自定义cnn网络识别验证码（附90%训练模型）

训练36300次正确率90%模型：http://download.csdn.net/detail/jsond/9852366

from  captcha.image import ImageCaptchaimport numpy as  npimport matplotlib.pyplot as  pltfrom  PIL import Imageimport randomimport tensorflow as tfnumber = ['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']char_set = number + alphabet + Alphabet##图片高IMAGE_HEIGHT = 60##图片宽IMAGE_WIDTH = 160##验证码长度MAX_CAPTCHA = 4##验证码选择空间CHAR_SET_LEN = len(char_set)##提前定义变量空间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)  ##节点保留率##生成n位验证码字符 这里n=4def random_captcha_text(char_set=char_set, captcha_size=4):    captcha_text = []    for i in range(captcha_size):        c = random.choice(char_set)        captcha_text.append(c)    return captcha_text##使用ImageCaptcha库生成验证码def gen_captcha_text_and_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##彩色图转化为灰度图def convert2gray(img):    if len(img.shape) > 2:        gray = np.mean(img, -1)        # 上面的转法较快，正规转法如下        # r, g, b = img[:,:,0], img[:,:,1], img[:,:,2]        # gray = 0.2989 * r + 0.5870 * g + 0.1140 * b        return gray    else:        return img##获取字符在 字符域中下标def getPos(char_set=char_set, char=None):    return char_set.index(char)##验证码字符转换为长向量def text2vec(text):    text_len = len(text)    if text_len > MAX_CAPTCHA:        raise ValueError('验证码最长4个字符')    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 + getPos(char=c)        vector[idx] = 1    return vector##获得1组验证码数据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 1:            text, image = gen_captcha_text_and_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##卷积层 附relu  max_pool drop操作def conn_layer(w_alpha=0.01, b_alpha=0.1, _keep_prob=0.7, input=None, last_size=None, cur_size=None):    w_c1 = tf.Variable(w_alpha * tf.random_normal([3, 3, last_size, cur_size]))    b_c1 = tf.Variable(b_alpha * tf.random_normal([cur_size]))    conv1 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(input, w_c1, strides=[1, 1, 1, 1], padding='SAME'), b_c1))    conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')    conv1 = tf.nn.dropout(conv1, keep_prob=_keep_prob)    return conv1##对卷积层到全链接层的数据进行变换def _get_conn_last_size(input):    shape = input.get_shape().as_list()    dim = 1    for d in shape[1:]:        dim *= d    input = tf.reshape(input, [-1, dim])    return input, dim##全链接层def _fc_layer(w_alpha=0.01, b_alpha=0.1, input=None, last_size=None, cur_size=None):    w_d = tf.Variable(w_alpha * tf.random_normal([last_size, cur_size]))    b_d = tf.Variable(b_alpha * tf.random_normal([cur_size]))    fc = tf.nn.bias_add(tf.matmul(input, w_d), b_d)    return fc##构建前向传播网络def crack_captcha_cnn(w_alpha=0.01, b_alpha=0.1):    x = tf.reshape(X, shape=[-1, IMAGE_HEIGHT, IMAGE_WIDTH, 1])    conv1 = conn_layer(input=x, last_size=1, cur_size=32)    conv2 = conn_layer(input=conv1, last_size=32, cur_size=64)    conn3 = conn_layer(input=conv2, last_size=64, cur_size=64)    input, dim = _get_conn_last_size(conn3)    fc_layer1 = _fc_layer(input=input, last_size=dim, cur_size=1024)    fc_layer1 = tf.nn.relu(fc_layer1)    fc_layer1 = tf.nn.dropout(fc_layer1, keep_prob)    fc_out = _fc_layer(input=fc_layer1, last_size=1024, cur_size=MAX_CAPTCHA * CHAR_SET_LEN)    return fc_out##反向传播def back_propagation():    output = crack_captcha_cnn()    ##学习率    loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(output, Y))    optm = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss)    predict = tf.reshape(output, [-1, MAX_CAPTCHA, CHAR_SET_LEN])    max_idx_p = tf.arg_max(predict, 2)    max_idx_l = tf.arg_max(tf.reshape(Y, [-1, MAX_CAPTCHA, CHAR_SET_LEN]), 2)    accuracy = tf.reduce_mean(tf.cast(tf.equal(max_idx_p, max_idx_l), tf.float32))    return loss, optm, accuracy##初次运行训练模型def train_first():    loss, optm, accuracy = back_propagation()    saver = tf.train.Saver()    with tf.Session() as  sess:        sess.run(tf.global_variables_initializer())        step = 0        while 1:            batch_x, batch_y = get_next_batch(64)            _, loss_ = sess.run([optm, loss], feed_dict={X: batch_x, Y: batch_y, keep_prob: 0.75})            if step % 50 == 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, loss_)                if acc > 0.80:  ##准确率大于0.80保存模型 可自行调整                    saver.save(sess, 'models/crack_capcha.model', global_step=step)                    break            step += 1##加载现有模型 继续进行训练def train_continue(step):    loss, optm, accuracy = back_propagation()    saver = tf.train.Saver()    with tf.Session() as sess:        path = "models/crack_capcha.model-" + str(step)        saver.restore(sess, path)        ##36300 36300 0.9325 0.0147698        while 1:            batch_x, batch_y = get_next_batch(100)            _, loss_ = sess.run([optm, loss], feed_dict={X: batch_x, Y: batch_y, keep_prob: 0.75})            if step % 50 == 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, loss_)                if acc >= 0.925:                    saver.save(sess, 'models/crack_capcha.model', global_step=step)                if acc >= 0.95:                    saver.save(sess, 'models/crack_capcha.model', global_step=step)                    break            step += 1##测试训练模型def crack_captcha(captcha_image, step):    output = crack_captcha_cnn()    saver = tf.train.Saver()    with tf.Session() as sess:        path = 'models/crack_capcha.model-' + str(step)        saver.restore(sess, path)        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 textif __name__ == '__main__':

    ##训练和测试开关

    train = 1    if train:        ##train_continue(36300)        train_first()    else:        text, image = gen_captcha_text_and_image()        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()        image = convert2gray(image)        image = image.flatten() / 255        predict_text = crack_captcha(image, 36300)        print("正确: {}  预测: {}".format(text, [char_set[char] for i, char in enumerate(predict_text)]))

测试1：

测试2：

提醒：经网友测试发现该模型的收敛速度较慢，全部字符空间的话要训练2000多次才开始收敛。

可以把字符空间代码
char_set = number + alphabet + Alphabet
修改为只有数字的字符空间
char_set = number   
大概训练1000次开始收敛