car-board-reg

基于CNN的车牌号识别
github：https://github.com/jmhIcoding/car-board-reg.git

数据集介绍

车牌构成

为简化实验，在该实验中默认车牌字符已经得到划分，因此车牌识别可以分解为三个区域的字符识别任务（多分类任务），共实现7个字符的识别。
例如：京A·F0236
其中第一部分 京 表示车牌所在的省市,后面紧跟的A是发牌单位,间隔符·后面的5个字符就是序号。
省市Province：
(“皖”, “沪”, “津”, “渝”, “冀”, “晋”, “蒙”, “辽”, “吉”, “黑”, “苏”, “浙”, “京”, “闽”, “赣”, “鲁”, “豫”, “鄂”, “湘”, “粤”, “桂”, “琼”, “川”, “贵”, “云”, “藏”, “陕”, “甘”, “青”, “宁”, “新”)
发牌单位Area：
(“A”,“B”,“C”,“D”,“E”,“F”,“G”,“H”,“I”,“J”,“K”,“L”,“M”,“N”,“O”,“P”,“Q”,“R”,“S”,“T”,“U”,“V”,“W”,“X”,“Y”,“Z”)
字符Letter:
(“0”,“1”,“2”,“3”,“4”,“5”,“6”,“7”,“8”,“9”,“A”,“B”,“C”,“D”,“E”,“F”,“G”,“H”,“J”,“K”,“L”,“M”,“N”,“P”,“Q”,“R”,“S”,“T”,“U”,“V”,“W”,“X”,“Y”,“Z”)

数据集目录:

└─dataset├─maps.py    ---->映射关系文件├─train     ---->训练集│  ├─area  │  ├─letter│  └─province└─val       ----->验证集├─area├─letter└─province

PIL读取Image文件

本例提供的训练集里面的每个图片都是20x20 的二值化后的灰度图，例如：

因此，我们需要使用PIL库或opencv库把灰度图转换为我们方便处理的数据形式。本人是先转化为list of list.

picReader.py

__author__ = 'jmh081701'
from PIL import  Image
def img2mat(img_filename):
#把所有的图片都resize为20x20img = Image.open(img_filename)img = img.resize((20,20))mat = [[img.getpixel((x,y)) for x in range(0,img.size[0])] for y in range(0,img.size[1])]return mat
def test():mat = img2mat("dataset\\test\\1.bmp")print(mat)print(mat[0][0],len(mat),len(mat[0]))
if __name__ == '__main__':test()

样例输出：

[[0, 0, 0, 0, 144, 212, 74, 17, 15, 60, 60, 62, 64, 67, 67, 68, 35, 0, 0, 0], [0, 0, 0, 0, 28, 119, 255, 101, 61, 233, 255, 255, 255, 255, 255, 255, 241, 44, 0, 0], [0, 0, 0, 0, 0, 15, 170, 92, 8, 14, 34, 31, 29, 29, 24, 74, 226, 38, 0, 0], [0, 0, 67, 220, 83, 4, 0, 0, 0, 84, 160, 0, 0, 0, 0, 52, 170, 11, 0, 0], [0, 0, 71, 255, 105, 10, 0, 0, 75, 230, 246, 124, 5, 0, 0, 49, 188, 19, 0, 0], [0, 0, 64, 255, 113, 15, 152, 216, 246, 255, 255, 255, 226, 225, 27, 46, 255, 59, 0, 0], [0, 0, 53, 255, 120, 22, 172, 249, 255, 255, 255, 255, 255, 255, 35, 33, 213, 61, 0, 0], [0, 0, 43, 255, 139, 105, 243, 254, 130, 231, 255, 139, 217, 255, 37, 35, 234, 63, 0, 0], [0, 0, 34, 247, 151, 68, 166, 248, 143, 225, 255, 159, 219, 255, 41, 37, 240, 50, 0, 0], [0, 0, 26, 240, 136, 38, 143, 246, 255, 255, 255, 255, 255, 255, 43, 29, 168, 0, 0, 0], [0, 0, 18, 231, 142, 44, 135, 246, 255, 255, 255, 230, 190, 98, 6, 25, 210, 49, 2, 0], [0, 0, 17, 223, 147, 49, 112, 214, 123, 226, 255, 147, 0, 0, 0, 28, 218, 10, 1, 0], [0, 0, 16, 212, 154, 56, 0, 0, 4, 69, 249, 149, 148, 216, 46, 18, 205, 94, 13, 0], [0, 0, 15, 200, 157, 59, 0, 11, 45, 255, 255, 242, 244, 255, 57, 3, 33, 13, 2, 0], [0, 0, 15, 196, 164, 66, 0, 66, 253, 198, 198, 198, 200, 225, 154, 87, 252, 90, 18, 0], [0, 0, 14, 184, 171, 73, 0, 8, 31, 1, 0, 0, 1, 16, 8, 13, 255, 110, 25, 0], [0, 0, 13, 175, 177, 79, 0, 0, 0, 0, 0, 0, 0, 0, 8, 37, 255, 117, 30, 0], [0, 0, 10, 134, 147, 69, 0, 0, 0, 0, 0, 0, 0, 0, 29, 127, 230, 24, 5, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 18, 2, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
0 20 20

