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【深度学习】(三)图像分类

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图像分类

 

文章目录

 

上一章介绍了深度学习的基础内容,这一章来学习一下图像分类的内容。图像分类是计算机视觉中最基础的一个任务,也是几乎所有的基准模型进行比较的任务。从最开始比较简单的10分类的灰度图像手写数字识别任务mnist,到后来更大一点的10分类的 cifar10和100分类的cifar100 任务,到后来的imagenet 任务,图像分类模型伴随着数据集的增长,一步一步提升到了今天的水平。现在,在imagenet 这样的超过1000万图像,超过2万类的数据集中,计算机的图像分类水准已经超过了人类。

 

一、ILSVRC竞赛

 

ILSVRC(ImageNet Large Scale Visual Recognition Challenge)是近年来机器视觉领域最受追捧也是最具权威的学术竞赛之一,代表了图像领域的最高水平。ILSVRC竞赛使得深度学习算法得到大力的发展,AI领域迎来了新一轮的热潮,CNN网络也不断迭代,图像分类的准确度也逐年上升,最终超越人类,完成竞赛的使命,2017年已经停办。

 

ImageNet数据集是ILSVRC竞赛使用的是数据集,由斯坦福大学李飞飞
教授主导,包含了超过1400万张全尺寸的有标记图片。ILSVRC比赛会每年从ImageNet数据集中抽出部分样本,以2012年为例,比赛的训练集包含1281167张图片,验证集包含50000张图片,测试集为100000张图片。

 

卷积神经网络在特征表示上具有极大的优越性,模型提取的特征随着网络深度的增加越来越抽象,越来越能表现图像主题语义,不确定性越少,识别能力越强。AlexNet
的成功证明了CNN 网络能够提升图像分类的效果,其使用了 8 层的网络结构,获得了 2012 年,ImageNet 数据集上图像分类的冠军,为训练深度卷积神经网络模型提供了参考。2014 年,冠军GoogleNet
另辟蹊径,从设计网络结构的角度来提升识别效果。其主要贡献是设计了Inception
模块结构来捕捉不同尺度的特征,通过 1×1 的卷积来进行降维。2014 年另外一个工作是VGG
(亚军),进一步证明了网络的深度在提升模型效果方面的重要性。2015 年,最重要的一篇文章是关于深度残差网络ResNet
,文章提出了拟合残差网络的方法,能够做到更好地训练更深层的网络。 2017年,SENet
是ImageNet(ImageNet收官赛)的冠军模型,和ResNet的出现类似,都在很大程度上减小了之前模型的错误率),并且复杂度低,新增参数和计算量小。

 

历届冠军做法:

 

二、卷积神经网络(CNN)发展

 

1.网络进化

 

网络:AlexNet–>VGG–>GoogLeNet–>ResNet

 

深度:8–>19–>22–>152

 

:sparkles:VGG结构简洁有效

容易修改,迁移到其他任务中
高层任务的基础网络

️性能竞争网络

GoogLeNet:Inception v1–>v4
Split-transform-merge
ResNet:ResNet1024–>ResNeXt
深度、宽度、基数

 

 

2.AlexNet网络

 

由于受到计算机性能的影响,虽然LeNet
在图像分类中取得了较好的成绩,但是并没有引起很多的关注。 知道2012年,Alex等人提出的AlexNet
网络在ImageNet大赛上以远超第二名的成绩夺冠,卷积神经网络乃至深度学习重新引起了广泛的关注。

AlexNet包含8层网络,有5个卷积层和3个全连接层
AlexNet第一层中的卷积核shape为11X11,第二层的卷积核形状缩小到5X5,之后全部采用3X3的卷积核
所有的池化层窗口大小为3X3,步长为2,最大池化采用Relu激活函数,代替sigmoid,梯度计算更简单,模型更容易训练
采用Dropout来控制模型复杂度,防止过拟合采用大量图像增强技术,比如翻转、裁剪和颜色变化,扩大数据集,防止过拟合
代码实现

# 导入工具包
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
# 模型构建
net = keras.models.Sequential([
    # 卷积:卷积核数量96,尺寸11*11,步长4,激活函数relu
    layers.Conv2D(filters=96, kernel_size=11, strides=4, activation='relu'),
    # 最大池化:尺寸3*3,步长2
    layers.MaxPool2D(pool_size=3, strides=2),
    # 卷积:卷积核数量256,尺寸5*5,激活函数relu,same卷积
    layers.Conv2D(filters=256, kernel_size=5, padding='same', activation='relu'),
    # 最大池化:尺寸3*3,步长3
    layers.MaxPool2D(pool_size=3, strides=2),
    # 卷积:卷积核数量384,尺寸3,激活函数relu,same卷积
    layers.Conv2D(filters=384, kernel_size=3, padding='same', activation='relu'),
    # 卷积:卷积核数量384,尺寸3,激活函数relu,same卷积
    layers.Conv2D(filters=384, kernel_size=3, padding='same', activation='relu'),
    # 卷积:卷积核数量256,尺寸3,激活函数relu,same卷积
    layers.Conv2D(filters=256, kernel_size=3, padding='same', activation='relu'),
    # 最大池化:尺寸3*3,步长2
    layers.MaxPool2D(pool_size=3, strides=2),
    # 展平特征图
    layers.Flatten(),
    # 全连接:4096神经元,relu
    layers.Dense(4096, activation='relu'),
    # 随机失活
    layers.Dropout(0.5),
    layers.Dense(4096, activation='relu'),
    layers.Dropout(0.5),
    # 输出层:多分类用softmax,二分类用sigmoid
    layers.Dense(10, activation='softmax')],
    name='AlexNet')
# 模拟输入
x = tf.random.uniform((1, 227, 227, 1))
y = net(x)
net.summary()

