使用Keras预训练模型ResNet50进行图像分类方式
Keras提供了一些用ImageNet训练过的模型：Xception，VGG16，VGG19，ResNet50，InceptionV3。在使用这些模型的时候，有一个参数include_top表示是否包含模型顶部的全连接层，如果包含，则可以将图像分为ImageNet中的1000类，如果不包含，则可以利用这些参数来做一些定制的事情。
在运行时自动下载有可能会失败，需要去网站中手动下载，放在“~/.keras/models/”中，使用WinPython则在“settings/.keras/models/”中。
修正：表示当前是训练模式还是测试模式的参数K.learning_phase()文中表述和使用有误，在该函数说明中可以看到：
Thelearningphaseflagisabooltensor(0=test,1=train)，所以0是测试模式，1是训练模式，部分网络结构下两者有差别。
这里使用ResNet50预训练模型，对Caltech101数据集进行图像分类。只有CPU，运行较慢，但是在训练集固定的情况下，较慢的过程只需要运行一次。
该预训练模型的中文文档介绍在http://keras-cn.readthedocs.io/en/latest/other/application/#resnet50。
我使用的版本：
1.Ubuntu16.04.3
2.Python2.7
3.Keras2.0.8
4.Tensoflow1.3.0
5.Numpy1.13.1
6.python-opencv2.4.9.1+dfsg-1.5ubuntu1
7.h5py2.7.0
从文件夹中提取图像数据的方式：
函数：
defeachFile(filepath):#将目录内的文件名放入列表中
pathDir=os.listdir(filepath)
out=[]
forallDirinpathDir:
child=allDir.decode('gbk')#.decode('gbk')是解决中文显示乱码问题
out.append(child)
returnout
defget_data(data_name,train_left=0.0,train_right=0.7,train_all=0.7,resize=True,data_format=None,t=''):#从文件夹中获取图像数据
file_name=os.path.join(pic_dir_out,data_name+t+'_'+str(train_left)+'_'+str(train_right)+'_'+str(Width)+"X"+str(Height)+".h5")
printfile_name
ifos.path.exists(file_name):#判断之前是否有存到文件中
f=h5py.File(file_name,'r')
ift=='train':
X_train=f['X_train'][:]
y_train=f['y_train'][:]
f.close()
return(X_train,y_train)
elift=='test':
X_test=f['X_test'][:]
y_test=f['y_test'][:]
f.close()
return(X_test,y_test)
else:
return
data_format=conv_utils.normalize_data_format(data_format)
pic_dir_set=eachFile(pic_dir_data)
X_train=[]
y_train=[]
X_test=[]
y_test=[]
label=0
forpic_dirinpic_dir_set:
printpic_dir_data+pic_dir
ifnotos.path.isdir(os.path.join(pic_dir_data,pic_dir)):
continue
pic_set=eachFile(os.path.join(pic_dir_data,pic_dir))
pic_index=0
train_count=int(len(pic_set)*train_all)
train_l=int(len(pic_set)*train_left)
train_r=int(len(pic_set)*train_right)
forpic_nameinpic_set:
ifnotos.path.isfile(os.path.join(pic_dir_data,pic_dir,pic_name)):
continue
img=cv2.imread(os.path.join(pic_dir_data,pic_dir,pic_name))
ifimgisNone:
continue
if(resize):
img=cv2.resize(img,(Width,Height))
img=img.reshape(-1,Width,Height,3)
if(pic_index=train_landpic_index0:
label+=1
f=h5py.File(file_name,'w')
ift=='train':
X_train=np.concatenate(X_train,axis=0)
y_train=np.array(y_train)
f.create_dataset('X_train',data=X_train)
f.create_dataset('y_train',data=y_train)
f.close()
return(X_train,y_train)
elift=='test':
X_test=np.concatenate(X_test,axis=0)
y_test=np.array(y_test)
f.create_dataset('X_test',data=X_test)
f.create_dataset('y_test',data=y_test)
f.close()
return(X_test,y_test)
else:
return
调用：
globalWidth,Height,pic_dir_out,pic_dir_data
Width=224
Height=224
num_classes=102#Caltech101为102cifar10为10
pic_dir_out='/home/ccuux3/pic_cnn/pic_out/'
pic_dir_data='/home/ccuux3/pic_cnn/pic_dataset/Caltech101/'
sub_dir='224_resnet50/'
ifnotos.path.isdir(os.path.join(pic_dir_out,sub_dir)):
os.mkdir(os.path.join(pic_dir_out,sub_dir))
pic_dir_mine=os.path.join(pic_dir_out,sub_dir)
(X_train,y_train)=get_data("Caltech101_color_data_",0.0,0.7,data_format='channels_last',t='train')
y_train=np_utils.to_categorical(y_train,num_classes)
载入预训练模型ResNet50，并将训练图像经过网络运算得到数据，不包含顶部的全连接层，得到的结果存成文件，以后可以直接调用（由于我内存不够，所以拆分了一下）：
input_tensor=Input(shape=(224,224,3))
