Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/8262#discussion_r37427126
--- Diff: docs/ml-ann.md ---
@@ -0,0 +1,129 @@
+---
+layout: global
+title: Multilayer perceptron classifier - ML
+displayTitle: <a href="ml-guide.html">ML</a> - Multilayer perceptron
classifier
+---
+
+
+`\[
+\newcommand{\R}{\mathbb{R}}
+\newcommand{\E}{\mathbb{E}}
+\newcommand{\x}{\mathbf{x}}
+\newcommand{\y}{\mathbf{y}}
+\newcommand{\wv}{\mathbf{w}}
+\newcommand{\av}{\mathbf{\alpha}}
+\newcommand{\bv}{\mathbf{b}}
+\newcommand{\N}{\mathbb{N}}
+\newcommand{\id}{\mathbf{I}}
+\newcommand{\ind}{\mathbf{1}}
+\newcommand{\0}{\mathbf{0}}
+\newcommand{\unit}{\mathbf{e}}
+\newcommand{\one}{\mathbf{1}}
+\newcommand{\zero}{\mathbf{0}}
+\]`
+
+
+Multilayer perceptron classifier (MLPC) is a classifier based on the
[feedforward artificial neural
network](https://en.wikipedia.org/wiki/Feedforward_neural_network).
+MLPC consists of multiple layers of nodes.
+Each layer is fully connected to the next layer in the network. Nodes in
the input layer represent the input data. All other nodes maps inputs to the
outputs
+by performing linear combination of the inputs with the node's weights
`$\wv$` and bias `$\bv$` and applying an activation function.
+It can be written in matrix form for MLPC with `$K+1$` layers as follows:
+`\[
+\mathrm{y}(\x) = \mathrm{f_K}(...\mathrm{f_2}(\wv_2^T\mathrm{f_1}(\wv_1^T
\x+b_1)+b_2)...+b_K)
+\]`
+Nodes in intermediate layers use sigmoid (logistic) function:
+`\[
+\mathrm{f}(z_i) = \frac{1}{1 + e^{-z_i}}
+\]`
+Nodes in the output layer use softmax function:
+`\[
+\mathrm{f}(z_i) = \frac{e^{z_i}}{\sum_{k=1}^N e^{z_k}}
+\]`
+The number of nodes `$N$` in the output layer corresponds to the number of
classes.
+
+MLPC employes backpropagation for learning the model. We use logistic loss
function for optimization and L-BFGS as optimization routine.
+
+**Examples**
+
+<div class="codetabs">
+
+<div data-lang="scala" markdown="1">
+
+{% highlight scala %}
+import org.apache.spark.ml.classification.MultilayerPerceptronClassifier
+import org.apache.spark.mllib.evaluation.MulticlassMetrics
+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").map { case
Row(p: Double, l: Double) => (p, l) }
+val metrics = new MulticlassMetrics(predictionAndLabels)
+println("Precision:" + metrics.precision)
+{% endhighlight %}
+
+</div>
+
+<div data-lang="java" markdown="1">
+
+{% highlight java %}
+
+import org.apache.spark.SparkConf;
+import org.apache.spark.SparkContext;
+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.mllib.evaluation.MulticlassMetrics;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.util.MLUtils;
+import org.apache.spark.sql.DataFrame;
+import org.apache.spark.sql.SQLContext;
+
+public class MultilayerPerceptronClassifierTest {
+
+ public static void main( String[] args )
+ {
+ SparkConf conf = new SparkConf().setAppName("Multilayer perceptron
classifier example").setMaster("local");
+ SparkContext sc = new SparkContext(conf);
+ SQLContext sql = new SQLContext(sc);
+ String path =
"data/mllib/sample_multiclass_classification_data.txt";
+ // Load training data
+ JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(sc,
path).toJavaRDD();
+ // Split the data into train and test
+ JavaRDD<LabeledPoint>[] splits = data.randomSplit(new
double[]{0.6, 0.4}, 1234L);
+ DataFrame train = sql.createDataFrame(splits[0],
LabeledPoint.class);
--- End diff --
`sql` -> `sqlContext`. I would suggest creating `DataFrame` first and then
call `randomSplit`.
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