模型设计

数据集已经把一个车牌的三个部分都分开了，所以可以设计三个模型分别去识别这三部分。在本例中，本人为了简单期间，三个部分用了用一个CNN 网络结构，但是每个网络结构里面的具体参数是各自独立的。
CNN网络结构：

输入层：20x20
第一层卷积：卷积核大小：3x3，卷积核个数：32，Stride 步长：1，Same 卷积
第二层卷积：卷积核大下：3x3，卷积核个数：64，Stride 步长：1，Same卷积
（两个卷积级联，效果就是5x5的卷积核，但是减少了参数个数）
第二层池化：池化大小：2x2，max pool,Stride 步长：2
第三层卷积：卷积核大小：3x3，卷积核个数：8，Stride 步长：1，Same卷积
第三层池化：池化大小：2x2，max pooling,Stride :2。应该得到8个5x5的特征图。
平坦化：得到8x5x5=200维的向量
第四层全连接：512个神经元，激活函数为relu。
第五层全连接：34个神经元，softmax激活函数。

第五层是分类层，一共有34个神经元，表示最多有34个类别。对于province来说，只有前31类有效；对于area来说只有前26类有效；对于letter来说，这34个神经元都有效。因此在生成训练集的时候，需要把正确的答案标签编码为34维的one-hot

数据处理

数据处理模块，主要仿照minist数据集的写法，编写一个类，实现next_train_batch,next_test_batch,next_valid_batch函数。
BaseTool.py

__author__ = 'jmh081701'
from dataset.map import  maps as aspectMap
import  os
from picReader import  img2mat
import  random
class data_generator:def __init__(self,aspect='area',seperate_ratio=0.1):''':param aspect: 打开什么样的训练集,[area,letter,province] 三选一:param seperate_ratio: 测试集划分比例 ,从训练集和验证集里面随机抽取seperate_ratio作为训练集:return:'''self.train_dir = "dataset\\train\\%s\\" %aspectself.val_dir   = "dataset\\val\\%s\\" % aspectself.seperate_ratio = seperate_ratioself.data_vector_set  = []      #保存所有的图片向量self.data_label_set   = []      #保存所有的标签self.train_set = []             #保存训练集的下标self.train_batch_index = 0self.valid_set = []             #保存验证集的下标self.valid_batch_index = 0self.test_set = []              #保存测试集的下标self.test_batch_index = 0self.classes = 0                #最大的classes为34,这个值会在载入train和test后有所变化self.data_set_cnt = 0self.load_train()self.load_valid()def load_train(self):for rt,dirs,files in os.walk(self.train_dir):self.classes = max(self.classes,len(dirs))if len(dirs)==0 :#说明到了叶子目录,里面放着就是图片label = int(rt.split('\\')[-1])for name in files:img_filename = os.path.join(rt,name)vec = img2mat(img_filename)self.data_vector_set.append(vec)self.data_label_set.append(label)if random.random() < self.seperate_ratio:self.test_set.append(self.data_set_cnt)else:self.train_set.append(self.data_set_cnt)self.data_set_cnt +=1def load_valid(self):for rt,dirs,files in os.walk(self.val_dir):self.classes = max(self.classes,len(dirs))if len(dirs)==0 :#说明到了叶子目录,里面放着就是图片label = int(rt.split('\\')[-1])for name in files:img_filename = os.path.join(rt,name)vec = img2mat(img_filename)self.data_vector_set.append(vec)self.data_label_set.append(label)if random.random() < self.seperate_ratio:self.test_set.append(self.data_set_cnt)else:self.valid_set.append(self.data_set_cnt)self.data_set_cnt +=1def next_train_batch(self,batch=100):input_x =[]input_y =[]for i in range(batch):input_x.append(self.data_vector_set[self.train_set[(self.train_batch_index + i)%len(self.train_set)]])y = [0] * self.classesy[self.data_label_set[self.train_set[(self.train_batch_index +i)%len(self.train_set)]]] = 1input_y.append(y)self.train_batch_index +=batchself.train_batch_index %=len(self.train_set)return  input_x,input_ydef next_valid_batch(self,batch=100):input_x =[]input_y =[]for i in range(batch):index = random.randint(0,len(self.valid_set)-1)input_x.append(self.data_vector_set[index])y = [0] * 34y[self.data_label_set[index]] = 1input_y.append(y)self.valid_batch_index +=batchself.valid_batch_index %=len(self.valid_set)return  input_x,input_y,self.train_epochdef next_test_batch(self,batch=100):input_x =[]input_y =[]for i in range(batch):input_x.append(self.data_vector_set[self.test_set[(self.test_batch_index + i)%len(self.test_set)]])y = [0] * self.classesy[self.data_label_set[self.test_set[(self.test_batch_index +i)%(len(self.test_set))]]] = 1input_y.append(y)self.test_batch_index +=batchself.test_batch_index %=len(self.test_set)return  input_x,input_y
if __name__ == '__main__':data_gen = data_generator()print(len(data_gen.test_set))print(data_gen.next_train_batch(50))print(data_gen.next_valid_batch(50)[1])print(data_gen.next_train_batch(30))

