test: minor style changes for mlp-test (#340)

examples/mnist/mlp-test.jl

@@ -2,32 +2,30 @@
 # features of MXNet.jl in this example in order to detect regression errors.
 
 module MNISTTest
+
 using MXNet
 using Base.Test
 
 include("mnist-data.jl")
 
 function get_mnist_mlp()
-  mlp = @mx.chain mx.Variable(:data)             =>
+  @mx.chain mx.Variable(:data)                   =>
     mx.FullyConnected(name=:fc1, num_hidden=128) =>
     mx.Activation(name=:relu1, act_type=:relu)   =>
     mx.FullyConnected(name=:fc2, num_hidden=64)  =>
     mx.Activation(name=:relu2, act_type=:relu)   =>
     mx.FullyConnected(name=:fc3, num_hidden=10)  =>
     mx.SoftmaxOutput(name=:softmax)
-  return mlp
 end
 
-function get_mnist_data(batch_size=100)
-  return get_mnist_providers(batch_size)
-end
+get_mnist_data(batch_size = 100) = get_mnist_providers(batch_size)
 
 function mnist_fit_and_predict(optimizer, initializer, n_epoch)
   mlp = get_mnist_mlp()
   train_provider, eval_provider = get_mnist_data()
 
   # setup model
-  model = mx.FeedForward(mlp, context=mx.cpu())
+  model = mx.FeedForward(mlp, context = mx.cpu())
 
   # fit parameters
   cp_prefix = "mnist-test-cp"
@@ -73,12 +71,25 @@ function mnist_fit_and_predict(optimizer, initializer, n_epoch)
 end
 
 function test_mnist_mlp()
+  info("MNIST::SGD")
   @test mnist_fit_and_predict(mx.SGD(lr=0.1, momentum=0.9), mx.UniformInitializer(0.01), 2) > 90
+
+  info("MNIST::ADAM")
   @test mnist_fit_and_predict(mx.ADAM(), mx.NormalInitializer(), 2) > 90
+
+  info("MNIST::AdaGrad")
   @test mnist_fit_and_predict(mx.AdaGrad(), mx.NormalInitializer(), 2) > 90
+
+  info("MNIST::AdaDelta")
   @test mnist_fit_and_predict(mx.AdaDelta(), mx.NormalInitializer(), 2) > 90
+
+  info("MNIST::AdaMax")
   @test mnist_fit_and_predict(mx.AdaMax(), mx.NormalInitializer(), 2) > 90
+
+  info("MNIST::RMSProp")
   @test mnist_fit_and_predict(mx.RMSProp(), mx.NormalInitializer(), 2) > 90
+
+  info("MNIST::Nadam")
   @test mnist_fit_and_predict(mx.Nadam(), mx.NormalInitializer(), 2) > 90
 end