基于python+tensorflow的cnn验证码识别

import os import tensorflow.compat.v1 as tf tf.disable_v2_behavior() from process import next_batch from getimg import CAPTCHA_HEIGHT, CAPTCHA_WIDTH, CAPTCHA_LEN, CAPTCHA_LIST from datetime import datetime # 随机生成权重 def weight_variable(shape, w_alpha=0.01): initial = w_alpha * tf.random_normal(shape) return tf.Variable(initial) # 随机生成偏置项 def bias_variable(shape, b_alpha=0.1): initial = b_alpha * tf.random_normal(shape) return tf.Variable(initial) # 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 def conv2d(x, w): return tf.nn.conv2d(x, w, strides=[1, 1, 1, 1], padding='SAME') # max pooling,取出区域内最大值为代表特征， 2x2pool，图片尺寸变为1/2 def max_pool_2x2(x): return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') # 三层卷积神经网络计算图 def cnn_graph(x, keep_prob, size, captcha_list=CAPTCHA_LIST, captcha_len=CAPTCHA_LEN): # 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]) # 第一层 # 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) # 第二层 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) # 第三层 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) # 全连接层 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) # 全连接层(输出层) 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 # 最小化loss def optimize_graph(y, y_conv): # 交叉熵计算loss # 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 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 optimizer # 偏差计算 def accuracy_graph(y, y_conv, width=len(CAPTCHA_LIST), height=CAPTCHA_LEN): # 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 # 训练cnn def train(height=CAPTCHA_HEIGHT, width=CAPTCHA_WIDTH, y_size=len(CAPTCHA_LIST) * CAPTCHA_LEN): 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: # 每批次64个样本 batch_x, batch_y = next_batch(64) sess.run(optimizer, feed_dict={x: batch_x, y: batch_y, keep_prob: 0.75}) print("step：", step) # 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() if __name__ == '__main__': train()