我通过使用Keras功能API自己找到了答案
from keras.applications import VGG16from keras.layers import Dropoutfrom keras.models import Modelmodel = VGG16(weights='imagenet')# Store the fully connected layersfc1 = model.layers[-3]fc2 = model.layers[-2]predictions = model.layers[-1]# Create the dropout layersdropout1 = Dropout(0.85)dropout2 = Dropout(0.85)# Reconnect the layersx = dropout1(fc1.output)x = fc2(x)x = dropout2(x)predictors = predictions(x)# Create a new modelmodel2 = 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) 0block1_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) 0block2_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) 0block3_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) 0block4_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) 0block5_conv3[0][0] ____________________________________________________________________________________________________flatten (Flatten) (None, 25088) 0block5_pool[0][0] ____________________________________________________________________________________________________fc1 (Dense)(None, 4096) 102764544 flatten[0][0] ____________________________________________________________________________________________________dropout_1 (Dropout) (None, 4096) 0fc1[0][0] ____________________________________________________________________________________________________fc2 (Dense)(None, 4096) 16781312 dropout_1[0][0] ____________________________________________________________________________________________________dropout_2 (Dropout) (None, 4096) 0fc2[1][0] ____________________________________________________________________________________________________predictions (Dense) (None, 1000) 4097000 dropout_2[0][0] ====================================================================================================Total params: 138,357,544Trainable params: 138,357,544Non-trainable params: 0____________________________________________________________________________________________________