base_model=ResNet50(input_tensor=input_tensor,include_top=False,weights='imagenet')
#base_model=ResNet50(input_tensor=input_tensor,include_top=False,weights=None)
get_resnet50_output=K.function([base_model.layers[0].input,K.learning_phase()],
[base_model.layers[-1].output])
file_name=os.path.join(pic_dir_mine,'resnet50_train_output'+'.h5')
ifos.path.exists(file_name):
f=h5py.File(file_name,'r')
resnet50_train_output=f['resnet50_train_output'][:]
f.close()
else:
resnet50_train_output=[]
delta=10
foriinrange(0,len(X_train),delta):
printi
one_resnet50_train_output=get_resnet50_output([X_train[i:i+delta],0])[0]
resnet50_train_output.append(one_resnet50_train_output)
resnet50_train_output=np.concatenate(resnet50_train_output,axis=0)
f=h5py.File(file_name,'w')
f.create_dataset('resnet50_train_output',data=resnet50_train_output)
f.close()
将ResNet50网络产生的结果用于图像分类：
input_tensor=Input(shape=(1,1,2048)) x=Flatten()(input_tensor) x=Dense(1024,activation='relu')(x) predictions=Dense(num_classes,activation='softmax')(x) model=Model(inputs=input_tensor,outputs=predictions) model.compile(optimizer=Adam(),loss='categorical_crossentropy',metrics=['accuracy'])
训练图像数据集：
print('\nTraining------------')#从文件中提取参数，训练后存在新的文件中
cm=0#修改这个参数可以多次训练
cm_str=''ifcm==0elsestr(cm)
cm2_str=''if(cm+1)==0elsestr(cm+1)
ifcm>=1:
model.load_weights(os.path.join(pic_dir_mine,'cnn_model_Caltech101_resnet50_'+cm_str+'.h5'))
model.fit(resnet50_train_output,y_train,epochs=10,batch_size=128,)
model.save_weights(os.path.join(pic_dir_mine,'cnn_model_Caltech101_resnet50_'+cm2_str+'.h5'))
测试图像数据集：
(X_test,y_test)=get_data("Caltech101_color_data_",0.0,0.7,data_format='channels_last',t='test')
y_test=np_utils.to_categorical(y_test,num_classes)
file_name=os.path.join(pic_dir_mine,'resnet50_test_output'+'.h5')
ifos.path.exists(file_name):
f=h5py.File(file_name,'r')
resnet50_test_output=f['resnet50_test_output'][:]
f.close()
else:
resnet50_test_output=[]
delta=10
foriinrange(0,len(X_test),delta):
printi
one_resnet50_test_output=get_resnet50_output([X_test[i:i+delta],0])[0]
resnet50_test_output.append(one_resnet50_test_output)
resnet50_test_output=np.concatenate(resnet50_test_output,axis=0)
f=h5py.File(file_name,'w')
f.create_dataset('resnet50_test_output',data=resnet50_test_output)
f.close()
print('\nTesting------------')#对测试集进行评估
class_name_list=get_name_list(pic_dir_data)#获取top-N的每类的准确率
pred=model.predict(resnet50_test_output,batch_size=32)
输出测试集各类别top-5的准确率：
N=5
pred_list=[]
forrowinpred:
pred_list.append(row.argsort()[-N:][::-1])#获取最大的N个值的下标
pred_array=np.array(pred_list)
test_arg=np.argmax(y_test,axis=1)
class_count=[0for_inxrange(num_classes)]
class_acc=[0for_inxrange(num_classes)]
foriinxrange(len(test_arg)):
class_count[test_arg[i]]+=1
iftest_arg[i]inpred_array[i]:
class_acc[test_arg[i]]+=1
print('top-'+str(N)+'allacc:',str(sum(class_acc))+'/'+str(len(test_arg)),sum(class_acc)/float(len(test_arg)))
foriinxrange(num_classes):
print(i,class_name_list[i],'acc:'+str(class_acc[i])+'/'+str(class_count[i]))
完整代码：
#-*-coding:utf-8-*-
importcv2
importnumpyasnp
importh5py
importos
fromkeras.utilsimportnp_utils,conv_utils
fromkeras.modelsimportModel
fromkeras.layersimportFlatten,Dense,Input
fromkeras.optimizersimportAdam
