bug fix: DLModel prediction

spark/dl/src/main/scala/org/apache/spark/ml/DLEstimator.scala

@@ -391,7 +391,8 @@ class DLModel[@specialized(Float, Double) T: ClassTag](
     val localBatchSize = $(batchSize)
 
     val resultRDD = dataFrame.rdd.mapPartitions { rowIter =>
-      val localModel = modelBroadCast.value()
+      // call the evaluate method to enable DLModel.train=False during the predict process
+      val localModel = modelBroadCast.value().evaluate()
       rowIter.grouped(localBatchSize).flatMap { rowBatch =>
         val samples = rowBatch.map { row =>
           val features = featureFunc(row, featureColIndex)

spark/dl/src/test/scala/com/intel/analytics/bigdl/optim/DLEstimatorSpec.scala

@@ -91,6 +91,27 @@ class DLEstimatorSpec extends FlatSpec with Matchers with BeforeAndAfter {
     assert(correct > nRecords * 0.8)
   }
 
+  "An DLEstimator" should "throws exception when DLModel is predicting with DLModel.train=True" in {
+    val model = new Sequential().add(Linear[Float](6, 2))
+      .add(Dropout[Float](initP = 0.5))
+      .add(LogSoftMax[Float])
+    val criterion = ClassNLLCriterion[Float]()
+    val estimator = new DLEstimator[Float](model, criterion, Array(6), Array(1))
+      .setBatchSize(nRecords)
+      .setOptimMethod(new LBFGS[Float]())
+      .setLearningRate(0.1)
+      .setMaxEpoch(maxEpoch)
+    val data = sc.parallelize(smallData)
+    val df = sqlContext.createDataFrame(data).toDF("features", "label")
+    val dlModel = estimator.fit(df)
+    dlModel.isInstanceOf[DLModel[_]] should be(true)
+    val correct = dlModel.transform(df).select("label", "prediction").rdd.filter {
+      case Row(label: Double, prediction: Seq[Double]) =>
+        label == prediction.indexOf(prediction.max) + 1
+    }.count()
+    assert(correct > nRecords * 0.8)
+  }
+
   "An DLEstimator" should "support different FEATURE types" in {
     val model = new Sequential().add(Linear[Float](6, 2)).add(LogSoftMax[Float])
     val criterion = ClassNLLCriterion[Float]()