本文代码用于训练定义的分类模型，用验证集获得模型训练效果，使用Tensorboard可视化！！！
import os
import numpy as np
import tensorflow as tf
from PIL import Image
from CNN_model import inference, loss_optimizerc_accuracy
Epochs = 80
Batch_Size = 64
Resize_Width = 128
Resize_Height = 64
imgs_path = 'extract_plate/'
Summary_Dir = 'logs/'
def read_imgs_label(imgs_path):
imgs_name = []
labels_name = []
# 获得图片路径和类别
for label_name in os.listdir(imgs_path):
for img_name in os.listdir(imgs_path+label_name):
imgs_name.append(imgs_path+label_name+'/'+img_name)
labels_name.append(label_name)
# 合并图片路径和类别为二维列表 2*n
imgs_labels = np.array([imgs_name,labels_name])
# 转置操作 每行为图片路径和类别 n*2
imgs_labels = imgs_labels.transpose()
# 打乱顺序
np.random.shuffle(imgs_labels)
# 分别获得图片路径和类别
random_imgs_path = list(imgs_labels[:,0])
random_labels_name = list(imgs_labels[:,1])
random_labels = [int(i) for i in random_labels_name]
return random_imgs_path,random_labels
def read_imgs_to_arr(imgs_path):
#存储图片
imgs = []
for img_path in imgs_path:
img = Image.open(img_path)
imgs.append(np.array(img))
return imgs
# 将数据集切分为训练集和验证集
def segmentation_train_val(imgs_path,labels, ratio=0.8):
num_imgs = len(imgs_path)
#获得切分比例对应的索引
idx = int(num_imgs * ratio)
#获得训练集和验证集
x_train = imgs_path[:idx]
y_train = labels[:idx]
x_val = imgs_path[idx:]
y_val = labels[idx:]
return x_train, y_train, x_val, y_val
# 按批次取数据
def mini_batches(imgs, labels, batch_size):
assert len(imgs) == len(labels)
for start_idx in range(0, len(imgs) - batch_size + 1, batch_size):
part_idx = slice(start_idx, start_idx + batch_size)
# 程序执行到yield语句的时候，程序暂停，
# 返回yield后面表达式的值，在下一次调用的时候，
# 从yield语句暂停的地方继续执行，如此循环，直到函数执行完
yield imgs[part_idx], labels[part_idx]
if __name__ == '__main__':
if not os.path.exists(Summary_Dir):
os.mkdir(Summary_Dir)
# 读取数据集(图片路径，标签列表)
imgs_path, labels = read_imgs_label(imgs_path)
# 切分数据集(训练集和验证集)
x_train, y_train, x_val, y_val = segmentation_train_val(imgs_path, labels)
x_train_num = len(x_train)
x_val_num = len(x_val)
print("训练集维度：",x_train_num," 验证集维度：",x_val_num)
# 读取图像到数组中
x_train = read_imgs_to_arr(x_train)
x_val = read_imgs_to_arr(x_val)
# 定义占位变量
with tf.name_scope('input'):
# dropout 失活率
keep_prob = tf.placeholder(tf.float32)
# 输入节点和标签节点
x_node = tf.placeholder(tf.float32, shape=[None, Resize_Height, Resize_Width,3], name='x_node')
y_node = tf.placeholder(tf.int32, shape=[None,], name='y_node')
# 获得前向传播输出
y_inference_out = inference(x_node,keep_prob)
# 定义损失函数和优化算法
train_val_loss, train_optimizer, train_val_accuracy = loss_optimizerc_accuracy(
y_inference_out, y_node, loss_name='loss', acc_name='acc')
# 合并所有变量操作
merged = tf.summary.merge_all()
# 训练神经网络
# 建立会话
with tf.Session() as sess:
# 变量初始化
sess.run(tf.global_variables_initializer())
# 初始化写日志的writer
train_writer = tf.summary.FileWriter(Summary_Dir+'/train', graph=sess.graph)
# 迭代训练
train_step = 0
for epoch in range(Epochs):
train_acc_sum, num_batch = 0, 0
for x_train_batch, y_train_batch in mini_batches(x_train, y_train, Batch_Size):
_, train_summary, train_loss_result, train_acc_result = sess.run(
[train_optimizer, merged, train_val_loss, train_val_accuracy],
feed_dict={x_node: x_train_batch, y_node: y_train_batch, keep_prob: 0.5})
train_acc_sum += train_acc_result
num_batch += 1
train_step +=1
# 将训练日志写入文件
train_writer.add_summary(train_summary, global_step=train_step)
# 获得当前epoch每个batch样本的准确率
print("epoch:{}".format(epoch))
print("train accuracy:{}".format(train_acc_sum / num_batch))
# 验证过程
val_loss_result, val_acc_result = sess.run([train_val_loss, train_val_accuracy],
feed_dict={x_node: x_val, y_node: y_val, keep_prob: 1})
print("val accuracy:{}".format(val_acc_result))
print('*' * 50)
train_writer.close()
验证结果：
本人使用的是1700多张训练集，基本保证100%的准确率。
网络结构对比实验
本人使用的GPU型号是1080Ti，以上是不同网络结构对比结果，实验目标是获得最优的车牌分类网络。
1. 3*3卷积核的作用？
a. 堆叠3个3*3卷积核和1个7*7卷积核具有相同的感受野，但参数更少；
b. 堆叠多个卷积核使得网络模型具有更多的非线性；
2. 卷积层和池化层的作用？
a. 卷积层可用于提取特征，其参数共享机制可减少训练参数；
b. 池化层可用于特征图压缩，提取主要特征，其也简化了网络计算复杂度。
参考资料：