fromkeras.applications.resnet50importResNet50
fromkerasimportbackendasK
defget_name_list(filepath):#获取各个类别的名字
pathDir=os.listdir(filepath)
out=[]
forallDirinpathDir:
ifos.path.isdir(os.path.join(filepath,allDir)):
child=allDir.decode('gbk')#.decode('gbk')是解决中文显示乱码问题
out.append(child)
returnout
defeachFile(filepath):#将目录内的文件名放入列表中
pathDir=os.listdir(filepath)
out=[]
forallDirinpathDir:
child=allDir.decode('gbk')#.decode('gbk')是解决中文显示乱码问题
out.append(child)
returnout
defget_data(data_name,train_left=0.0,train_right=0.7,train_all=0.7,resize=True,data_format=None,t=''):#从文件夹中获取图像数据
file_name=os.path.join(pic_dir_out,data_name+t+'_'+str(train_left)+'_'+str(train_right)+'_'+str(Width)+"X"+str(Height)+".h5")
printfile_name
ifos.path.exists(file_name):#判断之前是否有存到文件中
f=h5py.File(file_name,'r')
ift=='train':
X_train=f['X_train'][:]
y_train=f['y_train'][:]
f.close()
return(X_train,y_train)
elift=='test':
X_test=f['X_test'][:]
y_test=f['y_test'][:]
f.close()
return(X_test,y_test)
else:
return
data_format=conv_utils.normalize_data_format(data_format)
pic_dir_set=eachFile(pic_dir_data)
X_train=[]
y_train=[]
X_test=[]
y_test=[]
label=0
forpic_dirinpic_dir_set:
printpic_dir_data+pic_dir
ifnotos.path.isdir(os.path.join(pic_dir_data,pic_dir)):
continue
pic_set=eachFile(os.path.join(pic_dir_data,pic_dir))
pic_index=0
train_count=int(len(pic_set)*train_all)
train_l=int(len(pic_set)*train_left)
train_r=int(len(pic_set)*train_right)
forpic_nameinpic_set:
ifnotos.path.isfile(os.path.join(pic_dir_data,pic_dir,pic_name)):
continue
img=cv2.imread(os.path.join(pic_dir_data,pic_dir,pic_name))
ifimgisNone:
continue
if(resize):
img=cv2.resize(img,(Width,Height))
img=img.reshape(-1,Width,Height,3)
if(pic_index=train_landpic_index0:
label+=1
f=h5py.File(file_name,'w')
ift=='train':
X_train=np.concatenate(X_train,axis=0)
y_train=np.array(y_train)
f.create_dataset('X_train',data=X_train)
f.create_dataset('y_train',data=y_train)
f.close()
return(X_train,y_train)
elift=='test':
X_test=np.concatenate(X_test,axis=0)
y_test=np.array(y_test)
f.create_dataset('X_test',data=X_test)
f.create_dataset('y_test',data=y_test)
f.close()
return(X_test,y_test)
else:
return
defmain():
globalWidth,Height,pic_dir_out,pic_dir_data
Width=224
Height=224
num_classes=102#Caltech101为102cifar10为10
pic_dir_out='/home/ccuux3/pic_cnn/pic_out/'
pic_dir_data='/home/ccuux3/pic_cnn/pic_dataset/Caltech101/'
sub_dir='224_resnet50/'
ifnotos.path.isdir(os.path.join(pic_dir_out,sub_dir)):
os.mkdir(os.path.join(pic_dir_out,sub_dir))
pic_dir_mine=os.path.join(pic_dir_out,sub_dir)
(X_train,y_train)=get_data("Caltech101_color_data_",0.0,0.7,data_format='channels_last',t='train')
y_train=np_utils.to_categorical(y_train,num_classes)
input_tensor=Input(shape=(224,224,3))
base_model=ResNet50(input_tensor=input_tensor,include_top=False,weights='imagenet')
#base_model=ResNet50(input_tensor=input_tensor,include_top=False,weights=None)
get_resnet50_output=K.function([base_model.layers[0].input,K.learning_phase()],
[base_model.layers[-1].output])
file_name=os.path.join(pic_dir_mine,'resnet50_train_output'+'.h5')
ifos.path.exists(file_name):
f=h5py.File(file_name,'r')
resnet50_train_output=f['resnet50_train_output'][:]
f.close()
else:
resnet50_train_output=[]
delta=10
