神经网络与深度学习 使用Python实现基于梯度下降算法的神经网络和自制仿MNIST数据集的手写数字分类可视化程序 web版本
来源：互联网 发布：dragonfly python 编辑：程序博客网 时间：2024/05/06 10:38
github 地址:https://github.com/tudoupaisimalingshu/Handwritten-numeral-recognition
本文描述并实现了一个可视化的基于Python平台和梯度下降算法的神经网络的手写数字识别程序。采用web服务，用户在前台页面的手写板中输入手写数字，并可以输入神经网络进行学习，同时还能进行预测。较好的解决了MNIST数据库以及传统手写数字识别程序过于抽象的问题。但如效果图所示，准确度还大大有待提高，这也是今后努力的方向。
一、效果图
二、整体架构
前台手写板使用开源项目，项目地址：https://github.com/szimek/signature_pad
鉴于项目所需图片最终要压缩成28*28黑白图片，因此我做了些许调整，将手写板缩小，将笔迹放大，并将背景由无色透明改为白色不透明（signature_pad.js）
同时对主页html做了修改，添加了提交训练数据按钮、预测数字按钮，并为按钮添加了JavaScript代码。提交训练数据按钮点击后，将手写板和输入框数字使用Ajax传递到后台，后台将其保存到文件中，并加入训练数据集。预测数字按钮添加后，将手写板数据传递到后台，后台使用之前编写的神经网络进行预测，并将结果返回前台。
后台采用Python平台下的web.py，未采用笔者熟悉的JavaWeb平台，以方便调用前面的Python代码。
三、前端代码
1、页面HTML代码
<!doctype html><html lang="en"><head> <meta charset="utf-8"> <title>Signature Pad demo</title> <meta name="description" content="Signature Pad - HTML5 canvas based smooth signature drawing using variable width spline interpolation."> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta name="apple-mobile-web-app-capable" content="yes"> <meta name="apple-mobile-web-app-status-bar-style" content="black"> <link rel="stylesheet" href="css/signature-pad.css"> <script type="text/javascript"> var _gaq = _gaq || []; _gaq.push(['_setAccount', 'UA-39365077-1']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); </script> <script src="/static/js/jquery-1.8.2.js"></script> <script type="text/javascript"> function submitCanvas() { var canvas = document.getElementById("canvas"); var dataURL = canvas.toDataURL("image/png"); //alert(dataURL); url="Judge?dataURL="+dataURL; //alert(url); $.ajax({ type: "POST", url:"/Judge", async:false, dataType:"html", data:{dataURL:dataURL}, success:function(msg){ alert(msg); } }); } function joinExercise() { var number = document.getElementById("number").value; var canvas = document.getElementById("canvas"); var dataURL = canvas.toDataURL("image/png"); //alert(dataURL); url="Judge"; //alert(url); $.ajax({ type: "POST", url:"/Join", async:false, dataType:"html", data:{dataURL:dataURL,number:number}, success:function(msg){ alert(msg); } }); } </script></head><body onselectstart="return false"> <a id="github" href="https://github.com/szimek/signature_pad"> <img style="position: absolute; top: 0; right: 0; border: 0;" src="https://s3.amazonaws.com/github/ribbons/forkme_right_gray_6d6d6d.png" alt="Fork me on GitHub"> </a> <div id="signature-pad" class="m-signature-pad"> <div class="m-signature-pad--body"> <canvas id="canvas"></canvas> </div> <div class="m-signature-pad--footer"> <div class="description"> <input id="number" class="number"/> </div> <div class="left"> <button type="button" class="button clear" data-action="clear">Clear</button> </div> <div class="right"> <button type="button" class="button save" onclick="joinExercise()">joinExercise</button> <button type="button" class="button save" onclick="submitCanvas()">submitCanvas</button> </div> </div> </div> <script src="js/signature_pad.js"></script> <script src="js/app.js"></script></body></html>2、采用的signature_pad.js修改部分
function SignaturePad(canvas, options) { //this.minWidth = opts.minWidth || 0.5; //this.maxWidth = opts.maxWidth || 2.5; this.minWidth = opts.minWidth || 8.5; this.maxWidth = opts.maxWidth || 15.5; //20170806 //this.penColor = opts.penColor || 'black'; //this.backgroundColor = opts.backgroundColor || 'rgba(0,0,0,0)'; this.penColor = opts.penColor || 'black'; this.backgroundColor = opts.backgroundColor || 'rgba(255,255,255,1.0)'; };1、web.py后台核心代码
