多層感知機

演算法簡介：

        多層感知機是基於反向人工神經網路（feedforwardartificial neural network）。多層感知機含有多層節點，每層節點與網路的下一層節點完全連線。輸入層的節點代表輸入資料，其他層的節點通過將輸入資料與層上節點的權重w以及偏差b線性組合且應用一個啟用函式，得到該層輸出。多層感知機通過方向傳播來學習模型，其中我們使用邏輯損失函式以及L-BFGS。K＋1層多層感知機分類器可以寫成矩陣形式如下：

中間層節點使用sigmoid方程：

輸出層使用softmax方程：

輸出層中N代表類別數目。

引數：

featuresCol:

型別：字串型。

含義：特徵列名。

labelCol:

型別：字串型。

含義：標籤列名。

layers:

型別：整數陣列型。

含義：層規模，包括輸入規模以及輸出規模。

maxIter:

型別：整數型。

含義：迭代次數（>=0）。

predictionCol:

型別：字串型。

含義：預測結果列名。

seed:

型別：長整型。

含義：隨機種子。

stepSize:

型別：雙精度型。

含義：每次迭代優化步長。

tol:

型別：雙精度型。

含義：迭代演算法的收斂性。

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))
 

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));
 

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)))