卷积神经网络之验证码识别
import tensorflow as tf import glob import pandas as pd import numpy as np # 1）读取图片数据filename -> 标签值 def read_picture(): """ 读取验证码图片 :return: """ # 1、构造文件名队列 file_list = glob.glob("./GenPics/*.jpg") # print("file_list:\n", file_list) file_queue = tf.train.string_input_producer(file_list) # 2、读取与解码 # 读取 reader = tf.WholeFileReader() filename, image = reader.read(file_queue) # 解码 image_decode = tf.image.decode_jpeg(image) # 更新图片形状 image_decode.set_shape([20, 80, 3]) # print("image_decode:\n", image_decode) # 修改图片类型 image_cast = tf.cast(image_decode, tf.float32) # 3、构造批处理队列 filename_batch, image_batch = tf.train.batch([filename, image_cast], batch_size=100, num_threads=2, capacity=100) return filename_batch, image_batch # 2）解析csv文件，将标签值NZPP->[13, 25, 15, 15] def parse_csv(): # 解析CSV文件, 建立文件名和标签值对应表格 csv_data = pd.read_csv("./GenPics/labels.csv", names=["file_num", "chars"], index_col="file_num") labels = [] for label in csv_data["chars"]: tmp = [] for letter in label: tmp.append(ord(letter) - ord("A")) labels.append(tmp) csv_data["labels"] = labels return csv_data # 3）将filename和标签值联系起来 def filename2label(filenames, csv_data): """ 将filename和标签值联系起来 :param filenames: :param csv_data: :return: """ labels = [] # 将b'文件名中的数字提取出来并索引相应的标签值 for filename in filenames: digit_str = "".join(list(filter(str.isdigit, str(filename)))) label = csv_data.loc[int(digit_str), "labels"] labels.append(label) # print("labels:\n", labels) return np.array(labels) # 4）构建卷积神经网络->y_predict def create_weights(shape): return tf.Variable(initial_value=tf.random_normal(shape=shape, stddev=0.01)) def create_model(x): """ 构建卷积神经网络 :param x:[None, 20, 80, 3] :return: """ # 1）第一个卷积大层 with tf.variable_scope("conv1"): # 卷积层 # 定义filter和偏置 conv1_weights = create_weights(shape=[5, 5, 3, 32]) conv1_bias = create_weights(shape=[32]) conv1_x = tf.nn.conv2d(input=x, filter=conv1_weights, strides=[1, 1, 1, 1], padding="SAME") + conv1_bias # 激活层 relu1_x = tf.nn.relu(conv1_x) # 池化层 pool1_x = tf.nn.max_pool(value=relu1_x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME") # 2）第二个卷积大层 with tf.variable_scope("conv2"): # [None, 20, 80, 3] --> [None, 10, 40, 32] # 卷积层 # 定义filter和偏置 conv2_weights = create_weights(shape=[5, 5, 32, 64]) conv2_bias = create_weights(shape=[64]) conv2_x = tf.nn.conv2d(input=pool1_x, filter=conv2_weights, strides=[1, 1, 1, 1], padding="SAME") + conv2_bias # 激活层 relu2_x = tf.nn.relu(conv2_x) # 池化层 pool2_x = tf.nn.max_pool(value=relu2_x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME") # 3）全连接层 with tf.variable_scope("full_connection"): # [None, 10, 40, 32] -> [None, 5, 20, 64] # [None, 5, 20, 64] -> [None, 5 * 20 * 64] # [None, 5 * 20 * 64] * [5 * 20 * 64, 4 * 26] = [None, 4 * 26] x_fc = tf.reshape(pool2_x, shape=[-1, 5 * 20 * 64]) weights_fc = create_weights(shape=[5 * 20 * 64, 4 * 26]) bias_fc = create_weights(shape=[104]) y_predict = tf.matmul(x_fc, weights_fc) + bias_fc return y_predict # 5）构造损失函数 # 6）优化损失 # 7）计算准确率 # 8）开启会话、开启线程 if __name__ == "__main__": filename, image = read_picture() csv_data = parse_csv() # 1、准备数据 x = tf.placeholder(tf.float32, shape=[None, 20, 80, 3]) y_true = tf.placeholder(tf.float32, shape=[None, 4*26]) # 2、构建模型 y_predict = create_model(x) # 3、构造损失函数 loss_list = tf.nn.sigmoid_cross_entropy_with_logits(labels=y_true, logits=y_predict) loss = tf.reduce_mean(loss_list) # 4、优化损失 optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss) # 5、计算准确率 equal_list = tf.reduce_all( tf.equal(tf.argmax(tf.reshape(y_predict, shape=[-1, 4, 26]), axis=2), tf.argmax(tf.reshape(y_true, shape=[-1, 4, 26]), axis=2)), axis=1) accuracy = tf.reduce_mean(tf.cast(equal_list, tf.float32)) # 初始化变量 init = tf.global_variables_initializer() # 开启会话 with tf.Session() as sess: # 初始化变量 sess.run(init) # 开启线程 coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(sess=sess, coord=coord) for i in range(1000): filename_value, image_value = sess.run([filename, image]) # print("filename_value:\n", filename_value) # print("image_value:\n", image_value) labels = filename2label(filename_value, csv_data) # 将标签值转换成one-hot labels_value = tf.reshape(tf.one_hot(labels, depth=26), [-1, 4*26]).eval() _, error, accuracy_value = sess.run([optimizer, loss, accuracy], feed_dict={x: image_value, y_true: labels_value}) print("第%d次训练后损失为%f，准确率为%f" % (i+1, error, accuracy_value)) # 回收线程 coord.request_stop() coord.join(threads)