The ANN we have referred to so far is called a feed-forward neural network since the connections between the units and layers do not form a cycle and move only in one direction (from the input layer to the output layer).
Let's implement the feed-forward neural network example with simple Spark ML code:
object FeedForwardNetworkWithSpark {
def main(args:Array[String]): Unit ={
val recordReader:RecordReader = new CSVRecordReader(0,",")
val conf = new SparkConf()
.setMaster("spark://master:7077")
.setAppName("FeedForwardNetwork-Iris")
val sc = new SparkContext(conf)
val numInputs:Int = 4
val outputNum = 3
val iterations =1
val multiLayerConfig:MultiLayerConfiguration = new
NeuralNetConfiguration.Builder()
.seed(12345)
.iterations(iterations)
.optimizationAlgo(OptimizationAlgorithm...