在keras中的預訓練密集層之間添加丟失層

Question

在keras.applications中，有一個在imagenet上預訓練的VGG16模型。

from keras.applications import VGG16 
model = VGG16(weights='imagenet') 

這種模式具有以下結構。

Layer (type)      Output Shape   Param #  Connected to      
==================================================================================================== 
input_1 (InputLayer)    (None, 3, 224, 224) 0            
____________________________________________________________________________________________________ 
block1_conv1 (Convolution2D)  (None, 64, 224, 224) 1792  input_1[0][0]      
____________________________________________________________________________________________________ 
block1_conv2 (Convolution2D)  (None, 64, 224, 224) 36928  block1_conv1[0][0]    
____________________________________________________________________________________________________ 
block1_pool (MaxPooling2D)  (None, 64, 112, 112) 0   block1_conv2[0][0]    
____________________________________________________________________________________________________ 
block2_conv1 (Convolution2D)  (None, 128, 112, 112) 73856  block1_pool[0][0]     
____________________________________________________________________________________________________ 
block2_conv2 (Convolution2D)  (None, 128, 112, 112) 147584  block2_conv1[0][0]    
____________________________________________________________________________________________________ 
block2_pool (MaxPooling2D)  (None, 128, 56, 56) 0   block2_conv2[0][0]    
____________________________________________________________________________________________________ 
block3_conv1 (Convolution2D)  (None, 256, 56, 56) 295168  block2_pool[0][0]     
____________________________________________________________________________________________________ 
block3_conv2 (Convolution2D)  (None, 256, 56, 56) 590080  block3_conv1[0][0]    
____________________________________________________________________________________________________ 
block3_conv3 (Convolution2D)  (None, 256, 56, 56) 590080  block3_conv2[0][0]    
____________________________________________________________________________________________________ 
block3_pool (MaxPooling2D)  (None, 256, 28, 28) 0   block3_conv3[0][0]    
____________________________________________________________________________________________________ 
block4_conv1 (Convolution2D)  (None, 512, 28, 28) 1180160  block3_pool[0][0]     
____________________________________________________________________________________________________ 
block4_conv2 (Convolution2D)  (None, 512, 28, 28) 2359808  block4_conv1[0][0]    
____________________________________________________________________________________________________ 
block4_conv3 (Convolution2D)  (None, 512, 28, 28) 2359808  block4_conv2[0][0]    
____________________________________________________________________________________________________ 
block4_pool (MaxPooling2D)  (None, 512, 14, 14) 0   block4_conv3[0][0]    
____________________________________________________________________________________________________ 
block5_conv1 (Convolution2D)  (None, 512, 14, 14) 2359808  block4_pool[0][0]     
____________________________________________________________________________________________________ 
block5_conv2 (Convolution2D)  (None, 512, 14, 14) 2359808  block5_conv1[0][0]    
____________________________________________________________________________________________________ 
block5_conv3 (Convolution2D)  (None, 512, 14, 14) 2359808  block5_conv2[0][0]    
____________________________________________________________________________________________________ 
block5_pool (MaxPooling2D)  (None, 512, 7, 7)  0   block5_conv3[0][0]    
____________________________________________________________________________________________________ 
flatten (Flatten)    (None, 25088)   0   block5_pool[0][0]     
____________________________________________________________________________________________________ 
fc1 (Dense)      (None, 4096)   102764544 flatten[0][0]      
____________________________________________________________________________________________________ 
fc2 (Dense)      (None, 4096)   16781312 fc1[0][0]       
____________________________________________________________________________________________________ 
predictions (Dense)    (None, 1000)   4097000  fc2[0][0]       
==================================================================================================== 
Total params: 138,357,544 
Trainable params: 138,357,544 
Non-trainable params: 0 
____________________________________________________________________________________________________ 

我想微調這種模式與緻密層（FC1，FC2和預測）之間的差層，同時保持模型的所有預先訓練的權重不變。我知道可以用model.layers做一些技巧，但我還沒有找到任何地方如何在現有圖層之間添加圖層。

這樣做的最佳做法是什麼？

Answer 1

我自己找到了一個答案，這似乎工作。

from keras.applications import VGG16 
from keras.layers import Dropout 
from keras.models import Model 

model = VGG16(weights='imagenet') 