构建CNN模型

cnn_model.py

__author__ = 'jmh081701'
import  tensorflow as tf
from BaseTool import  data_generatorbatch_size = 100        # 每个batch的大小
learning_rate=1e-4      #学习速率
aspect = "area"
data_gen = data_generator(aspect)input_x  =tf.placeholder(dtype=tf.float32,shape=[None,20,20],name='input_x')
input_y  =tf.placeholder(dtype=tf.float32,shape=[None,34],name='input_y')with tf.name_scope('conv1'):W_C1 = tf.Variable(tf.truncated_normal(shape=[3,3,1,32],stddev=0.1))b_C1 = tf.Variable(tf.constant(0.1,tf.float32,shape=[32]))X=tf.reshape(input_x,[-1,20,20,1])featureMap_C1 = tf.nn.relu(tf.nn.conv2d(X,W_C1,strides=[1,1,1,1],padding='SAME') + b_C1 )with tf.name_scope('conv2'):W_C2 = tf.Variable(tf.truncated_normal(shape=[3,3,32,64],stddev=0.1))b_C2 = tf.Variable(tf.constant(0.1,tf.float32,shape=[64]))featureMap_C2 = tf.nn.relu(tf.nn.conv2d(featureMap_C1,W_C2,strides=[1,1,1,1],padding='SAME') + b_C2)with tf.name_scope('pooling2'):featureMap_S2 = tf.nn.max_pool(featureMap_C2,ksize=[1,2,2,1],strides=[1,2,2,1],padding='VALID')with tf.name_scope('conv3'):W_C3 = tf.Variable(tf.truncated_normal(shape=[3,3,64,8],stddev=0.1))b_C3 = tf.Variable(tf.constant(0.1,shape=[8],dtype=tf.float32))featureMap_C3 = tf.nn.relu(tf.nn.conv2d(featureMap_S2,filter=W_C3,strides=[1,1,1,1],padding='SAME')+ b_C3)with tf.name_scope('pooling3'):featureMap_S3 = tf.nn.max_pool(featureMap_C3,[1,2,2,1],[1,2,2,1],padding='VALID')with tf.name_scope('fulnet'):featureMap_flatten = tf.reshape(featureMap_S3,[-1,5*5*8])W_F4 = tf.Variable(tf.truncated_normal(shape=[5*5*8,512],stddev=0.1))b_F4 = tf.Variable(tf.constant(0.1,shape=[512],dtype=tf.float32))out_F4 = tf.nn.relu(tf.matmul(featureMap_flatten,W_F4) + b_F4)#out_F4 =tf.nn.dropout(out_F4,keep_prob=0.5)
with tf.name_scope('output'):W_OUTPUT = tf.Variable(tf.truncated_normal(shape=[512,34],stddev=0.1))b_OUTPUT = tf.Variable(tf.constant(0.1,shape=[34],dtype=tf.float32))logits = tf.matmul(out_F4,W_OUTPUT)+b_OUTPUTloss = tf.reduce_sum(tf.nn.softmax_cross_entropy_with_logits(labels=input_y,logits=logits))
train_op = tf.train.AdamOptimizer(learning_rate).minimize(loss)
predictY = tf.nn.softmax(logits)
y_pred=tf.arg_max(predictY,1)
bool_pred=tf.equal(tf.arg_max(input_y,1),y_pred)
right_rate=tf.reduce_mean(tf.to_float(bool_pred))saver = tf.train.Saver()
def load_model(sess,dir,modelname):ckpt=tf.train.get_checkpoint_state(dir)if ckpt and ckpt.model_checkpoint_path:print("*"*30)print("load lastest model......")saver.restore(sess,dir+".\\"+modelname)print("*"*30)
def save_model(sess,dir,modelname):saver.save(sess,dir+modelname)
dir = r".//"
modelname = aspectwith tf.Session() as sess:sess.run(tf.initialize_all_variables())step = 1display_interval=200max_epoch = 50epoch = 0acc = 0load_model(sess,dir=dir,modelname=modelname)while True  :if step % display_interval ==0:image_batch,label_batch,epoch = data_gen.next_valid_batch(batch_size)acc = sess.run(right_rate,feed_dict={input_x:image_batch,input_y:label_batch})print({'!'*30+str(epoch)+":"+str(step):acc})image_batch,label_batch,epoch = data_gen.next_train_batch(batch_size)sess.run([loss,train_op],{input_x:image_batch,input_y:label_batch})if(epoch> max_epoch):breakstep +=1while True :test_img,test_lab,test_epoch = data_gen.next_test_batch(batch_size)test_acc = sess.run(right_rate,{input_x:test_img,input_y:test_lab})acc = test_acc * 0.8 + acc * 0.2    #指数滑动平均if(test_epoch!=epoch):print({"Test Over..... acc:":acc})breaksave_model(sess,dir,modelname)

