我有一个保留摘要的模型：
Layer (type) Output Shape Param #
=================================================================
vgg19 (Model) (None, 4, 4, 512) 20024384
_________________________________________________________________
flatten_1 (Flatten) (None, 8192) 0
_________________________________________________________________
dense_1 (Dense) (None, 1024) 8389632
_________________________________________________________________
dropout_1 (Dropout) (None, 1024) 0
_________________________________________________________________
dense_2 (Dense) (None, 1024) 1049600
_________________________________________________________________
dense_3 (Dense) (None, 5) 5125
=================================================================
我需要vgg19不在单个层中扩展的版本。就像是 ：
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) (None, 128, 128, 3) 0
_________________________________________________________________
block1_conv1 (Conv2D) (None, 128, 128, 64) 1792
_________________________________________________________________
block1_conv2 (Conv2D) (None, 128, 128, 64) 36928
_________________________________________________________________
block1_pool (MaxPooling2D) (None, 64, 64, 64) 0
_________________________________________________________________
block2_conv1 (Conv2D) (None, 64, 64, 128) 73856
.
.
.
** end of vgg16 **
_________________________________________________________________
flatten_1 (Flatten) (None, 8192) 0
_________________________________________________________________
dense_1 (Dense) (None, 1024) 8389632
_________________________________________________________________
dropout_1 (Dropout) (None, 1024) 0
_________________________________________________________________
dense_2 (Dense) (None, 1024) 1049600
_________________________________________________________________
dense_3 (Dense) (None, 5) 5125
=================================================================
我试图逐层复制，但是遇到很多问题。有一种方法可以做到这一点，还可以复制权重吗？
答案 0 :(得分：0)
我不知道您是如何实现的，您可以看到我如何实现的代码。希望对您有所帮助。
from keras.applications.vgg19 import VGG19
from keras.models import Model
from keras.layers import *
model = VGG19(weights='imagenet', include_top=False, input_shape=(128,128,3))
flatten_1 = Flatten()(model.output)
dense_1 = Dense(1024)(flatten_1)
dropout_1 = Dropout(0.2)(dense_1)
dense_2 = Dense(1024)(dropout_1)
dense_3 = Dense(5)(dense_2)
model = Model(inputs=model.input, outputs=dense_3)
print(model.summary())
结果。
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) (None, 128, 128, 3) 0
_________________________________________________________________
block1_conv1 (Conv2D) (None, 128, 128, 64) 1792
_________________________________________________________________
block1_conv2 (Conv2D) (None, 128, 128, 64) 36928
_________________________________________________________________
block1_pool (MaxPooling2D) (None, 64, 64, 64) 0
_________________________________________________________________
block2_conv1 (Conv2D) (None, 64, 64, 128) 73856
_________________________________________________________________
block2_conv2 (Conv2D) (None, 64, 64, 128) 147584
_________________________________________________________________
block2_pool (MaxPooling2D) (None, 32, 32, 128) 0
_________________________________________________________________
block3_conv1 (Conv2D) (None, 32, 32, 256) 295168
_________________________________________________________________
block3_conv2 (Conv2D) (None, 32, 32, 256) 590080
_________________________________________________________________
block3_conv3 (Conv2D) (None, 32, 32, 256) 590080
_________________________________________________________________
block3_conv4 (Conv2D) (None, 32, 32, 256) 590080
_________________________________________________________________
block3_pool (MaxPooling2D) (None, 16, 16, 256) 0
_________________________________________________________________
block4_conv1 (Conv2D) (None, 16, 16, 512) 1180160
_________________________________________________________________
block4_conv2 (Conv2D) (None, 16, 16, 512) 2359808
_________________________________________________________________
block4_conv3 (Conv2D) (None, 16, 16, 512) 2359808
_________________________________________________________________
block4_conv4 (Conv2D) (None, 16, 16, 512) 2359808
_________________________________________________________________
block4_pool (MaxPooling2D) (None, 8, 8, 512) 0
_________________________________________________________________
block5_conv1 (Conv2D) (None, 8, 8, 512) 2359808
_________________________________________________________________
block5_conv2 (Conv2D) (None, 8, 8, 512) 2359808
_________________________________________________________________
block5_conv3 (Conv2D) (None, 8, 8, 512) 2359808
_________________________________________________________________
block5_conv4 (Conv2D) (None, 8, 8, 512) 2359808
_________________________________________________________________
block5_pool (MaxPooling2D) (None, 4, 4, 512) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 8192) 0
_________________________________________________________________
dense_1 (Dense) (None, 1024) 8389632
_________________________________________________________________
dropout_1 (Dropout) (None, 1024) 0
_________________________________________________________________
dense_2 (Dense) (None, 1024) 1049600
_________________________________________________________________
dense_3 (Dense) (None, 5) 5125
=================================================================
Total params: 29,468,741
Trainable params: 29,468,741
Non-trainable params: 0
_________________________________________________________________