# Store the fully connected layers 
fc1 = model.layers[-3] 
fc2 = model.layers[-2] 
predictions = model.layers[-1] 

# Create the dropout layers 
dropout1 = Dropout(0.85) 
dropout2 = Dropout(0.85) 

# Reconnect the layers 
x = dropout1(fc1.output) 
x = fc2(x) 
x = dropout2(x) 
predictors = predictions(x) 

# Create a new model 
model2 = Model(input=model.input, output=predictors) 

model2具有的屬性，因爲我想

____________________________________________________________________________________________________ 
Layer (type)      Output Shape   Param #  Connected to      
==================================================================================================== 
input_1 (InputLayer)    (None, 3, 224, 224) 0            
____________________________________________________________________________________________________ 
block1_conv1 (Convolution2D)  (None, 64, 224, 224) 1792  input_1[0][0]      
____________________________________________________________________________________________________ 
block1_conv2 (Convolution2D)  (None, 64, 224, 224) 36928  block1_conv1[0][0]    
____________________________________________________________________________________________________ 
block1_pool (MaxPooling2D)  (None, 64, 112, 112) 0   block1_conv2[0][0]    
____________________________________________________________________________________________________ 
block2_conv1 (Convolution2D)  (None, 128, 112, 112) 73856  block1_pool[0][0]     
____________________________________________________________________________________________________ 
block2_conv2 (Convolution2D)  (None, 128, 112, 112) 147584  block2_conv1[0][0]    
____________________________________________________________________________________________________ 
block2_pool (MaxPooling2D)  (None, 128, 56, 56) 0   block2_conv2[0][0]    
____________________________________________________________________________________________________ 
block3_conv1 (Convolution2D)  (None, 256, 56, 56) 295168  block2_pool[0][0]     
____________________________________________________________________________________________________ 
block3_conv2 (Convolution2D)  (None, 256, 56, 56) 590080  block3_conv1[0][0]    
____________________________________________________________________________________________________ 
block3_conv3 (Convolution2D)  (None, 256, 56, 56) 590080  block3_conv2[0][0]    
____________________________________________________________________________________________________ 
block3_pool (MaxPooling2D)  (None, 256, 28, 28) 0   block3_conv3[0][0]    
____________________________________________________________________________________________________ 
block4_conv1 (Convolution2D)  (None, 512, 28, 28) 1180160  block3_pool[0][0]     
____________________________________________________________________________________________________ 
block4_conv2 (Convolution2D)  (None, 512, 28, 28) 2359808  block4_conv1[0][0]    
____________________________________________________________________________________________________ 
block4_conv3 (Convolution2D)  (None, 512, 28, 28) 2359808  block4_conv2[0][0]    
____________________________________________________________________________________________________ 
block4_pool (MaxPooling2D)  (None, 512, 14, 14) 0   block4_conv3[0][0]    
____________________________________________________________________________________________________ 
block5_conv1 (Convolution2D)  (None, 512, 14, 14) 2359808  block4_pool[0][0]     
____________________________________________________________________________________________________ 
block5_conv2 (Convolution2D)  (None, 512, 14, 14) 2359808  block5_conv1[0][0]    
____________________________________________________________________________________________________ 
block5_conv3 (Convolution2D)  (None, 512, 14, 14) 2359808  block5_conv2[0][0]    
____________________________________________________________________________________________________ 
block5_pool (MaxPooling2D)  (None, 512, 7, 7)  0   block5_conv3[0][0]    
____________________________________________________________________________________________________ 
flatten (Flatten)    (None, 25088)   0   block5_pool[0][0]     
____________________________________________________________________________________________________ 
fc1 (Dense)      (None, 4096)   102764544 flatten[0][0]      
____________________________________________________________________________________________________ 
dropout_1 (Dropout)    (None, 4096)   0   fc1[0][0]       
____________________________________________________________________________________________________ 
fc2 (Dense)      (None, 4096)   16781312 dropout_1[0][0]     
____________________________________________________________________________________________________ 
dropout_2 (Dropout)    (None, 4096)   0   fc2[1][0]       
____________________________________________________________________________________________________ 
predictions (Dense)    (None, 1000)   4097000  dropout_2[0][0]     
==================================================================================================== 
Total params: 138,357,544 
Trainable params: 138,357,544 
Non-trainable params: 0 
____________________________________________________________________________________________________