我实际上是想用Keras获得VGG16的Sequential模型版本。功能版本可以通过以下方式获得：
from __future__ import division, print_function
import os, json
from glob import glob
import numpy as np
from scipy import misc, ndimage
from scipy.ndimage.interpolation import zoom
from keras import backend as K
from keras.layers.normalization import BatchNormalization
from keras.utils.data_utils import get_file
from keras.models import Sequential
from keras.layers.core import Flatten, Dense, Dropout, Lambda
from keras.layers.convolutional import Convolution2D, MaxPooling2D, ZeroPadding2D
from keras.layers.pooling import GlobalAveragePooling2D
from keras.optimizers import SGD, RMSprop, Adam
from keras.preprocessing import image
import keras
import keras.applications.vgg16
from keras.layers import Input
input_tensor = Input(shape=(224,224,3))
VGG_model=keras.applications.vgg16.VGG16(weights='imagenet',include_top= True,input_tensor=input_tensor)
其摘要如下：
VGG_model.summary()
Layer (type) Output Shape Param # Connected to
====================================================================================================
input_1 (InputLayer) (None, 224, 224, 3) 0
____________________________________________________________________________________________________
block1_conv1 (Convolution2D) (None, 224, 224, 64) 1792 input_1[0][0]
____________________________________________________________________________________________________
block1_conv2 (Convolution2D) (None, 224, 224, 64) 36928 block1_conv1[0][0]
____________________________________________________________________________________________________
block1_pool (MaxPooling2D) (None, 112, 112, 64) 0 block1_conv2[0][0]
____________________________________________________________________________________________________
block2_conv1 (Convolution2D) (None, 112, 112, 128) 73856 block1_pool[0][0]
____________________________________________________________________________________________________
block2_conv2 (Convolution2D) (None, 112, 112, 128) 147584 block2_conv1[0][0]
____________________________________________________________________________________________________
block2_pool (MaxPooling2D) (None, 56, 56, 128) 0 block2_conv2[0][0]
____________________________________________________________________________________________________
block3_conv1 (Convolution2D) (None, 56, 56, 256) 295168 block2_pool[0][0]
____________________________________________________________________________________________________
block3_conv2 (Convolution2D) (None, 56, 56, 256) 590080 block3_conv1[0][0]
____________________________________________________________________________________________________
block3_conv3 (Convolution2D) (None, 56, 56, 256) 590080 block3_conv2[0][0]
____________________________________________________________________________________________________
block3_pool (MaxPooling2D) (None, 28, 28, 256) 0 block3_conv3[0][0]
____________________________________________________________________________________________________
block4_conv1 (Convolution2D) (None, 28, 28, 512) 1180160 block3_pool[0][0]
____________________________________________________________________________________________________
block4_conv2 (Convolution2D) (None, 28, 28, 512) 2359808 block4_conv1[0][0]
____________________________________________________________________________________________________
block4_conv3 (Convolution2D) (None, 28, 28, 512) 2359808 block4_conv2[0][0]
____________________________________________________________________________________________________
block4_pool (MaxPooling2D) (None, 14, 14, 512) 0 block4_conv3[0][0]
____________________________________________________________________________________________________
block5_conv1 (Convolution2D) (None, 14, 14, 512) 2359808 block4_pool[0][0]
____________________________________________________________________________________________________
block5_conv2 (Convolution2D) (None, 14, 14, 512) 2359808 block5_conv1[0][0]
____________________________________________________________________________________________________
block5_conv3 (Convolution2D) (None, 14, 14, 512) 2359808 block5_conv2[0][0]
____________________________________________________________________________________________________
block5_pool (MaxPooling2D) (None, 7, 7, 512) 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
____________________________________________________________________________________________________
根据这个网站https://github.com/fchollet/keras/issues/3190，它说
Sequential(layers=functional_model.layers)
可以将功能模型转换为顺序模型。但是，如果我这样做：
model = Sequential(layers=VGG_model.layers)
model.summary()
导致
Layer (type) Output Shape Param # Connected to
====================================================================================================
input_1 (InputLayer) (None, 224, 224, 3) 0