 

3.VGG网络

 

VGG网络是在2014年由牛津大学计算机视觉组和谷歌公司的研究员共同开发的。VGG由5层卷积层、3层全连接层、softmax输出层构成,层与层之间使用最大池化分开,所有隐层的激活单元都采用ReLU函数。通过反复堆叠3X3的小卷积核和2X2的最大池化层,VGGNet成功的搭建了16-19层的深度卷积神经网络。VGG的结构图如下:

 

VGGNet 论文中全部使用了3X3的卷积核和2X2的池化核,通过不断加深网络结构来提升性能。下图所示为 VGGNet 各级别的网络结构图,以及随后的每一级别的参数量,从11层的网络一直到19层的网络都有详尽的性能测试。虽然从A到E每一级网络逐渐变深,但是网络的参数量并没有增长很多,这是因为参数量主要都消耗在最后3个全连接层。前面的卷积部分虽然很深,但是消耗的参数量不大,不过训练比较耗时的部分依然是卷积,因其计算量比较大。这其中的D、E也就是我们常说的 VGGNet-16 和 VGGNet-19。C相比B多了几个1X1的卷积层,1X1卷积的意义主要在于线性变换,而输入通道数和输出通道数不变,没有发生降维。

 

代码实现 VGG11

 

#tensorflow基于mnist数据集上的VGG11网络,可以直接运行
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
#tensorflow基于mnist实现VGG11
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
x = tf.placeholder(tf.float32, [None, 784])
y_ = tf.placeholder(tf.float32, [None, 10])
sess = tf.InteractiveSession()
#Layer1
W_conv1 =tf.Variable(tf.truncated_normal([3, 3, 1, 64],stddev=0.1))
b_conv1 = tf.Variable(tf.constant(0.1,shape=[64]))
#调整x的大小
x_image = tf.reshape(x, [-1,28,28,1])
h_conv1 = tf.nn.relu(tf.nn.conv2d(x_image, W_conv1,strides=[1, 1, 1, 1], padding='SAME') + b_conv1)
#Layer2 pooling
W_conv2 = tf.Variable(tf.truncated_normal([3, 3, 64, 64],stddev=0.1))
b_conv2 = tf.Variable(tf.constant(0.1,shape=[64]))
h_conv2 = tf.nn.relu(tf.nn.conv2d(h_conv1, W_conv2,strides=[1, 1, 1, 1], padding='SAME') + b_conv2)
h_pool2 = tf.nn.max_pool(h_conv2, ksize=[1, 2, 2, 1],
                        strides=[1, 2, 2, 1], padding='SAME')
#Layer3
W_conv3 = tf.Variable(tf.truncated_normal([3, 3, 64, 128],stddev=0.1))
b_conv3 = tf.Variable(tf.constant(0.1,shape=[128]))
h_conv3 = tf.nn.relu(tf.nn.conv2d(h_pool2, W_conv3,strides=[1, 1, 1, 1], padding='SAME') + b_conv3)
#Layer4 pooling
W_conv4 = tf.Variable(tf.truncated_normal([3, 3, 128, 128],stddev=0.1))
b_conv4 = tf.Variable(tf.constant(0.1,shape=[128]))
h_conv4 = tf.nn.relu(tf.nn.conv2d(h_conv3, W_conv4,strides=[1, 1, 1, 1], padding='SAME') + b_conv4)
h_pool4= tf.nn.max_pool(h_conv4, ksize=[1, 2, 2, 1],
                        strides=[1, 2, 2, 1], padding='SAME')
#Layer5
W_conv5 = tf.Variable(tf.truncated_normal([3, 3, 128, 256],stddev=0.1))
b_conv5 = tf.Variable(tf.constant(0.1,shape=[256]))
h_conv5 = tf.nn.relu(tf.nn.conv2d(h_pool4, W_conv5,strides=[1, 1, 1, 1], padding='SAME') + b_conv5)
#Layer6
W_conv6 = tf.Variable(tf.truncated_normal([3, 3, 256, 256],stddev=0.1))
b_conv6 = tf.Variable(tf.constant(0.1,shape=[256]))
h_conv6 = tf.nn.relu(tf.nn.conv2d(h_conv5, W_conv6,strides=[1, 1, 1, 1], padding='SAME') + b_conv6)
#Layer7
W_conv7 = tf.Variable(tf.truncated_normal([3, 3, 256, 256],stddev=0.1))
b_conv7 = tf.Variable(tf.constant(0.1,shape=[256]))
h_conv7 = tf.nn.relu(tf.nn.conv2d(h_conv6, W_conv7,strides=[1, 1, 1, 1], padding='SAME') + b_conv7)
#Layer8
W_conv8 = tf.Variable(tf.truncated_normal([3, 3, 256, 256],stddev=0.1))
b_conv8 = tf.Variable(tf.constant(0.1,shape=[256]))
h_conv8 = tf.nn.relu(tf.nn.conv2d(h_conv7, W_conv8,strides=[1, 1, 1, 1], padding='SAME') + b_conv8)
h_pool8 = tf.nn.max_pool(h_conv8, ksize=[1, 2, 2, 1],
                        strides=[1, 1, 1, 1], padding='SAME')
#Layer9-全连接层
W_fc1 = tf.Variable(tf.truncated_normal([7*7*256,1024],stddev=0.1))
b_fc1 = tf.Variable(tf.constant(0.1,shape=[1024]))
#对h_pool2数据进行铺平
h_pool2_flat = tf.reshape(h_pool8, [-1, 7*7*256])
#进行relu计算,matmul表示(wx+b)计算
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
#Layer10-全连接层,这里也可以是[1024,其它],大家可以尝试下
W_fc2 = tf.Variable(tf.truncated_normal([1024,1024],stddev=0.1))
b_fc2 = tf.Variable(tf.constant(0.1,shape=[1024]))
h_fc2 = tf.nn.relu(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
h_fc2_drop = tf.nn.dropout(h_fc2, keep_prob)
#Layer11-softmax层
W_fc3 = tf.Variable(tf.truncated_normal([1024,10],stddev=0.1))
b_fc3 = tf.Variable(tf.constant(0.1,shape=[10]))
y_conv = tf.matmul(h_fc2_drop, W_fc3) + b_fc3
#在这里通过tf.nn.softmax_cross_entropy_with_logits函数可以对y_conv完成softmax计算,同时计算交叉熵损失函数
cross_entropy = tf.reduce_mean(
    tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
#定义训练目标以及加速优化器
train_step = tf.train.AdamOptimizer(1e-3).minimize(cross_entropy)
#计算准确率
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
#初始化变量
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
for i in range(20000):
  batch = mnist.train.next_batch(10)
  if i%100 == 0:
    train_accuracy = accuracy.eval(feed_dict={
 
