Wrap some useful torch layers (intel#3)

* more torch layers

* update

* python wrapper

* update alias

* style

scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/python/PythonZooKeras.scala

@@ -26,6 +26,8 @@ import com.intel.analytics.bigdl.nn.Graph.ModuleNode
 import com.intel.analytics.bigdl.nn.abstractnn.Activity
 import com.intel.analytics.bigdl.nn.keras.KerasLayer
 import com.intel.analytics.zoo.pipeline.api.keras.layers._
+import com.intel.analytics.zoo.pipeline.api.keras.layers.extra._
+import com.intel.analytics.zoo.pipeline.api.keras.layers.utils.KerasUtils
 
 import scala.reflect.ClassTag
 
@@ -71,4 +73,71 @@ class PythonZooKeras[T: ClassTag](implicit ev: TensorNumeric[T]) extends PythonB
       bRegularizer, bias, toScalaShape(inputShape))
   }
 
+
+  // ================================= Torch layers in Keras Style =================================
+
+  def createZooKerasSelect(
+      dim: Int,
+      index: Int,
+      inputShape: JList[Int] = null): Select[T] = {
+    Select(dim, index, toScalaShape(inputShape))
+  }
+
+  def createZooKerasNarrow(
+      dim: Int,
+      offset: Int,
+      length: Int = 1,
+      inputShape: JList[Int] = null): Narrow[T] = {
+    Narrow(dim, offset, length, toScalaShape(inputShape))
+  }
+
+  def createZooKerasSqueeze(
+      dims: JList[Int],
+      inputShape: JList[Int] = null): Squeeze[T] = {
+    Squeeze(toScalaArray(dims), toScalaShape(inputShape))
+  }
+
+  def createZooKerasAddConstant(
+      constant: Double,
+      inputShape: JList[Int] = null): AddConstant[T] = {
+    AddConstant(constant, toScalaShape(inputShape))
+  }
+
+  def createZooKerasMulConstant(
+      constant: Double,
+      inputShape: JList[Int] = null): MulConstant[T] = {
+    MulConstant(constant, toScalaShape(inputShape))
+  }
+
+  def createZooKerasLRN2D(
+      alpha: Double = 1e-4,
+      k: Double = 1.0,
+      beta: Double = 0.75,
+      n: Int = 5,
+      dimOrdering: String = "th",
+      inputShape: JList[Int] = null): LRN2D[T] = {
+    LRN2D(alpha, k, beta, n, dimOrdering, toScalaShape(inputShape))
+  }
+
+  def createZooKerasShareConvolution2D(
+      nbFilter: Int,
+      nbRow: Int,
+      nbCol: Int,
+      init: String = "glorot_uniform",
+      activation: String = null,
+      subsample: JList[Int],
+      padH: Int = 0,
+      padW: Int = 0,
+      propagateBack: Boolean = true,
+      dimOrdering: String = "th",
+      wRegularizer: Regularizer[T] = null,
+      bRegularizer: Regularizer[T] = null,
+      bias: Boolean = true,
+      inputShape: JList[Int] = null): ShareConvolution2D[T] = {
+    new ShareConvolution2D(nbFilter, nbRow, nbCol, KerasUtils.getInitMethod(init),
+      KerasUtils.getKerasActivation(activation), toScalaArray(subsample),
+      padH, padW, propagateBack, KerasUtils.toBigDLFormat(dimOrdering),
+      wRegularizer, bRegularizer, bias, toScalaShape(inputShape))
+  }
+
 }