revive basic train code

src/cost.jl

@@ -0,0 +1,8 @@
+export mse, mse!
+
+function mse!(∇, pred, target)
+  map!(-, ∇, pred, target)
+  sumabs2(∇)/2
+end
+
+mse(pred, target) = mse(similar(pred), pred, target)

src/utils.jl

@@ -4,3 +4,26 @@ const AArray = AbstractArray
 
 onehot(label, labels) = [i == label for i in labels]
 onecold(pred, labels = 1:length(pred)) = labels[findfirst(pred, maximum(pred))]
+
+function train!(m::Model, train, test = []; epoch = 1, batch = 10, η = 0.1)
+    i = 0
+    ∇ = zeros(length(train[1][2]))
+    for _ in 1:epoch
+      for (x, y) in shuffle!(train)
+        i += 1
+        err = mse!(∇, m(x), y)
+        back!(m, ∇)
+        i % batch == 0 && update!(m, η/batch)
+      end
+      @show accuracy(m, test)
+    end
+    return m
+end
+
+function accuracy(m::Model, data)
+  correct = 0
+  for (x, y) in data
+    onecold(m(x)) == onecold(y) && (correct += 1)
+  end
+  return correct/length(data)
+end