Digit recognition example

src/main/scala/playground/TryOut.scala

@@ -4,6 +4,43 @@ import ml.dmlc.mxnet._
 import ml.dmlc.mxnet.optimizer.SGD
 
 object TryOut extends App {
+
   // model definition
   val data = Symbol.Variable("data")
+  val fc1 = Symbol.FullyConnected(name = "fc1")(Map("data" -> data, "num_hidden" -> 128))
+  val act1 = Symbol.Activation(name = "relu1")(Map("data" -> fc1, "act_type" -> "relu"))
+  val fc2 = Symbol.FullyConnected(name = "fc2")(Map("data" -> act1, "num_hidden" -> 64))
+  val act2 = Symbol.Activation(name = "relu2")(Map("data" -> fc2, "act_type" -> "relu"))
+  val fc3 = Symbol.FullyConnected(name = "fc3")(Map("data" -> act2, "num_hidden" -> 10))
+  val mlp = Symbol.SoftmaxOutput(name = "sm")(Map("data" -> fc3))
+
+  val trainDataIter = IO.MNISTIter(Map(
+    "image" -> "data/train-images-idx3-ubyte",
+    "label" -> "data/train-labels-idx1-ubyte",
+    "data_shape" -> "(1, 28, 28)",
+    "label_name" -> "sm_label",
+    "batch_size" -> "50",
+    "shuffle" -> "1",
+    "flat" -> "0",
+    "silent" -> "0",
+    "seed" -> "10"))
+
+  val valDataIter = IO.MNISTIter(Map(
+    "image" -> "data/t10k-images-idx3-ubyte",
+    "label" -> "data/t10k-labels-idx1-ubyte",
+    "data_shape" -> "(1, 28, 28)",
+    "label_name" -> "sm_label",
+    "batch_size" -> "50",
+    "shuffle" -> "1",
+    "flat" -> "0",
+    "silent" -> "0"))
+
+  val model = FeedForward.newBuilder(mlp)
+    .setContext(Context.cpu())
+    .setNumEpoch(10)
+    .setOptimizer(new SGD(learningRate = 0.1f, momentum = 0.9f, wd = 0.0001f))
+    .setTrainData(trainDataIter)
+    .setEvalData(valDataIter)
+    .build()
+
 }