實(shí)驗(yàn)使用tensorflow實(shí)現(xiàn)卷積神經(jīng)網(wǎng)絡(luò)對(duì)驗(yàn)證碼進(jìn)行識(shí)別，在識(shí)別的過(guò)程中遇到了優(yōu)化速度較慢的問(wèn)題，將調(diào)參的心得記錄如下。

• batch_size大小不要小于輸出值得個(gè)數(shù)
• 學(xué)習(xí)率大的情況下會(huì)出現(xiàn)損失忽大忽小的情況，學(xué)習(xí)率小的情況下容易出現(xiàn)訓(xùn)練速度過(guò)慢的問(wèn)題，可以人工嘗試多個(gè)不同的學(xué)習(xí)率觀察損失的變化，看是否能優(yōu)化，也可以使用動(dòng)態(tài)調(diào)整的方法。
• 使用ELU代替RELU
• 使用Batch Normalization可以提高訓(xùn)練的精確度和速度，并且可以替換到Droupout

在下面的代碼中包含在已有訓(xùn)練模型的基礎(chǔ)上訓(xùn)練模型和測(cè)試數(shù)據(jù)等功能。其中測(cè)試效果圖和代碼如下。

測(cè)試效果圖

#訓(xùn)練代碼
import tensorflow as tf
from captcha.image import  ImageCaptcha
import numpy as np
import matplotlib.pyplot as plt
from PIL import  Image
import random
import os

number=['0','1','2','3','4','5','6','7','8','9','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','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']
#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):
    captcha_text=[]
    for i in range(captcha_size):
        c=random.choice(char_set)
        captcha_text.append(c)
    return captcha_text

def gen_captcha_text_image():
    image=ImageCaptcha()
    captcha_text=random_captcha_text()
    captcha_text=''.join(captcha_text)
    captcha=image.generate(captcha_text)
    captcha_image=Image.open(captcha)
    captcha_image=np.array(captcha_image)
    return captcha_text,captcha_image


def convert2gray(img):
    if len(img.shape)>2:
        r, g, b = img[:, :, 0], img[:, :, 1], img[:, :, 2]
        gray = 0.2989 * r + 0.5870 * g + 0.1140 * b
        return gray
    else:
        return img


def text2vec(text):
    text_len = len(text)
    if text_len > max_captcha:
        raise ValueError('驗(yàn)證碼最長(zhǎng)4個(gè)字符')

    vector = np.zeros(max_captcha * char_set_len)

    def char2pos(c):
        if c == '_':
            k = 62
            return k
        k = ord(c) - 48
        if k > 9:
            k = ord(c) - 55
            if k > 35:
                k = ord(c) - 61
                if k > 61:
                    raise ValueError('No Map')
        return k

    for i, c in enumerate(text):
        idx = i * char_set_len + char2pos(c)
        vector[idx] = 1
    return vector


def get_next_batch(batch_size=128):
    batch_x=np.zeros([batch_size,image_height*image_width])
    batch_y=np.zeros([batch_size,max_captcha*char_set_len])

    def wrap_gen_captcha_text_and_image():
        while True:
            text, image = gen_captcha_text_image()
            if image.shape == (60, 160, 3):
                return text, image

    for i in range(batch_size):
        text, image = wrap_gen_captcha_text_and_image()
        image = convert2gray(image)

        batch_x[i, :] = image.flatten() / 255
        batch_y[i, :] = text2vec(text)

    return batch_x, batch_y

def cnn_structure(w_alpha=0.01, b_alpha=0.1):
    x = tf.reshape(X, shape=[-1, image_height, image_width, 1])


    wc1=tf.get_variable(name='wc1',shape=[3,3,1,32],dtype=tf.float32,initializer=tf.contrib.layers.xavier_initializer())
    #wc1 = tf.Variable(w_alpha * tf.random_normal([3, 3, 1, 32]))
    bc1 = tf.Variable(b_alpha * tf.random_normal([32]))
    conv1 = tf.nn.bias_add(tf.nn.conv2d(x, wc1, strides=[1, 1, 1, 1], padding='SAME'), bc1)
    conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
    #批標(biāo)準(zhǔn)化
    batch_mean, batch_var = tf.nn.moments(conv1, [0, 1, 2], keep_dims=True)
    shift = tf.Variable(tf.zeros([32]))
    scale = tf.Variable(tf.ones([32]))
    epsilon = 1e-3
    conv1 = tf.nn.batch_normalization(conv1, batch_mean, batch_var, shift, scale, epsilon)
    
