引言
TensorFlow是Google基于DistBelief进行研发的第二代人工智能学习系统,被广泛用于语音识别或图像识别等多项机器深度学习领域。其命名来源于本身的运行原理。Tensor(张量)意味着N维数组,Flow(流)意味着基于数据流图的计算,TensorFlow代表着张量从图象的一端流动到另一端计算过程,是将复杂的数据结构传输至人工智能神经网中进行分析和处理的过程。
TensorFlow完全开源,任何人都可以使用。可在小到一部智能手机、大到数千台数据中心服务器的各种设备上运行。
『机器学习进阶笔记』系列将深入解析TensorFlow系统的技术实践,从零开始,由浅入深,与大家一起走上机器学习的进阶之路。
前段时间心血来潮,想写几个爬虫,但是实操过的同学都知道,现在爬虫程序对很多网站都无效。
打开一看,其实是验证码问题,12306的坑爹验证码先不说,现在很多站都流行用“倒字验证码”:
需要在一排汉字中找到,简单看了下,大概7个汉字,不定数个汉字是倒置的,需要人为指出哪些汉字是倒置的,和以往常规的验证码识别有点区别,有点意思!!!
今天就给大家分享一下,如何用TensorFlow搞定“倒字验证码”,问题很明确,主要是:
- 数据问题?如何生成这些数据
- 切字+单个分类器,还是直接输出所有的倒置情况(保证验证码字数一致)
数据准备
有天沙神在群里发了个链接,python生成汉字的代码, 正好用得上,改改然后很快就搞定了,代码如下:
# -*- coding: utf-8 -*-
from PIL import Image,ImageDraw,ImageFont
import random
import math, string
import logging
# logger = logging.Logger(name='gen verification')
class RandomChar():
@staticmethod
def Unicode():
val = random.randint(0x4E00, 0x9FBF)
return unichr(val)
@staticmethod
def GB2312():
head = random.randint(0xB0, 0xCF)
body = random.randint(0xA, 0xF)
tail = random.randint(0, 0xF)
val = ( head << 8 ) | (body << 4) | tail
str = "%x" % val
return str.decode('hex').decode('gb2312')
class ImageChar():
def __init__(self, fontColor = (0, 0, 0),
size = (100, 40),
fontPath = '/Library/Fonts/Arial Unicode.ttf',
bgColor = (255, 255, 255),
fontSize = 20):
self.size = size
self.fontPath = fontPath
self.bgColor = bgColor
self.fontSize = fontSize
self.fontColor = fontColor
self.font = ImageFont.truetype(self.fontPath, self.fontSize)
self.image = Image.new('RGB', size, bgColor)
def drawText(self, pos, txt, fill):
draw = ImageDraw.Draw(self.image)
draw.text(pos, txt, font=self.font, fill=fill)
del draw
def drawTextV2(self, pos, txt, fill, angle=180):
image=Image.new('RGB', (25,25), (255,255,255))
draw = ImageDraw.Draw(image)
draw.text( (0, -3), txt, font=self.font, fill=fill)
w=image.rotate(angle, expand=1)
self.image.paste(w, box=pos)
del draw
def randRGB(self):
return (0,0,0)
def randChinese(self, num, num_flip):
gap = 1
start = 0
num_flip_list = random.sample(range(num), num_flip)
# logger.info('num flip list:{0}'.format(num_flip_list))
print 'num flip list:{0}'.format(num_flip_list)
char_list = []
for i in range(0, num):
char = RandomChar().GB2312()
char_list.append(char)
x = start + self.fontSize * i + gap + gap * i
if i in num_flip_list:
self.drawTextV2((x, 6), char, self.randRGB())
else:
self.drawText((x, 0), char, self.randRGB())
return char_list, num_flip_list
def save(self, path):
self.image.save(path)
err_num = 0
for i in range(10):
try:
ic = ImageChar(fontColor=(100,211, 90), size=(280,28), fontSize = 25)
num_flip = random.randint(3,6)
char_list, num_flip_list = ic.randChinese(10, num_flip)
ic.save(''.join(char_list)+'_'+''.join(str(i) for i in num_flip_list)+".jpeg")
except:
err_num += 1
continue
为了简单,没有在生成的字加一些噪声,颜色什么的干扰的东西, 生成的图像差不多这样
所以,接下来就是在tensorflow构建网络来识别了
模型
网络设计
受项亮的end2end定长的OCR识别的启发, 因为我这里的验证码都是长度为10的,所以我们只需要构造网络,最后的output有10个输出,每个输出为1个二类分类器:
其中,0表示正常,1表示倒置,这样就可以构造一个input到10个binary classification的网络,网络结构很简单(小伙伴们可以在这里都试试网络的设计,多试试黑科技),好吧,废话不多说,show
you code:
def network():
images = tf.placeholder(dtype=tf.float32, shape=[None, 28, 280, 1], name='image_batch')
labels = tf.placeholder(dtype=tf.int32, shape=[None, 10], name='label_batch')
endpoints = {}
conv_1 = slim.conv2d(images, 32, [5,5],1, padding='SAME')
avg_pool_1 = slim.avg_pool2d(conv_1, [2,2],[1,1], padding='SAME')
conv_2 = slim.conv2d(avg_pool_1, 32, [5,5], 1,padding='SAME')
