计算机视觉（5）用AlexNet模型训练验证码模型
背景：
AlexNet是2012年ImageNet竞赛冠军获得者Hinton和他的学生Alex Krizhevsky设计的。也是在那年之后，更多的更深的神经网络被提出，比如优秀的vgg,GoogLeNet。 这对于传统的机器学习分类算法而言，已经相当的出色。
源码：
# 运用Alexnet训练验证码 import os import matplotlib.pyplot as plt import numpy as np import tensorflow as tf # TF-Slim可以使建立、训练和评估神经网络更加简单，轻量级的库。 import tensorflow.contrib.slim as slim # 数据集路径 dataset_dir = r'C:\Users\Administrator\Desktop\Image\captcha' # 测试集占比 num_test = 0.2 # 批次大小 batch_size = 32 # 周期大小 epochs = 100 # 分类数 num_classes = 10 # 是否是训练状态 is_training = tf.placeholder(tf.bool) lr = tf.Variable(0.001, dtype=tf.float32) # 获取所有图标的路径和分类 def Get_filenames_and_classes(dataset_dir): photo_filenames = [] labels = [] for filename in os.listdir(dataset_dir): # 获取文件路径 path = os.path.join(dataset_dir, filename) photo_filenames.append(path) label = filename[0:4] num_labels = [] for i in range(4): num_labels.append(int(label[i])) labels.append(num_labels) return photo_filenames, labels def main(): # 获取图片路径和标签 photo_filenames, labels = Get_filenames_and_classes(dataset_dir) photo_filenames = np.array(photo_filenames) labels = np.array(labels) # 打乱数据 # 每次生成的随机数一样 np.random.seed(10) # 对0-5之间的序列进行随机排序 shuffle_indices = np.random.permutation(np.arange(len(photo_filenames))) photo_filenames_shuffled = photo_filenames[shuffle_indices] labels_shuffled = labels[shuffle_indices] # 切分训练集和测试集 test_sample_index = -1 * int(num_test * float(len(photo_filenames))) x_train, x_test = photo_filenames_shuffled[:test_sample_index], photo_filenames_shuffled[test_sample_index:] y_train, y_test = labels_shuffled[:test_sample_index], labels_shuffled[test_sample_index:] image, labels = parse_function(dataset_dir, labels) # 定义两个placeholder features_placeholder = tf.placeholder(photo_filenames_shuffled.dtype, [None]) labels_placeholder = tf.placeholder(labels_shuffled.dtype, [None, 4]) # 创建dataset对象 # 加载数据集 dataset = tf.data.Dataset.from_tensor_slices((features_placeholder, labels_placeholder)) # 处理图片 dataset = dataset.map(parse_function) # 训练周期 dataset = dataset.repeat(1) # 批次大小 dataset = dataset.batch(batch_size) # 初始化迭代器 iterator = dataset.make_initializable_iterator() # 获得一个批次数据和标签 data_batch, label_batch = iterator.get_next() with tf.Session() as sess: # 传入数据得到结果 logits0, logits1, logits2, logits3 = alexnet(data_batch, is_training) # 定义loss # sparse_softmax_cross_entropy：标签为整数 # softmax_cross_entropy：标签为one-hot独热编码 loss0 = tf.losses.sparse_softmax_cross_entropy( label_batch[:, 0], logits0) loss1 = tf.losses.sparse_softmax_cross_entropy( label_batch[:, 1], logits1) loss2 = tf.losses.sparse_softmax_cross_entropy( label_batch[:, 2], logits2) loss3 = tf.losses.sparse_softmax_cross_entropy( label_batch[:, 3], logits3) # 计算总的loss total_loss = (loss0 + loss1 + loss2 + loss3) / 4.0 # 优化total_loss,tensorflow内置的一个优化函数 optimizer = tf.train.AdamOptimizer(learning_rate=lr).minimize(total_loss) # 计算准确率 correct0 = tf.nn.in_top_k(logits0, label_batch[:, 0], 1) accuracy0 = tf.reduce_mean(tf.cast(correct0, tf.float32)) correct1 = tf.nn.in_top_k(logits1, label_batch[:, 1], 1) accuracy1 = tf.reduce_mean(tf.cast(correct1, tf.float32)) correct2 = tf.nn.in_top_k(logits2, label_batch[:, 2], 1) accuracy2 = tf.reduce_mean(tf.cast(correct2, tf.float32)) correct3 = tf.nn.in_top_k(logits3, label_batch[:, 3], 1) accuracy3 = tf.reduce_mean(tf.cast(correct3, tf.float32)) # 总的准确率，判断验证码中四个数字的准确率，1*1*1*0=0 则 flase total_correct = tf.cast(correct0, tf.float32) * tf.cast(correct1, tf.float32) * tf.cast(correct2, tf.float32) * tf.cast(correct3, tf.float32) total_accuracy = tf.reduce_mean(tf.cast(total_correct, tf.float32)) # 所有变量初始化 sess.run(tf.global_variables_initializer()) # 定义saver保存模型 saver = tf.train.Saver() # 训练epochs个周期 for i in range(epochs): if i % 30 == 0: sess.run(tf.assign(lr, lr / 3)) # 训练集传入迭代器中 sess.run(iterator.initializer, feed_dict={features_placeholder: x_train, labels_placeholder: y_train}) # 训练模型 while True: try: sess.run(optimizer, feed_dict={is_training: True}) except tf.errors.OutOfRangeError: # 所有数据训练完毕后跳出循环 break # 测试集放入迭代器中 sess.run(iterator.initializer, feed_dict={features_placeholder: x_test, labels_placeholder: y_test}) # 测试结果 while True: try: # 获得准确率和loss值 acc0, acc1, acc2, acc3, total_acc, l = \ sess.run([accuracy0, accuracy1, accuracy2, accuracy3, total_accuracy, total_loss], feed_dict={is_training: False}) # loss值统计 tf.add_to_collection('sum_losses', l) # 准确率统计 tf.add_to_collection('accuracy0', acc0) tf.add_to_collection('accuracy1', acc1) tf.add_to_collection('accuracy2', acc2) tf.add_to_collection('accuracy3', acc3) tf.add_to_collection('total_acc', total_acc) except tf.errors.OutOfRangeError: # loss值求平均 avg_loss = sess.run( tf.reduce_mean( tf.get_collection('sum_losses'))) # 准确率求平均 avg_acc0 = sess.run( tf.reduce_mean( tf.get_collection('accuracy0'))) avg_acc1 = sess.run( tf.reduce_mean( tf.get_collection('accuracy1'))) avg_acc2 = sess.run( tf.reduce_mean( tf.get_collection('accuracy2'))) avg_acc3 = sess.run( tf.reduce_mean( tf.get_collection('accuracy3'))) avg_total_acc = sess.run( tf.reduce_mean( tf.get_collection('total_acc'))) print('%d:loss=%.3f acc0=%.3f acc1=%.3f acc2=%.3f acc3=%.3f total_acc=%.3f' % (i, avg_loss, avg_acc0, avg_acc1, avg_acc2, avg_acc3, avg_total_acc)) # 清空loss统计 temp = tf.get_collection_ref('sum_losses') del temp[:] # 清空准确率统计 temp = tf.get_collection_ref('accuracy0') del temp[:] # 清空准确率统计 temp = tf.get_collection_ref('accuracy1') del temp[:] # 清空准确率统计 temp = tf.get_collection_ref('accuracy2') del temp[:] # 清空准确率统计 temp = tf.get_collection_ref('accuracy3') del temp[:] # 清空准确率统计 temp = tf.get_collection_ref('total_acc') del temp[:] # 所有数据测试完毕后跳出循环 break # 保存模型 saver.save(sess, r'C:\Users\Administrator\Desktop\Image\models\model.ckpt', global_step=epochs) # 图像处理函数 def parse_function(filenames, labels=None): image = tf.read_file(filenames) # 将图像解码 image = tf.image.decode_jpeg(image, channels=3) # 重置图像的大小 image = tf.image.resize_images(image, [224, 244]) # 图像预处理 image = tf.cast(image, tf.float32) / 255 image = tf.subtract(image, 0.5) image = tf.multiply(image, 2.0) return image, labels def alexnet(inputs, is_training=True): with slim.arg_scope([slim.conv2d, slim.fully_connected], activation_fn=tf.nn.relu, weights_initializer=tf.glorot_uniform_initializer(), biases_initializer=tf.constant_initializer(0)): net = slim.conv2d(inputs, 64, [11, 11], 4) net = slim.max_pool2d(net, [3, 3]) net = slim.conv2d(net, 192, [5, 5]) net = slim.max_pool2d(net, [3, 3]) net = slim.conv2d(net, 384, [3, 3]) net = slim.conv2d(net, 384, [3, 3]) net = slim.conv2d(net, 256, [3, 3]) net = slim.max_pool2d(net, [3, 3]) # 数据扁平化 net = slim.flatten(net) net = slim.fully_connected(net, 1024) net = slim.dropout(net, is_training=is_training) net0 = slim.fully_connected( net, num_classes, activation_fn=tf.nn.softmax) net1 = slim.fully_connected( net, num_classes, activation_fn=tf.nn.softmax) net2 = slim.fully_connected( net, num_classes, activation_fn=tf.nn.softmax) net3 = slim.fully_connected( net, num_classes, activation_fn=tf.nn.softmax) return net0, net1, net2, net3 if __name__ == '__main__': main()