【深度学习21天学习挑战赛】8、卷积神经网络（认识Xception模型）：动物识别

活动地址：CSDN21天学习挑战赛

• 🍨 本文为🔗365天深度学习训练营 中的学习记录博客
• 🍦 参考文章地址： 🔗深度学习100例 | 第24天-卷积神经网络（Xception）：动物识别
• 🍖 作者：K同学啊

1、 Xception模型主要思想

传统的卷积操作同时对输入的feature mapping的跨通道交互性（cross-channel correlations）、空间交互性（spatial correlations) 进行了映射。

Inception系列结构着力于将上述过程进行分解，在一定程度上实现了跨通道相关性和空间相关性的解耦。

在Inception的基础上进行改进，使用深度可分离卷积（depthwise separate convolution）替代传统的Inception块，实现跨通道相关性和空间相关性的完全解耦。此外，文章还引入了残差连接，最终提出了Xception的网络结构。

Xception是谷歌公司继Inception后，提出的InceptionV3的一种改进模型，其中Inception模块已被深度可分离卷积（depthwise separable convolution）替换。它与Inception-v1（23M）的参数数量大致相同。

2、深度可分离卷积

2.1 标准卷积

输入一个12×12×3的一个输入特征图，经过 5×5×3的卷积核得到一个8×8×1的输出特征图。如果我们此时有256个卷积核，我们将会得到一个8×8×256的输出特征图。

2.2 深度卷积

与标准卷积网络不一样的是，这里会将卷积核拆分成单通道形式，在不改变输入特征图像的深度的情况下，对每一通道进行卷积操作，这样就得到了和输入特征图通道数一致的输出特征图。如上图，输入12x12x3 的特征图，经过5x5x1x3的深度卷积之后，得到了8x8x3的输出特征图。输入和输出的维度是不变的3，这样就会有一个问题，通道数太少，特征图的维度太少，能获得足够的有效信息吗?

2.3逐点卷积

逐点卷积就是1*1卷积，主要作用就是对特征图进行升维和降维，如下图：

在深度卷积的过程中，我们得到了8x8x3的输出特征图，我们用256个1x1x3的卷积核对输入特征图进行卷积操作，输出的特征图和标准的卷积操作一样都是8x8x256了。

标准卷积与深度可分离卷积的过程对比如下：

可见，深度可分离卷积可以实现更少的参数，更少的运算量。

3、构建Xception模型

数据导入及预处理部分略

#====================================#
#     Xception的网络部分
#====================================#
from tensorflow.keras.preprocessing import image

from tensorflow.keras.models import Model
from tensorflow.keras import layers
from tensorflow.keras.layers import Dense,Input,BatchNormalization,Activation,Conv2D,SeparableConv2D,MaxPooling2D
from tensorflow.keras.layers import GlobalAveragePooling2D,GlobalMaxPooling2D
from tensorflow.keras import backend as K
from tensorflow.keras.applications.imagenet_utils import decode_predictions


def Xception(input_shape = [299,299,3],classes=1000):

    img_input = Input(shape=input_shape)
    
    #=================#
    #   Entry flow
    #=================#
    #  block1
    # 299,299,3 -> 149,149,64
    x = Conv2D(32, (3, 3), strides=(2, 2), use_bias=False, name='block1_conv1')(img_input)
    x = BatchNormalization(name='block1_conv1_bn')(x)
    x = Activation('relu', name='block1_conv1_act')(x)
    x = Conv2D(64, (3, 3), use_bias=False, name='block1_conv2')(x)
    x = BatchNormalization(name='block1_conv2_bn')(x)
    x = Activation('relu', name='block1_conv2_act')(x)


    # block2
    # 149,149,64 -> 75,75,128
    residual = Conv2D(128, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x)
    residual = BatchNormalization()(residual)

    x = SeparableConv2D(128, (3, 3), padding='same', use_bias=False, name='block2_sepconv1')(x)
    x = BatchNormalization(name='block2_sepconv1_bn')(x)
    x = Activation('relu', name='block2_sepconv2_act')(x)
    x = SeparableConv2D(128, (3, 3), padding='same', use_bias=False, name='block2_sepconv2')(x)
    x = BatchNormalization(name='block2_sepconv2_bn')(x)

    x = MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block2_pool')(x)
    x = layers.add([x, residual])

    # block3
    # 75,75,128 -> 38,38,256
    residual = Conv2D(256, (1, 1), strides=(2, 2),padding='same', use_bias=False)(x)
    residual = BatchNormalization()(residual)

    x = Activation('relu', name='block3_sepconv1_act')(x)
    x = SeparableConv2D(256, (3, 3), padding='same', use_bias=False, name='block3_sepconv1')(x)
    x = BatchNormalization(name='block3_sepconv1_bn')(x)
    x = Activation('relu', name='block3_sepconv2_act')(x)
    x = SeparableConv2D(256, (3, 3), padding='same', use_bias=False, name='block3_sepconv2')(x)
    x = BatchNormalization(name='block3_sepconv2_bn')(x)

    x = MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block3_pool')(x)
    x = layers.add([x, residual])

