python神經(jīng)網(wǎng)絡(luò)MobileNetV2模型的復(fù)現(xiàn)詳解

第python神經(jīng)網(wǎng)絡(luò)MobileNetV2模型的復(fù)現(xiàn)詳解目錄什么是MobileNetV2模型MobileNetV2網(wǎng)絡(luò)部分實(shí)現(xiàn)代碼圖片預(yù)測(cè)

什么是MobileNetV2模型

MobileNet它哥MobileNetV2也是很不錯(cuò)的呢

MobileNet模型是Google針對(duì)手機(jī)等嵌入式設(shè)備提出的一種輕量級(jí)的深層神經(jīng)網(wǎng)絡(luò)，其使用的核心思想便是depthwiseseparableconvolution。

MobileNetV2是MobileNet的升級(jí)版，它具有兩個(gè)特征點(diǎn)：

1、Invertedresiduals，在ResNet50里我們認(rèn)識(shí)到一個(gè)結(jié)構(gòu)，bottleneckdesign結(jié)構(gòu)，在3x3網(wǎng)絡(luò)結(jié)構(gòu)前利用1x1卷積降維，在3x3網(wǎng)絡(luò)結(jié)構(gòu)后，利用1x1卷積升維，相比直接使用3x3網(wǎng)絡(luò)卷積效果更好，參數(shù)更少，先進(jìn)行壓縮，再進(jìn)行擴(kuò)張。而在MobileNetV2網(wǎng)絡(luò)部分，其采用Invertedresiduals結(jié)構(gòu)，在3x3網(wǎng)絡(luò)結(jié)構(gòu)前利用1x1卷積升維，在3x3網(wǎng)絡(luò)結(jié)構(gòu)后，利用1x1卷積降維，先進(jìn)行擴(kuò)張，再進(jìn)行壓縮。

2、Linearbottlenecks，為了避免Relu對(duì)特征的破壞，在在3x3網(wǎng)絡(luò)結(jié)構(gòu)前利用1x1卷積升維，在3x3網(wǎng)絡(luò)結(jié)構(gòu)后，再利用1x1卷積降維后，不再進(jìn)行Relu6層，直接進(jìn)行殘差網(wǎng)絡(luò)的加法。

整體網(wǎng)絡(luò)結(jié)構(gòu)如下：（其中bottleneck進(jìn)行的操作就是上述的創(chuàng)新操作）

MobileNetV2網(wǎng)絡(luò)部分實(shí)現(xiàn)代碼

#-------------------------------------------------------------#

#MobileNetV2的網(wǎng)絡(luò)部分

#-------------------------------------------------------------#

importmath

importnumpyasnp

importtensorflowastf

fromtensorflow.kerasimportbackend

fromkerasimportbackendasK

fromkeras.preprocessingimportimage

fromkeras.modelsimportModel

fromkeras.layers.normalizationimportBatchNormalization

fromkeras.layersimportConv2D,Add,ZeroPadding2D,GlobalAveragePooling2D,Dropout,Dense

fromkeras.layersimportMaxPooling2D,Activation,DepthwiseConv2D,Input,GlobalMaxPooling2D

fromkeras.applicationsimportimagenet_utils

fromkeras.applications.imagenet_utilsimportdecode_predictions

fromkeras.utils.data_utilsimportget_file

#TODOChangepathtov1.1

BASE_WEIGHT_PATH=('/JonathanCMitchell/mobilenet_v2_keras/'

'releases/download/v1.1/')

#relu6！

defrelu6(x):

returnK.relu(x,max_value=6)

#用于計(jì)算padding的大小

defcorrect_pad(inputs,kernel_size):

img_dim=1

input_size=_shape(inputs)[img_dim:(img_dim+2)]

ifisinstance(kernel_size,int):

kernel_size=(kernel_size,kernel_size)

ifinput_size[0]isNone:

adjust=(1,1)

else:

adjust=(1-input_size[0]%2,1-input_size[1]%2)

correct=(kernel_size[0]//2,kernel_size[1]//2)

return((correct[0]-adjust[0],correct[0]),

(correct[1]-adjust[1],correct[1]))

#使其結(jié)果可以被8整除，因?yàn)槭褂玫搅伺蛎浵禂?shù)α

def_make_divisible(v,divisor,min_value=None):

ifmin_valueisNone:

min_value=divisor

new_v=max(min_value,int(v+divisor/2)//divisor*divisor)

ifnew_v0.9*v:

new_v+=divisor

returnnew_v

defMobileNetV2(input_shape=[224,224,3],

alpha=1.0,

include_top=True,

weights='imagenet',

classes=1000):

rows=input_shape[0]

img_input=Input(shape=input_shape)

#stem部分

#224,224,3-112,112,32

first_block_filters=_make_divisible(32*alpha,8)

x=ZeroPadding2D(padding=correct_pad(img_input,3),

name='Conv1_pad')(img_input)

x=Conv2D(first_block_filters,

kernel_size=3,

strides=(2,2),

padding='valid',

use_bias=False,

name='Conv1')(x)

x=BatchNormalization(epsilon=1e-3,

momentum=0.999,

name='bn_Conv1')(x)

x=Activation(relu6,name='Conv1_relu')(x)

#112,112,32-112,112,16

x=_inverted_res_block(x,filters=16,alpha=alpha,stride=1,

expansion=1,block_id=0)

