基于CNN的验证码识别神经网络实现

一、前言

1、什么是CNN？

2、TensorFlow进阶

二、实战

1、验证码生成

import randomimport numpy as npfrom PIL import Imagefrom captcha.image import ImageCaptchaNUMBER = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']LOW_CASE = ['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']UP_CASE = ['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']CAPTCHA_LIST = NUMBER + LOW_CASE + UP_CASECAPTCHA_LEN = 4CAPTCHA_HEIGHT = 60CAPTCHA_WIDTH = 160def random_captcha_text(char_set=CAPTCHA_LIST, captcha_size=CAPTCHA_LEN):    '''    随机生成验证码文本    :param char_set:    :param captcha_size:    :return:    '''    captcha_text = [random.choice(char_set) for _ in range(captcha_size)]    return ''.join(captcha_text)def gen_captcha_text_and_image(width=CAPTCHA_WIDTH, height=CAPTCHA_HEIGHT,save=None):    '''    生成随机验证码    :param width:    :param height:    :param save:    :return: np数组    '''    image = ImageCaptcha(width=width, height=height)    # 验证码文本    captcha_text = random_captcha_text()    captcha = image.generate(captcha_text)    # 保存    if save: image.write(captcha_text, captcha_text + '.jpg')    captcha_image = Image.open(captcha)    # 转化为np数组    captcha_image = np.array(captcha_image)    return captcha_text, captcha_image

基于captcha包做的简单验证码生成器，用来练手挺好的，直接看代码就行啦

2、权重、偏置及工具函数定义

def weight_variable(shape, w_alpha=0.01):    '''    增加噪音，随机生成权重    :param shape:    :param w_alpha:    :return:    '''    initial = w_alpha * tf.random_normal(shape)    return tf.Variable(initial)def bias_variable(shape, b_alpha=0.1):    '''    增加噪音，随机生成偏置项    :param shape:    :param b_alpha:    :return:    '''    initial = b_alpha * tf.random_normal(shape)    return tf.Variable(initial)def conv2d(x, w):    '''    局部变量线性组合，步长为1，模式‘SAME’代表卷积后图片尺寸不变，即零边距    :param x:    :param w:    :return:    '''    return tf.nn.conv2d(x, w, strides=[1, 1, 1, 1], padding='SAME')def max_pool_2x2(x):    '''    max pooling,取出区域内最大值为代表特征， 2x2pool，图片尺寸变为1/2    :param x:    :return:    '''    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')

3、CNN三层神经网络定义

def cnn_graph(x, keep_prob, size, captcha_list=CAPTCHA_LIST, captcha_len=CAPTCHA_LEN):    '''    三层卷积神经网络计算图    :param x:    :param keep_prob:    :param size:    :param captcha_list:    :param captcha_len:    :return:    '''    # 图片reshape为4维向量    image_height, image_width = size    x_image = tf.reshape(x, shape=[-1, image_height, image_width, 1])    # layer 1    # filter定义为3x3x1， 输出32个特征, 即32个filter    w_conv1 = weight_variable([3, 3, 1, 32])    b_conv1 = bias_variable([32])    # rulu激活函数    h_conv1 = tf.nn.relu(tf.nn.bias_add(conv2d(x_image, w_conv1), b_conv1))    # 池化    h_pool1 = max_pool_2x2(h_conv1)    # dropout防止过拟合    h_drop1 = tf.nn.dropout(h_pool1, keep_prob)    # layer 2    w_conv2 = weight_variable([3, 3, 32, 64])    b_conv2 = bias_variable([64])    h_conv2 = tf.nn.relu(tf.nn.bias_add(conv2d(h_drop1, w_conv2), b_conv2))    h_pool2 = max_pool_2x2(h_conv2)    h_drop2 = tf.nn.dropout(h_pool2, keep_prob)    # layer 3    w_conv3 = weight_variable([3, 3, 64, 64])    b_conv3 = bias_variable([64])    h_conv3 = tf.nn.relu(tf.nn.bias_add(conv2d(h_drop2, w_conv3), b_conv3))    h_pool3 = max_pool_2x2(h_conv3)    h_drop3 = tf.nn.dropout(h_pool3, keep_prob)    # full connect layer    image_height = int(h_drop3.shape[1])    image_width = int(h_drop3.shape[2])    w_fc = weight_variable([image_height*image_width*64, 1024])    b_fc = bias_variable([1024])    h_drop3_re = tf.reshape(h_drop3, [-1, image_height*image_width*64])    h_fc = tf.nn.relu(tf.add(tf.matmul(h_drop3_re, w_fc), b_fc))    h_drop_fc = tf.nn.dropout(h_fc, keep_prob)    # out layer    w_out = weight_variable([1024, len(captcha_list)*captcha_len])    b_out = bias_variable([len(captcha_list)*captcha_len])    y_conv = tf.add(tf.matmul(h_drop_fc, w_out), b_out)    return y_conv