训练结果：

area：

{'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!3:200': 0.34}
{'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!6:400': 0.61}
{'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!9:600': 0.78}
{'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!13:800': 0.73}
{'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!16:1000': 0.8}
{'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!19:1200': 0.88}
{'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!23:1400': 0.76}
{'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!25:1600': 0.86}
{'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!28:1800': 0.89}
{'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!32:2000': 0.83}
{'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!35:2200': 0.87}
{'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!38:2400': 0.93}
{'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!42:2600': 0.89}
{'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!45:2800': 0.9}
{'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!48:3000': 0.95}
{'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!50:3200': 0.97}
{'Test Over..... acc:': 0.9042283506058594}

province:

******************************
load lastest model......
******************************
{'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!7:200': 0.9}
{'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!15:400': 0.88}
{'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!22:600': 0.91}
{'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!29:800': 0.92}
{'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!36:1000': 0.95}
{'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!44:1200': 0.99}
{'Test Over..... acc:': 0.7786719818115235}

可以看到，模型有点过拟合了，因为验证集表现的很好，但是测试gg。
于是加上dropout

out_F4 =tf.nn.dropout(out_F4,keep_prob=0.5)

结果，还是很差，哈哈哈，垃圾网络，233

******************************
load lastest model......
******************************
{'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!7:200': 0.91}
{'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!15:400': 0.92}
{'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!24:600': 0.94}
{'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!32:800': 0.92}
{'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!40:1000': 0.95}
{'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!48:1200': 0.98}
{'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!55:1400': 0.94}
{'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!63:1600': 0.96}
{'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!71:1800': 0.95}
{'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!79:2000': 0.99}
{'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!87:2200': 0.95}
{'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!95:2400': 0.98}
{'Test Over..... acc:': 0.857055978012085}

letter:
字符比较多，把max_epoch加大到500来，最后测试准确率为96.17%

{'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!483:57600': 1.0}
{'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!485:57800': 0.99}
{'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!487:58000': 1.0}
{'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!488:58200': 1.0}
{'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!490:58400': 0.99}
{'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!492:58600': 1.0}
{'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!494:58800': 1.0}
{'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!495:59000': 1.0}
{'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!496:59200': 1.0}
{'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!498:59400': 1.0}
{'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!499:59600': 1.0}
{'Test Over..... acc:': 0.9617528679144185}

