TensorFlow多任务学习以及验证码识别

一、TensorFlow多任务学习

Multi-task Learning交替训练示意图：

Multi-task Learning – 联合训练示意图：

二、TensorFlow验证码识别

验证码生成代码示例：

# 验证码生成库
from captcha.image import ImageCaptcha  # pip install captcha
import numpy as np
from PIL import Image
import random
import sys
 
number = ['0','1','2','3','4','5','6','7','8','9']
# alphabet = ['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']
# ALPHABET = ['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']

def random_captcha_text(char_set=number, captcha_size=4):
    # 验证码列表
    captcha_text = []
    for i in range(captcha_size):
        #随机选择
        c = random.choice(char_set)
        #加入验证码列表
        captcha_text.append(c)
    return captcha_text
 
# 生成字符对应的验证码
def gen_captcha_text_and_image():
    image = ImageCaptcha()
    #获得随机生成的验证码
    captcha_text = random_captcha_text()
    #把验证码列表转为字符串
    captcha_text = ''.join(captcha_text)
    #生成验证码
    captcha = image.generate(captcha_text)
    image.write(captcha_text, 'captcha/images/' + captcha_text + '.jpg')  # 写到文件

#数量少于10000，因为重名
num = 10000
if __name__ == '__main__':
    for i in range(num):
        gen_captcha_text_and_image()
        sys.stdout.write('\r>> Creating image %d/%d' % (i+1, num))
        sys.stdout.flush()
    sys.stdout.write('\n')
    sys.stdout.flush()
                        
    print("生成完毕")
           

生成tfrecord文件代码示例：

import tensorflow as tf
import os
import random
import math
import sys
from PIL import Image
import numpy as np


# In[2]:

#验证集数量
_NUM_TEST = 500

#随机种子
_RANDOM_SEED = 0

#数据集路径
DATASET_DIR = "D:/Tensorflow/captcha/images/"

#tfrecord文件存放路径
TFRECORD_DIR = "D:/Tensorflow/captcha/"


#判断tfrecord文件是否存在
def _dataset_exists(dataset_dir):
    for split_name in ['train', 'test']:
        output_filename = os.path.join(dataset_dir,split_name + '.tfrecords')
        if not tf.gfile.Exists(output_filename):
            return False
    return True

#获取所有验证码图片
def _get_filenames_and_classes(dataset_dir):
    photo_filenames = []
    for filename in os.listdir(dataset_dir):
        #获取文件路径
        path = os.path.join(dataset_dir, filename)
        photo_filenames.append(path)
    return photo_filenames

def int64_feature(values):
    if not isinstance(values, (tuple, list)):
        values = [values]
    return tf.train.Feature(int64_list=tf.train.Int64List(value=values))

def bytes_feature(values):
    return tf.train.Feature(bytes_list=tf.train.BytesList(value=[values]))

def image_to_tfexample(image_data, label0, label1, label2, label3):
    #Abstract base class for protocol messages.
    return tf.train.Example(features=tf.train.Features(feature={
      'image': bytes_feature(image_data),
      'label0': int64_feature(label0),
      'label1': int64_feature(label1),
      'label2': int64_feature(label2),
      'label3': int64_feature(label3),
    }))

#把数据转为TFRecord格式
def _convert_dataset(split_name, filenames, dataset_dir):
    assert split_name in ['train', 'test']

    with tf.Session() as sess:
        #定义tfrecord文件的路径+名字
        output_filename = os.path.join(TFRECORD_DIR,split_name + '.tfrecords')
        with tf.python_io.TFRecordWriter(output_filename) as tfrecord_writer:
            for i,filename in enumerate(filenames):
                try:
                    sys.stdout.write('\r>> Converting image %d/%d' % (i+1, len(filenames)))
                    sys.stdout.flush()

                    #读取图片
                    image_data = Image.open(filename)  
                    #根据模型的结构resize
                    image_data = image_data.resize((224, 224))
                    #灰度化
                    image_data = np.array(image_data.convert('L'))
                    #将图片转化为bytes
                    image_data = image_data.tobytes()              

                    #获取label
                    labels = filename.split('/')[-1][0:4]
                    num_labels = []
                    for j in range(4):
                        num_labels.append(int(labels[j]))
                                            
