跟我学算法- tensorflow 卷积神经网络训练验证码
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使用captcha.image.Image 生成随机验证码，随机生成的验证码为0到9的数字，验证码有4位数字组成,这是一个自己生成验证码，自己不断训练的模型
使用三层卷积层，三层池化层，二层全连接层来进行组合
第一步：定义生成随机验证码图片
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, captha_size=4): captha_texts = [] for i in range(captha_size): # 随机抽取数字，添加到列表中 captha_texts.append(random.choice(char_set)) return captha_texts def gen_captcha_text_and_image(): image = ImageCaptcha() captcha_texts = random_captcha_text() # 列表转换为字符串 captcha_texts = ‘‘.join(captcha_texts) # 产生图片 captcha = image.generate(captcha_texts) captcha_image = Image.open(captcha) captcha_image = np.array(captcha_image) # 返回字符串和图片 return captcha_texts, captcha_image
第二步： 生成训练样本
# 把彩图转换为灰度图 def convert2gray(image): if len(image.shape)> 2: grey = np.mean(image, -1) return grey else: return image # 把文本转换为可用的标签维度是40 def text2vec(text): text_len = len(text) int(text[0]) if text_len > MAX_CAPTCHA: raise ValueError(‘验证码最长4个字符‘) vec = np.zeros(MAX_CAPTCHA*CHAR_SET_LEN) for index, c in enumerate(text): now_index = index * CHAR_SET_LEN + int(c.strip()) vec[now_index] = 1 return vec # 生成训练样本 def get_next_batch(batch_size=128): batch_x = np.zeros([batch_size, IMAGE_HEIGHT*IMAGE_WEIGHT]) batch_y = np.zeros([batch_size, MAX_CAPTCHA*CHAR_SET_LEN]) # 有时候生成的图像大小不是(60, 160, 3), 重新生成 def wrap_gen_captcha_text_and_image(): text, image = gen_captcha_text_and_image() while True: 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) # 转换成的一维的灰度图，使得其范围为(0, 1) batch_x[i, :] = image.flatten() / 255 # (image.flatten()-128)/128 mean为0 # 把输入的文本转换为标签类型 batch_y[i, :] = text2vec(text) return batch_x, batch_y
第三步： 定义CNN，这里的CNN为3层卷积，3层池化， 2层全连接
# 定义CNN def crack_captcha_cnn(w_alpha=0.01, b_alpha=0.1): # [-1, IMAGE_HEIGHT, IMAGE_WEIGHT, 1] -1表示batch_size,1表示样本深度，也就是RGB通道的个数 x = tf.reshape(X, [-1, IMAGE_HEIGHT, IMAGE_WEIGHT, 1]) # 创建w_c1和b_c1的初始化变量 w_c1 = tf.Variable(w_alpha*tf.random_normal([3, 3, 1, 32])) b_c1 = tf.Variable(b_alpha*tf.random_normal([32])) # 进行卷积操作 conv1 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(x, w_c1, strides=(1, 1, 1, 1), padding=‘SAME‘), b_c1)) # 进行池化操作 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) w_c2 = tf.Variable(w_alpha * tf.random_normal([3, 3, 32, 64])) b_c2 = tf.Variable(b_alpha * tf.random_normal([64])) conv2 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv1, w_c2, strides=(1, 1, 1, 1), padding=‘SAME‘), b_c2)) conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding=‘SAME‘) conv2 = tf.nn.dropout(conv2, keep_prob) w_c3 = tf.Variable(w_alpha * tf.random_normal([3, 3, 64, 64])) b_c3 = tf.Variable(b_alpha * tf.random_normal([64])) conv3 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv2, w_c3, strides=(1, 1, 1, 1), padding=‘SAME‘), b_c3)) conv3 = tf.nn.max_pool(conv3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding=‘SAME‘) conv3 = tf.nn.dropout(conv3, keep_prob) # 第一个全连接层 #8*20*64表示conv3的维度， 60/2/2/2 = 8 160/2/2/2=20 w_d = tf.Variable(w_alpha * tf.random_normal([8 * 20 * 64, 1024])) b_d = tf.Variable(b_alpha * tf.random_normal([1024])) dense = tf.reshape(conv3, [-1, w_d.get_shape().as_list()[0]]) print(tf.matmul(dense, w_d).shape, b_d.shape) dense = tf.nn.relu(tf.add(tf.matmul(dense, w_d), b_d)) dense = tf.nn.dropout(dense, keep_prob) # 第二个全连接层， 不需要激活层 w_out = tf.Variable(w_alpha * tf.random_normal([1024, MAX_CAPTCHA*CHAR_SET_LEN])) b_out = tf.Variable(b_alpha * tf.random_normal([MAX_CAPTCHA*CHAR_SET_LEN])) out = tf.add(tf.matmul(dense, w_out), b_out) return out
第四步： 定义训练CNN函数
def train_crack_captcha_cnn(): output = crack_captcha_cnn() loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=output, labels=Y)) optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss) 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) accr = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) saver = tf.train.Saver() with tf.Session() as sess: #变量初始化 sess.run(tf.global_variables_initializer()) step = 0 # 让它一直都训练直到精度大于0.5 while True: # 生成64个样本 batch_x, batch_y = get_next_batch(batch_size=64) __, _loss = sess.run([optimizer, loss], feed_dict={X:batch_x, Y:batch_y, keep_prob: 0.75}) print(step, _loss) # 每一百次循环计算一次返回值 if step%100 == 0 : batch_text_x, batch_text_y = get_next_batch(batch_size=128) acc = sess.run(accr, feed_dict={X:batch_text_x, Y:batch_text_y, keep_prob:1.}) print(acc) # 如果准确率大于0.5就保存模型 if acc > 0.5: saver.save(sess, ‘.model/crack_captcha/model‘) break step += 1
第五步： 定义训练好后的预测模型
# 用于训练好后的模型进行预测 def crack_captcha(captcha_image): output = crack_captcha_cnn() # 初始化保存数据 saver = tf.train.Saver() with tf.Session() as sess: # 重新加载sess saver.restore(sess, ‘.model/crack_captcha/model‘) predict = tf.reshape(output, [-1, MAX_CAPTCHA, CHAR_SET_LEN], 2) # 获得CNN之后的结果 text_list = sess.run(predict, feed_dict={X:[captcha_image], keep_prob:1}) # 让输出结果变成一个列表 text = text_list[0].tolist() return text
第六步：主要函数用来进行训练，或者测试
if __name__ == ‘__main__‘: #获得文本和图片 train = 0 # 当train=0时进行训练 if train==0: number = [‘0‘, ‘1‘, ‘2‘, ‘3‘, ‘4‘, ‘5‘, ‘6‘, ‘7‘, ‘8‘, ‘9‘] text, image = gen_captcha_text_and_image() IMAGE_HEIGHT = 60 IMAGE_WEIGHT = 160 MAX_CAPTCHA = len(text) print(‘验证码文本最长字符数‘, MAX_CAPTCHA) char_set = number CHAR_SET_LEN = len(char_set) X = tf.placeholder(tf.float32, [None, IMAGE_HEIGHT*IMAGE_WEIGHT]) Y = tf.placeholder(tf.float32, [None, MAX_CAPTCHA*CHAR_SET_LEN]) keep_prob = tf.placeholder(tf.float32) train_crack_captcha_cnn() # 当trian=1时进行测试 elif train == 1: text, image = gen_captcha_text_and_image() # 将模型转换为灰度图以后再进行测试 image = convert2gray(image) image = image.flatten() / 255 IMAGE_HEIGHT = 60 IMAGE_WEIGHT = 160 MAX_CAPTCHA = len(text) print(‘验证码文本最长字符数‘, MAX_CAPTCHA) char_set = number CHAR_SET_LEN = len(char_set) X = tf.placeholder(tf.float32, [None, IMAGE_HEIGHT * IMAGE_WEIGHT]) Y = tf.placeholder(tf.float32, [None, MAX_CAPTCHA * CHAR_SET_LEN]) keep_prob = tf.placeholder(tf.float32) pred_text = crack_captcha(image) print(‘真实值‘, text, ‘测试值‘, pred_text)
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