如何从多层感知分类中获取神经元权重

Question

我在pySpark（使用spark1.6.0）中使用MLP多类分类器，或多或少遵循here中的示例。在

因为我对一次训练模型感兴趣，然后在不同的数据集上使用已经训练过的模型，所以我想检索神经元权重（就像使用pickle包为pythonsklearn解释的here）。在

但是，在阅读documentation之后，我无法获得模型的权重和内部参数。在

如果有帮助，我的代码是：

# Importing PySpark libraries
from pyspark import SparkConf, SparkContext
from pyspark.sql import SQLContext, HiveContext
from pyspark.ml.classification import MultilayerPerceptronClassifier
from pyspark.ml.evaluation import MulticlassClassificationEvaluator

#%% Codigo inicio

if __name__ == "__main__":

    conf  = SparkConf().setAppName("prueba_features")
    sc    = SparkContext(conf=conf)
    hc    = HiveContext(sc)
    sqlc  = SQLContext(sc)

    # Load training data
    data = sqlc.read.format("libsvm")\
        .load("/user/sample_multiclass_classification_data.txt")

    # print data
    print("\nData set: \n{}".format(data))

    # Split the data into train and test
    splits = data.randomSplit([0.6, 0.4], 1234)
    train = splits[0]
    test = splits[1]

    # print sets
    print("\nTraining set: \n{}".format(train))
    print("\nTest set: \n{}".format(test))

    # 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 precision on the test set
    result = model.transform(test)
    predictionAndLabels = result.select("prediction", "label")
    evaluator_prec = MulticlassClassificationEvaluator(metricName="precision")
    evaluator_rec = MulticlassClassificationEvaluator(metricName="recall")
    evaluator_f1 = MulticlassClassificationEvaluator(metricName="f1")

    # print fitting precision and results
    print("\nResults: \n{}".format(result))

    print("\nKPIs")
    print("Precision: " + str(evaluator_prec.evaluate(predictionAndLabels)))
    print("Recall: " + str(evaluator_rec.evaluate(predictionAndLabels)))
    print("F1-score: " + str(evaluator_f1.evaluate(predictionAndLabels)))

    # we end the SparkContext
    sc.stop()

如果可能的话，有人知道如何使用pySpark MLP吗？在

Answer 1

您要查找的方法是^{}：

weights

the weights of layers.

New in version 2.0.0.

正如注释所说，您需要将Spark版本更新到至少2.0才能使用它。在