在keras中的预训练密集层之间添加辍学图层

1 回答

• 20

  我通过使用Keras functional API找到了答案

  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 有我想要的辍学层

  ____________________________________________________________________________________________________
  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
  ____________________________________________________________________________________________________
  

  回复于 2024-05-03T20:49:50+08:00