diff --git a/.travis.yml b/.travis.yml index d90ad059d3..38eeb116c4 100644 --- a/.travis.yml +++ b/.travis.yml @@ -4,7 +4,7 @@ os: - linux - osx julia: - - 1.0 + - 1.1 - nightly matrix: allow_failures: diff --git a/test/runtests.jl b/test/runtests.jl index b227b1f17e..300ce8bb52 100644 --- a/test/runtests.jl +++ b/test/runtests.jl @@ -27,9 +27,9 @@ linear_analytic = (u0,p,t) -> [exp(-(t^2)/2)/(1+t+t^3) + t^2] prob = ODEProblem(ODEFunction(linear,analytic=linear_analytic),[1f0],(0.0f0,1.0f0)) chain = Flux.Chain(Dense(1,5,σ),Dense(5,1)) opt = Flux.ADAM(0.1, (0.9, 0.95)) -sol = solve(prob,NeuralNetDiffEq.nnode(chain,opt),dt=1/5f0) +sol = solve(prob,NeuralNetDiffEq.nnode(chain,opt),verbose = true, dt=1/5f0) err = sol.errors[:l2] -sol = solve(prob,NeuralNetDiffEq.nnode(chain,opt),dt=1/20f0) +sol = solve(prob,NeuralNetDiffEq.nnode(chain,opt),verbose = true, dt=1/20f0) sol.errors[:l2]/err < 0.5 #= @@ -45,9 +45,9 @@ linear_analytic = (u0,p,t) -> exp(-t/5)*(u0 + sin(t)) prob = ODEProblem(ODEFunction(linear,analytic=linear_analytic),0.0f0,(0.0f0,1.0f0)) chain = Flux.Chain(Dense(1,5,σ),Dense(5,1)) opt = Flux.ADAM(0.1, (0.9, 0.95)) -sol = solve(prob,NeuralNetDiffEq.nnode(chain,opt),dt=1/5f0) +sol = solve(prob,NeuralNetDiffEq.nnode(chain,opt),verbose = true, dt=1/5f0) err = sol.errors[:l2] -sol = solve(prob,NeuralNetDiffEq.nnode(chain,opt),dt=1/20f0) +sol = solve(prob,NeuralNetDiffEq.nnode(chain,opt),verbose = true, dt=1/20f0) sol.errors[:l2]/err < 0.5 #=