example: fix batchsize config in regression-example (#342)

examples/regression-example.jl

@@ -13,18 +13,31 @@ generate_inputs(mean, var, size) = rand(MvNormal(mean, var), size)
 output(data) = sin.(data[1:1,:]).*sin.(data[2:2,:])./(data[1:1,:].*data[2:2,:])
 
 # create training and evaluation data sets
-mean=[0.0;0.0]
-var=[1.0 0.0;0.0 1.0]
+mean=[0.0; 0.0]
+var=[1.0 0.0; 0.0 1.0]
 samplesize  = 5000
 TrainInput = generate_inputs(mean, var, samplesize)
 TrainOutput = output(TrainInput)
 ValidationInput = generate_inputs(mean, var, samplesize)
 ValidationOutput = output(ValidationInput)
 
 # how to set up data providers using data in memory
-batchsize = 100 # can adjust this later, but must be defined now for next line
-trainprovider = mx.ArrayDataProvider(:data => TrainInput, batch_size=batchsize, shuffle=true, :label => TrainOutput)
-evalprovider = mx.ArrayDataProvider(:data => ValidationInput, batch_size=batchsize, shuffle=true, :label => ValidationOutput)
+function data_source(batchsize = 100)
+  train = mx.ArrayDataProvider(
+    :data => TrainInput,
+    :label => TrainOutput,
+    batch_size = batchsize,
+    shuffle = true,
+    )
+  valid = mx.ArrayDataProvider(
+    :data => ValidationInput,
+    :label => ValidationOutput,
+    batch_size = batchsize,
+    shuffle = true,
+    )
+
+  train, valid
+end
 
 # create a two hidden layer MPL: try varying num_hidden, and change tanh to relu,
 # or add/remove a layer
@@ -35,7 +48,7 @@ net = @mx.chain     mx.Variable(:data) =>
                     mx.Activation(act_type=:tanh) =>
                     mx.FullyConnected(num_hidden=3) =>
                     mx.Activation(act_type=:tanh) =>
-                    mx.FullyConnected(num_hidden=1) =>        
+                    mx.FullyConnected(num_hidden=1) =>
                     mx.LinearRegressionOutput(mx.Variable(:label))
 
 # final model definition, don't change, except if using gpu
@@ -47,11 +60,22 @@ optimizer = mx.ADAM()
 
 # train, reporting loss for training and evaluation sets
 # initial training with small batch size, to get to a good neighborhood
-batchsize = 200
-mx.fit(model, optimizer, initializer=mx.NormalInitializer(0.0,0.1), eval_metric=mx.MSE(), trainprovider, eval_data=evalprovider, n_epoch = 20)
+trainprovider, evalprovider = data_source(#= batchsize =# 200)
+mx.fit(model, optimizer, trainprovider,
+       initializer = mx.NormalInitializer(0.0, 0.1),
+       eval_metric = mx.MSE{mx.NDArray{Float32,1}}(),
+       eval_data = evalprovider,
+       n_epoch = 20,
+       callbacks = [mx.speedometer()])
 # more training with the full sample
-batchsize = samplesize
-mx.fit(model, optimizer, eval_metric=mx.MSE(), trainprovider, eval_data=evalprovider, n_epoch = 20)
+trainprovider, evalprovider = data_source(#= batchsize =# samplesize)
+mx.fit(model, optimizer, trainprovider,
+       initializer = mx.NormalInitializer(0.0, 0.1),
+       eval_metric = mx.MSE{mx.NDArray{Float32,1}}(),
+       eval_data = evalprovider,
+       n_epoch = 500,  # previous setting is batchsize = 200, epoch = 20
+                       # implies we did (5000 / 200) * 20 times update in previous `fit`
+       callbacks = [mx.speedometer()])
 
 # obtain predictions
 plotprovider = mx.ArrayDataProvider(:data => ValidationInput, :label => ValidationOutput)