python -keras：将两个模型合并为一个顺序

Question

如何使用两个模型创建一个模型顺序？ 我有两个模型，一个是一个KERAS应用程序（VGG16模型）和一个自定义模型，我想将它们合并为一个顺序模型。

我尝试以这种方式进行操作：

VGG16_model = tf.keras.applications.VGG16(
    include_top=False,
    weights='imagenet',
    pooling='avg'
)

teacher = tf.keras.Sequential(
    [
        VGG16_model,
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dense(512, activation=('relu')),
        tf.keras.layers.Dropout(0.2),
        tf.keras.layers.Dense(256, activation=('relu')),
        tf.keras.layers.Dropout(0.2),
        tf.keras.layers.Dense(10, activation=('softmax'))
    ],
    name = 'teacher',
)

但是当我打印模型的摘要时，我有这个东西： https://i.sstatic.net/xsktg.png

但是我想拥有我的总结VGG16型号的所有层，我该怎么做？

Answer 1

尝试这样的事情：

import tensorflow as tf

VGG16_model = tf.keras.applications.VGG16(
    include_top=False,
    weights='imagenet',
    pooling='avg'
)

teacher = tf.keras.Sequential(name = 'teacher')
for l in VGG16_model.layers:
  teacher.add(l)

teacher.add(tf.keras.layers.Flatten())
teacher.add(tf.keras.layers.Dense(512, activation=('relu')))
teacher.add(tf.keras.layers.Dropout(0.2))
teacher.add(tf.keras.layers.Dense(256, activation=('relu')))
teacher.add(tf.keras.layers.Dropout(0.2))
teacher.add(tf.keras.layers.Dense(10, activation=('softmax')))

print(teacher.summary())

Model: "teacher"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 block1_conv1 (Conv2D)       (None, None, None, 64)    1792      
                                                                 
 block1_conv2 (Conv2D)       (None, None, None, 64)    36928     
                                                                 
 block1_pool (MaxPooling2D)  (None, None, None, 64)    0         
                                                                 
 block2_conv1 (Conv2D)       (None, None, None, 128)   73856     
                                                                 
 block2_conv2 (Conv2D)       (None, None, None, 128)   147584    
                                                                 
 block2_pool (MaxPooling2D)  (None, None, None, 128)   0         
                                                                 
 block3_conv1 (Conv2D)       (None, None, None, 256)   295168    
                                                                 
 block3_conv2 (Conv2D)       (None, None, None, 256)   590080    
                                                                 
 block3_conv3 (Conv2D)       (None, None, None, 256)   590080    
                                                                 
 block3_pool (MaxPooling2D)  (None, None, None, 256)   0         
                                                                 
 block4_conv1 (Conv2D)       (None, None, None, 512)   1180160   
                                                                 
 block4_conv2 (Conv2D)       (None, None, None, 512)   2359808   
                                                                 
 block4_conv3 (Conv2D)       (None, None, None, 512)   2359808   
                                                                 
 block4_pool (MaxPooling2D)  (None, None, None, 512)   0         
                                                                 
 block5_conv1 (Conv2D)       (None, None, None, 512)   2359808   
                                                                 
 block5_conv2 (Conv2D)       (None, None, None, 512)   2359808   
                                                                 
 block5_conv3 (Conv2D)       (None, None, None, 512)   2359808   
                                                                 
 block5_pool (MaxPooling2D)  (None, None, None, 512)   0         
                                                                 
 global_average_pooling2d_2   (None, 512)              0         
 (GlobalAveragePooling2D)                                        
                                                                 
 flatten_1 (Flatten)         (None, 512)               0         
                                                                 
 dense_3 (Dense)             (None, 512)               262656    
                                                                 
 dropout_2 (Dropout)         (None, 512)               0         
                                                                 
 dense_4 (Dense)             (None, 256)               131328    
                                                                 
 dropout_3 (Dropout)         (None, 256)               0         
                                                                 
 dense_5 (Dense)             (None, 10)                2570      
                                                                 
=================================================================
Total params: 15,111,242
Trainable params: 15,111,242
Non-trainable params: 0
_________________________________________________________________
None