/*******************************************************************************
  * Copyright (c) 2015-2018 Skymind, Inc.
  *
  * This program and the accompanying materials are made available under the
  * terms of the Apache License, Version 2.0 which is available at
  * https://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.
  *
  * SPDX-License-Identifier: Apache-2.0
 ******************************************************************************/
package org.deeplearning4j.scalnet.layers.noise

import org.deeplearning4j.nn.conf.dropout.{ GaussianDropout => JGaussianDropout }
import org.deeplearning4j.nn.conf.layers.DropoutLayer
import org.deeplearning4j.scalnet.layers.core.Layer

/**
  * GaussianDropout layer
  *
  * @author Max Pumperla
  */
class GaussianDropout(nOut: List[Int], nIn: List[Int], rate: Double, override val name: String) extends Layer {

  override def compile: org.deeplearning4j.nn.conf.layers.Layer =
    new DropoutLayer.Builder()
      .dropOut(new JGaussianDropout(rate))
      .nIn(inputShape.last)
      .nOut(outputShape.last)
      .name(name)
      .build()

  override val outputShape: List[Int] = nOut

  override val inputShape: List[Int] = nIn

  override def reshapeInput(newIn: List[Int]): GaussianDropout =
    new GaussianDropout(nOut, newIn, rate, name)
}

object GaussianDropout {
  def apply(nOut: Int, nIn: Int = 0, rate: Double, name: String = ""): GaussianDropout =
    new GaussianDropout(List(nOut), List(nIn), rate, name)
}
