数字识别手写神经网络实现基于MNIST数据集
MNIST数据集自行baidu下载，解压后放到项目文件里
git详细代码仓库：https://github.com/justDoForever/deep_learning/digital_recognition.py (python 2.7)
理论分析: https://www.zybuluo.com/hanbingtao/note/476663
代码如下:
# encoding:utf-8
#神经网络实现数字识别
import struct
import sys
from datetime import datetime
from fcn import Network
def transpose(param):
pass
class Loader(object):
#数据基础加载器
def __init__(self,path,number):
'''
:param path: 数据文件路经
:param number: 文件中样本个数
'''
self.path = path;
self.number = number;
def get_file_content(self):
file = open(self.path,'rb')
content = file.read()
file.close();
return content;
def to_int(self,byte):
#将unsigned char转成int
return struct.unpack('B',byte)[0]
class ImgaeLoader(Loader):
#这里的self即指向ImageLoader基类自身也指向Loader父类
def load(self):
#首先得到文件内容
content = self.get_file_content()
data_set = []
for i in range(self.number):
data_set.append(self.get_one_sample(self.get_picture(content,i)))
return data_set
def get_picture(self, content, index):
#获得图片二维矩阵
#t=图片样本前16位不是标签所以要从第17位开始（content[16])
start = index * 28 * 28 + 16;
picture = [];
for i in range(28):
picture.append([])
for j in range(28):
picture[i].append(self.to_int(content[start + i * 28 + j]))
# if self.to_int(content[start + i * 28 + j]) != 0:
# sys.stdout.write('*')
# else:
# sys.stdout.write(' ')
# print ' '
return picture;
def get_one_sample(self, picture):
#将图片二维矩阵转为一维输入向量
sample = [];
for i in range(28):
for j in range(28):
sample.append(picture[i][j])
return sample;
class LabelLoder(Loader):
def load(self):
#标签前8位不是标签所以要从第9位开始（content[8])
content = self.get_file_content();
label_set = [];
for i in range(self.number):
label_set.append(self.norm(content[i + 8]))
return label_set;
def norm(self,label):
#将label值一个0到9的数字转为一个10维的输出向量
label_vec = [];
label_value = self.to_int(label)
for i in range(10):
if i == label_value:
label_vec.append(0.9)
else:
label_vec.append(0.1)
return label_vec
def get_train_data_set():
image_loader = ImgaeLoader('train-images.idx3-ubyte',60)
label_loader = LabelLoder('train-labels.idx1-ubyte',60)
return image_loader.load(),label_loader.load();
def get_test_data_set():
image_loader = ImgaeLoader('t10k-images.idx3-ubyte',10)
label_loader = LabelLoder('t10k-labels.idx1-ubyte',10)
return image_loader.load(),label_loader.load()
def now():
return datetime.now().strftime("%c")
def get_result(input):
max_value = - 1;
max_index = -1;
for i in range(len(input)):
if input[i] > max_value:
max_index = i;
max_value = input[i]
return max_index;
def evaluate(net, test_data_set, test_labels):
error = 0;
for i in range(len(test_labels)):
#样本和预测输出的虽然都是10维的一个向量，但是不能直接比较相等来代表预测是否正确
#因为预测值的10维向量不是0.1,0.9组成的，只能通过最大值的位置作为结果来比较
label = get_result(test_labels[i])
predict = get_result(net.predict(test_data_set[i]))
if label != predict:
error += 1;
return float(error) / float(len(test_labels))
def train_and_evaluate():
epoch = 1;
last_error_ratio = 1.0;
#获取数据集并处理
# train_data_set,tain_labels = transpose(get_train_data_set())
# test_data_set,test_labels = transpose(get_test_data_set())
train_data_set, tain_labels = get_train_data_set()
test_data_set, test_labels = get_test_data_set()
#搭建神经网络
net = Network([784,300,10]);
#训练
while True:
net.train(tain_labels,train_data_set,1,0.01)
epoch += 1;
print '%s epoch: %d \n' % (now(),epoch)
if epoch % 10 == 0:
#每训练10次就通过测试样本计算错误率
error_ratio = evaluate(net,test_data_set,test_labels)
print '%s epoch: %d error ratio : %f\n' % (now(),epoch,error_ratio)
#如果错误率升高就停止训练防止过拟合，否则更新last_error_ratio为最新的错误率
if error_ratio > last_error_ratio:
break;
else:
last_error_ratio = error_ratio;
if __name__ == '__main__':
train_and_evaluate();
# train_data_set, tain_labels = get_train_data_set()
# print train_data_set,' ',tain_labels
# a = [[1,2],[4,5]]
# print a[-1]
作者：Justdoforever
