大数据处理实验（五）使用mllib完成mnist手写识别任务

http://yann.lecun.com/exdb/mnist/

将解压得到的数据集文件与csv_to_libsvm.py和mnist_to_csv.py文件（见下方代码）放置在同一目录下先执行mnist_to_csv.py

def convert(imgf, labelf, outf, n):
    f = open(imgf, "rb")
    o = open(outf, "w")
    l = open(labelf, "rb")

    f.read(16)
    l.read(8)
    images = []

    for i in range(n):
        image = [ord(l.read(1))]
        for j in range(28 * 28):
            image.append(ord(f.read(1)))
        images.append(image)

    for image in images:
        o.write(",".join(str(pix) for pix in image) + "n")
    f.close()
    o.close()
    l.close()


# 数据集在 http://yann.lecun.com/exdb/mnist/ 下载
convert("train-images.idx3-ubyte", "train-labels.idx1-ubyte",
        "mnist_train.csv", 60000)
convert("t10k-images.idx3-ubyte", "t10k-labels.idx1-ubyte",
        "mnist_test.csv", 10000)

执行完成后在根目录下产生以下两个.csv文件:

mnist_train.csv
mnist_test.csv

再执行csv_to_libsvm.py

import csv


def execute(data, savepath):

    csv_reader = csv.reader(open(data))
    f = open(savepath, 'wb')
    for line in csv_reader:
        label = line[0]
        features = line[1:]
        libsvm_line = label + ' '

        for index, feature in enumerate(features):
            libsvm_line += str(index + 1) + ':' + feature + ' '
        f.write(bytes(libsvm_line.strip() + 'n', 'UTF-8'))

    f.close()


execute('mnist_train.csv', 'mnist_train.libsvm')
execute('mnist_test.csv', 'mnist_test.libsvm')

执行完成后在根目录下产生以下两个.libsvm文件:

mnist_test.libsvm
mnist_train.libsvm

进入spark-master

sudo docker exec -it spark-master /bin/bash

输入命令将数据集文件移动到共享文件夹下（即实验四yml文件里配置的路径）

sudo mv mnist_test.libsvm  /home/ryu/spark
sudo mv mnist_train.libsvm  /home/ryu/spark

spark-shell位于/spark/bin目录下，使用./spark-shell命令进入spark-shell。

读取训练集

val train = spark.read.format("libsvm").load("/data/mnist_train.libsvm")

读取测试集

val test = spark.read.format("libsvm").load("/data/mnist_test.libsvm")

定义网络结构。如果计算机性能不好可以降低隐藏层的参数。

val layers = Array[Int](784, 784, 784, 10)

导入多层感知机与多分类评价器。

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

使用多层感知机初始化训练器。

val trainer = new MultilayerPerceptronClassifier().setLayers(layers).setBlockSize(128).setSeed(1234L).setMaxIter(100)

var model = trainer.fit(train)

输入测试集进行识别

val result = model.transform(test)

获取测试结果中的预测结果与实际结果

val predictionAndLabels = result.select("prediction", "label")

初始化评价器

val evaluator = new MulticlassClassificationEvaluator().setMetricName("accuracy")

计算识别精度

println(s"Test set accuracy = ${evaluator.evaluate(predictionAndLabels)}")

在result上创建临时视图

result.toDF.createOrReplaceTempView("deep_learning")

使用Spark SQL的方式计算识别精度

spark.sql("select (select count(*) from deep_learning where label=prediction)/count(*) as accuracy from deep_learning").show()