avg_pool_2 = slim.avg_pool2d(conv_2, [2,2],[1,1], padding='SAME')
conv_3 = slim.conv2d(avg_pool_2, 32, [3,3])
avg_pool_3 = slim.avg_pool2d(conv_3, [2,2], [1,1])
flatten = slim.flatten(avg_pool_3)
fc1 = slim.fully_connected(flatten, 512, activation_fn=None)
out0 = slim.fully_connected(fc1,2, activation_fn=None)
out1 = slim.fully_connected(fc1,2, activation_fn=None)
out2 = slim.fully_connected(fc1,2, activation_fn=None)
out3 = slim.fully_connected(fc1,2, activation_fn=None)
out4 = slim.fully_connected(fc1,2, activation_fn=None)
out5 = slim.fully_connected(fc1,2, activation_fn=None)
out6 = slim.fully_connected(fc1,2, activation_fn=None)
out7 = slim.fully_connected(fc1,2, activation_fn=None)
out8 = slim.fully_connected(fc1,2, activation_fn=None)
out9 = slim.fully_connected(fc1,2, activation_fn=None)
global_step = tf.Variable(initial_value=0)
out0_argmax = tf.expand_dims(tf.argmax(out0, 1), 1)
out1_argmax = tf.expand_dims(tf.argmax(out1, 1), 1)
out2_argmax = tf.expand_dims(tf.argmax(out2, 1), 1)
out3_argmax = tf.expand_dims(tf.argmax(out3, 1), 1)
out4_argmax = tf.expand_dims(tf.argmax(out4, 1), 1)
out5_argmax = tf.expand_dims(tf.argmax(out5, 1), 1)
out6_argmax = tf.expand_dims(tf.argmax(out6, 1), 1)
out7_argmax = tf.expand_dims(tf.argmax(out7, 1), 1)
out8_argmax = tf.expand_dims(tf.argmax(out8, 1), 1)
out9_argmax = tf.expand_dims(tf.argmax(out9, 1), 1)
out_score = tf.concat([out0, out1, out2, out3, out4, out5, out6, out7, out8, out9], axis=1)
out_final = tf.cast(tf.concat([out0_argmax, out1_argmax, out2_argmax, out3_argmax, out4_argmax, out5_argmax, out6_argmax, out7_argmax, out8_argmax, out9_argmax], axis=1), tf.int32)
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=out0, labels=tf.one_hot(labels[:,0],depth=2)))
loss1 = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=out1, labels=tf.one_hot(labels[:,1],depth=2)))
loss2 = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=out2, labels=tf.one_hot(labels[:,2],depth=2)))
loss3 = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=out3, labels=tf.one_hot(labels[:,3],depth=2)))
loss4 = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=out4, labels=tf.one_hot(labels[:,4],depth=2)))
loss5 = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=out5, labels=tf.one_hot(labels[:,5],depth=2)))
loss6 = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=out6, labels=tf.one_hot(labels[:,6],depth=2)))
loss7 = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=out7, labels=tf.one_hot(labels[:,7],depth=2)))
loss8 = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=out8, labels=tf.one_hot(labels[:,8],depth=2)))
loss9 = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=out9, labels=tf.one_hot(labels[:,9],depth=2)))
loss_list= [loss, loss1, loss2, loss3,loss4, loss5, loss6, loss7, loss8, loss9]
loss_sum = tf.reduce_sum(loss_list)
train_op = tf.train.AdamOptimizer(learning_rate=0.0001).minimize(loss_sum, global_step=global_step)
accuracy = tf.reduce_mean(tf.cast(tf.reduce_all(tf.equal(out_final, labels), axis=1), tf.float32))
tf.summary.scalar('loss_sum', loss_sum)
tf.summary.scalar('accuracy', accuracy)
merged_summary_op = tf.summary.merge_all()
endpoints['global_step'] = global_step
endpoints['images'] = images
endpoints['labels'] = labels
endpoints['train_op'] = train_op
endpoints['loss_sum'] = loss_sum
endpoints['accuracy'] = accuracy
endpoints['merged_summary_op'] = merged_summary_op
endpoints['out_final'] = out_final
endpoints['out_score'] = out_score
