Skip to main content

Browse the archive

https://github.com/JuliaDiffEq/DiffEqFlux.jl
13 April 2020, 12:04:22 UTC
Revision 05a3545d642163ae5d88423cc2f725723b402acd authored by Christopher Rackauckas on 17 July 2019, 02:54:28 UTC, committed by GitHub on 17 July 2019, 02:54:28 UTC 
Update README.md
1 parent e522ff0
Raw File
Tip revision: 05a3545d642163ae5d88423cc2f725723b402acd authored by Christopher Rackauckas on 17 July 2019, 02:54:28 UTC
Update README.md
Tip revision: 05a3545
layers_sde.jl 
using Flux, DiffEqFlux, StochasticDiffEq, Plots

function lotka_volterra(du,u,p,t)
  x, y = u
  α, β, δ, γ = p
  du[1] = dx = α*x - β*x*y
  du[2] = dy = -δ*y + γ*x*y
end
function lotka_volterra_noise(du,u,p,t)
  du[1] = 0.1u[1]
  du[2] = 0.1u[2]
end
prob = SDEProblem(lotka_volterra,lotka_volterra_noise,[1.0,1.0],(0.0,10.0))
p = param([2.2, 1.0, 2.0, 0.4])
function predict_fd_sde()
  diffeq_fd(p,sol->sol[1,:],101,prob,SOSRI(),saveat=0.1)
end
loss_fd_sde() = sum(abs2,x-1 for x in predict_fd_sde())
loss_fd_sde()
Flux.back!(loss_fd_sde())

function predict_rd_sde()
  Array(diffeq_rd(p,prob,SOSRI(),saveat=0.1))
end
loss_rd_sde() = sum(abs2,x-1 for x in predict_rd_sde())
loss_rd_sde()
@test_broken Flux.back!(loss_rd_sde())
back to top