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

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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

import com.intel.analytics.bigdl.nn.abstractnn.{Initializable, TensorModule}
import com.intel.analytics.bigdl.tensor.Tensor
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
import com.intel.analytics.bigdl.utils.RandomGenerator._
import com.intel.analytics.bigdl.utils.{T, Table}

import scala.reflect.ClassTag

/**
 * multiply a single scalar factor to the incoming data
 */

@SerialVersionUID(7706562484586989118L)
class Mul[T: ClassTag](implicit ev: TensorNumeric[T])
  extends TensorModule[T] with Initializable {

  val weight = Tensor[T](1)
  val gradWeight = Tensor[T](1)

  {
    val wInit = RandomUniform(-1.0, 1.0)
    setInitMethod(wInit)
  }

  override def reset(): Unit = {
    weightInitMethod.init(weight, VariableFormat.ONE_D)
    zeroGradParameters()
  }

  override def updateOutput(input: Tensor[T]): Tensor[T] = {
    output.resizeAs(input).copy(input)
    output.mul(weight(Array(1)))
    output
  }

  override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = {
    gradInput.resizeAs(input).zero()
    gradInput.add(weight(Array(1)), gradOutput)
    gradInput
  }


  override def accGradParameters(input: Tensor[T], gradOutput: Tensor[T]): Unit = {
    if (scaleW != 0) {
      gradWeight.add(ev.times(input.dot(gradOutput), ev.fromType[Double](scaleW)))
    }
  }

  override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = {
    (Array(this.weight), Array(this.gradWeight))
  }

  override def canEqual(other: Any): Boolean = other.isInstanceOf[Mul[T]]

  override def equals(other: Any): Boolean = other match {
    case that: Mul[T] =>
      super.equals(that) &&
        (that canEqual this) &&
        weight == that.weight &&
        gradWeight == that.gradWeight
    case _ => false
  }

  override def hashCode(): Int = {
    def getHashCode(a: Any): Int = if (a == null) 0 else a.hashCode()
    val state = Seq(super.hashCode(), weight, gradWeight)
    state.map(getHashCode).foldLeft(0)((a, b) => 31 * a + b)
  }
}

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