From ac500746af444a387ae27a316e6610710a7c36c2 Mon Sep 17 00:00:00 2001 From: luyishisi <543429245@qq.com> Date: Thu, 9 Mar 2017 14:20:34 +0800 Subject: [PATCH] =?UTF-8?q?=E6=95=B4=E7=90=86=E9=AA=8C=E8=AF=81=E7=A0=81?= =?UTF-8?q?=E4=B8=8B=E6=96=87=E4=BB=B6=E5=A4=B9=EF=BC=8C=E6=B7=BB=E5=8A=A0?= =?UTF-8?q?TensorFlow=E7=A0=B4=E8=A7=A3=E7=9A=84=E6=8F=8F=E8=BF=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../README.md" | 21 ++ .../knn_num_captcha/19.jpeg" | Bin .../knn_num_captcha/3.png" | Bin .../knn_num_captcha/33.jpeg" | Bin .../knn_num_captcha/45.jpeg" | Bin .../knn_num_captcha/58.jpeg" | Bin .../knn_num_captcha/7.jpeg" | Bin .../knn_num_captcha/L.jpeg" | Bin .../knn_num_captcha/L.png" | Bin .../knn_num_captcha/README.md" | 0 .../knn_num_captcha/login.py" | 0 .../knn_num_captcha/recognise.py" | 0 .../num/icon/0/0_1.jpeg" | Bin 416 -> 0 bytes .../num/icon/0/0_2.jpeg" | Bin 414 -> 0 bytes .../num/icon/1/23.jpeg" | Bin 405 -> 0 bytes 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1 + return vector +# 向量转回文本 +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)) + return "".join(text) + +""" +#向量(大小MAX_CAPTCHA*CHAR_SET_LEN)用0,1编码 每63个编码一个字符,这样顺利有,字符也有 +vec = text2vec("F5Sd") +text = vec2text(vec) +print(text) # F5Sd +vec = text2vec("SFd5") +text = vec2text(vec) +print(text) # SFd5 +""" + +# 生成一个训练batchv 一个批次为 默认128 张图片 转换为向量 +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]) + + # 有时生成图像大小不是(60, 160, 3) 反复获取验证码直到该验证码符合标准格式。 + 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): + #获取图片,并灰度转换 + text, image = wrap_gen_captcha_text_and_image() + image = convert2gray(image) + + # flatten 图片一维化 以及对应的文字内容也一维化,形成一个128行每行一个图片及对应文本 + batch_x[i,:] = image.flatten() / 255 # (image.flatten()-128)/128 mean为0 + batch_y[i,:] = text2vec(text) + + return batch_x, batch_y + +#################################################################### + +# 申请三个占位符 +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 + +# 定义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 # 3 个 转换层 + 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 # 最后连接层 + 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) + + # 输出层 + 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) + #out = tf.nn.softmax(out) + return out + +# 训练 +def train_crack_captcha_cnn(): + output = crack_captcha_cnn() + # loss 损失数值 + # loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(output, Y)) + loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=output, labels=Y)) + # 最后一层用来分类的softmax和sigmoid有什么不同? + # optimizer 为了加快训练 learning_rate 应该开始大,然后慢慢衰 + 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计算一次准确率 + if step % 100 == 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) + # 如果准确率大于50%,保存模型,完成训练 + if acc > 0.5: + saver.save(sess, "crack_capcha.model", global_step=step) + break + step += 1 + +def crack_captcha(captcha_image): + output = crack_captcha_cnn() + + saver = tf.train.Saver() + with tf.Session() as sess: + saver.restore(sess, tf.train.latest_checkpoint('.')) + + 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() + vector = np.zeros(MAX_CAPTCHA*CHAR_SET_LEN) + i = 0 + for n in text: + vector[i*CHAR_SET_LEN + n] = 1 + i += 1 + return vec2text(vector) + +if __name__ == '__main__': + + text, image = gen_captcha_text_and_image() + image = convert2gray(image) #生成一张新图 + image = image.flatten() / 255 # 将图片一维化 + predict_text = crack_captcha(image) #导入模型识别 + print("正确: {} 预测: {}".format(text, predict_text)) + #train_crack_captcha_cnn() diff --git "a/1.\351\252\214\350\257\201\347\240\201/tensorflow/text_model.py" "b/1.\351\252\214\350\257\201\347\240\201/tensorflow_cnn/text_model.py" similarity index 100% rename from "1.\351\252\214\350\257\201\347\240\201/tensorflow/text_model.py" rename to "1.\351\252\214\350\257\201\347\240\201/tensorflow_cnn/text_model.py"