org.deeplearning4j.scalnet.layers.convolutional.Convolution2D.scala Maven / Gradle / Ivy

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/*
 *
 *  * Copyright 2016 Skymind,Inc.
 *  *
 *  *    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 org.deeplearning4j.scalnet.layers.convolutional

import org.deeplearning4j.nn.conf.layers.ConvolutionLayer
import org.deeplearning4j.nn.weights.WeightInit
import org.deeplearning4j.scalnet.layers.Layer
import org.deeplearning4j.scalnet.regularizers.{NoRegularizer, WeightRegularizer}


/**
  * 2D convolution for structured image-like inputs. Input should have
  * three dimensions: height (number of rows), width (number of columns),
  * and number of channels. Convolution is over height and width.
  *
  * @author David Kale
  */
class Convolution2D(
    nFilter: Int,
    kernelSize: List[Int],
    nChannels: Int = 0,
    stride: List[Int] = List(1, 1),
    padding: List[Int] = List(0, 0),
    val weightInit: WeightInit = WeightInit.VI,
    val activation: String = "identity",
    val regularizer: WeightRegularizer = NoRegularizer(),
    val dropOut: Double = 0.0,
    override val name: String = null)
  extends Convolution(kernelSize, stride, padding, nChannels, nFilter)
    with Layer {

  override def compile: org.deeplearning4j.nn.conf.layers.Layer =
    new ConvolutionLayer.Builder(kernelSize.head, kernelSize.last)
      .nIn(inputShape.last)
      .nOut(outputShape.last)
      .stride(stride.head, stride.last)
      .padding(padding.head, padding.last)
      .weightInit(weightInit)
      .activation(activation)
      .l1(regularizer.l1)
      .l2(regularizer.l2)
      .dropOut(dropOut)
      .name(name)
      .build()
}