用TensorFlow訓練卷積神經網路——識別驗證碼
阿新 • • 發佈：2018-11-01
需要用到的包：numpy、tensorflow、captcha、matplotlib、PIL、random
import numpy as np
import tensorflow as tf # 深度學習庫
from captcha.image import ImageCaptcha # 用來生成驗證碼
import matplotlib.pyplot as plt # 用來將驗證碼可視化出來
from PIL import Image # 將驗證碼存為圖片
import random # 為了生成隨機驗證碼第一步：先生成可以用來訓練的驗證碼資料：
# 使用下面定義好的函式,去生成驗證碼,然後將驗證碼轉為圖片,再將圖片轉為文字向量 def random_captcha_text(char_set=number, captcha_size=4): pass def gen_captcha_text_and_image(): pass def convert2gray(img): pass def text2vec(text): pass def vec2text(vec): pass
第二步：生成訓練batch，即生成等等要喂入卷積神經網路的輸入引數batch_x，batch_y。
def get_next_batch(batch_size=128):
pass第三步：建立卷積神經網路，使用3層隱藏層和一層全連線層
def crack_captcha_cnn(w_alpha=0.01, b_alpha=0.1):
pass第四步：訓練卷積神經網路模型
def train_crack_captcha_cnn():
pass第五步：測試模型準確率
def crack_captcha(captcha_image): pass
完整程式碼如下，可以修改準確率要求在自己電腦上跑跑，注意：
train = 0 為訓練模型
train = 1 為測試模型
在測試模型時，別忘了修改訓練模型，將模型（crack_capcha.model-1810）替換為自己訓練好模型，在同級目錄下的model目錄中（crack_capcha.model-XXXX）
import numpy as np import tensorflow as tf from captcha.image import ImageCaptcha import matplotlib.pyplot as plt from PIL import Image import random 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+alphabet+ALPHABET, captcha_size=4): 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_and_image(): image = ImageCaptcha() captcha_text = random_captcha_text() captcha_text = ''.join(captcha_text) captcha = image.generate(captcha_text) # image.write(captcha_text, captcha_text + '.jpg') captcha_image = Image.open(captcha) captcha_image = np.array(captcha_image) return captcha_text, captcha_image def convert2gray(img): if len(img.shape) > 2: gray = np.mean(img, -1) # 上面的轉法較快，正規轉法如下 # r, g, b = img[:,:,0], img[:,:,1], img[:,:,2] # gray = 0.2989 * r + 0.5870 * g + 0.1140 * b return gray else: return img def text2vec(text): text_len = len(text) if text_len > MAX_CAPTCHA: raise ValueError('驗證碼最長4個字元') 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 + int(c) vector[idx] = 1 return vector # 向量轉回文字 def vec2text(vec): """ char_pos = vec.nonzero()[0] text=[] for i, c in enumerate(char_pos): char_at_pos = i #c/63 char_idx = c % CHAR_SET_LEN if char_idx < 10: char_code = char_idx + ord('0') elif char_idx <36: char_code = char_idx - 10 + ord('A') elif char_idx < 62: char_code = char_idx- 36 + ord('a') elif char_idx == 62: char_code = ord('_') else: raise ValueError('error') text.append(chr(char_code)) """ text=[] char_pos = vec.nonzero()[0] for i, c in enumerate(char_pos): number = i % 10 text.append(str(number)) return "".join(text) # 生成一個訓練batch 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]) # 有時生成影象大小不是(60, 160, 3) def wrap_gen_captcha_text_and_image(): while True: 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 # (image.flatten()-128)/128 mean為0 batch_y[i,:] = text2vec(text) return batch_x, batch_y # 定義CNN def crack_captcha_cnn(w_alpha=0.01, b_alpha=0.1): x = tf.reshape(X, shape=[-1, IMAGE_HEIGHT, IMAGE_WIDTH, 1]) # w_c1_alpha = np.sqrt(2.0/(IMAGE_HEIGHT*IMAGE_WIDTH)) # # w_c2_alpha = np.sqrt(2.0/(3*3*32)) # w_c3_alpha = np.sqrt(2.0/(3*3*64)) # w_d1_alpha = np.sqrt(2.0/(8*32*64)) # out_alpha = np.sqrt(2.0/1024) # 3 conv layer w_c1 = tf.Variable(w_alpha*tf.random_normal([3, 3, 1, 32])) b_c1 = tf.Variable(b_alpha*tf.random_normal([32])) conv1 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(x, 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) 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) # Fully connected layer 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]]) dense = tf.nn.relu(tf.add(tf.matmul(dense, w_d), b_d)) dense = tf.nn.dropout(dense, keep_prob) 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 # 訓練 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) accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) saver = tf.train.Saver() with tf.Session() as sess: sess.run(tf.global_variables_initializer()) step = 0 while True: batch_x, batch_y = get_next_batch(64) _, loss_ = sess.run([optimizer, loss], feed_dict={X: batch_x, Y: batch_y, keep_prob: 0.75}) print(step, loss_) # 每100 step計算一次準確率 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) # 如果準確率大於85%,儲存模型,完成訓練 if acc > 0.98: saver.save(sess, "./model/crack_capcha.model", global_step=step) break step += 1 # 測試 def crack_captcha(captcha_image): output = crack_captcha_cnn() saver = tf.train.Saver() with tf.Session() as sess: saver.restore(sess, "./model/crack_capcha.model-1810") 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__': train = 1 if train == 0: 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'] text, image = gen_captcha_text_and_image() print("驗證碼影象channel:", image.shape) # (60, 160, 3) # 影象大小 IMAGE_HEIGHT = 60 IMAGE_WIDTH = 160 MAX_CAPTCHA = len(text) print("驗證碼文字最長字元數", MAX_CAPTCHA) # 文字轉向量 # char_set = number + alphabet + ALPHABET + ['_'] # 如果驗證碼長度小於4, '_'用來補齊 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) # dropout train_crack_captcha_cnn() if train == 1: number = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'] IMAGE_HEIGHT = 60 IMAGE_WIDTH = 160 char_set = number CHAR_SET_LEN = len(char_set) 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() MAX_CAPTCHA = len(text) image = convert2gray(image) image = image.flatten() / 255 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) # dropout predict_text = crack_captcha(image) print("正確: {} 預測: {}".format(text, predict_text))
程式碼理論上來說是可以用的，但是準確率還是不好，大家可以修改準確率去跑，以及可以把字母等字元加入進去，訓練學習。