Add tensorboard support (intel-analytics#2667)

* add tensorboard support

* fix style

* fix style2

* fix test

scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/estimator/Estimator.scala

@@ -19,6 +19,7 @@ import com.intel.analytics.bigdl.{Criterion, Module}
 import com.intel.analytics.bigdl.dataset.MiniBatch
 import com.intel.analytics.bigdl.optim._
 import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
+import com.intel.analytics.bigdl.visualization.{TrainSummary, ValidationSummary}
 import com.intel.analytics.zoo.feature.{DiskFeatureSet, DistributedFeatureSet, FeatureSet}
 import com.intel.analytics.zoo.pipeline.api.keras.models.InternalDistriOptimizer
 import org.apache.log4j.Logger
@@ -72,6 +73,10 @@ class Estimator[T: ClassTag] private[zoo](
   protected val gradientClipping: ArrayBuffer[GradientClipping] =
     new ArrayBuffer[GradientClipping]()
 
+  protected var logDir: String = null
+
+  protected var appName: String = null
+
   /**
    * Clear gradient clipping parameters. In this case, gradient clipping will not be applied.
    * In order to take effect, it needs to be called before fit.
@@ -100,6 +105,22 @@ class Estimator[T: ClassTag] private[zoo](
   def setGradientClippingByL2Norm(clipNorm: Double): Unit = {
     this.gradientClipping.append(L2NormClipping(clipNorm))
   }
+
+  def setTensorBoard(logDir: String, appName: String): Unit = {
+    this.logDir = logDir
+    this.appName = appName
+  }
+
+  def getTrainSummary(tag: String): Array[(Long, Float, Double)] = {
+    this.internalEstimator.asInstanceOf[InternalDistriOptimizer[T]].getTrainSummary(tag)
+  }
+
+  def getValidationSummary(tag: String): Array[(Long, Float, Double)] = {
+    this.internalEstimator.asInstanceOf[InternalDistriOptimizer[T]].getValidationSummary(tag)
+  }
+
+
+
   /**
    * Train model with provided trainSet and criterion.
    * The training will end until the endTrigger is triggered.
@@ -128,6 +149,13 @@ class Estimator[T: ClassTag] private[zoo](
             .setCheckpointDir(modelDir)
             .setOptimMethods(optimMethods)
             .setNumOfSlice(d.numOfSlice)
+          if ((logDir != null)  && (appName != null)) {
+            val trainSummary = TrainSummary(logDir, appName)
+            val valSummary = ValidationSummary(logDir, appName)
+            internalEstimator.asInstanceOf[Optimizer[_, _]]
+              .setTrainSummary(trainSummary)
+              .setValidationSummary(valSummary)
+          }
         }
       case _ => throw new IllegalArgumentException("Unsupported FeatureSet type.")
     }