense(1, 3, tanh), Dense(3, 1, tanh)) tspan = (0.0f0,10.0f0) ffjord_test = FFJORD(nn,tspan, Tsit5()) data_train = Float32.(rand(Normal(6.0,0.7), 1, 100)) function loss_adjoint(θ) logpx = ffjord_test(data_train,θ) loss = -mean(logpx) end adtype = GalacticOptim.AutoZygote() optf = GalacticOptim.OptimizationFunction((x, p) -> loss_adjoint(x), adtype) optfunc = GalacticOptim.instantiate_function(optf, ffjord_test.p, adtype, nothing) optprob = GalacticOptim.OptimizationProblem(optfunc, ffjord_test.p) res1 = GalacticOptim.solve(optprob, ADAM(0.1), maxiters = 100) # Retrain using the LBFGS optimizer optprob2 = remake(optprob,u0 = res1.u) res2 = GalacticOptim.solve(optprob2, LBFGS(), allow_f_increases = false)
We can use DiffEqFlux.jl to define, train and output the densities computed by CNF layers. In the same way as a neural ODE, the layer takes a neural network that defines its derivative function (see  for a reference). A possible way to define a CNF layer, would be:
using DiffEqFlux, DifferentialEquations, Distributions, GalacticOptim nn = Chain(Dense(1, 3, tanh), Dense(3, 1, tanh)) tspan = (0.0f0,10.0f0) ffjord_test = FFJORD(nn,tspan, Tsit5())
where we also pass as an input the desired timespan for which the differential equation that defines
log p_x and
z(t) will be solved.
First, let's get an array from a normal distribution as the training data
data_train = Float32.(rand(Normal(6.0,0.7), 1, 100))
Now we define a loss function that we wish to minimize
function loss_adjoint(θ) logpx = ffjord_test(data_train,θ) loss = -mean(logpx) end
In this example, we wish to choose the parameters of the network such that the likelihood of the re-parameterized variable is maximized. Other loss functions may be used depending on the application. Furthermore, the CNF layer gives the log of the density of the variable x, as one may guess from the code above.
We then train the neural network to learn the distribution of
Here we showcase starting the optimization with
ADAM to more quickly find a minimum, and then honing in on the minimum by using
# Train using the ADAM optimizer res1 = DiffEqFlux.sciml_train(loss_adjoint, ffjord_test.p, ADAM(0.1), cb = cb, maxiters = 100) # output * Status: success * Candidate solution u: [-1.88e+00, 2.44e+00, 2.01e-01, ...] Minimum: 1.240627e+00 * Found with Algorithm: ADAM Initial Point: [9.33e-01, 1.13e+00, 2.92e-01, ...]
We then complete the training using a different optimizer starting from where
# Retrain using the LBFGS optimizer res2 = DiffEqFlux.sciml_train(loss_adjoint, res1.u, LBFGS(), allow_f_increases = false) # output * Status: success * Candidate solution u: [-1.06e+00, 2.24e+00, 8.77e-01, ...] Minimum: 1.157672e+00 * Found with Algorithm: L-BFGS Initial Point: [-1.88e+00, 2.44e+00, 2.01e-01, ...] * Convergence measures |x - x'| = 0.00e+00 ≰ 0.0e+00 |x - x'|/|x'| = 0.00e+00 ≰ 0.0e+00 |f(x) - f(x')| = 0.00e+00 ≤ 0.0e+00 |f(x) - f(x')|/|f(x')| = 0.00e+00 ≤ 0.0e+00 |g(x)| = 4.09e-03 ≰ 1.0e-08 * Work counters Seconds run: 514 (vs limit Inf) Iterations: 44 f(x) calls: 244 ∇f(x) calls: 244
 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.