        x:batch[0], y_: batch[1], keep_prob: 1.0})
    print("step %d, training accuracy %g"%(i, train_accuracy))
  train_step.run(feed_dict={
 x: batch[0], y_: batch[1], keep_prob: 0.5})
#保存模型
save_path = saver.save(sess, "./model/save_net.ckpt")
print("test accuracy %g"%accuracy.eval(feed_dict={
 
    x: mnist.test.images[:3000], y_: mnist.test.labels[:3000], keep_prob: 1.0}))

 

4.GoogLeNet网络

 

Google Inception Net通常被称为Google Inception V1,在ILSVRC-2014比赛中由论文<Going deeper with convolutions>提出.

 

Inception V1有22层,比AlexNet的8层和VGGNet的19层还要深.参数量(500万)仅有AlexNet参数量(6000万)的1/12,但准确率远胜于AlexNet的准确率.

 

Inception V1降低参数量的目的:

 

 

    1. 参数越多模型越庞大,需要模型学习的数据量就越大,且高质量的数据非常昂贵.

 

    1. 参数越多,消耗的计算资源越多.

 

 

Inception V1网络的特点:

模型层数更深(22层),表达能力更强.
去除最后的全连接层,用全局平均池化层(即将图片尺寸变为1X1)来代替它.(借鉴了NIN)
使用Inception Module提高了参数利用效率.

Inception V2网络的特点:
Batch Normalization 白化:使每一层的输出都规范化到N(0,1)
解决Interal Covariate Shift问题
允许较高学习率
取代部分Dropout
5X5卷积核–>2个3X3卷积核

Inception V3网络的特点:

高效的降尺寸
不增加计算量
取消浅层的辅助分类器
深层辅助分类器只在训练后期有用

GoogLeNet网络结构:

 

对于我们搭建的Inception模块,所需要使用到参数有#1×1, #3x3reduce, #3×3, #5x5reduce, #5×5, poolproj,这6个参数,分别对应着所使用的卷积核个数,参数设置如下表所示:

 

代码实现 Inception V3

 

import tensorflow as tf
slim = tf.contrib.slim
trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev)
# 生成默认参数
def inception_v3_arg_scope(weight_decay=0.00004,                      # L2正则weight_decay
                           stddev=0.1,                                # 标准差
                           batch_norm_var_collection='moving_vars'):
    batch_norm_params = {
 
