多层感知机（MLP）算法原理及Spark MLlib调用实例（Scala/Java/Python）

多层感知机

算法简介：

多层感知机是基于反向人工神经网络（feedforwardartificial neural network）。多层感知机含有多层节点，每层节点与网络的下一层节点完全连接。输入层的节点代表输入数据，其他层的节点通过将输入数据与层上节点的权重w以及偏差b线性组合且应用一个激活函数，得到该层输出。多层感知机通过方向传播来学习模型，其中我们使用逻辑损失函数以及L-BFGS。K＋1层多层感知机分类器可以写成矩阵形式如下：



中间层节点使用sigmoid方程：



输出层使用softmax方程：



输出层中N代表类别数目。

参数：

featuresCol:

类型：字符串型。

含义：特征列名。

labelCol:

类型：字符串型。

含义：标签列名。

layers:

类型：整数数组型。

含义：层规模，包括输入规模以及输出规模。

maxIter:

类型：整数型。

含义：迭代次数（>=0）。

predictionCol:

类型：字符串型。

含义：预测结果列名。

seed:

类型：长整型。

含义：随机种子。

stepSize:

类型：双精度型。

含义：每次迭代优化步长。

tol:

类型：双精度型。

含义：迭代算法的收敛性。

示例：

Scala:

import org.apache.spark.ml.classification.MultilayerPerceptronClassifier
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator

// Load the data stored in LIBSVM format as a DataFrame.
val data = spark.read.format("libsvm")
.load("data/mllib/sample_multiclass_classification_data.txt")
// 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 accuracy on the test set
val result = model.transform(test)
val predictionAndLabels = result.select("prediction", "label")
val evaluator = new MulticlassClassificationEvaluator()
.setMetricName("accuracy")
println("Accuracy: " + evaluator.evaluate(predictionAndLabels))

Java:

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;

// 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("Accuracy = " + evaluator.evaluate(predictionAndLabels));

Python：

from pyspark.ml.classification import MultilayerPerceptronClassifier
from pyspark.ml.evaluation import MulticlassClassificationEvaluator

# Load training data
data = spark.read.format("libsvm")\
.load("data/mllib/sample_multiclass_classification_data.txt")
# Split the data into train and test
splits = data.randomSplit([0.6, 0.4], 1234)
train = splits[0]
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)
layers = [4, 5, 4, 3]
# create the trainer and set its parameters
trainer = MultilayerPerceptronClassifier(maxIter=100, layers=layers, blockSize=128, seed=1234)
# train the model
model = trainer.fit(train)
# compute accuracy on the test set
result = model.transform(test)
predictionAndLabels = result.select("prediction", "label")
evaluator = MulticlassClassificationEvaluator(metricName="accuracy")
print("Accuracy: " + str(evaluator.evaluate(predictionAndLabels)))