content: /*
   * 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 org.apache.spark.examples.ml;
  
  // $example on$
  import org.apache.spark.sql.Dataset;
  import org.apache.spark.sql.Row;
  import org.apache.spark.sql.SparkSession;
  import org.apache.spark.ml.classification.MultilayerPerceptronClassificationModel;
  import org.apache.spark.ml.classification.MultilayerPerceptronClassifier;
  import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator;
  // $example off$
  
  /**
   * An example for Multilayer Perceptron Classification.
   */
  public class JavaMultilayerPerceptronClassifierExample {
  
    public static void main(String[] args) {
      SparkSession spark = SparkSession
        .builder()
        .appName("JavaMultilayerPerceptronClassifierExample")
        .getOrCreate();
  
      // $example on$
      // Load training data
      String path = "data/mllib/sample_multiclass_classification_data.txt";
      Dataset<Row> dataFrame = spark.read().format("libsvm").load(path);
  
      // Split the data into train and test
      Dataset<Row>[] splits = dataFrame.randomSplit(new double[]{0.6, 0.4}, 1234L);
      Dataset<Row> train = splits[0];
      Dataset<Row> test = splits[1];
  
      // specify layers for the neural network:
      // input layer of size 4 (features), two intermediate of size 5 and 4
      // and output of size 3 (classes)
      int[] layers = new int[] {4, 5, 4, 3};
  
      // create the trainer and set its parameters
      MultilayerPerceptronClassifier trainer = new MultilayerPerceptronClassifier()
        .setLayers(layers)
        .setBlockSize(128)
        .setSeed(1234L)
        .setMaxIter(100);
  
      // train the model
      MultilayerPerceptronClassificationModel model = trainer.fit(train);
  
      // compute accuracy on the test set
      Dataset<Row> result = model.transform(test);
      Dataset<Row> predictionAndLabels = result.select("prediction", "label");
      MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator()
        .setMetricName("accuracy");
  
      System.out.println("Test set accuracy = " + evaluator.evaluate(predictionAndLabels));
      // $example off$
  
      spark.stop();
    }
  }
  