        'decay': 0.9997,
        'epsilon':0.001,
        'updates_collections': tf.GraphKeys.UPDATE_OPS,
        'variables_collections':{
 
            'beta': None,
            'gamma': None,
            'moving_mean': [batch_norm_var_collection],
            'moving_variance': [batch_norm_var_collection],
        }
    }
    # 提供了新的范围名称scope name
    # 对slim.conv2d和slim.fully_connected两个函数的参数自动赋值
    with slim.arg_scope([slim.conv2d, slim.fully_connected],
                        weights_regularizer=slim.l2_regularizer(weight_decay)):
        with slim.arg_scope(
            [slim.conv2d], # 对卷积层的参数赋默认值
            weights_initializer=tf.truncated_normal_initializer(stddev=stddev), # 权重初始化器
            activation_fn=tf.nn.relu,  # 激活函数用ReLU
            normalizer_params=batch_norm_params) as sc: # 标准化器参数用batch_norm_params
            return sc
# inputs为输入图片数据的tensor(299x299x3),scope为包含了函数默认参数的环境
def inception_v3_base(inputs, scope=None):
    # 保存某些关键节点
    end_points = {
 }
    # 定义InceptionV3的网络结构
    with tf.variable_scope(scope, 'InceptionV3', [inputs]):
        # 设置卷积/最大池化/平均池化的默认步长为1,padding模式为VALID
        # 设置Inception模块组的默认参数
        with slim.arg_scope([slim.conv2d,       # 创建卷积层
                             slim.max_pool2d,   # 输出的通道数
                             slim.avg_pool2d],  # 卷积核尺寸
                            stride=1,           # 步长
                            padding='VALID'):   # padding模式
            # 经3个3x3的卷积层后,输入数据(299x299x3)变为(35x35x192),空间尺寸降低,输出通道增加
            net = slim.conv2d(inputs, 32, [3,3], stride=2, scope='Conv2d_1a_3x3')
            net = slim.conv2d(net, 32, [3, 3], scope='Conv2d_2a_3x3')
            net = slim.conv2d(net, 64, [3, 3], padding='SAME', scope='Conv2d_2b_3x3')
            net = slim.max_pool2d(net, [3, 3], stride=2, scope='MaxPool_3a_3x3')
            net = slim.conv2d(net, 80, [1, 1], scope='Conv2d_3b_1x1')
            net = slim.conv2d(net, 192, [3, 3], scope='Conv2d_4a_3x3')
            net = slim.max_pool2d(net, [3, 3], stride=2, scope='MaxPool_5a_3x3')
        # 设置卷积/最大池化/平均池化的默认步长为1,padding模式为SAME
        # 步长为1,padding模式为SAME,所以图像尺寸不会变,仍为35x35
        with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d], stride=1, padding='SAME'):
            # 设置Inception Moduel名称为Mixed_5b
            with tf.variable_scope('Mixed_5b'):
                # 第1个分支:64输出通道的1x1卷积
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
                # 第2个分支:48输出通道的1x1卷积,连接64输出通道的5x5卷积
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net, 48, [1, 1], scope='Con2d_0a_1x1')
                    branch_1 = slim.conv2d(branch_1, 64, [5, 5], scope='Conv2d_0b_5x5')
                # 第3个分支:64输出通道的1x1卷积,连接两个96输出通道的3x3卷积
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
                    branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0b_3x3')
                    branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0c_3x3')
                # 第4个分支:3x3的平均池化,连接32输出通道的1x1卷积
                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
                    branch_3 = slim.conv2d(branch_3, 32, [1, 1], scope='Conv2d_0b_1x1')
                # 4个分支输出通道数之和=64+64+96+32=256,输出tensor为35x35x256
                net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
            # 第1个Inception模块组的第2个Inception Module
            with tf.variable_scope('Mixed_5c'):
                # 第1个分支:64输出通道的1x1卷积
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
                # 第2个分支:48输出通道的1x1卷积,连接64输出通道的5x5卷积
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net, 48, [1, 1], scope='Conv2d_0b_1x1')
                    branch_1 = slim.conv2d(branch_1, 64, [5, 5], scope='Conv2d_0c_5x5')
                # 第3个分支:64输出通道的1x1卷积,连接两个96输出通道的3x3卷积
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
                    branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0b_3x3')
                    branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0c_3x3')
                # 第4个分支:3x3的平均池化,连接64输出通道的1x1卷积
                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
                    branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
                # 输出tensor尺寸为35x35x288
                net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
            # 第1个Inception模块组的第3个Inception Module
            with tf.variable_scope('Mixed_5d'):
                # 第1个分支:64输出通道的1x1卷积
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
                # 第2个分支:48输出通道的1x1卷积,连接64输出通道的5x5卷积
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net, 48, [1, 1], scope='Conv2d_0a_1x1')
                    branch_1 = slim.conv2d(branch_1, 64, [5, 5], scope='Conv2d_0b_5x5')
                # 第3个分支:64输出通道的1x1卷积,连接两个96输出通道的3x3卷积