                    #生成protocol数据类型
                    example = image_to_tfexample(image_data, num_labels[0], num_labels[1], num_labels[2], num_labels[3])
                    tfrecord_writer.write(example.SerializeToString())
                    
                except IOError as e:
                    print('Could not read:',filename)
                    print('Error:',e)
                    print('Skip it\n')
    sys.stdout.write('\n')
    sys.stdout.flush()

#判断tfrecord文件是否存在
if _dataset_exists(TFRECORD_DIR):
    print('tfcecord文件已存在')
else:
    #获得所有图片
    photo_filenames = _get_filenames_and_classes(DATASET_DIR)

    #把数据切分为训练集和测试集,并打乱
    random.seed(_RANDOM_SEED)
    random.shuffle(photo_filenames)
    training_filenames = photo_filenames[_NUM_TEST:]
    testing_filenames = photo_filenames[:_NUM_TEST]

    #数据转换
    _convert_dataset('train', training_filenames, DATASET_DIR)
    _convert_dataset('test', testing_filenames, DATASET_DIR)
    print('生成tfcecord文件')
           

验证码识别代码示例：

import os
import tensorflow as tf 
from PIL import Image
from nets import nets_factory
import numpy as np


# In[2]:

# 不同字符数量
CHAR_SET_LEN = 10
# 图片高度
IMAGE_HEIGHT = 60 
# 图片宽度
IMAGE_WIDTH = 160  
# 批次
BATCH_SIZE = 25
# tfrecord文件存放路径
TFRECORD_FILE = "D:/Tensorflow/captcha/train.tfrecords"

# placeholder
x = tf.placeholder(tf.float32, [None, 224, 224])  
y0 = tf.placeholder(tf.float32, [None]) 
y1 = tf.placeholder(tf.float32, [None]) 
y2 = tf.placeholder(tf.float32, [None]) 
y3 = tf.placeholder(tf.float32, [None])

# 学习率
lr = tf.Variable(0.003, dtype=tf.float32)

# 从tfrecord读出数据
def read_and_decode(filename):
    # 根据文件名生成一个队列
    filename_queue = tf.train.string_input_producer([filename])
    reader = tf.TFRecordReader()
    # 返回文件名和文件
    _, serialized_example = reader.read(filename_queue)   
    features = tf.parse_single_example(serialized_example,
                                       features={
                                           'image' : tf.FixedLenFeature([], tf.string),
                                           'label0': tf.FixedLenFeature([], tf.int64),
                                           'label1': tf.FixedLenFeature([], tf.int64),
                                           'label2': tf.FixedLenFeature([], tf.int64),
                                           'label3': tf.FixedLenFeature([], tf.int64),
                                       })
    # 获取图片数据
    image = tf.decode_raw(features['image'], tf.uint8)
    # tf.train.shuffle_batch必须确定shape
    image = tf.reshape(image, [224, 224])
    # 图片预处理
    image = tf.cast(image, tf.float32) / 255.0
    image = tf.subtract(image, 0.5)
    image = tf.multiply(image, 2.0)
    # 获取label
    label0 = tf.cast(features['label0'], tf.int32)
    label1 = tf.cast(features['label1'], tf.int32)
    label2 = tf.cast(features['label2'], tf.int32)
    label3 = tf.cast(features['label3'], tf.int32)

    return image, label0, label1, label2, label3


# In[3]:

# 获取图片数据和标签
image, label0, label1, label2, label3 = read_and_decode(TFRECORD_FILE)

#使用shuffle_batch可以随机打乱
image_batch, label_batch0, label_batch1, label_batch2, label_batch3 = tf.train.shuffle_batch(
        [image, label0, label1, label2, label3], batch_size = BATCH_SIZE,
        capacity = 50000, min_after_dequeue=10000, num_threads=1)

#定义网络结构
train_network_fn = nets_factory.get_network_fn(
    'alexnet_v2',
    num_classes=CHAR_SET_LEN,
    weight_decay=0.0005,
    is_training=True)
 
    
with tf.Session() as sess:
    # inputs: a tensor of size [batch_size, height, width, channels]
    X = tf.reshape(x, [BATCH_SIZE, 224, 224, 1])
    # 数据输入网络得到输出值
    logits0,logits1,logits2,logits3,end_points = train_network_fn(X)
    