    #放在最大池化之后的relu
    conv1 = tf.nn.elu(conv1)
    conv1 = tf.nn.dropout(conv1, keep_prob)

    wc2=tf.get_variable(name='wc2',shape=[3,3,32,64],dtype=tf.float32,initializer=tf.contrib.layers.xavier_initializer())
   # wc2 = tf.Variable(w_alpha * tf.random_normal([3, 3, 32, 64]))
    bc2 = tf.Variable(b_alpha * tf.random_normal([64]))
    conv2 = tf.nn.bias_add(tf.nn.conv2d(conv1, wc2, strides=[1, 1, 1, 1], padding='SAME'), bc2)
    conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
    #批標(biāo)準(zhǔn)化
    batch_mean, batch_var = tf.nn.moments(conv2, [0, 1, 2], keep_dims=True)
    shift = tf.Variable(tf.zeros([64]))
    scale = tf.Variable(tf.ones([64]))
    epsilon = 1e-3
    conv2 = tf.nn.batch_normalization(conv2, batch_mean, batch_var, shift, scale, epsilon)
    
    conv2 = tf.nn.elu(conv2)
    conv2 = tf.nn.dropout(conv2, keep_prob)

    wc3=tf.get_variable(name='wc3',shape=[3,3,64,128],dtype=tf.float32,initializer=tf.contrib.layers.xavier_initializer())
    #wc3 = tf.Variable(w_alpha * tf.random_normal([3, 3, 64, 128]))
    bc3 = tf.Variable(b_alpha * tf.random_normal([128]))
    conv3 = tf.nn.bias_add(tf.nn.conv2d(conv2, wc3, strides=[1, 1, 1, 1], padding='SAME'), bc3)
    conv3 = tf.nn.max_pool(conv3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
    #批標(biāo)準(zhǔn)化 通過(guò)相當(dāng)于一個(gè)圖
    batch_mean, batch_var = tf.nn.moments(conv3, [0, 1, 2], keep_dims=True)
    shift = tf.Variable(tf.zeros([128]))
    scale = tf.Variable(tf.ones([128]))
    epsilon = 1e-3
    conv3 = tf.nn.batch_normalization(conv3, batch_mean, batch_var, shift, scale, epsilon)
    
    conv3 = tf.nn.elu(conv3)
    conv3 = tf.nn.dropout(conv3, keep_prob)


    wd1=tf.get_variable(name='wd1',shape=[8*20*128,1024],dtype=tf.float32,initializer=tf.contrib.layers.xavier_initializer())
    #wd1 = tf.Variable(w_alpha * tf.random_normal([7*20*128,1024]))
    bd1 = tf.Variable(b_alpha * tf.random_normal([1024]))
    dense = tf.reshape(conv3, [-1, wd1.get_shape().as_list()[0]])
    
    
    dense = tf.nn.elu(tf.add(tf.matmul(dense, wd1), bd1))
    dense = tf.nn.dropout(dense, keep_prob)

    wout=tf.get_variable('name',shape=[1024,max_captcha * char_set_len],dtype=tf.float32,initializer=tf.contrib.layers.xavier_initializer())
    #wout = tf.Variable(w_alpha * tf.random_normal([1024, max_captcha * char_set_len]))
    bout = tf.Variable(b_alpha * tf.random_normal([max_captcha * char_set_len]))
    out = tf.add(tf.matmul(dense, wout), bout)
    return out

def train_cnn():
    output=cnn_structure()
    cost=tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=output,labels=Y))
    #嘗試降低學(xué)習(xí)率，從原來(lái)模型的基礎(chǔ)上繼續(xù)訓(xùn)練
    optimizer=tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)
    predict=tf.reshape(output,[-1,max_captcha,char_set_len])
    max_idx_p = tf.argmax(predict, 2)
    max_idx_l = tf.argmax(tf.reshape(Y, [-1, max_captcha, char_set_len]), 2)
    correct_pred = tf.equal(max_idx_p, max_idx_l)
    accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

    saver=tf.train.Saver()
    modelRestore = True
    path = "./model"
    with tf.Session() as sess:
        
        #先初始化變量
        init = tf.global_variables_initializer()
        sess.run(init)
        # 重新加載模型
        if modelRestore and os.path.isfile(os.path.join(path, 'checkpoint')):#判斷是否要加載并且是否存在訓(xùn)練好的模型
            ckpt = tf.train.get_checkpoint_state(path)    # 讀取最后一個(gè)模型的路徑  
            print( ckpt.model_checkpoint_path)
            saver.restore(sess, ckpt.model_checkpoint_path) #加載模型
#         init = tf.global_variables_initializer()
#         sess.run(init)
        step = 0
        while True:
            batch_x, batch_y = get_next_batch(1024)
            _, cost_= sess.run([optimizer, cost], feed_dict={X: batch_x, Y: batch_y, keep_prob: 1})
            print(step, cost_)
            if step % 10 == 0:
                batch_x_test, batch_y_test = get_next_batch(100)
                acc = sess.run(accuracy, feed_dict={X: batch_x_test, Y: batch_y_test, keep_prob: 1.})
                print(step, acc)
                if acc > 0.97:
                    saver.save(sess, "./model/crack_capcha.model", global_step=step)
                    break
            step += 1