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
                    branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0b_3x3')
                    branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0c_3x3')
                # 第4个分支:3x3的平均池化,连接64输出通道的1x1卷积
                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
                    branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
                # 输出tensor尺寸为35x35x288
                net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
            # 第2个Inception模块组
            with tf.variable_scope('Mixed_6a'):
                # 第1个分支:3x3卷积,步长为2,padding模式为VALID,因此图像被压缩为17x17
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 384, [3, 3], stride=2 , padding='VALID', scope='Conv2d_1a_1x1')
                # 第2个分支:64输出通道的1x1卷积,连接2个96输出通道的3x3卷积
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
                    branch_1 = slim.conv2d(branch_1, 96, [3, 3], scope='Conv2d_0b_3x3')
                    # 步长为2,padding模式为VALID,因此图像被压缩为17x17
                    branch_1 = slim.conv2d(branch_1, 96, [3, 3], stride=2, padding='VALID', scope='Conv2d_1a_1x1')
                # 第3个分支:3x3的最大池化层,步长为2,padding模式为VALID,因此图像被压缩为17x17x256
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID', scope='MaxPool_1a_3x3')
                net = tf.concat([branch_0, branch_1, branch_2], 3)
            # 第2个Inception模块组,包含5个Inception Module
            with tf.variable_scope('Mixed_6b'):
                # 第1个分支:192输出通道的1x1卷积
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
                # 第2个分支:128输出通道的1x1卷积,接128输出通道的1x7卷积,接192输出通道的7x1卷积
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
                    branch_1 = slim.conv2d(branch_1, 128, [1, 7], scope='Conv2d_0b_1x7')
                    branch_1 = slim.conv2d(branch_1, 192, [7, 1], scope='Conv2d_0c_7x1')
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
                    branch_2 = slim.conv2d(branch_2, 128, [7, 1], scope='Conv2d_0b_7x1')
                    branch_2 = slim.conv2d(branch_2, 128, [1, 7], scope='Conv2d_0c_1x7')
                    branch_2 = slim.conv2d(branch_2, 128, [7, 1], scope='Conv2d_0d_7x1')
                    branch_2 = slim.conv2d(branch_2, 192, [1, 7], scope='Conv2d_0e_1x7')
                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
                    branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv2d_0b_1x1')
                # 输出tensor尺寸=17x17x(192+192+192+192)=17x17x768
                net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
            # 经过一个Inception Module输出tensor尺寸不变,但特征相当于被精炼类一遍
            # 第3个Inception模块组
            with tf.variable_scope('Mixed_6c'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
                    branch_1 = slim.conv2d(branch_1, 160, [1, 7], scope='Conv2d_0b_1x7')
                    branch_1 = slim.conv2d(branch_1, 192, [7, 1], scope='Conv2d_0c_7x1')
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
                    branch_2 = slim.conv2d(branch_2, 160, [7, 1], scope='Conv2d_0b_7x1')
                    branch_2 = slim.conv2d(branch_2, 160, [1, 7], scope='Conv2d_0c_1x7')
                    branch_2 = slim.conv2d(branch_2, 160, [7, 1], scope='Conv2d_0d_7x1')
                    branch_2 = slim.conv2d(branch_2, 192, [1, 7], scope='Conv2d_0e_1x7')
                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
                    branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv2d_0b_1x1')
                # 输出tensor尺寸为17x17x768
                net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
            # 第4个Inception模块组
            with tf.variable_scope('Mixed_6d'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
                    branch_1 = slim.conv2d(branch_1, 160, [1, 7], scope='Conv2d_0b_1x7')
                    branch_1 = slim.conv2d(branch_1, 192, [7, 1], scope='Conv2d_0c_7x1')
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
                    branch_2 = slim.conv2d(branch_2, 160, [7, 1], scope='Conv2d_0b_7x1')
                    branch_2 = slim.conv2d(branch_2, 160, [1, 7], scope='Conv2d_0c_1x7')
                    branch_2 = slim.conv2d(branch_2, 160, [7, 1], scope='Conv2d_0d_7x1')
                    branch_2 = slim.conv2d(branch_2, 192, [1, 7], scope='Conv2d_0e_1x7')
                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
                    branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv2d_0b_1x1')
                # 输出tensor尺寸为17x17x768
                net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
            # 第5个Inception模块组
            with tf.variable_scope('Mixed_6e'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