____________________________________________________________________________________________________
block1_conv1 (Convolution2D) (None, 224, 224, 64) 1792 input_1[0][0]
input_1[0][0]
input_1[0][0]
____________________________________________________________________________________________________
block1_conv2 (Convolution2D) (None, 224, 224, 64) 36928 block1_conv1[0][0]
block1_conv1[1][0]
block1_conv1[2][0]
____________________________________________________________________________________________________
block1_pool (MaxPooling2D) (None, 112, 112, 64) 0 block1_conv2[0][0]
block1_conv2[1][0]
block1_conv2[2][0]
____________________________________________________________________________________________________
block2_conv1 (Convolution2D) (None, 112, 112, 128) 73856 block1_pool[0][0]
block1_pool[1][0]
block1_pool[2][0]
____________________________________________________________________________________________________
block2_conv2 (Convolution2D) (None, 112, 112, 128) 147584 block2_conv1[0][0]
block2_conv1[1][0]
block2_conv1[2][0]
____________________________________________________________________________________________________
block2_pool (MaxPooling2D) (None, 56, 56, 128) 0 block2_conv2[0][0]
block2_conv2[1][0]
block2_conv2[2][0]
____________________________________________________________________________________________________
block3_conv1 (Convolution2D) (None, 56, 56, 256) 295168 block2_pool[0][0]
block2_pool[1][0]
block2_pool[2][0]
____________________________________________________________________________________________________
block3_conv2 (Convolution2D) (None, 56, 56, 256) 590080 block3_conv1[0][0]
block3_conv1[1][0]
block3_conv1[2][0]
____________________________________________________________________________________________________
block3_conv3 (Convolution2D) (None, 56, 56, 256) 590080 block3_conv2[0][0]
block3_conv2[1][0]
block3_conv2[2][0]
____________________________________________________________________________________________________
block3_pool (MaxPooling2D) (None, 28, 28, 256) 0 block3_conv3[0][0]
block3_conv3[1][0]
block3_conv3[2][0]
____________________________________________________________________________________________________
block4_conv1 (Convolution2D) (None, 28, 28, 512) 1180160 block3_pool[0][0]
block3_pool[1][0]
block3_pool[2][0]
____________________________________________________________________________________________________
block4_conv2 (Convolution2D) (None, 28, 28, 512) 2359808 block4_conv1[0][0]
block4_conv1[1][0]
block4_conv1[2][0]
____________________________________________________________________________________________________
block4_conv3 (Convolution2D) (None, 28, 28, 512) 2359808 block4_conv2[0][0]
block4_conv2[1][0]
block4_conv2[2][0]
____________________________________________________________________________________________________
block4_pool (MaxPooling2D) (None, 14, 14, 512) 0 block4_conv3[0][0]
block4_conv3[1][0]
block4_conv3[2][0]
____________________________________________________________________________________________________
block5_conv1 (Convolution2D) (None, 14, 14, 512) 2359808 block4_pool[0][0]
block4_pool[1][0]
block4_pool[2][0]
____________________________________________________________________________________________________
block5_conv2 (Convolution2D) (None, 14, 14, 512) 2359808 block5_conv1[0][0]
block5_conv1[1][0]
block5_conv1[2][0]
____________________________________________________________________________________________________
block5_conv3 (Convolution2D) (None, 14, 14, 512) 2359808 block5_conv2[0][0]
block5_conv2[1][0]
block5_conv2[2][0]
____________________________________________________________________________________________________
block5_pool (MaxPooling2D) (None, 7, 7, 512) 0 block5_conv3[0][0]
block5_conv3[1][0]
block5_conv3[2][0]
____________________________________________________________________________________________________
flatten (Flatten) (None, 25088) 0 block5_pool[0][0]
block5_pool[1][0]
block5_pool[2][0]
____________________________________________________________________________________________________
fc1 (Dense) (None, 4096) 102764544 flatten[0][0]
flatten[1][0]
flatten[2][0]
____________________________________________________________________________________________________
fc2 (Dense) (None, 4096) 16781312 fc1[0][0]
fc1[1][0]
fc1[2][0]
____________________________________________________________________________________________________
predictions (Dense) (None, 1000) 4097000 fc2[0][0]
fc2[1][0]
fc2[2][0]
====================================================================================================
Total params: 138,357,544
Trainable params: 138,357,544
Non-trainable params: 0
_
这与原始功能模型不同，因为新图层连接到前一层3次。人们说使用功能模型更强大。但我想做的只是弹出最终的预测层。功能模型不能这样做......
