在同学的演讲中了解到了TensorFlow，上网查TensorFlow最初是由Google大脑小组的工程师开发出来的，用于的是机器学习和深度神经网络方面的研究。现在已经完全开源，可以拿这个用于语音识别和图像识别等机器学习。
于是找了个例子练习下，以前做验证码破解都是直接使用别人的库，效率低识别差，好的需要自己写算法或者是购买第三方的，比较麻烦。使用了这个机器学习的验证码识别，感觉这个cnn的识别效果还是不错的。下面具体来看下怎么实现深度学习验证码破解。
验证码主要是用于防刷，传统的验证码算法是通过把验证码分割为单个的字符，然后逐个的识别，如果复杂的验证码字母之间相互重叠，那么就无法识别了，这里采用的cnn可以对验证码整体进行有效的识别。 学习的要点：
1、captcha库生成验证码 2、如何将验证码识别问题转化为分类问题 3、可以训练自己的验证码识别模型
所有的训练模型，数据才是关键，这里采用的captcha库生成验证码，captcha可以生成语音和图片验证码，验证码是由数字、大小写字母（也可以自定义特殊字符），长度为4，共有62^4种组合。
本文的系统为centos，首先安装captcha库：
sudo pip install captcha验证码生成器：
采用python中生成器方式来生成训练数据，这样的好处是不需要提前生成大量的数据，而是在训练的过程中生成数据，可以无限的生成数据。
创建一个源文件：
vim generate_captcha.py参考代码：
#!/usr/bin/python # -*- coding: utf-8 -* from captcha.image import ImageCaptcha from PIL import Image import numpy as np import random import string class generateCaptcha(): def __init__(self, width = 160,#验证码图片的宽 height = 60,#验证码图片的高 char_num = 4,#验证码字符个数 characters = string.digits + string.ascii_uppercase + string.ascii_lowercase):#验证码组成，数字+大写字母+小写字母 self.width = width self.height = height self.char_num = char_num self.characters = characters self.classes = len(characters) def gen_captcha(self,batch_size = 50): X = np.zeros([batch_size,self.height,self.width,1]) img = np.zeros((self.height,self.width),dtype=np.uint8) Y = np.zeros([batch_size,self.char_num,self.classes]) image = ImageCaptcha(width = self.width,height = self.height) while True: for i in range(batch_size): captcha_str = ''.join(random.sample(self.characters,self.char_num)) img = image.generate_image(captcha_str).convert('L') img = np.array(img.getdata()) X[i] = np.reshape(img,[self.height,self.width,1])/255.0 for j,ch in enumerate(captcha_str): Y[i,j,self.characters.find(ch)] = 1 Y = np.reshape(Y,(batch_size,self.char_num*self.classes)) yield X,Y def decode_captcha(self,y): y = np.reshape(y,(len(y),self.char_num,self.classes)) return ''.join(self.characters[x] for x in np.argmax(y,axis = 2)[0,:]) def get_parameter(self): return self.width,self.height,self.char_num,self.characters,self.classes def gen_test_captcha(self): image = ImageCaptcha(width = self.width,height = self.height) captcha_str = ''.join(random.sample(self.characters,self.char_num)) img = image.generate_image(captcha_str) img.save(captcha_str + '.jpg')然后执行源文件：
python >>>import generate_captcha >>>g = generate_captcha.generateCaptcha() >>>g.gen_test_captcha()然后可以在当前目录下查看生成的验证码
将验证码识别问题转化为分类问题，总共62^4中类型，采用4个one-hot编码分别表示4中字符取值
cnn验证码识别模型
3 层隐藏层、2 层全连接层，对每层都进行 dropout。 input——>conv——>pool——>dropout——>conv——>pool——>dropout——>conv——>pool——>dropout——>fully connected layer——>dropout——>fully connected layer——>output
创建源文件captcha_model.py，参考代码如下：
#!/usr/bin/python # -*- coding: utf-8 -* import tensorflow as tf import math class captchaModel(): def __init__(self, width = 160, height = 60, char_num = 4, classes = 62): self.width = width self.height = height self.char_num = char_num self.classes = classes def conv2d(self,x, W): return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') def max_pool_2x2(self,x): return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') def weight_variable(self,shape): initial = tf.truncated_normal(shape, stddev=0.1) return tf.Variable(initial) def bias_variable(self,shape): initial = tf.constant(0.1, shape=shape) return tf.Variable(initial) def create_model(self,x_images,keep_prob): #first layer w_conv1 = self.weight_variable([5, 5, 1, 32]) b_conv1 = self.bias_variable([32]) h_conv1 = tf.nn.relu(tf.nn.bias_add(self.conv2d(x_images, w_conv1), b_conv1)) h_pool1 = self.max_pool_2x2(h_conv1) h_dropout1 = tf.nn.dropout(h_pool1,keep_prob) conv_width = math.ceil(self.width/2) conv_height = math.ceil(self.height/2) #second layer w_conv2 = self.weight_variable([5, 5, 32, 64]) b_conv2 = self.bias_variable([64]) h_conv2 = tf.nn.relu(tf.nn.bias_add(self.conv2d(h_dropout1, w_conv2), b_conv2)) h_pool2 = self.max_pool_2x2(h_conv2) h_dropout2 = tf.nn.dropout(h_pool2,keep_prob) conv_width = math.ceil(conv_width/2) conv_height = math.ceil(conv_height/2) #third layer w_conv3 = self.weight_variable([5, 5, 64, 64]) b_conv3 = self.bias_variable([64]) h_conv3 = tf.nn.relu(tf.nn.bias_add(self.conv2d(h_dropout2, w_conv3), b_conv3)) h_pool3 = self.max_pool_2x2(h_conv3) h_dropout3 = tf.nn.dropout(h_pool3,keep_prob) conv_width = math.ceil(conv_width/2) conv_height = math.ceil(conv_height/2) #first fully layer conv_width = int(conv_width) conv_height = int(conv_height) w_fc1 = self.weight_variable([64*conv_width*conv_height,1024]) b_fc1 = self.bias_variable([1024]) h_dropout3_flat = tf.reshape(h_dropout3,[-1,64*conv_width*conv_height]) h_fc1 = tf.nn.relu(tf.nn.bias_add(tf.matmul(h_dropout3_flat, w_fc1), b_fc1)) h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) #second fully layer w_fc2 = self.weight_variable([1024,self.char_num*self.classes]) b_fc2 = self.bias_variable([self.char_num*self.classes]) y_conv = tf.add(tf.matmul(h_fc1_drop, w_fc2), b_fc2) return y_conv训练cnn验证码识别模型：
这里采用了64个训练样本，每100次循环采用100个测试样本检查识别准确度，当准确度大于99%时，训练结束。
这里测试的话，可以将代码中的 if acc > 0.99 的准确度降低来减少训练的时间。
下面是一个已经训练好的训练集，可以下载下来看看最终的验证效果：
首先通过命令下载到本地：
wget http://tensorflow-1253902462.cosgz.myqcloud.com/captcha/capcha_model.zip unzip capcha_model.zip在当前目录下创建文件train_captcha.py，内容参考下面代码：
#!/usr/bin/python import tensorflow as tf import numpy as np import string import generate_captcha import captcha_model if __name__ == '__main__': captcha = generate_captcha.generateCaptcha() width,height,char_num,characters,classes = captcha.get_parameter() x = tf.placeholder(tf.float32, [None, height,width,1]) y_ = tf.placeholder(tf.float32, [None, char_num*classes]) keep_prob = tf.placeholder(tf.float32) model = captcha_model.captchaModel(width,height,char_num,classes) y_conv = model.create_model(x,keep_prob) cross_entropy = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=y_,logits=y_conv)) train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) predict = tf.reshape(y_conv, [-1,char_num, classes]) real = tf.reshape(y_,[-1,char_num, classes]) correct_prediction = tf.equal(tf.argmax(predict,2), tf.argmax(real,2)) correct_prediction = tf.cast(correct_prediction, tf.float32) accuracy = tf.reduce_mean(correct_prediction) saver = tf.train.Saver() with tf.Session() as sess: sess.run(tf.global_variables_initializer()) step = 0 while True: batch_x,batch_y = next(captcha.gen_captcha(64)) _,loss = sess.run([train_step,cross_entropy],feed_dict={x: batch_x, y_: batch_y, keep_prob: 0.75}) print ('step:%d,loss:%f' % (step,loss)) if step % 100 == 0: batch_x_test,batch_y_test = next(captcha.gen_captcha(100)) acc = sess.run(accuracy, feed_dict={x: batch_x_test, y_: batch_y_test, keep_prob: 1.}) print ('###############################################step:%d,accuracy:%f' % (step,acc)) if acc > 0.99: saver.save(sess,"capcha_model.ckpt") break step += 1测试识别模型：
创建源文件predict_captcha.py，参考代码如下：
#!/usr/bin/python from PIL import Image, ImageFilter import tensorflow as tf import numpy as np import string import sys import generate_captcha import captcha_model if __name__ == '__main__': captcha = generate_captcha.generateCaptcha() width,height,char_num,characters,classes = captcha.get_parameter() gray_image = Image.open(sys.argv[1]).convert('L') img = np.array(gray_image.getdata()) test_x = np.reshape(img,[height,width,1])/255.0 x = tf.placeholder(tf.float32, [None, height,width,1]) keep_prob = tf.placeholder(tf.float32) model = captcha_model.captchaModel(width,height,char_num,classes) y_conv = model.create_model(x,keep_prob) predict = tf.argmax(tf.reshape(y_conv, [-1,char_num, classes]),2) init_op = tf.global_variables_initializer() saver = tf.train.Saver() gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.95) with tf.Session(config=tf.ConfigProto(log_device_placement=False,gpu_options=gpu_options)) as sess: sess.run(init_op) saver.restore(sess, "capcha_model.ckpt") pre_list = sess.run(predict,feed_dict={x: [test_x], keep_prob: 1}) for i in pre_list: s = '' for j in i: s += characters[j] print s然后执行源文件，python predict_captcha.py Kz2J.jpg
执行结果：Kz2J
在训练时间足够长的情况下，你可以采用验证码生成器生成测试数据，cnn 训练出来的验证码识别模型还是很强大的，大小写的 z 都可以区分，甚至有时候人都无法区分，该模型也可以正确的识别。这个验证码识别还是可以破解大部分简单的验证码的，但是如果碰到中文的，那就需要一个集群来进行训练了。机器学习是非常强大的，可以说是大数据和人工智能的桥接处，以后多多了解。
腾讯云的TensorFlowAPI体验实验：https://cloud.tencent.com/developer/labs/lab/10187 TensorFlow中文社区文档：http://www.tensorfly.cn/tfdoc/get_started/introduction.html