                    branch_1 = slim.conv2d(branch_1, 192, [1, 7], scope='Conv2d_0b_1x7')
                    branch_1 = slim.conv2d(branch_1, 192, [7, 1], scope='Conv2d_0c_7x1')
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
                    branch_2 = slim.conv2d(branch_2, 192, [7, 1], scope='Conv2d_0b_7x1')
                    branch_2 = slim.conv2d(branch_2, 192, [1, 7], scope='Conv2d_0c_1x7')
                    branch_2 = slim.conv2d(branch_2, 192, [7, 1], scope='Conv2d_0d_7x1')
                    branch_2 = slim.conv2d(branch_2, 192, [1, 7], scope='Conv2d_0e_1x7')
                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
                    branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv2d_0b_1x1')
                # 输出tensor尺寸为17x17x768
                net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
            # 将Mixed_6e存储于end_points中
            end_points['Mixed_6e'] = net
            # 第3个Inception模块
            # 第1个Inception模块组
            with tf.variable_scope('Mixed_7a'):
                # 第1个分支:192输出通道的1x1卷积,接320输出通道的3x3卷积 步长为2
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
                    branch_0 = slim.conv2d(branch_0, 320, [3, 3], stride=2, padding='VALID', scope='Conv2d_0a_3x3')
                # 第2个分支:4个卷积层
                with tf.variable_scope('Branch_1'):
                    # 192输出通道的1x1卷积
                    branch_1 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
                    # 192输出通道的1x7卷积
                    branch_1 = slim.conv2d(branch_1, 192, [1, 7], scope='Conv2d_0b_1x7')
                    # 192输出通道的7x1卷积
                    branch_1 = slim.conv2d(branch_1, 192, [7, 1], scope='Conv2d_0c_7x1')
                    # 192输出通道的3x3卷积 步长为2,输出8x8x192
                    branch_1 = slim.conv2d(branch_1, 192, [3, 3], stride=2, padding='VALID', scope='Conv2d_1a_3x3')
                # 第3个分支:3x3的最大池化层,输出8x8x768
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID', scope='MaxPool_1a_3x3')
                # 输出tensor尺寸:8x8x(320+192+768)=8x8x1280,尺寸缩小,通道数增加
                net = tf.concat([branch_0, branch_1, branch_2], 3)
            # 第2个Inception模块组
            with tf.variable_scope('Mixed_7b'):
                # 第1个分支:320输出通道的1x1卷积
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 320, [1, 1], scope='Conv2d_0a_1x1')
                # 第2个分支:384输出通道的1x1卷积
                # 分支内拆分为两个分支:384输出通道的1x3卷积+384输出通道的3x1卷积
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net, 384, [1, 1], scope='Conv2d_0a_1x1')
                    branch_1 = tf.concat([
                               slim.conv2d(branch_1, 384, [1, 3], scope='Conv2d_0b_1x3'),
                               slim.conv2d(branch_1, 384, [3, 1], scope='Conv2d_0b_3x1')], 3)
                # 第3个分支:448输出通道的1x1卷积,接384输出通道的3x3卷积,分支内拆分为两个分支
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.conv2d(net, 448, [1, 1], scope='Conv2d_0a_1x1')
                    branch_2 = slim.conv2d(branch_2, 384, [3, 3], scope='Conv2d_0b_3x3')
                    # 分支内拆分为两个分支:384输出通道的1x3卷积+384输出通道的3x1卷积
                    branch_2 = tf.concat([
                               slim.conv2d(branch_2, 384, [1, 3], scope='Conv2d_0c_1x3'),
                               slim.conv2d(branch_2, 384, [3, 1], scope='Conv2d_0d_3x1')], 3)
                # 第4个分支:3x3的平均池化层,接192输出通道的1x1卷积,输出8x8x768
                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
                    branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv2d_0b_1x1')
                # 输出tensor尺寸:8x8x(320+768+768+192)=8x8x2048
                net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
            # 第3个Inception模块组
            with tf.variable_scope('Mixed_7c'):
                # 第1个分支:320输出通道的1x1卷积
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 320, [1, 1], scope='Conv2d_0a_1x1')
                # 第2个分支:384输出通道的1x1卷积
                # 分支内拆分为两个分支:384输出通道的1x3卷积+384输出通道的3x1卷积
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net, 384, [1, 1], scope='Conv2d_0a_1x1')
                    branch_1 = tf.concat([
                               slim.conv2d(branch_1, 384, [1, 3], scope='Conv2d_0b_1x3'),
                               slim.conv2d(branch_1, 384, [3, 1], scope='Conv2d_0c_3x1')], 3)
                # 第3个分支:448输出通道的1x1卷积,接384输出通道的3x3卷积,分支内拆分为两个分支
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.conv2d(net, 448, [1, 1], scope='Conv2d_0a_1x1')
                    branch_2 = slim.conv2d(branch_2, 384, [3, 3], scope='Conv2d_0b_3x3')
                    # 分支内拆分为两个分支:384输出通道的1x3卷积+384输出通道的3x1卷积
                    branch_2 = tf.concat([
                               slim.conv2d(branch_2, 384, [1, 3], scope='Conv2d_0c_1x3'),
                               slim.conv2d(branch_2, 384, [3, 1], scope='Conv2d_0d_3x1')], 3)