import web#web.py库import base64#base64库，字符串<=>图片import numpy as nprender = web.template.render('templates')import network#神经网络import mnist_loader#MINIST加载net = network.Network([784, 30, 10])#新建一个三层神经网络training_data, validation_data, test_data = mnist_loader.load_data_wrapper()#获得训练数据，验证数据，测试数据training_data = list(training_data)import mnist_data_loder#自定义手写数据库加载loder = mnist_data_loder.DataLoder()#自定义手写数据库加载#training_data = list()urls = ( '/index', 'index', '/Judge','judge', '/Join','join', '/static/(.*)','StaticFile' , '/(.*.css)', 'StaticFile', # 处理css文件 '/(.*.js)', 'StaticFile', # 处理js文件 '/(.*)', 'hello')#web.py URL映射app = web.application(urls, globals())#新建web应用#如果是join请求，分发到此类处理class join: def POST(self): print("join POST ") print("input:", web.input().get("dataURL")) print("input:",web.input().get("number")) number = (int)(web.input().get("number")) dataURL = str(web.input().get("dataURL")) base64_str = dataURL.replace("data:image/png;base64,", "") #获得手写图片的base64字符串 #print(base64_str) ori_image_data = base64.b64decode(base64_str) #将base64字符串转换为图片数据 import uuid #唯一随机数 name = str(uuid.uuid1()) + ".jpg" fout = open(name, 'wb') fout.write(ori_image_data) fout.close() #将图片数据保存为图片文件 import test4 data = test4.Data2().getTestPicArray(name) #将图片文件进行压缩等处理，得到784*1数组 #print("data.shape=",data.shape) training_result = self.vectorized_result(number) #将图片对应的正确分类转化为10*1向量 #print("training_result.shape=", training_result.shape) #print("training_result=", training_result) loder.join(data,training_result) #将数据加入自定义加载器中 training_data, validation_data, test_data = loder.zip() #使用自定义加载器将数据打包成MNIST格式 training_data = list(training_data) import threading #t1 = threading.Thread(target=net.SGD, args=(training_data,30,10,3.0,test_data)) #t1.start() #net.SGD(training_data,30,10,3.0,test_data=test_data) #TODO 尝试将手写数据写入文本文件，不再使用MNIST """ len= 2len data[0]= 784data[0].shape= (784, 1)len data[1]= 10data[1].shape= (10, 1) """ return "加入训练集并后台开始训练" """ training_inputs = [np.reshape(x, (784, 1)) for x in tr_d[0]] training_results = [vectorized_result(y) for y in tr_d[1]] training_data = zip(training_inputs, training_results) """ def vectorized_result(self,j): """Return a 10-dimensional unit vector with a 1.0 in the jth position and zeroes elsewhere. This is used to convert a digit (0...9) into a corresponding desired output from the neural network.""" e = np.zeros((10, 1)) e[j] = 1.0 return eclass judge: def GET(self): print("judge GET") def POST(self): print("judge POST ") print("input:",web.input().get("dataURL")) dataURL = str(web.input().get("dataURL")) base64_str = dataURL.replace("data:image/png;base64,","") print(base64_str) ori_image_data = base64.b64decode(base64_str) import uuid name = str(uuid.uuid1()) + ".jpg" fout = open(name, 'wb') fout.write(ori_image_data) fout.close() import test4 data = test4.Data2().getTestPicArray(name) #print(data) print("开始训练，训练集有:",len(training_data)) net.SGD(training_data, 30, 10, 3.0, test_data=test_data) print("训练完成，开始预测") return "预测的结果为:",net.cjtest(data)class StaticFile: def GET(self, file): print("调用StaticFile GET. file=",file) web.seeother('/static/'+file); #重定向class index: def GET(self): html = open("index.html",encoding="utf-8") return html.read()class hello: def GET(self, name): html = open("index.html", encoding="utf-8") return html.read() """ print("name=", name) if not name: return render.hello2("welcome") # return open(r"hello2.html").read() return render.hello2(name) :param name: :return: """if __name__ == "__main__": #net.SGD(training_data, 30, 10, 2.0, test_data=test_data) print("神经网络初始化完成，启动web服务中...") app.run()2、自定义仿MINIST数据加载类
import pickleimport gzip# Third-party librariesimport numpy as npclass DataLoder(): def __init__(self): tr_d, va_d, te_d = self.load_data() #self.training_inputs = [np.reshape(x, (784, 1)) for x in tr_d[0]] #self.training_results = [self.vectorized_result(y) for y in tr_d[1]] #以上是MNIST加载过程 self.training_inputs = [] self.training_results = [] #自定义则清空 #self.numbers = [] #training_inputs = [] #training_results = [] import csv #导入csv模块，将数据以csv格式存储起来 try: csv_reader = csv.reader(open('shouxie.csv', encoding='utf-8')) for row in csv_reader: if len(row) == 794: # print(row) # print(row[0:784]) # print(row[784:794]) input = [] for i in row[0:784]: input.append(float(i)) result = [] for f in row[784:794]: # print(f) result.append(float(f)) print("input", input) print("result", result) inputs = np.zeros((784, 1)) for i in range(0, 784): inputs[i] = input[i] # print("inputs", inputs) e = np.zeros((10, 1)) for i in range(0, 10): e[i] = result[i] # print("result'=", e) self.training_inputs.append(inputs) self.training_results.append(e) except: print("文件打开失败") print("从shouxie.csv中读入", len(self.training_inputs), "条数据") training_data = zip(self.training_inputs, self.training_results) print("初始化训练数据成功，现有",len(self.training_inputs),"条数据") def join2(self, training_input, number): self.training_inputs.append(training_input) self.numbers.append(number) print("加入训练数据成功，现有", len(self.training_inputs), "条数据") for training_input, training_result in zip(self.training_inputs, self.training_results): data = [] for i in training_input: data.append(i[0]) data.append(number) self.spamwriter.writerow(data) def join(self,training_input,training_result): self.training_inputs.append(training_input) self.training_results.append(training_result) print("加入训练数据成功，现有",len(self.training_inputs),"条数据") import csv with open('shouxie.csv', 'w+') as csvfile: spamwriter = csv.writer(csvfile, dialect='excel') for training_input,training_result in zip(self.training_inputs,self.training_results): data = [] for i in training_input: data.append(i[0]) for j in training_result: data.append(j[0]) spamwriter.writerow(data) print("data",data) csvfile.close() print("训练数据已保存到文件中") def zip(self): tr_d, va_d, te_d = self.load_data() training_data = zip(self.training_inputs, self.training_results) validation_inputs = [np.reshape(x, (784, 1)) for x in va_d[0]] validation_data = zip(validation_inputs, va_d[1]) test_inputs = [np.reshape(x, (784, 1)) for x in te_d[0]] test_data = zip(test_inputs, te_d[1]) return (training_data, validation_data, test_data) def load_data(self): training_inputs = [] training_results = [] import csv try: csv_reader = csv.reader(open('shouxie.csv', encoding='utf-8')) for row in csv_reader: if len(row) == 794: # print(row) # print(row[0:784]) # print(row[784:794]) input = [] for i in row[0:784]: input.append(float(i)) result = [] for f in row[784:794]: # print(f) result.append(float(f)) print("input", input) print("result", result) inputs = np.zeros((784, 1)) for i in range(0, 784): inputs[i] = input[i] #print("inputs", inputs) e = np.zeros((10, 1)) for i in range(0, 10): e[i] = result[i] #print("result'=", e) training_inputs.append(inputs) training_results.append(e) except: print("文件打开失败") print("从shouxie.csv中读入", len(training_inputs), "条数据") training_data = zip(training_inputs, training_results) f = gzip.open('mnist.pkl.gz', 'rb') #training_data, validation_data, test_data = pickle.load(f, encoding="latin1") training_data, validation_data, test_data = pickle.load(f, encoding="latin1") f.close() return (training_data, validation_data, test_data) def load_data_wrapper(self): tr_d, va_d, te_d = self.load_data() training_inputs = [np.reshape(x, (784, 1)) for x in tr_d[0]] training_results = [self.vectorized_result(y) for y in tr_d[1]] training_data = zip(training_inputs, training_results) validation_inputs = [np.reshape(x, (784, 1)) for x in va_d[0]] validation_data = zip(validation_inputs, va_d[1]) test_inputs = [np.reshape(x, (784, 1)) for x in te_d[0]] test_data = zip(test_inputs, te_d[1]) """ for data in training_data: print("-------------一条数据---------------") print("len=", len(data)) print("len data[0]=", len(data[0])) print("data[0].shape=",data[0].shape) print("len data[1]=", len(data[1])) print("data[1].shape=", data[1].shape) print("---------------------------------") """ #print(training_data[0]) return (training_data, validation_data, test_data) def vectorized_result(self,j): e = np.zeros((10, 1)) e[j] = 1.0 return e #load_data_wrapper()3、预测方法
def cjtest(self,data): #print(self.feedforward(data)) return np.argmax(self.feedforward(data))
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