    # 把标签转成one_hot的形式
    one_hot_labels0 = tf.one_hot(indices=tf.cast(y0, tf.int32), depth=CHAR_SET_LEN)
    one_hot_labels1 = tf.one_hot(indices=tf.cast(y1, tf.int32), depth=CHAR_SET_LEN)
    one_hot_labels2 = tf.one_hot(indices=tf.cast(y2, tf.int32), depth=CHAR_SET_LEN)
    one_hot_labels3 = tf.one_hot(indices=tf.cast(y3, tf.int32), depth=CHAR_SET_LEN)
    
    # 计算loss
    loss0 = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits0,labels=one_hot_labels0)) 
    loss1 = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits1,labels=one_hot_labels1)) 
    loss2 = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits2,labels=one_hot_labels2)) 
    loss3 = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits3,labels=one_hot_labels3)) 
    # 计算总的loss
    total_loss = (loss0+loss1+loss2+loss3)/4.0
    # 优化total_loss
    optimizer = tf.train.AdamOptimizer(learning_rate=lr).minimize(total_loss) 
    
    # 计算准确率
    correct_prediction0 = tf.equal(tf.argmax(one_hot_labels0,1),tf.argmax(logits0,1))
    accuracy0 = tf.reduce_mean(tf.cast(correct_prediction0,tf.float32))
    
    correct_prediction1 = tf.equal(tf.argmax(one_hot_labels1,1),tf.argmax(logits1,1))
    accuracy1 = tf.reduce_mean(tf.cast(correct_prediction1,tf.float32))
    
    correct_prediction2 = tf.equal(tf.argmax(one_hot_labels2,1),tf.argmax(logits2,1))
    accuracy2 = tf.reduce_mean(tf.cast(correct_prediction2,tf.float32))
    
    correct_prediction3 = tf.equal(tf.argmax(one_hot_labels3,1),tf.argmax(logits3,1))
    accuracy3 = tf.reduce_mean(tf.cast(correct_prediction3,tf.float32)) 
    
    # 用于保存模型
    saver = tf.train.Saver()
    # 初始化
    sess.run(tf.global_variables_initializer())
    
    # 创建一个协调器，管理线程
    coord = tf.train.Coordinator()
    # 启动QueueRunner, 此时文件名队列已经进队
    threads = tf.train.start_queue_runners(sess=sess, coord=coord)

    for i in range(6001):
        # 获取一个批次的数据和标签
        b_image, b_label0, b_label1 ,b_label2 ,b_label3 = sess.run([image_batch, label_batch0, label_batch1, label_batch2, label_batch3])
        # 优化模型
        sess.run(optimizer, feed_dict={x: b_image, y0:b_label0, y1: b_label1, y2: b_label2, y3: b_label3})  

        # 每迭代20次计算一次loss和准确率  
        if i % 20 == 0:  
            # 每迭代2000次降低一次学习率
            if i%2000 == 0:
                sess.run(tf.assign(lr, lr/3))
            acc0,acc1,acc2,acc3,loss_ = sess.run([accuracy0,accuracy1,accuracy2,accuracy3,total_loss],feed_dict={x: b_image,
                                                                                                                y0: b_label0,
                                                                                                                y1: b_label1,
                                                                                                                y2: b_label2,
                                                                                                                y3: b_label3})      
            learning_rate = sess.run(lr)
            print ("Iter:%d  Loss:%.3f  Accuracy:%.2f,%.2f,%.2f,%.2f  Learning_rate:%.4f" % (i,loss_,acc0,acc1,acc2,acc3,learning_rate))
             
            # 保存模型
            # if acc0 > 0.90 and acc1 > 0.90 and acc2 > 0.90 and acc3 > 0.90: 
            if i==6000:
                saver.save(sess, "./captcha/models/crack_captcha.model", global_step=i)  
                break 
                
    # 通知其他线程关闭
    coord.request_stop()
    # 其他所有线程关闭之后，这一函数才能返回
    coord.join(threads)