SReLU

spark/dl/src/main/scala/com/intel/analytics/bigdl/nn/SReLU.scala

@@ -16,7 +16,7 @@
 
 package com.intel.analytics.bigdl.nn
 
-import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, Initializable, TensorModule}
+import com.intel.analytics.bigdl.nn.abstractnn._
 import com.intel.analytics.bigdl.tensor.Tensor
 import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
 import com.intel.analytics.bigdl.utils.serializer._
@@ -48,7 +48,7 @@ import scala.reflect.ClassTag
 
 @SerialVersionUID(7173457290010080259L)
 class SReLU[T: ClassTag](val shape: Array[Int], val sharedAxes: Array[Int] = null)(
-  implicit ev: TensorNumeric[T]) extends TensorModule[T] with Initializable {
+  implicit ev: TensorNumeric[T]) extends TensorModule[T] with Initializable with IdentityOutputShape {
   import SReLU._
   val weightsLen = 4
   val weights: Array[Tensor[T]] = Array.fill[Tensor[T]](4)(Tensor[T]())

spark/dl/src/main/scala/com/intel/analytics/bigdl/nn/keras/SReLU.scala

@@ -0,0 +1,55 @@
+/*
+ * Copyright 2016 The BigDL Authors.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.intel.analytics.bigdl.nn.keras
+
+import com.intel.analytics.bigdl.nn.abstractnn._
+import com.intel.analytics.bigdl.tensor.Tensor
+import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
+import com.intel.analytics.bigdl.utils.Shape
+
+import scala.reflect.ClassTag
+
+class SReLU[T: ClassTag](SharedAxes: Array[Int] = null,
+                         var inputShape: Shape = null
+  )(implicit ev: TensorNumeric[T])
+  extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) {
+
+  override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = {
+    val shape = inputShape.toSingle().toArray
+    if (SharedAxes == null) {
+      val layer = com.intel.analytics.bigdl.nn.SReLU(shape.slice(1, shape.length))
+      layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]]
+    } else
+    {
+      val layer = com.intel.analytics.bigdl.nn.SReLU(shape.slice(1, shape.length), SharedAxes)
+      layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]]
+    }
+  }
+}
+
+
+object SReLU {
+
+  def apply[@specialized(Float, Double) T: ClassTag](
+    SharedAxes: Array[Int] = null,
+    inputShape: Shape = null
+    )(implicit ev: TensorNumeric[T]) : SReLU[T] = {
+    new SReLU[T](
+      SharedAxes,
+      inputShape)
+  }
+}

spark/dl/src/test/scala/com/intel/analytics/bigdl/keras/nn/SReLUSpec.scala

@@ -0,0 +1,59 @@
+/*
+ * Copyright 2016 The BigDL Authors.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.intel.analytics.bigdl.keras.nn
+
+import com.intel.analytics.bigdl.keras.KerasBaseSpec
+import com.intel.analytics.bigdl.nn.keras.{Sequential => KSequential}
+import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule
+import com.intel.analytics.bigdl.nn.keras.SReLU
+import com.intel.analytics.bigdl.tensor.Tensor
+import com.intel.analytics.bigdl.utils.Shape
+
+class SReLUSpec extends KerasBaseSpec{
+
+  "SReLU" should "be the same as Keras" in {
+    val kerasCode =
+      """
+        |input_tensor = Input(shape=[2, 3])
+        |input = np.random.uniform(-1, 1, [1, 2, 3])
+        |# input = np.array([[[0.1, 0.2, 0.3], [0.1, 0.2, 0.3]]])
+        |output_tensor = SReLU(a_left_init='one', t_right_init='one')(input_tensor)
+        |# output_tensor = SReLU()(input_tensor)
+        |model = Model(input=input_tensor, output=output_tensor)
+      """.stripMargin
+    val seq = KSequential[Float]()
+    val srelu = SReLU[Float](null, inputShape = Shape(2, 3))
+    seq.add(srelu)
+    checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]],
+      kerasCode)
+  }
+
+  "SReLU 3D" should "be the same as Keras" in {
+    val kerasCode =
+      """
+        |input_tensor = Input(shape=[3, 24])
+        |input = np.random.random([2, 3, 24])
+        |output_tensor = SReLU(shared_axes=[1, 2])(input_tensor)
+        |model = Model(input=input_tensor, output=output_tensor)
+      """.stripMargin
+    val seq = KSequential[Float]()
+    val srelu = SReLU[Float](Array(1, 2), inputShape = Shape(3, 24))
+    seq.add(srelu)
+    checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]],
+      kerasCode)
+  }
+}