return endpoints
注意tf1.0和tf0.n里面有一些api接口改了,这里的代码是tf1.0的要改成0.n大概需要改下tf.concat,另外因为label是10为binary的数,所以这里很显然,loss续传重新设计,这里把10为的loss全加起来作为去优化的object func
其实我觉得这里可以用更多的方法,因为这里的label可以看出一个10位上的数据分布,因为我们的predict和原先的groundtruth的分布的差异,比如kl距离都可以尝试,我这里简单粗暴的直接加起来了,另外就是tf.reduce_all的使用
意思是只要其中有任意为false,则结果为false,恰好满足我这里必须10位都相同才视为识别正确
其他的代码没什么特别好说明的,想要学习这些api的直接run下代码就好了,再有不懂的直接在我文章下面留言,我会第一时间回复
模型训练
def train():
train_feeder = DataIterator(data_dir=FLAGS.train_data_dir)
test_feeder = DataIterator(data_dir=FLAGS.test_data_dir)
with tf.Session() as sess:
train_images, train_labels = train_feeder.input_pipeline(batch_size=FLAGS.batch_size, aug=True)
test_images, test_labels = test_feeder.input_pipeline(batch_size=FLAGS.batch_size)
endpoints = network()
sess.run(tf.global_variables_initializer())
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
saver = tf.train.Saver()
train_writer = tf.summary.FileWriter('./log' + '/train',sess.graph)
test_writer = tf.summary.FileWriter('./log' + '/val')
start_step = 0
if FLAGS.restore:
ckpt = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
if ckpt:
saver.restore(sess, ckpt)
print "restore from the checkpoint {0}".format(ckpt)
start_step += int(ckpt.split('-')[-1])
logger.info(':::Training Start:::')
try:
while not coord.should_stop():
start_time = time.time()
train_images_batch, train_labels_batch = sess.run([train_images, train_labels])
feed_dict = {endpoints['images']: train_images_batch, endpoints['labels']: train_labels_batch}
_, loss_val, train_summary, step = sess.run([endpoints['train_op'], endpoints['loss_sum'], endpoints['merged_summary_op'], endpoints['global_step']], feed_dict=feed_dict)
train_writer.add_summary(train_summary, step)
end_time = time.time()
logger.info("[train] the step {0} takes {1} loss {2}".format(step, end_time-start_time, loss_val))
if step > FLAGS.max_steps:
break
if step % FLAGS.eval_steps == 1:
logger.info('========Begin eval stage =========')
start_time = time.time()
# can't run
test_images_batch, test_labels_batch = sess.run([test_images, test_labels])
logger.info('[test] gen test batch spend {0}'.format(time.time()-start_time))
feed_dict = {
endpoints['images']: test_images_batch,
endpoints['labels']: test_labels_batch
}
accuracy_val,test_summary = sess.run([endpoints['accuracy'], endpoints['merged_summary_op']], feed_dict=feed_dict)
end_time = time.time()
test_writer.add_summary(test_summary, step)
logger.info( '[test] the step {0} accuracy {1} spend time {2}'.format(step, accuracy_val, (end_time-start_time)))
if step % FLAGS.save_steps == 1:
logger.info('Save the ckpt of {0}'.format(step))
saver.save(sess, os.path.join(FLAGS.checkpoint_dir, 'my-model'), global_step=endpoints['global_step'])
except tf.errors.OutOfRangeError:
# print "============train finished========="
logger.info('==================Train Finished================')
saver.save(sess, os.path.join(FLAGS.checkpoint_dir, 'my-model'), global_step=endpoints['global_step'])
finally:
coord.request_stop()
coord.join(threads)
训练过程会新建两个DataIterator的对象,这样就可以在训练的过程中对test的dataset来做训练的验证,训练过程的loss和accuracy见下图:
大家可以看看大概到了4000多个step的时候,train和val的batch就的accuracy就特别高了,可能是我这里的train和val的dataset都太simple的,所以模型的训练很快就可以达到很高的性能,为了测试这里的模型是不是扯淡,
照常写了validation来做所有test dataset的数据集上accuracy的验证
模型验证
def validation():
# it should be fixed by using placeholder with epoch num in train stage
logger.info("=======Validation Beigin=======")
