使用Keras预训练模型ResNet50进行图像分类方式

站长资源 2024/11/24 佚名

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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：

The learning phase flag is a bool tensor (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.Ubuntu 16.04.3

2.Python 2.7

3.Keras 2.0.8

4.Tensoflow 1.3.0

5.Numpy 1.13.1

6.python-opencv 2.4.9.1+dfsg-1.5ubuntu1

7.h5py 2.7.0

从文件夹中提取图像数据的方式：

函数：

def eachFile(filepath):     #将目录内的文件名放入列表中
 pathDir = os.listdir(filepath)
 out = []
 for allDir in pathDir:
  child = allDir.decode('gbk') # .decode('gbk')是解决中文显示乱码问题
  out.append(child)
 return out
 
def get_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") 
 print file_name
 if os.path.exists(file_name):   #判断之前是否有存到文件中
  f = h5py.File(file_name,'r')
  if t=='train':
   X_train = f['X_train'][:]
   y_train = f['y_train'][:]
   f.close()
   return (X_train, y_train)
  elif t=='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
 for pic_dir in pic_dir_set:
  print pic_dir_data+pic_dir
  if not os.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)
  for pic_name in pic_set:
   if not os.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))
   if img is None:
    continue
   if (resize):
    img = cv2.resize(img,(Width,Height)) 
    img = img.reshape(-1,Width,Height,3)
   if (pic_index < train_count):
    if t=='train':
     if (pic_index >= train_l and pic_index < train_r):
      X_train.append(img)
      y_train.append(label) 
   else:
    if t=='test':
     X_test.append(img)
     y_test.append(label)
   pic_index += 1
  if len(pic_set) <> 0:  
   label += 1
 
 f = h5py.File(file_name,'w') 
 if t=='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)
 elif t=='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：

 global Width, Height, pic_dir_out, pic_dir_data
 Width = 224
 Height = 224
 num_classes = 102    #Caltech101为102 cifar10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/'
 if not os.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')
 if os.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
  for i in range(0,len(X_train),delta):
   print i
   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 = '' if cm==0 else str(cm)
 cm2_str = '' if (cm+1)==0 else str(cm+1) 
 if cm >= 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')
 if os.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
  for i in range(0,len(X_test),delta):
   print i
   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 = []
 for row in pred:
  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 = [0 for _ in xrange(num_classes)]
 class_acc = [0 for _ in xrange(num_classes)]
 for i in xrange(len(test_arg)):
  class_count[test_arg[i]] += 1
  if test_arg[i] in pred_array[i]:
   class_acc[test_arg[i]] += 1
 print('top-'+str(N)+' all acc:',str(sum(class_acc))+'/'+str(len(test_arg)),sum(class_acc)/float(len(test_arg)))
 for i in xrange(num_classes):
  print (i, class_name_list[i], 'acc: '+str(class_acc[i])+'/'+str(class_count[i]))

完整代码：

# -*- coding: utf-8 -*-
import cv2
import numpy as np
import h5py
import os
 
from keras.utils import np_utils, conv_utils
from keras.models import Model
from keras.layers import Flatten, Dense, Input 
from keras.optimizers import Adam
from keras.applications.resnet50 import ResNet50
from keras import backend as K
 
def get_name_list(filepath):    #获取各个类别的名字
 pathDir = os.listdir(filepath)
 out = []
 for allDir in pathDir:
  if os.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)
 return out
 
def eachFile(filepath):     #将目录内的文件名放入列表中
 pathDir = os.listdir(filepath)
 out = []
 for allDir in pathDir:
  child = allDir.decode('gbk') # .decode('gbk')是解决中文显示乱码问题
  out.append(child)
 return out
 
