Merge pull request #23 from SciML/nn

Add basic iris NN example

Author: utkarsh530

Project.toml

@@ -6,9 +6,11 @@ version = "1.0.0-DEV"
 [deps]
 CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
 DiffEqGPU = "071ae1c0-96b5-11e9-1965-c90190d839ea"
+MLDatasets = "eb30cadb-4394-5ae3-aed4-317e484a6458"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SciMLBase = "0bca4576-84f4-4d90-8ffe-ffa030f20462"
 Setfield = "efcf1570-3423-57d1-acb7-fd33fddbac46"
+SimpleChains = "de6bee2f-e2f4-4ec7-b6ed-219cc6f6e9e5"
 StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
 
 [compat]

examples/neural_network/nn.jl

@@ -0,0 +1,62 @@
+using SimpleChains
+using IterTools
+using MLDatasets
+using Random
+dataset =  MLDatasets.Iris().dataframe
+
+data = Array(dataset)
+data = data[shuffle(1:end), :]
+
+function mapstrtoclass(flower)
+    if string(flower) == "Iris-setosa"
+        return UInt32(1)
+    elseif string(flower) == "Iris-versicolor"
+        return UInt32(2)
+    elseif string(flower) == "Iris-virginica"
+        return UInt32(3)
+    end
+end
+ytrain = map(mapstrtoclass, data[:, 5])
+lenet = SimpleChain(
+    static(4),
+    TurboDense{true}(tanh, 20),
+    TurboDense{true}(identity, 3),
+)
+lenetloss = SimpleChains.add_loss(lenet, LogitCrossEntropyLoss(ytrain))
+
+p = SimpleChains.init_params(lenet);
+xtrain = Float32.(Array(data[:, 1:4]'))
+G = SimpleChains.alloc_threaded_grad(lenet);
+
+lenetloss(xtrain, p)
+
+report = let mlpdloss = lenetloss, X=xtrain
+p -> begin
+      let train = mlpdloss(X, p)
+        @info "Loss:" train
+      end
+    end
+end
+
+for _ in 1:3
+    @time SimpleChains.train_unbatched!(
+      G, p, lenetloss, xtrain, SimpleChains.ADAM(), 5000
+    );
+    report(p)
+end
+
+p = SimpleChains.init_params(lenet);
+
+lenetloss(xtrain, p)
+
+using Optimization, PSOGPU
+
+lb = -ones(length(p)) .* 10
+ub = ones(length(p)) .* 10
+prob = OptimizationProblem((u,data) -> lenetloss(data, u), p, xtrain; lb = lb, ub = ub)
+
+n_particles = 1000
+
+sol = solve(prob,
+    ParallelPSOKernel(n_particles; gpu = false, threaded = true),
+    maxiters = 1000)