com.intel.analytics.bigdl.nn.TanhShrink.scala Maven / Gradle / Ivy

/*
 * 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

import com.intel.analytics.bigdl.nn.abstractnn.TensorModule
import com.intel.analytics.bigdl.tensor.Tensor
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric

import scala.reflect.ClassTag

/**
 * A simple layer for each element of the input tensor, do the following operation
 * during the forward process:
 *    [f(x) = tanh(x) - 1]
 */

@SerialVersionUID(7783278258985544682L)
class TanhShrink[T: ClassTag](
  implicit ev: TensorNumeric[T]) extends TensorModule[T] {
  private val tanh = new Tanh[T]()

  override def updateOutput(input: Tensor[T]): Tensor[T] = {
    val th = tanh.forward(input)
    output.resizeAs(input).copy(input)
    output.add(ev.fromType[Int](-1), th)
    output
  }

  override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = {
    val dth = tanh.updateGradInput(input, gradOutput)
    gradInput.resizeAs(input).copy(gradOutput)
    gradInput.add(ev.fromType[Int](-1), dth)
    gradInput
  }

  override def toString: String = s"nn.TanhShrink"
}

object TanhShrink {
  def apply[@specialized(Float, Double) T: ClassTag]()
      (implicit ev: TensorNumeric[T]) : TanhShrink[T] = {
    new TanhShrink[T]()
  }
}