foriinrange(0,len(X_train),delta):
printi
one_resnet50_train_output=get_resnet50_output([X_train[i:i+delta],0])[0]
resnet50_train_output.append(one_resnet50_train_output)
resnet50_train_output=np.concatenate(resnet50_train_output,axis=0)
f=h5py.File(file_name,'w')
f.create_dataset('resnet50_train_output',data=resnet50_train_output)
f.close()
input_tensor=Input(shape=(1,1,2048))
x=Flatten()(input_tensor)
x=Dense(1024,activation='relu')(x)
predictions=Dense(num_classes,activation='softmax')(x)
model=Model(inputs=input_tensor,outputs=predictions)
model.compile(optimizer=Adam(),loss='categorical_crossentropy',metrics=['accuracy'])
print('\nTraining------------')#从文件中提取参数，训练后存在新的文件中
cm=0#修改这个参数可以多次训练
cm_str=''ifcm==0elsestr(cm)
cm2_str=''if(cm+1)==0elsestr(cm+1)
ifcm>=1:
model.load_weights(os.path.join(pic_dir_mine,'cnn_model_Caltech101_resnet50_'+cm_str+'.h5'))
model.fit(resnet50_train_output,y_train,epochs=10,batch_size=128,)
model.save_weights(os.path.join(pic_dir_mine,'cnn_model_Caltech101_resnet50_'+cm2_str+'.h5'))
(X_test,y_test)=get_data("Caltech101_color_data_",0.0,0.7,data_format='channels_last',t='test')
y_test=np_utils.to_categorical(y_test,num_classes)
file_name=os.path.join(pic_dir_mine,'resnet50_test_output'+'.h5')
ifos.path.exists(file_name):
f=h5py.File(file_name,'r')
resnet50_test_output=f['resnet50_test_output'][:]
f.close()
else:
resnet50_test_output=[]
delta=10
foriinrange(0,len(X_test),delta):
printi
one_resnet50_test_output=get_resnet50_output([X_test[i:i+delta],0])[0]
resnet50_test_output.append(one_resnet50_test_output)
resnet50_test_output=np.concatenate(resnet50_test_output,axis=0)
f=h5py.File(file_name,'w')
f.create_dataset('resnet50_test_output',data=resnet50_test_output)
f.close()
print('\nTesting------------')#对测试集进行评估
class_name_list=get_name_list(pic_dir_data)#获取top-N的每类的准确率
pred=model.predict(resnet50_test_output,batch_size=32)
f=h5py.File(os.path.join(pic_dir_mine,'pred_'+cm2_str+'.h5'),'w')
f.create_dataset('pred',data=pred)
f.close()
N=1
pred_list=[]
forrowinpred:
pred_list.append(row.argsort()[-N:][::-1])#获取最大的N个值的下标
pred_array=np.array(pred_list)
test_arg=np.argmax(y_test,axis=1)
class_count=[0for_inxrange(num_classes)]
class_acc=[0for_inxrange(num_classes)]
foriinxrange(len(test_arg)):
class_count[test_arg[i]]+=1
iftest_arg[i]inpred_array[i]:
class_acc[test_arg[i]]+=1
print('top-'+str(N)+'allacc:',str(sum(class_acc))+'/'+str(len(test_arg)),sum(class_acc)/float(len(test_arg)))
foriinxrange(num_classes):
print(i,class_name_list[i],'acc:'+str(class_acc[i])+'/'+str(class_count[i]))
print('----------------------------------------------------')
N=5
pred_list=[]
forrowinpred:
pred_list.append(row.argsort()[-N:][::-1])#获取最大的N个值的下标
pred_array=np.array(pred_list)
test_arg=np.argmax(y_test,axis=1)
class_count=[0for_inxrange(num_classes)]
class_acc=[0for_inxrange(num_classes)]
foriinxrange(len(test_arg)):
class_count[test_arg[i]]+=1
iftest_arg[i]inpred_array[i]:
class_acc[test_arg[i]]+=1
print('top-'+str(N)+'allacc:',str(sum(class_acc))+'/'+str(len(test_arg)),sum(class_acc)/float(len(test_arg)))
foriinxrange(num_classes):
print(i,class_name_list[i],'acc:'+str(class_acc[i])+'/'+str(class_count[i]))
if__name__=='__main__':
main()
运行结果：
UsingTensorFlowbackend.