    # block4
    # 38,38,256 -> 19,19,728
    residual = Conv2D(728, (1, 1), strides=(2, 2),padding='same', use_bias=False)(x)
    residual = BatchNormalization()(residual)

    x = Activation('relu', name='block4_sepconv1_act')(x)
    x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name='block4_sepconv1')(x)
    x = BatchNormalization(name='block4_sepconv1_bn')(x)
    x = Activation('relu', name='block4_sepconv2_act')(x)
    x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name='block4_sepconv2')(x)
    x = BatchNormalization(name='block4_sepconv2_bn')(x)

    x = MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block4_pool')(x)
    x = layers.add([x, residual])

    #=================#
    # Middle flow
    #=================#
    # block5--block12
    # 19,19,728 -> 19,19,728
    for i in range(8):
        residual = x
        prefix = 'block' + str(i + 5)

        x = Activation('relu', name=prefix + '_sepconv1_act')(x)
        x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv1')(x)
        x = BatchNormalization(name=prefix + '_sepconv1_bn')(x)
        x = Activation('relu', name=prefix + '_sepconv2_act')(x)
        x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv2')(x)
        x = BatchNormalization(name=prefix + '_sepconv2_bn')(x)
        x = Activation('relu', name=prefix + '_sepconv3_act')(x)
        x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv3')(x)
        x = BatchNormalization(name=prefix + '_sepconv3_bn')(x)

        x = layers.add([x, residual])

    #=================#
    #    Exit flow
    #=================#
    # block13
    # 19,19,728 -> 10,10,1024
    residual = Conv2D(1024, (1, 1), strides=(2, 2),
                      padding='same', use_bias=False)(x)
    residual = BatchNormalization()(residual)

    x = Activation('relu', name='block13_sepconv1_act')(x)
    x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name='block13_sepconv1')(x)
    x = BatchNormalization(name='block13_sepconv1_bn')(x)
    x = Activation('relu', name='block13_sepconv2_act')(x)
    x = SeparableConv2D(1024, (3, 3), padding='same', use_bias=False, name='block13_sepconv2')(x)
    x = BatchNormalization(name='block13_sepconv2_bn')(x)

    x = MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block13_pool')(x)
    x = layers.add([x, residual])

    # block14
    # 10,10,1024 -> 10,10,2048
    x = SeparableConv2D(1536, (3, 3), padding='same', use_bias=False, name='block14_sepconv1')(x)
    x = BatchNormalization(name='block14_sepconv1_bn')(x)
    x = Activation('relu', name='block14_sepconv1_act')(x)

    x = SeparableConv2D(2048, (3, 3), padding='same', use_bias=False, name='block14_sepconv2')(x)
    x = BatchNormalization(name='block14_sepconv2_bn')(x)
    x = Activation('relu', name='block14_sepconv2_act')(x)

    x = GlobalAveragePooling2D(name='avg_pool')(x)
    x = Dense(classes, activation='softmax', name='predictions')(x)

    inputs = img_input

    model = Model(inputs, x, name='xception')

    return model
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132

打印模型如下：

Model: "xception"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_1 (InputLayer)            [(None, 299, 299, 3) 0                                            
__________________________________________________________________________________________________
block1_conv1 (Conv2D)           (None, 149, 149, 32) 864         input_1[0][0]                    
__________________________________________________________________________________________________
block1_conv1_bn (BatchNormaliza (None, 149, 149, 32) 128         block1_conv1[0][0]               
__________________________________________________________________________________________________
......
__________________________________________________________________________________________________
block14_sepconv2 (SeparableConv (None, 10, 10, 2048) 3159552     block14_sepconv1_act[0][0]       
__________________________________________________________________________________________________
block14_sepconv2_bn (BatchNorma (None, 10, 10, 2048) 8192        block14_sepconv2[0][0]           
__________________________________________________________________________________________________
block14_sepconv2_act (Activatio (None, 10, 10, 2048) 0           block14_sepconv2_bn[0][0]        
__________________________________________________________________________________________________
avg_pool (GlobalAveragePooling2 (None, 2048)         0           block14_sepconv2_act[0][0]       
__________________________________________________________________________________________________
predictions (Dense)             (None, 1000)         2049000     avg_pool[0][0]                   
==================================================================================================
Total params: 22,910,480
Trainable params: 22,855,952
Non-trainable params: 54,528
__________________________________________________________________________________________________
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26

模型学习率、编译、训练部分略

4、评估

5、启发

Xception作为Inception v3的改进，主要是在Inception v3的基础上引入了depthwise separable convolution，在基本不增加网络复杂度的前提下提高了模型的效果。