Merge branch 'size'

Author: ChrisRackauckas

Project.toml

@@ -1,7 +1,7 @@
 name = "DiffEqFlux"
 uuid = "aae7a2af-3d4f-5e19-a356-7da93b79d9d0"
 authors = ["Chris Rackauckas <[email protected]>"]
-version = "0.6.1"
+version = "0.7.0"
 
 [deps]
 Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"

test/runtests.jl

@@ -10,6 +10,7 @@ if GROUP == "All"
     @safetestset "Layers Tests" begin include("layers.jl") end
     @safetestset "Layers SDE" begin include("layers_sde.jl") end
     @safetestset "Layers DDE" begin include("layers_dde.jl") end
+    @safetestset "Size Handling in Adjoint Tests" begin include("size_handling_adjoint.jl") end
     @safetestset "odenet" begin include("odenet.jl") end
     @safetestset "Neural DE Tests" begin include("neural_de.jl") end
     @safetestset "Partial Neural Tests" begin include("partial_neural.jl") end

test/size_handling_adjoint.jl

@@ -0,0 +1,39 @@
+using OrdinaryDiffEq, Test
+
+p = [1.5 1.0;3.0 1.0]
+function lotka_volterra(du,u,p,t)
+  du[1] = p[1,1]*u[1] - p[1,2]*u[1]*u[2]
+  du[2] = -p[2,1]*u[2] + p[2,2]*u[1]*u[2]
+end
+
+u0 = [1.0,1.0]
+tspan = (0.0,10.0)
+
+prob = ODEProblem(lotka_volterra,u0,tspan,p)
+sol = solve(prob,Tsit5())
+using Plots
+plot(sol)
+
+using Flux, DiffEqFlux
+p = param([2.2 1.0;2.0 0.4]) # Tweaked Initial Parameter Array
+params = Flux.Params([p])
+
+function predict_adjoint() # Our 1-layer neural network
+  diffeq_adjoint(p,prob,Tsit5(),saveat=0.0:0.1:10.0)
+end
+
+loss_adjoint() = sum(abs2,x-1 for x in predict_adjoint())
+
+data = Iterators.repeated((), 100)
+opt = ADAM(0.1)
+cb = function () #callback function to observe training
+  display(loss_adjoint())
+end
+
+predict_adjoint()
+
+# Display the ODE with the initial parameter values.
+cb()
+Flux.train!(loss_adjoint, params, data, opt, cb = cb)
+
+@test loss_adjoint() < 1