test_feeder = DataIterator(data_dir='../data/test_data/')
predict_labels_list = []
groundtruth = []
with tf.Session() as sess:
test_images, test_labels = test_feeder.input_pipeline(batch_size=FLAGS.batch_size,num_epochs=1)
endpoints = network()
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
saver = tf.train.Saver()
ckpt = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
if ckpt:
saver.restore(sess, ckpt)
logger.info('restore from the checkpoint {0}'.format(ckpt))
logger.info('======Start Validation=======')
try:
i = 0
acc_sum = 0.0
while not coord.should_stop():
i += 1
start_time = time.time()
test_images_batch, test_labels_batch = sess.run([test_images, test_labels])
feed_dict = {endpoints['images']:test_images_batch, endpoints['labels']: test_labels_batch}
labels_batch, predict_labels_batch, acc = sess.run([endpoints['labels'],endpoints['out_final'], endpoints['accuracy']], feed_dict=feed_dict)
predict_labels_list += predict_labels_batch.tolist()
groundtruth += labels_batch.tolist()
acc_sum += acc
logger.info('the batch {0} takes {1} seconds, accuracy {2}'.format(i, time.time()-start_time, acc))
except tf.errors.OutOfRangeError:
logger.info('==================Validation Finished===================')
logger.info('The finally accuracy {0}'.format(acc_sum/i))
finally:
coord.request_stop()
coord.join(threads)
return {'predictions':predict_labels_list, 'gt_labels':groundtruth}
因为我这边太暴力了,在生成数据集的时候,训练数据100w(有生成异常,最终80多万张),测试数据60w(最后好像有40多w成功生成的), 跑一次全测试数据集也好长时间,但是看了好久的输出,都是下面的结果,吓死我了,全是1:
模型inference
有点害怕是不是代码写错的原因,再次确认,写了个inference:
def inference(image):
logger.info('============inference==========')
temp_image = Image.open(image).convert('L')
# temp_image = temp_image.resize((FLAGS.image_height, FLAGS.image_size),Image.ANTIALIAS)
temp_image = np.asarray(temp_image) / 255.0
temp_image = temp_image.reshape([-1, 28, 280, 1])
sess = tf.Session()
logger.info('========start inference============')
# images = tf.placeholder(dtype=tf.float32, shape=[None, 280, 28, 1])
endpoints = network()
saver = tf.train.Saver()
ckpt = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
if ckpt:
saver.restore(sess, ckpt)
feed_dict = {endpoints['images']: temp_image}
predict_val, predict_index = sess.run([endpoints['out_score'],endpoints['out_final']], feed_dict=feed_dict)
sess.close()
return predict_val, predict_index
随机找了张图像,然后做了下inference,结果是
这里稍微做下解释../data/test_data/092e9ae8-ee91-11e6-91c1-525400551618_69128.jpeg文件名前面有uuid+’_’+’label’组成,最开始我是把uuid部分用汉字表示,这样生成的图像既有汉字信息又有label信息,
在我本地mac上是ok的可以生成的,但是在ubuntu的云主机上,可能是汉字的不支持的原因,写入文件为汉字的时候,就生成不了,太忙了暂时没有解决这个问题。这里的label 69128转换为10位binary
就是0110001011,看看我的结果
所以好像是没有大的问题。
总结
照例做个总结,综上,这次主要是设计一个小的网络做一个单input,多output的网络,然后确定一个loss作为目标函数来最小化,整个网络很简单,但是确实是很有效,另外因为数据还有字数都是自己DIY的,当然不能直接使用,但是选择好合适字体,做一些扭曲变换,应该会很快cover到真正的生产环境上来,对此很有信心。
有兴趣的小伙帮可以fork下项目,然后探索下把倒字验证码搞定,以上的东西纯属抛砖引玉,有兴趣的小伙伴一起玩呀。
所有的代码都在tensorflow-101/zhihu_code,感觉有意思的star下吧,我们一起来玩。
——————
相关阅读推荐:
机器学习进阶笔记之八 | TensorFlow与中文手写汉字识别
机器学习进阶笔记之六 | 深入理解Fast Neural Style
机器学习进阶笔记之五 | 深入理解VGG\Residual Network
本文由『UCloud内核与虚拟化研发团队』提供。
关于作者:
Burness(@想飞的石头), UCloud平台研发中心深度学习研发工程师,tflearn Contributor & tensorflow Contributor,做过电商推荐、精准化营销相关算法工作,专注于分布式深度学习框架、计算机视觉算法研究,平时喜欢玩玩算法,研究研究开源的项目,偶尔也会去一些数据比赛打打酱油,生活中是个极客,对新技术、新技能痴迷。
你可以在Github上找到他:hacker.duanshishi.com/
「UCloud机构号」将独家分享云计算领域的技术洞见、行业资讯以及一切你想知道的相关讯息。欢迎提问&求关注 o(*////▽////*)q~
现在注册使用UCloud,还有首充返现优惠,最高可返3000元代金券!活动传送门:用UCloud!3000元限量版礼盒等你来拆!
另,欢迎添加UCloud运营小妹个人微信号:Surdur,陪聊很专业:)
以上。