                # 第4个分支:3x3的平均池化层,接192输出通道的1x1卷积,输出8x8x768
                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
                    branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv2d_0b_1x1')
                # 输出tensor尺寸:8x8x(320+768+768+192)=8x8x2048
                net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
            return net, end_points
# 全局平均池化
def inception_v3(inputs,
                 num_classes=1000,          # 最后分类数量
                 is_training=True,          # 是否是训练过程的标志
                 dropout_keep_prob=0.8,     # Dropout保留节点的比例
                 prediction_fn=slim.softmax,# 进行分类的函数
                 spatial_squeeze=True,      # 是否对输出进行squeeze操作,即去除维数为1的维度
                 reuse=None,                # tf.variable_scope的reuse默认值
                 scope='InceptionV3'):      # tf.variable_scope的scope默认值
    with tf.variable_scope(scope, 'InceptionV3', [inputs, num_classes], reuse=reuse) as scope:
        with slim.arg_scope([slim.batch_norm, slim.dropout], is_training=is_training):
            net, end_points = inception_v3_base(inputs, scope=scope)
            # 设置卷积/最大池化/平均池化的默认步长为1,padding模式为SAME
            with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d], stride=1, padding='SAME'):
                aux_logits = end_points['Mixed_6e']
                # 辅助分类节点
                with tf.variable_scope('AuxLogits'):
                    # 5x5的平均池化,步长设为3,padding模式设为VALID
                    aux_logits = slim.avg_pool2d(aux_logits, [5, 5], stride=3, padding='VALID', scope='AvgPool_1a_5x5')
                    aux_logits = slim.conv2d(aux_logits, 128, [1, 1], scope='Conv2d_1b_1x1')
                    aux_logits = slim.conv2d(aux_logits,768, [5, 5], weights_initializer=trunc_normal(0.01), padding='VALID', scope='Conv2d_2a_5x5')
                    aux_logits = slim.conv2d(aux_logits, num_classes, [1, 1], activation_fn=None,
                                             normalizer_fn=None, weights_initializer=trunc_normal(0.001), scope='Conv2d_2b_1x1')
                    if spatial_squeeze:
                        # 进行squeeze操作,去除维数为1的维度
                        aux_logits = tf.squeeze(aux_logits, [1, 2], name='SpatialSqueeze')
                    end_points['AuxLogits'] = aux_logits
            # 处理正常的分类预测
            with tf.variable_scope('Logits'):
                # 8x8的平均池化层
                net = slim.avg_pool2d(net, [8, 8], padding='VALID', scope='AvgPool_1a_8x8')
                # Dropout层
                net = slim.dropout(net, keep_prob=dropout_keep_prob, scope='Dropout_1b')
                end_points['PreLogits'] = net
                logits = slim.conv2d(net, num_classes, [1, 1], activation_fn= None, normalizer_fn=None,scope='Conv2d_1c_1x1')
                if spatial_squeeze:
                    # 进行squeeze操作,去除维数为1的维度
                    logits = tf.squeeze(logits, [1, 2], name='SpatialSqueeze')
            # 辅助节点
            end_points['Logits'] = logits
            # 利用Softmax对结果进行分类预测
            end_points['Predictions'] = prediction_fn(logits, scope='Predictions')
    return logits, end_points
import math
from datetime import datetime
import time
# 评估每轮计算占用的时间
# 输入TensorFlow的Session,需要测评的算子target,测试的名称info_string
def time_tensorflow_run(session, target, info_string):
    # 定义预热轮数(忽略前10轮,不考虑显存加载等因素的影响)
    num_steps_burn_in = 10
    total_duration = 0.0
    total_duration_squared = 0.0
    for i in range(num_batches + num_steps_burn_in):
        start_time = time.time()
        _ = session.run(target)
        # 持续时间
        duration = time.time()- start_time
        if i >= num_steps_burn_in:
            # 只考量10轮迭代之后的计算时间
            if not i % 10:
                print '%s: step %d, duration = %.3f' % (datetime.now().strftime('%X'), i - num_steps_burn_in, duration)
            # 记录总时间
            total_duration += duration
            total_duration_squared += duration * duration
    # 计算每轮迭代的平均耗时mn,和标准差sd
    mn = total_duration / num_batches
    vr = total_duration_squared / num_batches - mn * mn
    sd = math.sqrt(vr)
    # 打印出每轮迭代耗时
    print '%s: %s across %d steps, %.3f +/- %.3f sec / batch' % (datetime.now().strftime('%X'), info_string, num_batches, mn, sd)
# Inception V3运行性能测试
if __name__ == '__main__':
    batch_size = 32
    height, width = 299, 299
    inputs = tf.random_uniform((batch_size, height, width, 3))
    with slim.arg_scope(inception_v3_arg_scope()):
        # 传入inputs获取logits,end_points
        logits, end_points = inception_v3(inputs, is_training=False)
    # 初始化
    init = tf.global_variables_initializer()
    sess = tf.Session()
    sess.run(init)
    num_batches = 100
    # 测试Inception V3的forward性能
    time_tensorflow_run(sess, logits, 'Forward')

