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A Scala wrapper for Deeplearning4j, inspired by Keras. Scala + DL + Spark + GPUs

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/*******************************************************************************
  * 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.pooling

import org.deeplearning4j.nn.conf.layers.{ GlobalPoolingLayer, PoolingType }
import org.deeplearning4j.scalnet.layers.core.{ Layer, Node }

/**
  * 1D global max pooling layer.
  *
  * @author Max Pumperla
  */
class GlobalMaxPooling1D(nIn: Option[List[Int]] = None, override val name: String = null) extends Node with Layer {

  override def inputShape: List[Int] = nIn.getOrElse(List(0))

  override def outputShape: List[Int] = {
    val nOutChannels: Int =
      if (inputShape.nonEmpty) inputShape.last
      else 0
    if (inputShape.lengthCompare(2) == 0) {
      List[Int](inputShape.head, nOutChannels)
    } else if (nOutChannels > 0) List(nOutChannels)
    else List()
  }

  override def reshapeInput(nIn: List[Int]): GlobalMaxPooling1D =
    new GlobalMaxPooling1D(Some(nIn), name)

  override def compile: org.deeplearning4j.nn.conf.layers.Layer =
    new GlobalPoolingLayer.Builder()
      .poolingType(PoolingType.MAX)
      .name(name)
      .build()
}

object GlobalMaxPooling1D {
  def apply(nIn: Option[List[Int]] = None, name: String = null): GlobalMaxPooling1D =
    new GlobalMaxPooling1D(nIn, name)
}




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