Python搭建BP神经网络测试mnist数据集

mnist数据集官网:http://yann.lecun.com/exdb/mnist/

mnist数据集是一个被广泛应用(“嚼烂”)的手写体数字数据集,包含60000个训练样本及10000个测试样本,以字节形式存储。在官网下载到的数据是安装包形式,安装包及其解压后数据形式如下:


我们需要注意的是哪个文件是什么数据集,我将对应关系陈列如下:

t10k-images    :    测试图像数据集

t10k-labels      :测试标签数据集

train-images    :    训练图像数据集

train-labels    :    训练标签数据集

关于标签和图像的对应关系我不在此处表达,因为看到这篇文章的同学们应该都是对数据集有一定了解的同学们。

我在搭建神经网络测试数据的时候,参考了网上很多的代码,也搭建了很多不同的网络,引用mnist数据集的方法也测试了许多次。但也失败了好多,我最终找到了如下方法可以达到预期目标。


首先将mnist数据集转换为CSV格式:

(参考网站:https://blog.csdn.net/Albert201605/article/details/79893585)

我将个人转换代码张贴如下:

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 pixin image) +'\n')

        f.close()

        o.close()

        l.close()


train_image_path ='E:/College/Graduate_Paper/mnist_test/train-images.idx3-ubyte'

train_label_path ='E:/College/Graduate_Paper/mnist_test/train-labels.idx1-ubyte'

test_image_path ='E:/College/Graduate_Paper/mnist_test/t10k-images.idx3-ubyte'

test_label_path ='E:/College/Graduate_Paper/mnist_test/t10k-labels.idx1-ubyte'

convert( train_image_path , train_label_path ,'E:/College/Graduate_Paper/mnist_test/mnist_train.csv' ,60000 )

convert( test_image_path , test_label_path ,'E:/College/Graduate_Paper/mnist_test/mnist_test.csv' ,10000 )

print('Convert finished!')

转换完成后文件格式如下所示:


在此时,我们依旧无法自然语言方式直接读取测试集内的数据。


其次,将CSV格式的数据集读入神经网络进行训练测试:

(参考网址:https://blog.csdn.net/ebzxw/article/details/81591437)

代码张贴如下:

import numpy

import scipy.special


class neuralNetwork:

    def __init__(self, inputnodes, hiddennodes, outputnodes, learningrate):

        self.inodes = inputnodes

        self.hnodes = hiddennodes

        self.onodes = outputnodes

        self.lr = learningrate

        self.wih = (numpy.random.normal(0.0,pow(self.hnodes, -0.5), (self.hnodes,self.inodes)))#shape (200,784)

        self.who = (numpy.random.normal(0.0,pow(self.onodes, -0.5), (self.onodes,self.hnodes)))#shape (10,200)

        self.activation_function =lambda x: scipy.special.expit(x)

pass

    print('初始化神经网络完成')

    def train(self, inputs_list, targets_list):

        inputs = numpy.array(inputs_list,ndmin=2).T#shape (784,1)

        targets = numpy.array(targets_list,ndmin=2).T#shape (10,1)

        hidden_inputs = numpy.dot(self.wih, inputs)#shape (200,1)

        hidden_outputs =self.activation_function(hidden_inputs)

        final_inputs = numpy.dot(self.who, hidden_outputs)#shape (10,1)

        final_outputs =self.activation_function(final_inputs)

        output_errors = targets - final_outputs#shape (10,1)

        hidden_errors = numpy.dot(self.who.T, output_errors)#shape (200,1)

        self.who +=self.lr * numpy.dot((output_errors * final_outputs * (1.0 - final_outputs)),

numpy.transpose(hidden_outputs))

        self.wih +=self.lr * numpy.dot((hidden_errors * hidden_outputs * (1.0 - hidden_outputs)),

numpy.transpose(inputs))

pass

    print('神经网络训练完成')

    def query(self, inputs_list):

        inputs = numpy.array(inputs_list,ndmin=2).T

        hidden_inputs = numpy.dot(self.wih, inputs)

        hidden_outputs =self.activation_function(hidden_inputs)

        final_inputs = numpy.dot(self.who, hidden_outputs)

        final_outputs =self.activation_function(final_inputs)

return final_outputs

print('神经网络测试完成')


#设置神经网络初始参数

input_nodes =784    # 28 * 28 = 784

hidden_nodes =200

output_nodes =10

learning_rate =0.1

n = neuralNetwork(input_nodes, hidden_nodes, output_nodes, learning_rate)

print('神经网络参数传入完成')


#训练神经网络 

training_data_file =open('E:/College/Graduate_Paper/mnist_test/mnist_train.csv','r')

training_data_list = training_data_file.readlines()

training_data_file.close()

# epochs is the number of times the training data set is used for training

epochs =5

for ein range(epochs):

    for recordin training_data_list:

        all_values = record.split(',')

        inputs = (numpy.asfarray(all_values[1:]) /255.0 *0.99) +0.01

        targets = numpy.zeros(output_nodes) +0.01

        targets[int(all_values[0])] =0.99

        n.train(inputs, targets)

        pass


    print('%d times train result in the followings:'%e)

    test_data_file =open('E:/College/Graduate_Paper/mnist_test/mnist_test.csv','r')

    test_data_list = test_data_file.readlines()

    test_data_file.close()

    scorecard = []


    for recordin test_data_list:

        all_values = record.split(',')

        correct_label =int(all_values[0])

        inputs = (numpy.asfarray(all_values[1:]) /255.0 *0.99) +0.01

        outputs = n.query(inputs)

        label = numpy.argmax(outputs)

        if (label == correct_label):

            scorecard.append(1)

        else:

            scorecard.append(0)

        pass

    scorecard_array = numpy.asarray(scorecard)

    print('performance = ', scorecard_array.sum() / scorecard_array.size)

    pass


代码运行结果展示如下:


更改参数对神经网络识别正确率影响如下所示:


测试数据仅供参考,转载请注明出处。若有疑问,请私信我(不经常上),看到后会尽快与您讨论。若有侵权,请联系我删除此文。

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