 

5.ResNet网络

 

ResNet是一个应用十分广泛的卷积神经网络的特征提取网络,在2016年由大名鼎鼎的何恺明(He-Kaiming)及其团队提出,他曾以第一作者身份拿过2次CVPR最佳论文奖(2009年和2016年),其中2016年CVPR最佳论文就是这个深度残差网络。

 

ResNet残差网络特点:

全是3X3卷积核
卷积步长2取代池化
使用BN
取消Max池化、全连接和Dropout
网络更深

各种ResNet残差网络:

 

以Resnet18为例,它是由残差块堆叠而成的网络–1个卷积层+8个残差块(每个残差块有2个卷积层)+1个全连接层,如下图:

 

代码实现 ResNet18

 

#coding:utf-8
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import  tensorflow as tf
from    tensorflow import keras
from    tensorflow.keras import layers, Sequential
#构建残差块
class BasicBlock(layers.Layer):
    def __init__(self, filter_num, stride=1):
        super(BasicBlock, self).__init__()
        #卷积层(过滤器尺寸3*3,过滤器个数filter_num(可变),步长为stride(可变),padding为same(输出尺寸=输入尺寸/步长)
        self.conv1 = layers.Conv2D(filter_num, (3, 3), strides=stride, padding='same')
        #BatchNormalization标准化
        self.bn1 = layers.BatchNormalization()
        #激活函数选择relu
        self.relu = layers.Activation('relu')
        #卷积层(过滤器尺寸3*3,过滤器个数filter_num(可变),步长为1,padding为same(输出尺寸=输入尺寸/步长)
        self.conv2 = layers.Conv2D(filter_num, (3, 3), strides=1, padding='same')
        self.bn2 = layers.BatchNormalization()
        #对步长进行判断,以减少参数量(如果步长等于1,则为原x;否则将x输入一个过滤器为1、步长为stride的卷积层中)
        if stride != 1:
            #建立一个容器
            self.downsample = Sequential()
            # 卷积层(过滤器尺寸1*1,过滤器个数filter_num(可变),步长为stride(可变));输出尺寸=向下取整((输入尺寸-过滤器尺寸)/步长)+1)
            self.downsample.add(layers.Conv2D(filter_num, (1, 1), strides=stride))
        else:
            self.downsample = lambda x:x
    #前向传播
    def call(self, inputs, training=None):
        # [b, h, w, c]
        out = self.conv1(inputs)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.bn2(out)
        identity = self.downsample(inputs)
        output = layers.add([out, identity])
        output = tf.nn.relu(output)
        return output
#建立残差网络模型
class ResNet(keras.Model):
    #通过在__init__中定义层的实现
    def __init__(self, layer_dims, num_classes=100): #resnet18的layer_dims为[2, 2, 2, 2]
        super(ResNet, self).__init__()
        self.stem = Sequential([layers.Conv2D(64, (3, 3), strides=(1, 1)),
                                layers.BatchNormalization(),
                                layers.Activation('relu'),
                                layers.MaxPool2D(pool_size=(2, 2), strides=(1, 1), padding='same')
                                ])
        self.layer1 = self.build_resblock(64,  layer_dims[0])
        self.layer2 = self.build_resblock(128, layer_dims[1], stride=2)
        self.layer3 = self.build_resblock(256, layer_dims[2], stride=2)
        self.layer4 = self.build_resblock(512, layer_dims[3], stride=2)
        # output: [b, 512, h, w],
        #GlobalAveragePooling2D(是平均池化的一个特例,主要是用来解决全连接的问题)是将输入特征图的每一个通道求平均得到一个数值,它不需要指定pool_size和strides等参数。返回的tensor是[batch_size, channels],例如:128个9*9的feature map,对每个feature map取最大值直接得到一个128维的特征向量。
        self.avgpool = layers.GlobalAveragePooling2D()
        self.fc = layers.Dense(num_classes)

    #在call函数中实现前向过程
    def call(self, inputs, training=None):
        x = self.stem(inputs)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        # [b, c]
        x = self.avgpool(x)
        # [b, 100]
        x = self.fc(x)
        return x
   #串联多个残差块
    def build_resblock(self, filter_num, blocks, stride=1):
        res_blocks = Sequential()
        # may down sample
        res_blocks.add(BasicBlock(filter_num, stride))
        for _ in range(1, blocks):
            res_blocks.add(BasicBlock(filter_num, stride=1))
        return res_blocks
def resnet18():
    return ResNet([2, 2, 2, 2])

 

今天介绍了图像分类中各种CNN的迭代过程,网络越来越深,网络的复杂度也不断增加,但图像分类的准确度连年上升。下一节开始介绍另一大类研究方向–图像识别,包括各种各样新的网络结构和算法,敬请期待

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