/home/ccuux3/pic_cnn/pic_out/Caltech101_color_data_train_0.0_0.7_224X224.h5
Training------------
Epoch1/10
6353/6353[==============================]-5s-loss:1.1269-acc:0.7494
Epoch2/10
6353/6353[==============================]-4s-loss:0.1603-acc:0.9536
Epoch3/10
6353/6353[==============================]-4s-loss:0.0580-acc:0.9855
Epoch4/10
6353/6353[==============================]-4s-loss:0.0312-acc:0.9931
Epoch5/10
6353/6353[==============================]-4s-loss:0.0182-acc:0.9956
Epoch6/10
6353/6353[==============================]-4s-loss:0.0111-acc:0.9976
Epoch7/10
6353/6353[==============================]-4s-loss:0.0090-acc:0.9981
Epoch8/10
6353/6353[==============================]-4s-loss:0.0082-acc:0.9987
Epoch9/10
6353/6353[==============================]-4s-loss:0.0069-acc:0.9994
Epoch10/10
6353/6353[==============================]-4s-loss:0.0087-acc:0.9987
/home/ccuux3/pic_cnn/pic_out/Caltech101_color_data_test_0.0_0.7_224X224.h5
Testing------------
('top-1allacc:','2597/2792',0.9301575931232091)
(0,u'62.mayfly','acc:10/12')
(1,u'66.Motorbikes','acc:240/240')
(2,u'68.octopus','acc:7/11')
(3,u'94.umbrella','acc:21/23')
(4,u'90.strawberry','acc:10/11')
(5,u'86.stapler','acc:13/14')
(6,u'83.sea_horse','acc:15/18')
(7,u'72.pigeon','acc:13/14')
(8,u'89.stop_sign','acc:19/20')
(9,u'4.BACKGROUND_Google','acc:125/141')
(10,u'22.cougar_face','acc:18/21')
(11,u'81.scissors','acc:9/12')
(12,u'100.wrench','acc:8/12')
(13,u'57.Leopards','acc:60/60')
(14,u'46.hawksbill','acc:29/30')
(15,u'30.dolphin','acc:19/20')
(16,u'9.bonsai','acc:39/39')
(17,u'35.euphonium','acc:18/20')
(18,u'44.gramophone','acc:16/16')
(19,u'74.platypus','acc:7/11')
(20,u'14.camera','acc:15/15')
(21,u'55.lamp','acc:15/19')
(22,u'38.Faces_easy','acc:129/131')
(23,u'54.ketch','acc:28/35')
(24,u'33.elephant','acc:18/20')
(25,u'3.ant','acc:8/13')
(26,u'49.helicopter','acc:26/27')
(27,u'36.ewer','acc:26/26')
(28,u'78.rooster','acc:14/15')
(29,u'70.pagoda','acc:15/15')
(30,u'58.llama','acc:20/24')
(31,u'5.barrel','acc:15/15')
(32,u'101.yin_yang','acc:18/18')
(33,u'18.cellphone','acc:18/18')
(34,u'59.lobster','acc:7/13')
(35,u'17.ceiling_fan','acc:14/15')
(36,u'16.car_side','acc:37/37')
(37,u'50.ibis','acc:24/24')
(38,u'76.revolver','acc:23/25')
(39,u'84.snoopy','acc:7/11')
(40,u'87.starfish','acc:26/26')
(41,u'12.buddha','acc:24/26')
(42,u'52.joshua_tree','acc:20/20')
(43,u'43.gerenuk','acc:10/11')
(44,u'65.minaret','acc:23/23')
(45,u'91.sunflower','acc:26/26')
(46,u'56.laptop','acc:24/25')
(47,u'77.rhino','acc:17/18')
(48,u'1.airplanes','acc:239/240')