添加自适应学习速率

前面的代码中学习速率是固定的，这是不合理的。训练前期模型可以以较高的学习速率学习，后期再以较低的学习速率学习。方法是每隔一定数量的epoch 就让学习速率缩小。

__author__ = 'jmh081701'
import  tensorflow as tf
from BaseTool import  data_generatorbatch_size = 100        # 每个batch的大小
lr=0.001      # 学习速率,每20个epoch 减少为原来的80%
aspect = "letter"
data_gen = data_generator(aspect)input_x  =tf.placeholder(dtype=tf.float32,shape=[None,20,20],name='input_x')
input_y  =tf.placeholder(dtype=tf.float32,shape=[None,34],name='input_y')
input_learning_rate = tf.placeholder(dtype=tf.float32,name='learning_rate') #把learning rate变成placeholder就可以啦with tf.name_scope('conv1'):W_C1 = tf.Variable(tf.truncated_normal(shape=[3,3,1,32],stddev=0.1))b_C1 = tf.Variable(tf.constant(0.1,tf.float32,shape=[32]))X=tf.reshape(input_x,[-1,20,20,1])featureMap_C1 = tf.nn.relu(tf.nn.conv2d(X,W_C1,strides=[1,1,1,1],padding='SAME') + b_C1 )with tf.name_scope('conv2'):W_C2 = tf.Variable(tf.truncated_normal(shape=[3,3,32,64],stddev=0.1))b_C2 = tf.Variable(tf.constant(0.1,tf.float32,shape=[64]))featureMap_C2 = tf.nn.relu(tf.nn.conv2d(featureMap_C1,W_C2,strides=[1,1,1,1],padding='SAME') + b_C2)with tf.name_scope('pooling2'):featureMap_S2 = tf.nn.max_pool(featureMap_C2,ksize=[1,2,2,1],strides=[1,2,2,1],padding='VALID')with tf.name_scope('conv3'):W_C3 = tf.Variable(tf.truncated_normal(shape=[3,3,64,8],stddev=0.1))b_C3 = tf.Variable(tf.constant(0.1,shape=[8],dtype=tf.float32))featureMap_C3 = tf.nn.relu(tf.nn.conv2d(featureMap_S2,filter=W_C3,strides=[1,1,1,1],padding='SAME')+ b_C3)with tf.name_scope('pooling3'):featureMap_S3 = tf.nn.max_pool(featureMap_C3,[1,2,2,1],[1,2,2,1],padding='VALID')with tf.name_scope('fulnet'):featureMap_flatten = tf.reshape(featureMap_S3,[-1,5*5*8])W_F4 = tf.Variable(tf.truncated_normal(shape=[5*5*8,512],stddev=0.1))b_F4 = tf.Variable(tf.constant(0.1,shape=[512],dtype=tf.float32))out_F4 = tf.nn.relu(tf.matmul(featureMap_flatten,W_F4) + b_F4)out_F4 =tf.nn.dropout(out_F4,keep_prob=0.5)
with tf.name_scope('output'):W_OUTPUT = tf.Variable(tf.truncated_normal(shape=[512,34],stddev=0.1))b_OUTPUT = tf.Variable(tf.constant(0.1,shape=[34],dtype=tf.float32))logits = tf.matmul(out_F4,W_OUTPUT)+b_OUTPUTloss = tf.reduce_sum(tf.nn.softmax_cross_entropy_with_logits(labels=input_y,logits=logits))
train_op = tf.train.AdamOptimizer(learning_rate=input_learning_rate).minimize(loss)
predictY = tf.nn.softmax(logits)
y_pred=tf.arg_max(predictY,1)
bool_pred=tf.equal(tf.arg_max(input_y,1),y_pred)
right_rate=tf.reduce_mean(tf.to_float(bool_pred))saver = tf.train.Saver()
def load_model(sess,dir,modelname):ckpt=tf.train.get_checkpoint_state(dir)if ckpt and ckpt.model_checkpoint_path:print("*"*30)print("load lastest model......")saver.restore(sess,dir+".\\"+modelname)print("*"*30)
def save_model(sess,dir,modelname):saver.save(sess,dir+modelname)
dir = r".\\parameter\\%s\\"%aspect
modelname = aspectwith tf.Session() as sess:sess.run(tf.initialize_all_variables())step = 1display_interval=200max_epoch = 500epoch = 1acc = 0load_model(sess,dir=dir,modelname=modelname)while True  :if step % display_interval ==0:image_batch,label_batch,epoch = data_gen.next_valid_batch(batch_size)acc = sess.run(right_rate,feed_dict={input_x:image_batch,input_y:label_batch,input_learning_rate:lr})print({'!'*30+str(epoch)+":"+str(step):acc})image_batch,label_batch,epoch = data_gen.next_train_batch(batch_size)sess.run([loss,train_op],{input_x:image_batch,input_y:label_batch})if(epoch> max_epoch):breakstep +=1if (epoch % 20) ==0:lr =lr * 0.8while True :test_img,test_lab,test_epoch = data_gen.next_test_batch(batch_size)test_acc = sess.run(right_rate,feed_dict={input_x:test_img,input_y:test_lab,input_learning_rate:lr})acc = test_acc * 0.8 + acc * 0.2    #指数滑动平均if(test_epoch!=epoch):print({"Test Over..... acc:":acc})breaksave_model(sess,dir,modelname)

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