4、优化及偏差

def optimize_graph(y, y_conv):    '''    优化计算图    :param y:    :param y_conv:    :return:    '''    # 交叉熵计算loss 注意logits输入是在函数内部进行sigmod操作    # sigmod_cross适用于每个类别相互独立但不互斥，如图中可以有字母和数字    # softmax_cross适用于每个类别独立且排斥的情况，如数字和字母不可以同时出现    loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=y_conv, labels=y))    # 最小化loss优化    optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss)    return optimizerdef accuracy_graph(y, y_conv, width=len(CAPTCHA_LIST), height=CAPTCHA_LEN):    '''    偏差计算图    :param y:    :param y_conv:    :param width:    :param height:    :return:    '''    # 这里区分了大小写 实际上验证码一般不区分大小写    # 预测值    predict = tf.reshape(y_conv, [-1, height, width])    max_predict_idx = tf.argmax(predict, 2)    # 标签    label = tf.reshape(y, [-1, height, width])    max_label_idx = tf.argmax(label, 2)    correct_p = tf.equal(max_predict_idx, max_label_idx)    accuracy = tf.reduce_mean(tf.cast(correct_p, tf.float32))    return accuracy

5、训练

def train(height=CAPTCHA_HEIGHT, width=CAPTCHA_WIDTH, y_size=len(CAPTCHA_LIST)*CAPTCHA_LEN):    '''    cnn训练    :param height:    :param width:    :param y_size:    :return:    '''    # cnn在图像大小是2的倍数时性能最高, 如果图像大小不是2的倍数，可以在图像边缘补无用像素    # 在图像上补2行，下补3行，左补2行，右补2行    # np.pad(image,((2,3),(2,2)), 'constant', constant_values=(255,))    acc_rate = 0.95    # 按照图片大小申请占位符    x = tf.placeholder(tf.float32, [None, height * width])    y = tf.placeholder(tf.float32, [None, y_size])    # 防止过拟合 训练时启用 测试时不启用    keep_prob = tf.placeholder(tf.float32)    # cnn模型    y_conv = cnn_graph(x, keep_prob, (height, width))    # 最优化    optimizer = optimize_graph(y, y_conv)    # 偏差    accuracy = accuracy_graph(y, y_conv)    # 启动会话.开始训练    saver = tf.train.Saver()    sess = tf.Session()    sess.run(tf.global_variables_initializer())    step = 0    while 1:        batch_x, batch_y = next_batch(64)        sess.run(optimizer, feed_dict={x: batch_x, y: batch_y, keep_prob: 0.75})        # 每训练一百次测试一次        if step % 100 == 0:            batch_x_test, batch_y_test = next_batch(100)            acc = sess.run(accuracy, feed_dict={x: batch_x_test, y: batch_y_test, keep_prob: 1.0})            print(datetime.now().strftime('%c'), ' step:', step, ' accuracy:', acc)            # 偏差满足要求，保存模型            if acc > acc_rate:                model_path = os.getcwd() + os.sep + str(acc_rate) + "captcha.model"                saver.save(sess, model_path, global_step=step)                acc_rate += 0.01                if acc_rate > 0.99: break        step += 1    sess.close()

这里设定准确率到达95%就保存模型，实际训练半个多小时可以达到98%的准确率

三、其他

详细代码可以在我的github上找到: https://github.com/lpty/tensorflow_tutorial