com.lewuathe.dllib.solver.MultiLayerPerceptron.scala Maven / Gradle / Ivy

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/*
 * Licensed to the Apache Software Foundation (ASF) under one
 * or more contributor license agreements.  See the NOTICE file
 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * to you 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.lewuathe.dllib.solver

import org.apache.spark.ml.linalg.Vector
import org.apache.spark.ml.param.ParamMap
import org.apache.spark.sql.Dataset

import breeze.linalg.{Vector => brzVector}

import com.lewuathe.dllib.Blob
import com.lewuathe.dllib.network.Network

/**
  * Simple multilayer perceptron implementing backpropagation.
  * @param uid
  * @param network
  */
class MultiLayerPerceptron(override val uid: String, network: Network)
    extends Solver[Vector, MultiLayerPerceptron, MultiLayerPerceptronModel](
      network) {
  override def copy(extra: ParamMap): MultiLayerPerceptron = defaultCopy(extra)

  override protected def train(
      dataset: Dataset[_]): MultiLayerPerceptronModel = {
    val newModel   = trainInternal(dataset, model)
    val newNetwork = new Network(newModel, network.graph)
    copyValues(new MultiLayerPerceptronModel(uid, newNetwork))
  }
}

class MultiLayerPerceptronModel(override val uid: String, network: Network)
    extends SolverModel[Vector, MultiLayerPerceptronModel](network) {
  override protected def predict(features: Vector): Double = {
    val brzFeatures = brzVector[Double](features.toArray)
    predictInternal(Blob.uni(brzFeatures))
  }
}