Fix mx.chain var reference in macro (#299)

fix #298

src/nn-factory.jl

@@ -21,7 +21,7 @@ fully connected layers.
 
 Returns the constructed MLP.
 """
-function MLP(input, spec; hidden_activation::Base.Symbol=:relu, prefix=gensym())
+function MLP(input, spec; hidden_activation::Symbol=:relu, prefix=gensym())
   spec = convert(Vector{Union{Int,Tuple}}, spec)
 
   n_layer = length(spec)

src/symbolic-node.jl

@@ -893,7 +893,8 @@ macro chain(layers)
             return esc(layer)
         else
             if @capture(layer, f_(x__))
-                return :($f($last_layer, $(x...)))
+                x′ = esc.(x)
+                return :($f($last_layer, $(x′...)))
             else
                 throw(AssertionError("$layer is not a valid function call and cannot be chained."))
             end

test/unittest/symbolic-node.jl

@@ -23,6 +23,19 @@ function test_chain()
   @test mx.list_arguments(model) == [:data,:fc1_weight,:fc1_bias,:fc2_weight,:fc2_bias]
   @test mx.list_outputs(model) == [:fc2_output]
   @test mx.list_auxiliary_states(model) == Symbol[]
+
+  let layerconfig = [20, 10, 6]
+    model = @mx.chain mx.Variable(:data) =>
+      mx.MLP(layerconfig, prefix=:magic_) =>
+      mx.LinearRegressionOutput(mx.Variable(:label))
+
+    @test mx.list_arguments(model) == [
+      :data,
+      :magic_fc1_weight, :magic_fc1_bias,
+      :magic_fc2_weight, :magic_fc2_bias,
+      :magic_fc3_weight, :magic_fc3_bias,
+      :label]
+  end
 end
 
 function test_internal()