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diff --git a/docs/ml-ann.md b/docs/ml-ann.md new file mode 100644 index 0000000000..d5ddd92af1 --- /dev/null +++ b/docs/ml-ann.md @@ -0,0 +1,123 @@ +--- +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.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 %} + +</div> + +<div data-lang="java" markdown="1"> + +{% 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> + +</div> diff --git a/docs/ml-guide.md b/docs/ml-guide.md index c64fff7c03..de8fead352 100644 --- a/docs/ml-guide.md +++ b/docs/ml-guide.md @@ -179,6 +179,7 @@ There are now several algorithms in the Pipelines API which are not in the lower * [Decision Trees for Classification and Regression](ml-decision-tree.html) * [Ensembles](ml-ensembles.html) * [Linear methods with elastic net regularization](ml-linear-methods.html) +* [Multilayer perceptron classifier](ml-ann.html) # Code Examples |