答案 0 :(得分：2)
我也一直在努力解决这个问题，以前的海报几乎就在那里，但遗漏了一个特别的细节，以前让我感到难过。 确实，你可以做＆＃34; pop＆＃34;即使是使用Functional API创建的模型，但它还有一些工作要做。
这是我的模特（Just plain vanilla VGG16）
model.summary()
____________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ==================================================================================================== input_6 (InputLayer) (None, 224, 224, 3) 0 ____________________________________________________________________________________________________ block1_conv1 (Convolution2D) (None, 224, 224, 64) 1792 input_6[0][0] ____________________________________________________________________________________________________ block1_conv2 (Convolution2D) (None, 224, 224, 64) 36928 block1_conv1[0][0] ____________________________________________________________________________________________________ block1_pool (MaxPooling2D) (None, 112, 112, 64) 0 block1_conv2[0][0] ____________________________________________________________________________________________________ block2_conv1 (Convolution2D) (None, 112, 112, 128) 73856 block1_pool[0][0] ____________________________________________________________________________________________________ block2_conv2 (Convolution2D) (None, 112, 112, 128) 147584 block2_conv1[0][0] ____________________________________________________________________________________________________ block2_pool (MaxPooling2D) (None, 56, 56, 128) 0 block2_conv2[0][0] ____________________________________________________________________________________________________ block3_conv1 (Convolution2D) (None, 56, 56, 256) 295168 block2_pool[0][0] ____________________________________________________________________________________________________ block3_conv2 (Convolution2D) (None, 56, 56, 256) 590080 block3_conv1[0][0] ____________________________________________________________________________________________________ block3_conv3 (Convolution2D) (None, 56, 56, 256) 590080 block3_conv2[0][0] ____________________________________________________________________________________________________ block3_pool (MaxPooling2D) (None, 28, 28, 256) 0 block3_conv3[0][0] ____________________________________________________________________________________________________ block4_conv1 (Convolution2D) (None, 28, 28, 512) 1180160 block3_pool[0][0] ____________________________________________________________________________________________________ block4_conv2 (Convolution2D) (None, 28, 28, 512) 2359808 block4_conv1[0][0] ____________________________________________________________________________________________________ block4_conv3 (Convolution2D) (None, 28, 28, 512) 2359808 block4_conv2[0][0] ____________________________________________________________________________________________________ block4_pool (MaxPooling2D) (None, 14, 14, 512) 0 block4_conv3[0][0] ____________________________________________________________________________________________________ block5_conv1 (Convolution2D) (None, 14, 14, 512) 2359808 block4_pool[0][0] ____________________________________________________________________________________________________ block5_conv2 (Convolution2D) (None, 14, 14, 512) 2359808 block5_conv1[0][0] ____________________________________________________________________________________________________ block5_conv3 (Convolution2D) (None, 14, 14, 512) 2359808 block5_conv2[0][0] ____________________________________________________________________________________________________ block5_pool (MaxPooling2D) (None, 7, 7, 512) 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 ____________________________________________________________________________________________________
然后我＆＃34;弹出＆＃34;最后一层但不使用pop，只需使用Functional API
#Get the last but one layer/tensor from the old model
last_layer = model.layers[-2].output
#Define the new layer/tensor for the new model
new_model = Dense(2, activation='softmax', name='Binary_predictions')(last_layer)
#Create the new model, with the old models input and the new_model tensor as the output
new_model = Model(model.input, new_model, name='Finetuned_VGG16')
#Set all layers,except the last one to not trainable
for layer in new_model.layers[:-1]: layer.trainable=False
#Compile the new model
new_model.compile(optimizer=Adam(lr=learning_rate),
loss='categorical_crossentropy', metrics=['accuracy'])
#now train with the new outputs (cats and dogs!)