(49,u'88.stegosaurus','acc:16/18')
(50,u'23.crab','acc:17/22')
(51,u'8.binocular','acc:8/10')
(52,u'31.dragonfly','acc:18/21')
(53,u'6.bass','acc:15/17')
(54,u'95.watch','acc:72/72')
(55,u'0.accordion','acc:17/17')
(56,u'98.wild_cat','acc:9/11')
(57,u'67.nautilus','acc:16/17')
(58,u'40.flamingo','acc:20/21')
(59,u'92.tick','acc:12/15')
(60,u'47.headphone','acc:12/13')
(61,u'24.crayfish','acc:15/21')
(62,u'97.wheelchair','acc:17/18')
(63,u'27.cup','acc:15/18')
(64,u'25.crocodile','acc:14/15')
(65,u'2.anchor','acc:7/13')
(66,u'19.chair','acc:17/19')
(67,u'39.ferry','acc:21/21')
(68,u'60.lotus','acc:16/20')
(69,u'13.butterfly','acc:26/28')
(70,u'34.emu','acc:14/16')
(71,u'64.metronome','acc:10/10')
(72,u'82.scorpion','acc:24/26')
(73,u'7.beaver','acc:12/14')
(74,u'48.hedgehog','acc:16/17')
(75,u'37.Faces','acc:131/131')
(76,u'45.grand_piano','acc:30/30')
(77,u'79.saxophone','acc:11/12')
(78,u'26.crocodile_head','acc:9/16')
(79,u'80.schooner','acc:15/19')
(80,u'93.trilobite','acc:26/26')
(81,u'28.dalmatian','acc:21/21')
(82,u'10.brain','acc:28/30')
(83,u'61.mandolin','acc:10/13')
(84,u'11.brontosaurus','acc:11/13')
(85,u'63.menorah','acc:25/27')
(86,u'85.soccer_ball','acc:20/20')
(87,u'51.inline_skate','acc:9/10')
(88,u'71.panda','acc:11/12')
(89,u'53.kangaroo','acc:24/26')
(90,u'99.windsor_chair','acc:16/17')
(91,u'42.garfield','acc:11/11')
(92,u'29.dollar_bill','acc:16/16')
(93,u'20.chandelier','acc:30/33')
(94,u'96.water_lilly','acc:6/12')
(95,u'41.flamingo_head','acc:13/14')
(96,u'73.pizza','acc:13/16')
(97,u'21.cougar_body','acc:15/15')
(98,u'75.pyramid','acc:16/18')
(99,u'69.okapi','acc:12/12')
(100,u'15.cannon','acc:11/13')
(101,u'32.electric_guitar','acc:19/23')
----------------------------------------------------
('top-5allacc:','2759/2792',0.9881805157593123)
(0,u'62.mayfly','acc:12/12')
(1,u'66.Motorbikes','acc:240/240')
(2,u'68.octopus','acc:11/11')
(3,u'94.umbrella','acc:23/23')
(4,u'90.strawberry','acc:11/11')
(5,u'86.stapler','acc:14/14')
(6,u'83.sea_horse','acc:16/18')
(7,u'72.pigeon','acc:14/14')
(8,u'89.stop_sign','acc:20/20')
(9,u'4.BACKGROUND_Google','acc:141/141')
(10,u'22.cougar_face','acc:19/21')
(11,u'81.scissors','acc:11/12')
(12,u'100.wrench','acc:10/12')
(13,u'57.Leopards','acc:60/60')
(14,u'46.hawksbill','acc:30/30')
(15,u'30.dolphin','acc:20/20')
(16,u'9.bonsai','acc:39/39')
(17,u'35.euphonium','acc:20/20')
(18,u'44.gramophone','acc:16/16')
(19,u'74.platypus','acc:9/11')
(20,u'14.camera','acc:15/15')
(21,u'55.lamp','acc:18/19')
(22,u'38.Faces_easy','acc:131/131')
(23,u'54.ketch','acc:34/35')
