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/*
* 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.{InitializationMethod, Ones, Xavier, Zeros}
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
/**
* S-shaped Rectified Linear Unit.
* It follows:
* f(x) = t^r + a^r(x - t^r) for x >= t^r,
* f(x) = x for t^r > x > t^l,
* f(x) = t^l + a^l(x - t^l) for x <= t^l.
*
* When you use this layer as the first layer of a model, you need to provide the argument
* inputShape (a Single Shape, does not include the batch dimension).
*
* @param tLeftInit Initialization function for the left part intercept. Default is Zeros.
* You can also pass in corresponding string representations such as 'zero'
* or 'normal', etc. for simple init methods in the factory method.
* @param aLeftInit Initialization function for the left part slope. Default is Xavier.
* You can also pass in corresponding string representations such as
* 'glorot_uniform', etc. for simple init methods in the factory method.
* @param tRightInit Initialization function for the right part intercept. Default is Xavier.
* You can also pass in corresponding string representations such as
* 'glorot_uniform', etc. for simple init methods in the factory method.
* @param aRightInit Initialization function for the right part slope. Default is Ones.
* You can also pass in corresponding string representations such as 'one'
* or 'normal', etc. for simple init methods in the factory method.
* @param sharedAxes Array of Int. The axes along which to share learnable parameters
* for the activation function. Default is null.
* For example, if the incoming feature maps are from a 2D convolution
* with output shape (batch, height, width, channels),
* and you wish to share parameters across space
* so that each filter only has one set of parameters,
* set 'sharedAxes = Array(1,2)'.
* @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now.
*/
class SReLU[T: ClassTag](
val tLeftInit: InitializationMethod = Zeros,
val aLeftInit: InitializationMethod = Xavier,
val tRightInit: InitializationMethod = Xavier,
val aRightInit: InitializationMethod = Ones,
val sharedAxes: Array[Int] = null,
val inputShape: Shape = null)(implicit ev: TensorNumeric[T])
extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape))
with IdentityOutputShape {
override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = {
val shape = inputShape.toSingle().toArray
val layer = com.intel.analytics.bigdl.nn.SReLU(shape.slice(1, shape.length), sharedAxes)
layer.setInitMethod(Array(tLeftInit, aLeftInit, tRightInit, aRightInit))
layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]]
}
}
object SReLU {
def apply[@specialized(Float, Double) T: ClassTag](
tLeftInit: String = "zero",
aLeftInit: String = "glorot_uniform",
tRightInit: String = "glorot_uniform",
aRightInit: String = "one",
sharedAxes: Array[Int] = null,
inputShape: Shape = null)(implicit ev: TensorNumeric[T]): SReLU[T] = {
new SReLU[T](KerasUtils.getInitMethod(tLeftInit), KerasUtils.getInitMethod(aLeftInit),
KerasUtils.getInitMethod(tRightInit), KerasUtils.getInitMethod(aRightInit),
sharedAxes, inputShape)
}
}