这会创建一个新模型（new_model），最后一层被替换，旧图层被修复（不可训练）。
new_model.summary()
____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
====================================================================================================
input_6 (InputLayer) (None, 224, 224, 3) 0
____________________________________________________________________________________________________
block1_conv1 (Convolution2D) (None, 224, 224, 64) 1792 input_6[0][0]
____________________________________________________________________________________________________
block1_conv2 (Convolution2D) (None, 224, 224, 64) 36928 block1_conv1[0][0]
____________________________________________________________________________________________________
block1_pool (MaxPooling2D) (None, 112, 112, 64) 0 block1_conv2[0][0]
____________________________________________________________________________________________________
block2_conv1 (Convolution2D) (None, 112, 112, 128) 73856 block1_pool[0][0]
____________________________________________________________________________________________________
block2_conv2 (Convolution2D) (None, 112, 112, 128) 147584 block2_conv1[0][0]
____________________________________________________________________________________________________
block2_pool (MaxPooling2D) (None, 56, 56, 128) 0 block2_conv2[0][0]
____________________________________________________________________________________________________
block3_conv1 (Convolution2D) (None, 56, 56, 256) 295168 block2_pool[0][0]
____________________________________________________________________________________________________
block3_conv2 (Convolution2D) (None, 56, 56, 256) 590080 block3_conv1[0][0]
____________________________________________________________________________________________________
block3_conv3 (Convolution2D) (None, 56, 56, 256) 590080 block3_conv2[0][0]
____________________________________________________________________________________________________
block3_pool (MaxPooling2D) (None, 28, 28, 256) 0 block3_conv3[0][0]
____________________________________________________________________________________________________
block4_conv1 (Convolution2D) (None, 28, 28, 512) 1180160 block3_pool[0][0]
____________________________________________________________________________________________________
block4_conv2 (Convolution2D) (None, 28, 28, 512) 2359808 block4_conv1[0][0]
____________________________________________________________________________________________________
block4_conv3 (Convolution2D) (None, 28, 28, 512) 2359808 block4_conv2[0][0]
____________________________________________________________________________________________________
block4_pool (MaxPooling2D) (None, 14, 14, 512) 0 block4_conv3[0][0]
____________________________________________________________________________________________________
block5_conv1 (Convolution2D) (None, 14, 14, 512) 2359808 block4_pool[0][0]
____________________________________________________________________________________________________
block5_conv2 (Convolution2D) (None, 14, 14, 512) 2359808 block5_conv1[0][0]
____________________________________________________________________________________________________
block5_conv3 (Convolution2D) (None, 14, 14, 512) 2359808 block5_conv2[0][0]
____________________________________________________________________________________________________
block5_pool (MaxPooling2D) (None, 7, 7, 512) 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]
____________________________________________________________________________________________________
Binary_predictions (Dense) (None, 2) 8194 fc2[0][0]
====================================================================================================
Total params: 134,268,738
Trainable params: 8,194
Non-trainable params: 134,260,544
棘手的部分是将.output作为最后一层，因为这使得它成为Tensor。然后使用Tensor作为新Dense图层的输入，并使其成为新模型中的最终输出......
希望有帮助...
吞
答案 1 :(得分：1)
你可以＆＃34; pop＆＃34;最后一层只需定义另一个Model，将前一层作为输出：
poppedModel = Model(VGG_model.input,VGG_model.layers[-2].output)
此模型将与原始模型共享完全相同的权重，并且训练将影响两个模型。
您可以在poppedModel之后添加自己的图层（甚至模型），没问题：
popOut = poppedModel(input_tensor)
newLayOut = SomeKerasLayer(blablabla)(popOut)
anotherModel = Model(input_tensor, newLayOut)
#anotherModel will also share weights with poppedModel and VGG_model in the layers they have in common.
但重要的是，如果您打算在anotherModel中训练新图层而不影响VGG权重，则可以使用poppedModel.trainable = False创建poppedModel.layers[i].trainable = False及其中的每一层anotherModel在编译flex: 1 2 10%;之前。