def crack_captcha(captcha_image):
    output = cnn_structure()

    saver = tf.train.Saver()
    with tf.Session() as sess:
        saver.restore(sess, "./model/crack_capcha.model-710")

        predict = tf.argmax(tf.reshape(output, [-1, max_captcha, char_set_len]), 2)
        text_list = sess.run(predict, feed_dict={X: [captcha_image], keep_prob: 1.})
        text = text_list[0].tolist()
        return text

if __name__=='__main__':
        text,image=gen_captcha_text_image()
        print("驗(yàn)證碼大小：",image.shape)#(60,160,3)

        image_height=60
        image_width=160
        max_captcha=len(text)
        print("驗(yàn)證碼文本最長(zhǎng)字符數(shù)",max_captcha)
        char_set=number
        char_set_len=len(char_set)

        X = tf.placeholder(tf.float32, [None, image_height * image_width])
        Y = tf.placeholder(tf.float32, [None, max_captcha * char_set_len])
        keep_prob = tf.placeholder(tf.float32)
        train_cnn()

#測(cè)試代碼
import tensorflow as tf
from captcha.image import  ImageCaptcha
import numpy as np
import matplotlib.pyplot as plt
from PIL import  Image
import random

model_path = './model'
image_height = 60
image_width = 160
number=['0','1','2','3','4','5','6','7','8','9','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','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']
char_set = number
char_set_len = len(char_set)

def random_captcha_text(char_set=number,captcha_size=4):
    captcha_text=[]
    for i in range(captcha_size):
        c=random.choice(char_set)
        captcha_text.append(c)
    return captcha_text

def gen_captcha_text_image():
    image=ImageCaptcha()
    captcha_text=random_captcha_text()
    captcha_text=''.join(captcha_text)
    captcha=image.generate(captcha_text)
    captcha_image=Image.open(captcha)
    captcha_image=np.array(captcha_image)
    return captcha_text,captcha_image


def convert2gray(img):
    if len(img.shape)>2:
        r, g, b = img[:, :, 0], img[:, :, 1], img[:, :, 2]
        gray = 0.2989 * r + 0.5870 * g + 0.1140 * b
        return gray
    else:
        return img


def text2vec(text):
    text_len = len(text)
    if text_len > max_captcha:
        raise ValueError('驗(yàn)證碼最長(zhǎng)4個(gè)字符')

    vector = np.zeros(max_captcha * char_set_len)

    def char2pos(c):
        if c == '_':
            k = 62
            return k
        k = ord(c) - 48
        if k > 9:
            k = ord(c) - 55
            if k > 35:
                k = ord(c) - 61
                if k > 61:
                    raise ValueError('No Map')
        return k

    for i, c in enumerate(text):
        idx = i * char_set_len + char2pos(c)
        vector[idx] = 1
    return vector



text, image = gen_captcha_text_image()
print("原始數(shù)據(jù):",text)
f = plt.figure()
ax = f.add_subplot(111)
ax.text(0.1, 0.9, text, ha='center', va='center', transform=ax.transAxes)
plt.imshow(image)

# plt.show()

max_captcha = len(text)
image = convert2gray(image)
#獲取訓(xùn)練的圖片數(shù)據(jù)
image = image.flatten() / 255
y_label = np.array(range(620)).reshape(1,620)
saver = tf.train.import_meta_graph(model_path + '/crack_capcha.model-1390.meta')# 加載圖結(jié)構(gòu)
gragh = tf.get_default_graph()# 獲取當(dāng)前圖，為了后續(xù)訓(xùn)練時(shí)恢復(fù)變量
# tensor_name_list = [tensor.name for tensor in gragh.as_graph_def().node]# 得到當(dāng)前圖中所有變量的名稱(chēng)


x = gragh.get_tensor_by_name('Placeholder:0')# 獲取輸入變量（占位符，由于保存時(shí)未定義名稱(chēng)，tf自動(dòng)賦名稱(chēng)“Placeholder”）
keep_prob = gragh.get_tensor_by_name('Placeholder_2:0')# 獲取dropout的保留參數(shù)

pred = gragh.get_tensor_by_name('Add_1:0')# 獲取網(wǎng)絡(luò)輸出值
predict = tf.argmax(tf.reshape(pred, [-1, max_captcha, char_set_len]), 2)

model_path = "./model"
with tf.Session() as sess:
    saver.restore(sess, tf.train.latest_checkpoint(model_path))# 加載變量值
    print('finish loading model!')   
    text = sess.run(predict, feed_dict = {x:[image], keep_prob:1})
    text = text[0].tolist()
    text = [number[index] for index in text]
    print("預(yù)測(cè)數(shù)據(jù):",text)