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"笨方法"学习CNN图像识别(三)—— ResNet网络训练及预测

秋意凉 1年前   阅读数 174 0

在本文中,你将学习到以下内容:

  • TensorFlow中调用ResNet网络
  • 训练网络并保存模型
  • 加载模型预测结果

前言

在深度学习中,随着网络深度的增加,模型优化会变得越来越困难,甚至会发生梯度爆炸,导致整个网络训练无法收敛。ResNet(Residual Networks)的提出解决了这个问题。在这里我们直接调用ResNet网络进行训练,讲解ResNet细节的文章有很多,这里找了一篇供参考

搭建训练网络

如果你看过了前面的准备工作,图片预处理制作tfrecord格式,默认已经有tfrecord格式的数据文件了。我们接着搭建网络,来处理100类商标图片的分类问题。将制作好的tfrecord数据通过队列系统传入ResNet网络进行训练。

首先导入必要的库:

import tensorflow as tf
import tensorflow.contrib.slim.nets as nets

nets库里面集成了现有的很多网络(AlexNet,Inception,ResNet,VGG)可以直接调用,我们在这里使用ResNet_50,即50层的网络训练。

接下来我们先定义一个读取tfrecord文件的函数:

def read_and_decode_tfrecord(filename):
    filename_deque = tf.train.string_input_producer(filename)
    reader = tf.TFRecordReader()
    _, serialized_example = reader.read(filename_deque)
    features = tf.parse_single_example(serialized_example, features={
        'label': tf.FixedLenFeature([], tf.int64),
        'img_raw': tf.FixedLenFeature([], tf.string)})
    label = tf.cast(features['label'], tf.int32)
    img = tf.decode_raw(features['img_raw'], tf.uint8)
    img = tf.reshape(img, [224, 224, 3])
    img = tf.cast(img, tf.float32) / 255.0      #将矩阵归一化0-1之间
    return img, label

定义模型保存地址,batch_sizes设置的小一点训练效果更好,将当前目录下的tfrecord文件放入列表中:

save_dir = r"./train_image_63.model"  # 模型保存路径
batch_size_ = 2
lr = tf.Variable(0.0001, dtype=tf.float32)  # 学习速率
x = tf.placeholder(tf.float32, [None, 224, 224, 3])  # 图片大小为224*224*3
y_ = tf.placeholder(tf.float32, [None])
train_list = ['traindata_63.tfrecords-000', 'traindata_63.tfrecords-001', 'traindata_63.tfrecords-002','traindata_63.tfrecords-003', 'traindata_63.tfrecords-004', 'traindata_63.tfrecords-005','traindata_63.tfrecords-006', 'traindata_63.tfrecords-007', 'traindata_63.tfrecords-008','traindata_63.tfrecords-009', 'traindata_63.tfrecords-010', 'traindata_63.tfrecords-011','traindata_63.tfrecords-012', 'traindata_63.tfrecords-013', 'traindata_63.tfrecords-014',
'traindata_63.tfrecords-015', 'traindata_63.tfrecords-016', 'traindata_63.tfrecords-017','traindata_63.tfrecords-018', 'traindata_63.tfrecords-019', 'traindata_63.tfrecords-020','traindata_63.tfrecords-021']    #制作成的所有tfrecord数据,每个最多包含1000个图片数据

# 随机打乱顺序
img, label = read_and_decode_tfrecord(train_list)
img_batch, label_batch = tf.train.shuffle_batch([img, label], num_threads=2, batch_size=batch_size_, capacity=10000,min_after_dequeue=9900)

注意这里使用了tf.train.shuffle_batch随机打乱队列里面的数据顺序,num_threads表示线程数,capacity表示队列的容量,在这里设置成10000, min_after_dequeue队列里保留的最小数据量,并且控制着随机的程度,设置成9900的意思是,当队列中的数据出列100个,剩下9900个的时候,就要重新补充100个数据进来并打乱顺序。如果你要按顺序导入队列,改成tf.train.batch函数,并删除min_after_dequeue参数。这些参数都要根据自己的电脑配置进行相应的设置。

接下来将label值进行onehot编码,直接调用tf.one_hot函数。因为我们这里有100类,depth设置成100:

# 将label值进行onehot编码
one_hot_labels = tf.one_hot(indices=tf.cast(y_, tf.int32), depth=100)
pred, end_points = nets.resnet_v2.resnet_v2_50(x, num_classes=100, is_training=True)
pred = tf.reshape(pred, shape=[-1, 100])

