人工智能已经是当下一大热点，各个行业都在探讨人工智能将为自身带来怎样的改变，包括出行、居家、安全等不同方面，我们都可以看到人工智能的应用可能性。
在这样的趋势下，很多公司开始加入人工智能的开发研究当中。但是基于大数据、深度学习的人工智能技术，需要强大的计算能力来支持。一般的物理服务器或云端的ecs服务器勉强可以支持完成计算，但是效率上来讲局限很大。
GPU高速服务器，就是为了解决这种难题而推出的，在相同的任务下，GPU服务器的表现远远优于ECS服务器的计算能力。我们利用TensorFlow的验证码识别训练，比较了在训练CNN模型方面ECS服务器和GPU服务器各自的速度，过程以及结果如下。
数据学习
安装 captcha 库
pip install captcha获取训练数据
本教程使用的验证码由数字、大写字母、小写字母组成，每个验证码包含 4 个字符，总共有 62^4 种组合，所以一共有 62^4 种不同的验证码。
示例代码：
现在您可以在 /home/ubuntu 目录下创建源文件 generate_captcha.py，内容可参考：
示例代码：/home/ubuntu/generate_captcha.py
#-*- 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')
X = np.zeros([1,self.height,self.width,1])
Y = np.zeros([1,self.char_num,self.classes])
img = img.convert('L')
img = np.array(img.getdata())
X[0] = np.reshape(img,[self.height,self.width,1])/255.0
for j,ch in enumerate(captcha_str):
Y[0,j,self.characters.find(ch)] = 1
Y = np.reshape(Y,(1,self.char_num*self.classes))
return X,Y理解训练数据
- X：一个 mini-batch 的训练数据，其 shape 为 [ batch_size, height, width, 1 ]，batch_size 表示每批次多少个训练数据，height 表示验证码图片的高，width 表示验证码图片的宽，1 表示图片的通道。
- Y：X 中每个训练数据属于哪一类验证码，其形状为 [ batch_size, class ] ，对验证码中每个字符进行 One-Hot 编码，所以 class 大小为 4*62。
执行:
- 获取验证码和对应的分类
d /home/ubuntu;
python
from generate_captcha import generateCaptcha
g = generateCaptcha()
X,Y = g.gen_test_captcha()- 查看训练数据
X.shape
Y.shape可以在 /home/ubuntu 目录下查看生成的验证码，jpg 格式的图片可以点击查看。
模型学习
任务时间：时间未知
CNN 模型
总共 5 层网络，前 3 层为卷积层，第 4、5 层为全连接层。对 4 层隐藏层都进行 dropout。网络结构如下所示： input——>conv——>pool——>dropout——>conv——>pool——>dropout——>conv——>pool——>dropout——>fully connected layer——>dropout——>fully connected layer——>output
示例代码：
现在您可以在 /home/ubuntu 目录下创建源文件 captcha_model.py，内容可参考：
示例代码：/home/ubuntu/captcha_model.py
# -*- 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 模型
每批次采用 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，采用 GPU 需要 4-5 个小时左右，CPU 大概需要 20 个小时左右。
示例代码：
现在您可以在 /home/ubuntu 目录下创建源文件 train_captcha.py，内容可参考：
示例代码：/home/ubuntu/train_captcha.py
#-*- coding:utf-8 -*-
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 = 1
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然后执行:
cd /home/ubuntu;
python train_captcha.py执行结果：
Ecs服务器结果
GPU服务器结果
可以看到，在训练结果中，ECS服务器的训练时间为151893s，而GPU服务器讲训练时间减少到了29570s，速度提升五倍以上！显然，使用GPU服务器进行深度学习训练、研发人工智能技术，将极大的提升效率。
<代码来源于腾讯云开发者实验室>
<两种试验用服务器来源于新睿云>