【实验楼】基于BP神经网络的手写体识别——python3版

用自己的机器跑BP神经网络手写体识别，刚开始因为Python2与3之间存在不兼容，所以需要对代码本身做一定的修改。（实验地址：https://www.shiyanlou.com/courses/593）

更新：Python3代码已上传到我的GitHub中：点击打开链接

 

实验楼参考文档：https://www.shiyanlou.com/courses/593/labs/1966/document

代码提供（Python2版）:https://github.com/aosabook/500lines/tree/master/ocr

 

环境我用的是Python3.6。对参考文档中的代码主要修改了以下几个部分：

1、ocr.py文件中train函数

 

    def train(self, training_data_array):
        for data in training_data_array:
            # 前向传播得到结果向量
            y1 = np.dot(np.mat(self.theta1), np.mat(data['y0']).T)  # 修改后
            sum1 = y1 + np.mat(self.input_layer_bias)
            y1 = self.sigmoid(sum1)

            y2 = np.dot(np.array(self.theta2), y1)
            y2 = np.add(y2, self.hidden_layer_bias)
            y2 = self.sigmoid(y2)

            # 后向传播得到误差向量
            actual_vals = [0] * 10
            actual_vals[data['label']] = 1  # 修改后
            output_errors = np.mat(actual_vals).T - np.mat(y2)
            hidden_errors = np.multiply(np.dot(np.mat(self.theta2).T, output_errors), self.sigmoid_prime(sum1))

            # 更新权重矩阵与偏置向量
            self.theta1 += self.LEARNING_RATE * np.dot(np.mat(hidden_errors), np.mat(data['y0']))  # 修改后
            self.theta2 += self.LEARNING_RATE * np.dot(np.mat(output_errors), np.mat(y1).T)
            self.hidden_layer_bias += self.LEARNING_RATE * output_errors
            self.input_layer_bias += self.LEARNING_RATE * hidden_errors

2、运行中报TypeError: a bytes-like object is required, not 'str'。定位后在server.py中处理接收到的POST请求一块，因为json.dumps()返回是str，需要对此部分解码即可。

 

 

self.send_response(response_code)
        self.send_header("Content-type", "application/json")
        self.send_header("Access-Control-Allow-Origin", "*")
        self.end_headers()
        if response:
            self.wfile.write(json.dumps(response).encode())  # 修改后
        return

3、Python3中不在用BaseHTTPServer，而是并入http.server中，所以此处导入http.server即可。贴出完整的server.py文件：

 

 

# -*- coding: UTF-8 -*-
import http.server  # 修改后
import json
from ocr import OCRNeuralNetwork
import numpy as np
import random

# 服务器端配置
HOST_NAME = 'localhost'
PORT_NUMBER = 9000
# 这个值是通过运行神经网络设计脚本得到的最优值
HIDDEN_NODE_COUNT = 15

# 加载数据集
data_matrix = np.loadtxt(open('data.csv', 'rb'), delimiter=',')
data_labels = np.loadtxt(open('dataLabels.csv', 'rb'))

# 转换成list类型
data_matrix = data_matrix.tolist()
data_labels = data_labels.tolist()

# 数据集一共5000个数据，train_indice存储用来训练的数据的序号
train_indice = list(range(5000))
# 打乱训练顺序
random.shuffle(train_indice)

nn = OCRNeuralNetwork(HIDDEN_NODE_COUNT, data_matrix, data_labels, train_indice);

class JSONHandler(http.server.BaseHTTPRequestHandler):   # 修改后
    """处理接收到的POST请求"""
    def do_POST(self):
        response_code = 200
        response = ""
        var_len = int(self.headers.get('Content-Length'))
        content = self.rfile.read(var_len)
        payload = json.loads(content)

        # 如果是训练请求，训练然后保存训练完的神经网络
        if payload.get('train'):
            nn.train(payload['trainArray'])
            nn.save()
        # 如果是预测请求，返回预测值
        elif payload.get('predict'):
            try:
                print(nn.predict(data_matrix[0]))
                response = {"type": "test", "result": str(nn.predict(payload['image']))}
            except:
                response_code = 500
        else:
            response_code = 400

        self.send_response(response_code)
        self.send_header("Content-type", "application/json")
        self.send_header("Access-Control-Allow-Origin", "*")
        self.end_headers()
        if response:
            self.wfile.write(json.dumps(response).encode())  # 修改后
        return

if __name__ == '__main__':
    server_class = http.server.HTTPServer  # 修改后
    httpd = server_class((HOST_NAME, PORT_NUMBER), JSONHandler)

    try:
        # 启动服务器
        httpd.serve_forever()
    except KeyboardInterrupt:
        pass
    else:
        print("Unexpected server exception occurred.")
    finally:
        httpd.server_close()

运行结果：