def get_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") 
 print file_name
 if os.path.exists(file_name):   #判断之前是否有存到文件中
  f = h5py.File(file_name,'r')
  if t=='train':
   X_train = f['X_train'][:]
   y_train = f['y_train'][:]
   f.close()
   return (X_train, y_train)
  elif t=='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
 for pic_dir in pic_dir_set:
  print pic_dir_data+pic_dir
  if not os.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)
  for pic_name in pic_set:
   if not os.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))
   if img is None:
    continue
   if (resize):
    img = cv2.resize(img,(Width,Height)) 
    img = img.reshape(-1,Width,Height,3)
   if (pic_index < train_count):
    if t=='train':
     if (pic_index >= train_l and pic_index < train_r):
      X_train.append(img)
      y_train.append(label) 
   else:
    if t=='test':
     X_test.append(img)
     y_test.append(label)
   pic_index += 1
  if len(pic_set) <> 0:  
   label += 1
 
 f = h5py.File(file_name,'w') 
 if t=='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)
 elif t=='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
 
def main():
 global Width, Height, pic_dir_out, pic_dir_data
 Width = 224
 Height = 224
 num_classes = 102    #Caltech101为102 cifar10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/'
 if not os.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')
 if os.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
  for i in range(0,len(X_train),delta):
   print i
   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 = '' if cm==0 else str(cm)
 cm2_str = '' if (cm+1)==0 else str(cm+1) 
 if cm >= 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')
 if os.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
  for i in range(0,len(X_test),delta):
   print i
   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 = []
 for row in pred:
  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 = [0 for _ in xrange(num_classes)]
 class_acc = [0 for _ in xrange(num_classes)]
 for i in xrange(len(test_arg)):
  class_count[test_arg[i]] += 1
  if test_arg[i] in pred_array[i]:
   class_acc[test_arg[i]] += 1
 print('top-'+str(N)+' all acc:',str(sum(class_acc))+'/'+str(len(test_arg)),sum(class_acc)/float(len(test_arg)))
 for i in xrange(num_classes):
  print (i, class_name_list[i], 'acc: '+str(class_acc[i])+'/'+str(class_count[i]))
 
 print('----------------------------------------------------')
 N = 5
 pred_list = []
 for row in pred:
  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 = [0 for _ in xrange(num_classes)]
 class_acc = [0 for _ in xrange(num_classes)]
 for i in xrange(len(test_arg)):
  class_count[test_arg[i]] += 1
  if test_arg[i] in pred_array[i]:
   class_acc[test_arg[i]] += 1
 print('top-'+str(N)+' all acc:',str(sum(class_acc))+'/'+str(len(test_arg)),sum(class_acc)/float(len(test_arg)))
 for i in xrange(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：

Using TensorFlow backend.
/home/ccuux3/pic_cnn/pic_out/Caltech101_color_data_train_0.0_0.7_224X224.h5

Training ------------
Epoch 1/10
6353/6353 [==============================] - 5s - loss: 1.1269 - acc: 0.7494  
Epoch 2/10
6353/6353 [==============================] - 4s - loss: 0.1603 - acc: 0.9536  
Epoch 3/10
6353/6353 [==============================] - 4s - loss: 0.0580 - acc: 0.9855  
Epoch 4/10
6353/6353 [==============================] - 4s - loss: 0.0312 - acc: 0.9931  
Epoch 5/10
6353/6353 [==============================] - 4s - loss: 0.0182 - acc: 0.9956  
Epoch 6/10
6353/6353 [==============================] - 4s - loss: 0.0111 - acc: 0.9976  
Epoch 7/10
6353/6353 [==============================] - 4s - loss: 0.0090 - acc: 0.9981  
Epoch 8/10
6353/6353 [==============================] - 4s - loss: 0.0082 - acc: 0.9987  
Epoch 9/10
6353/6353 [==============================] - 4s - loss: 0.0069 - acc: 0.9994  
Epoch 10/10
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-1 all acc:', '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-5 all acc:', '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。

Keras,预训练模型,ResNet50,图像分类

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使用Keras预训练模型ResNet50进行图像分类方式

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