#112,112,16-56,56,24

x=_inverted_res_block(x,filters=24,alpha=alpha,stride=2,

expansion=6,block_id=1)

x=_inverted_res_block(x,filters=24,alpha=alpha,stride=1,

expansion=6,block_id=2)

#56,56,24-28,28,32

x=_inverted_res_block(x,filters=32,alpha=alpha,stride=2,

expansion=6,block_id=3)

x=_inverted_res_block(x,filters=32,alpha=alpha,stride=1,

expansion=6,block_id=4)

x=_inverted_res_block(x,filters=32,alpha=alpha,stride=1,

expansion=6,block_id=5)

#28,28,32-14,14,64

x=_inverted_res_block(x,filters=64,alpha=alpha,stride=2,

expansion=6,block_id=6)

x=_inverted_res_block(x,filters=64,alpha=alpha,stride=1,

expansion=6,block_id=7)

x=_inverted_res_block(x,filters=64,alpha=alpha,stride=1,

expansion=6,block_id=8)

x=_inverted_res_block(x,filters=64,alpha=alpha,stride=1,

expansion=6,block_id=9)

#14,14,64-14,14,96

x=_inverted_res_block(x,filters=96,alpha=alpha,stride=1,

expansion=6,block_id=10)

x=_inverted_res_block(x,filters=96,alpha=alpha,stride=1,

expansion=6,block_id=11)

x=_inverted_res_block(x,filters=96,alpha=alpha,stride=1,

expansion=6,block_id=12)

#14,14,96-7,7,160

x=_inverted_res_block(x,filters=160,alpha=alpha,stride=2,

expansion=6,block_id=13)

x=_inverted_res_block(x,filters=160,alpha=alpha,stride=1,

expansion=6,block_id=14)

x=_inverted_res_block(x,filters=160,alpha=alpha,stride=1,

expansion=6,block_id=15)

#7,7,160-7,7,320

x=_inverted_res_block(x,filters=320,alpha=alpha,stride=1,

expansion=6,block_id=16)

ifalpha1.0:

last_block_filters=_make_divisible(1280*alpha,8)

else:

last_block_filters=1280

#7,7,320-7,7,1280

x=Conv2D(last_block_filters,

kernel_size=1,

use_bias=False,

name='Conv_1')(x)

x=BatchNormalization(epsilon=1e-3,

momentum=0.999,

name='Conv_1_bn')(x)

x=Activation(relu6,name='out_relu')(x)

x=GlobalAveragePooling2D()(x)

x=Dense(classes,activation='softmax',

use_bias=True,name='Logits')(x)

inputs=img_input

model=Model(inputs,x,name='mobilenetv2_%0.2f_%s'%(alpha,rows))

#Loadweights.

ifweights=='imagenet':

ifinclude_top:

model_name=('mobilenet_v2_weights_tf_dim_ordering_tf_kernels_'+

str(alpha)+'_'+str(rows)+'.h5')

weight_path=BASE_WEIGHT_PATH+model_name

weights_path=get_file(

model_name,weight_path,cache_subdir='models')

else:

model_name=('mobilenet_v2_weights_tf_dim_ordering_tf_kernels_'+

str(alpha)+'_'+str(rows)+'_no_top'+'.h5')

weight_path=BASE_WEIGHT_PATH+model_name

weights_path=get_file(

model_name,weight_path,cache_subdir='models')

model.load_weights(weights_path)

elifweightsisnotNone:

model.load_weights(weights)

returnmodel

def_inverted_res_block(inputs,expansion,stride,alpha,filters,block_id):

in_channels=_shape(inputs)[-1]

pointwise_conv_filters=int(filters*alpha)

pointwise_filters=_make_divisible(pointwise_conv_filters,8)

x=inputs

prefix='block_{}_'.format(block_id)

#part1數(shù)據(jù)擴(kuò)張

ifblock_id:

#Expand

x=Conv2D(expansion*in_channels,

kernel_size=1,

padding='same',

use_bias=False,

activation=None,

name=prefix+'expand')(x)

x=BatchNormalization(epsilon=1e-3,

momentum=0.999,

name=prefix+'expand_BN')(x)

x=Activation(relu6,name=prefix+'expand_relu')(x)

else:

prefix='expanded_conv_'

ifstride==2:

x=ZeroPadding2D(padding=correct_pad(x,3),

name=prefix+'pad')(x)

#part2可分離卷積

x=DepthwiseConv2D(kernel_size=3,

strides=stride,

activation=None,

use_bias=False,

padding='same'ifstride==1else'valid',

name=prefix+'depthwise')(x)

x=BatchNormalization(epsilon=1e-3,

momentum=0.999,

name=prefix+'depthwise_BN')(x)

x=Activation(relu6,name=prefix+'depthwise_relu')(x)

#part3壓縮特征，而且不使用relu函數(shù)，保證特征不被破壞

x=Conv2D(pointwise_filters,

kernel_size=1,

padding='same',

use_bias=False,

activation=None,

name=prefix+'project')(x)

x=BatchNormalization(epsilon=1e-3,

momentum=0.999,

name=prefix+'project_BN')(x)

ifin_channels==pointwise_filtersandstride==1:

returnAdd(name=prefix+'add')([inputs,x])

returnx

圖片預(yù)測(cè)

建立網(wǎng)絡(luò)后，可以用以下的代碼進(jìn)行預(yù)測(cè)。

defpreprocess_input(x):

x/=255.

x-=0.5

x*=2.

returnx

if__name__=='__main__':

model=MobileNetV2(input_shape=(224,