我们通过nets.resnet_v2.resnet_v2_50直接调用ResNet_50网络,同样num_classes等于类别总数,is_training表示我们是否要训练网络里面固定层的参数,True表示所有参数都重新训练,False表示只训练后面几层的参数。

网络搭好后,我们继续定义损失函数和优化器,损失函数选择sigmoid交叉熵,优化器选择Adam:

# 定义损失函数和优化器
loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=pred, labels=one_hot_labels))
optimizer = tf.train.AdamOptimizer(learning_rate=lr).minimize(loss)

定义准确率函数,tf.argmax函数返回最大值所在位置:

# 准确度
a = tf.argmax(pred, 1)
b = tf.argmax(one_hot_labels, 1)
correct_pred = tf.equal(a, b)
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

最后我们构建Session,让网络跑起来:

saver = tf.train.Saver()
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())

    # 创建一个协调器,管理线程
    coord = tf.train.Coordinator()
    # 启动QueueRunner,此时文件名队列已经进队
    threads = tf.train.start_queue_runners(sess=sess, coord=coord)
    i = 0
    while True:
        i += 1
        b_image, b_label = sess.run([img_batch, label_batch])
        _, loss_, y_t, y_p, a_, b_ = sess.run([optimizer, loss, one_hot_labels, pred, a, b], feed_dict={x: b_image,y_: b_label})
        print('step: {}, train_loss: {}'.format(i, loss_))
        if i % 20 == 0:
            _loss, acc_train = sess.run([loss, accuracy], feed_dict={x: b_image, y_: b_label})
            print('--------------------------------------------------------')
            print('step: {}  train_acc: {}  loss: {}'.format(i, acc_train, _loss))
            print('--------------------------------------------------------')
            if i == 200000:
                saver.save(sess, save_dir, global_step=i)
            elif i == 300000:
                saver.save(sess, save_dir, global_step=i)
            elif i == 400000:
                saver.save(sess, save_dir, global_step=i)
                break
    coord.request_stop()
    # 其他所有线程关闭之后,这一函数才能返回
    coord.join(threads)

当我们使用队列系统时,在Session部分一定要创建一个协调器管理线程。我们每20步输出一次准确率,在200000,300000,400000步的时候自动保存模型。

训练结束后会得到如下模型文件,我在这里只保留了300000步的模型:

模型文件

附上训练网络完整代码:

import tensorflow as tf
import tensorflow.contrib.slim.nets as nets



def read_and_decode_tfrecord(filename):
    filename_deque = tf.train.string_input_producer(filename)
    reader = tf.TFRecordReader()
    _, serialized_example = reader.read(filename_deque)
    features = tf.parse_single_example(serialized_example, features={
        'label': tf.FixedLenFeature([], tf.int64),
        'img_raw': tf.FixedLenFeature([], tf.string)})
    label = tf.cast(features['label'], tf.int32)
    img = tf.decode_raw(features['img_raw'], tf.uint8)
    img = tf.reshape(img, [224, 224, 3])
    img = tf.cast(img, tf.float32) / 255.0        #将矩阵归一化0-1之间
    return img, label

save_dir = r"./train_image_63.model"
batch_size_ = 2
lr = tf.Variable(0.0001, dtype=tf.float32)
x = tf.placeholder(tf.float32, [None, 224, 224, 3])
y_ = tf.placeholder(tf.float32, [None])

train_list = ['traindata_63.tfrecords-000','traindata_63.tfrecords-001','traindata_63.tfrecords-002','traindata_63.tfrecords-003','traindata_63.tfrecords-004','traindata_63.tfrecords-005','traindata_63.tfrecords-006','traindata_63.tfrecords-007','traindata_63.tfrecords-008''traindata_63.tfrecords-009','traindata_63.tfrecords-010','traindata_63.tfrecords-011','traindata_63.tfrecords-012','traindata_63.tfrecords-013','traindata_63.tfrecords-014','traindata_63.tfrecords-015','traindata_63.tfrecords-016','traindata_63.tfrecords-017','traindata_63.tfrecords-018','traindata_63.tfrecords-019','traindata_63.tfrecords-020','traindata_63.tfrecords-021']

# 随机打乱顺序
img, label = read_and_decode_tfrecord(train_list)
img_batch, label_batch = tf.train.shuffle_batch([img, label], num_threads=2, batch_size=batch_size_, capacity=10000,min_after_dequeue=9900)

