loop over simulated data sets

test/neural_net_solution.jl

@@ -43,36 +43,31 @@ s = ParameterSchedulers.Stateful(CosAnneal(.001, 1e-6, 500))
 optim = Flux.setup(Flux.Adam(), neural_net)  # will store optimiser momentum, etc.
 
 
-# for i in 1:10
-n_simul = 1000
-n_burnin = 500
+for i in 1:10
+    n_simul = 1000
+    n_burnin = 500
 
-shcks = randn(n_shocks, n_burnin + n_simul)
+    shcks = randn(n_shocks, n_burnin + n_simul)
 
-sims = get_irf(Smets_Wouters_2007, shocks = shcks, periods = 0, levels = true)
+    sims = get_irf(Smets_Wouters_2007, shocks = shcks, periods = 0, levels = true)
 
-normalised_sims = Flux.normalise(collect(sims[:,n_burnin:end,1]), dims=1)
+    normalised_sims = Flux.normalise(collect(sims[:,n_burnin:end,1]), dims=1)
 
-normalised_sim_slices = Float32.(vcat(normalised_sims[:,1:end - 1], shcks[:,n_burnin + 1:n_burnin + n_simul]))
+    normalised_sim_slices = Float32.(vcat(normalised_sims[:,1:end - 1], shcks[:,n_burnin + 1:n_burnin + n_simul]))
 
-normalised_out_slices = Float32.(normalised_sims[:,2:end])
+    normalised_out_slices = Float32.(normalised_sims[:,2:end])
 
-# loss() = sqrt(sum(abs2, out_slices - neural_net(sim_slices)))
-# loss() = Flux.mse(neural_net(sim_slices), out_slices)
-
-# Training loop, using the whole data set 1000 times:
-losses = []
-for epoch in 1:5000
-    # for (x, y) in loader
+    # Training loop, using the whole data set 1000 times:
+    losses = []
+    for epoch in 1:1000
         lss, grads = Flux.withgradient(neural_net) do nn
-            # Evaluate model and loss inside gradient context:
             sqrt(Flux.mse(nn(normalised_sim_slices), normalised_out_slices))
         end
         Flux.adjust!(optim, ParameterSchedulers.next!(s))
         Flux.update!(optim, neural_net, grads[1])
         push!(losses, loss)  # logging, outside gradient context
-        if epoch % 100 == 0 println("Epoch: $epoch; Loss: $lss; Opt state: $(optim.layers[1].weight.rule)") end
-    # end
+        if epoch % 10 == 0 println("Epoch: $epoch; Loss: $lss; Opt state: $(optim.layers[1].weight.rule)") end
+    end
 end