【总结】keras下，利用VGG16和resnet50预训练模型，完成多类别动物图片分类任务（下）

最新推荐文章于 2025-05-16 10:45:00 发布

岁辞秋

最新推荐文章于 2025-05-16 10:45:00 发布

阅读量3k

点赞数 2

CC 4.0 BY-SA版权

分类专栏：任务总结文章标签：任务总结

本文链接：https://blog.youkuaiyun.com/ZZU_chenhao/article/details/97667057

多类别动物图片分类任务（下）

在最后，我们将在之前完成模型的基础上，利用模型微调，来进一步提高val_acc。

查看ResNet50的模型结构
想要进行模型微调，前提自然是知道我们可以调整那些层，关于这一点，我们可以利用summary函数来实现。
代码实现

model.summary()

模型层次

__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_1 (InputLayer)            (None, 356, 356, 3)  0                                            
__________________________________________________________________________________________________
conv1_pad (ZeroPadding2D)       (None, 362, 362, 3)  0           input_1[0][0]                    
__________________________________________________________________________________________________
conv1 (Conv2D)                  (None, 178, 178, 64) 9472        conv1_pad[0][0]                  
__________________________________________________________________________________________________
bn_conv1 (BatchNormalization)   (None, 178, 178, 64) 256         conv1[0][0]                      
__________________________________________________________________________________________________
activation_1 (Activation)       (None, 178, 178, 64) 0           bn_conv1[0][0]                   
__________________________________________________________________________________________________
pool1_pad (ZeroPadding2D)       (None, 180, 180, 64) 0           activation_1[0][0]               
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D)  (None, 89, 89, 64)   0           pool1_pad[0][0]                  
__________________________________________________________________________________________________
res2a_branch2a (Conv2D)         (None, 89, 89, 64)   4160        max_pooling2d_1[0][0]            
__________________________________________________________________________________________________
bn2a_branch2a (BatchNormalizati (None, 89, 89, 64)   256         res2a_branch2a[0][0]             
__________________________________________________________________________________________________
activation_2 (Activation)       (None, 89, 89, 64)   0           bn2a_branch2a[0][0]              
__________________________________________________________________________________________________
res2a_branch2b (Conv2D)         (None, 89, 89, 64)   36928       activation_2[0][0]               
__________________________________________________________________________________________________
bn2a_branch2b (BatchNormalizati (None, 89, 89, 64)   256         res2a_branch2b[0][0]             
__________________________________________________________________________________________________
activation_3 (Activation)       (None, 89, 89, 64)   0           bn2a_branch2b[0][0]              
__________________________________________________________________________________________________
res2a_branch2c (Conv2D)         (None, 89, 89, 256)  16640       activation_3[0][0]               
__________________________________________________________________________________________________
res2a_branch1 (Conv2D)          (None, 89, 89, 256)  16640       max_pooling2d_1[0][0]            
__________________________________________________________________________________________________
bn2a_branch2c (BatchNormalizati (None, 89, 89, 256)  1024        res2a_branch2c[0][0]             
__________________________________________________________________________________________________
bn2a_branch1 (BatchNormalizatio (None, 89, 89, 256)  1024        res2a_branch1[0][0]              
__________________________________________________________________________________________________
add_1 (Add)                     (None, 89, 89, 256)  0           bn2a_branch2c[0][0]              
                                                                 bn2a_branch1[0][0]               
__________________________________________________________________________________________________
activation_4 (Activation)       (None, 89, 89, 256)  0           add_1[0][0]                      
__________________________________________________________________________________________________
res2b_branch2a (Conv2D)         (None, 89, 89, 64)   16448       activation_4[0][0]               
__________________________________________________________________________________________________
bn2b_branch2a (BatchNormalizati (None, 89, 89, 64)   256         res2b_branch2a[0][0]             
__________________________________________________________________________________________________
activation_5 (Activation)       (None, 89, 89, 64)   0           bn2b_branch2a[0][0]              
__________________________________________________________________________________________________
res2b_branch2b (Conv2D)         (None, 89, 89, 64)   36928       activation_5[0][0]               
__________________________________________________________________________________________________
bn2b_branch2b (BatchNormalizati (None, 89, 89, 64)   256         res2b_branch2b[0][0]             
__________________________________________________________________________________________________
activation_6 (Activation)       (None, 89, 89, 64)   0           bn2b_branch2b[0][0]              
__________________________________________________________________________________________________
res2b_branch2c (Conv2D)         (None, 89, 89, 256)  16640       activation_6[0][0]               
__________________________________________________________________________________________________
bn2b_branch2c (BatchNormalizati (None, 89, 89, 256)  1024        res2b_branch2c[0][0]             
__________________________________________________________________________________________________
add_2 (Add)                     (None, 89, 89, 256)  0           bn2b_branch2c[0][0]              
                                                                 activation_4[0][0]               
__________________________________________________________________________________________________
activation_7 (Activation)       (None, 89, 89, 256)  0           add_2[0][0]                      
__________________________________________________________________________________________________
res2c_branch2a (Conv2D)         (None, 89, 89, 64)   16448       activation_7[0][0]               
__________________________________________________________________________________________________
bn2c_branch2a (BatchNormalizati (None, 89, 89, 64)   256         res2c_branch2a[0][0]             
__________________________________________________________________________________________________
activation_8 (Activation)       (None, 89, 89, 64)   0           bn2c_branch2a[0][0]              
__________________________________________________________________________________________________
res2c_branch2b (Conv2D)         (None, 89, 89, 64)   36928       activation_8[0][0]               
__________________________________________________________________________________________________
bn2c_branch2b (BatchNormalizati (None, 89, 89, 64)   256         res2c_branch2b[0][0]             
__________________________________________________________________________________________________
activation_9 (Activation)       (None, 89, 89, 64)   0           bn2c_branch2b[0][0]              
__________________________________________________________________________________________________
res2c_branch2c (Conv2D)         (None, 89, 89, 256)  16640       activation_9[0][0]               
__________________________________________________________________________________________________
bn2c_branch2c (BatchNormalizati (None, 89, 89, 256)  1024        res2c_branch2c[0][0]             
__________________________________________________________________________________________________
add_3 (Add)                     (None, 89, 89, 256)  0           bn2c_branch2c[0][0]              
                                                                 activation_7[0][0]               
__________________________________________________________________________________________________
activation_10 (Activation)      (None, 89, 89,