(24,u'33.elephant','acc:20/20')
(25,u'3.ant','acc:10/13')
(26,u'49.helicopter','acc:27/27')
(27,u'36.ewer','acc:26/26')
(28,u'78.rooster','acc:15/15')
(29,u'70.pagoda','acc:15/15')
(30,u'58.llama','acc:24/24')
(31,u'5.barrel','acc:15/15')
(32,u'101.yin_yang','acc:18/18')
(33,u'18.cellphone','acc:18/18')
(34,u'59.lobster','acc:13/13')
(35,u'17.ceiling_fan','acc:14/15')
(36,u'16.car_side','acc:37/37')
(37,u'50.ibis','acc:24/24')
(38,u'76.revolver','acc:25/25')
(39,u'84.snoopy','acc:10/11')
(40,u'87.starfish','acc:26/26')
(41,u'12.buddha','acc:25/26')
(42,u'52.joshua_tree','acc:20/20')
(43,u'43.gerenuk','acc:11/11')
(44,u'65.minaret','acc:23/23')
(45,u'91.sunflower','acc:26/26')
(46,u'56.laptop','acc:25/25')
(47,u'77.rhino','acc:18/18')
(48,u'1.airplanes','acc:240/240')
(49,u'88.stegosaurus','acc:18/18')
(50,u'23.crab','acc:22/22')
(51,u'8.binocular','acc:10/10')
(52,u'31.dragonfly','acc:20/21')
(53,u'6.bass','acc:16/17')
(54,u'95.watch','acc:72/72')
(55,u'0.accordion','acc:17/17')
(56,u'98.wild_cat','acc:11/11')
(57,u'67.nautilus','acc:17/17')
(58,u'40.flamingo','acc:21/21')
(59,u'92.tick','acc:13/15')
(60,u'47.headphone','acc:12/13')
(61,u'24.crayfish','acc:21/21')
(62,u'97.wheelchair','acc:18/18')
(63,u'27.cup','acc:16/18')
(64,u'25.crocodile','acc:15/15')
(65,u'2.anchor','acc:12/13')
(66,u'19.chair','acc:19/19')
(67,u'39.ferry','acc:21/21')
(68,u'60.lotus','acc:19/20')
(69,u'13.butterfly','acc:27/28')
(70,u'34.emu','acc:16/16')
(71,u'64.metronome','acc:10/10')
(72,u'82.scorpion','acc:26/26')
(73,u'7.beaver','acc:14/14')
(74,u'48.hedgehog','acc:17/17')
(75,u'37.Faces','acc:131/131')
(76,u'45.grand_piano','acc:30/30')
(77,u'79.saxophone','acc:12/12')
(78,u'26.crocodile_head','acc:14/16')
(79,u'80.schooner','acc:19/19')
(80,u'93.trilobite','acc:26/26')
(81,u'28.dalmatian','acc:21/21')
(82,u'10.brain','acc:30/30')
(83,u'61.mandolin','acc:13/13')
(84,u'11.brontosaurus','acc:13/13')
(85,u'63.menorah','acc:25/27')
(86,u'85.soccer_ball','acc:20/20')
(87,u'51.inline_skate','acc:10/10')
(88,u'71.panda','acc:12/12')
(89,u'53.kangaroo','acc:26/26')
(90,u'99.windsor_chair','acc:17/17')
(91,u'42.garfield','acc:11/11')
(92,u'29.dollar_bill','acc:16/16')
(93,u'20.chandelier','acc:32/33')
(94,u'96.water_lilly','acc:12/12')
(95,u'41.flamingo_head','acc:14/14')
(96,u'73.pizza','acc:16/16')
(97,u'21.cougar_body','acc:15/15')
(98,u'75.pyramid','acc:18/18')
(99,u'69.okapi','acc:12/12')
(100,u'15.cannon','acc:12/13')
(101,u'32.electric_guitar','acc:23/23')
以上这篇使用Keras预训练模型ResNet50进行图像分类方式就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持毛票票。