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diff --git a/docs/ml-ann.md b/docs/ml-ann.md index d5ddd92af1..6e763e8f41 100644 --- a/docs/ml-ann.md +++ b/docs/ml-ann.md @@ -48,76 +48,15 @@ MLPC employes backpropagation for learning the model. We use logistic loss funct <div class="codetabs"> <div data-lang="scala" markdown="1"> - -{% highlight scala %} -import org.apache.spark.ml.classification.MultilayerPerceptronClassifier -import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator -import org.apache.spark.mllib.util.MLUtils -import org.apache.spark.sql.Row - -// Load training data -val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_multiclass_classification_data.txt").toDF() -// Split the data into train and test -val splits = data.randomSplit(Array(0.6, 0.4), seed = 1234L) -val train = splits(0) -val 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) -val layers = Array[Int](4, 5, 4, 3) -// create the trainer and set its parameters -val trainer = new MultilayerPerceptronClassifier() - .setLayers(layers) - .setBlockSize(128) - .setSeed(1234L) - .setMaxIter(100) -// train the model -val model = trainer.fit(train) -// compute precision on the test set -val result = model.transform(test) -val predictionAndLabels = result.select("prediction", "label") -val evaluator = new MulticlassClassificationEvaluator() - .setMetricName("precision") -println("Precision:" + evaluator.evaluate(predictionAndLabels)) -{% endhighlight %} - +{% include_example scala/org/apache/spark/examples/ml/MultilayerPerceptronClassifierExample.scala %} </div> <div data-lang="java" markdown="1"> +{% include_example java/org/apache/spark/examples/ml/JavaMultilayerPerceptronClassifierExample.java %} +</div> -{% highlight java %} -import org.apache.spark.api.java.JavaRDD; -import org.apache.spark.ml.classification.MultilayerPerceptronClassificationModel; -import org.apache.spark.ml.classification.MultilayerPerceptronClassifier; -import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator; -import org.apache.spark.mllib.regression.LabeledPoint; -import org.apache.spark.mllib.util.MLUtils; - -// Load training data -String path = "data/mllib/sample_multiclass_classification_data.txt"; -JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(sc, path).toJavaRDD(); -DataFrame dataFrame = sqlContext.createDataFrame(data, LabeledPoint.class); -// Split the data into train and test -DataFrame[] splits = dataFrame.randomSplit(new double[]{0.6, 0.4}, 1234L); -DataFrame train = splits[0]; -DataFrame 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 precision on the test set -DataFrame result = model.transform(test); -DataFrame predictionAndLabels = result.select("prediction", "label"); -MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator() - .setMetricName("precision"); -System.out.println("Precision = " + evaluator.evaluate(predictionAndLabels)); -{% endhighlight %} +<div data-lang="python" markdown="1"> +{% include_example python/ml/multilayer_perceptron_classification.py %} </div> </div> |