# 将label值进行onehot编码
one_hot_labels = tf.one_hot(indices=tf.cast(y_, tf.int32), depth=100)
pred, end_points = nets.resnet_v2.resnet_v2_50(x, num_classes=100, is_training=True)
pred = tf.reshape(pred, shape=[-1, 100])


# 定义损失函数和优化器
loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=pred, labels=one_hot_labels))
optimizer = tf.train.AdamOptimizer(learning_rate=lr).minimize(loss)

# 准确度
a = tf.argmax(pred, 1)
b = tf.argmax(one_hot_labels, 1)
correct_pred = tf.equal(a, b)
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())
    # 创建一个协调器,管理线程
    coord = tf.train.Coordinator()
    # 启动QueueRunner,此时文件名队列已经进队
    threads = tf.train.start_queue_runners(sess=sess, coord=coord)
    i = 0
    while True:
        i += 1
        b_image, b_label = sess.run([img_batch, label_batch])
        _, loss_, y_t, y_p, a_, b_ = sess.run([optimizer, loss, one_hot_labels, pred, a, b], feed_dict={x: b_image,y_: b_label})
        print('step: {}, train_loss: {}'.format(i, loss_))
        if i % 20 == 0:
            _loss, acc_train = sess.run([loss, accuracy], feed_dict={x: b_image, y_: b_label})
            print('--------------------------------------------------------')
            print('step: {}  train_acc: {}  loss: {}'.format(i, acc_train, _loss))
            print('--------------------------------------------------------')
            if i == 200000:
                saver.save(sess, save_dir, global_step=i)
            elif i == 300000:
                saver.save(sess, save_dir, global_step=i)
            elif i == 400000:
                saver.save(sess, save_dir, global_step=i)
                break
    coord.request_stop()
    # 其他所有线程关闭之后,这一函数才能返回
    coord.join(threads)

预测结果

我们利用1000张测试数据评估我们的模型,直接放代码:

import tensorflow as tf
import tensorflow.contrib.slim.nets as nets
from PIL import Image
import os

test_dir = r'./test'     # 原始的test文件夹,含带预测的图片
model_dir = r'./train_image_63.model-300000'     # 模型地址
test_txt_dir = r'./test.txt'     # 原始的test.txt文件
result_dir = r'./result.txt'     # 生成输出结果
x = tf.placeholder(tf.float32, [None, 224, 224, 3])
classes = ['1', '10', '100', '11', '12', '13', '14', '15', '16', '17', '18', '19', '2', '20', '21', '22', '23', '24','25', '26', '27', '28', '29', '3', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '4', '40','41', '42', '43', '44', '45', '46', '47', '48', '49', '5', '50', '51', '52', '53', '54', '55', '56', '57','58', '59', '6', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '7', '70', '71', '72', '73','74', '75', '76', '77', '78', '79', '8', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '9','90', '91', '92', '93', '94', '95', '96', '97', '98', '99']# 标签顺序

pred, end_points = nets.resnet_v2.resnet_v2_50(x, num_classes=100, is_training=True)
pred = tf.reshape(pred, shape=[-1, 100])
a = tf.argmax(pred, 1)
saver = tf.train.Saver()
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    saver.restore(sess, model_dir)
    with open(test_txt_dir, 'r') as f:
        data = f.readlines()
        for i in data:
            test_name = i.split()[0]
            for pic in os.listdir(test_dir):
                if pic == test_name:
                    img_path = os.path.join(test_dir, pic)
                    img = Image.open(img_path)
                    img = img.resize((224, 224))
                    img = tf.reshape(img, [1, 224, 224, 3])
                    img1 = tf.reshape(img, [1, 224, 224, 3])
                    img = tf.cast(img, tf.float32) / 255.0
                    b_image, b_image_raw = sess.run([img, img1])
                    t_label = sess.run(a, feed_dict={x: b_image})
                    index_ = t_label[0]
                    predict = classes[index_]
                    with open(result_dir, 'a') as f1:
                        print(test_name, predict, file=f1)
                    break

需要注意的是test数据集并没有处理成tfrecord格式,在这里直接将图片一张张导入用模型预测,生成的结果文件主要是为了提交比赛使用。原始数据和模型我会放在这里,密码:8xbi。有兴趣自提。

至此,我们就完成了一个CNN图像识别项目。

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