如何从下面的架构中提取瓶颈层？

Question

我创建了一个模型（如下）。训练后，我想从该模型的瓶颈层获取输出张量。 因此，我尝试创建提取层的模型并使用该模型进行预测。

from keras.layers.convolutional import Conv2D, MaxPooling2D
from keras.models import Sequential
from keras.layers import Dense, Activation

nstrides = (1,1)
inputs = layers.Input(imshape)

conv01 = layers.Conv2D(32, 4, activation = 'relu', 
                       strides = nstrides, padding="same")(inputs)
conv1 = layers.Conv2D(32, 4, activation = 'relu', 
                       strides = nstrides, padding="same")(conv01)
pool1 = layers.MaxPooling2D(pool_size=(2, 2))(conv1)

.
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#block4
conv04 = layers.Conv2D(256, 4, activation = 'relu', 
                       strides = nstrides, padding="same")(pool3)
conv4 = layers.Conv2D(256, 4, activation = 'relu', 
                       strides = nstrides, padding="same")(conv04)
pool4 = layers.MaxPooling2D(pool_size=(2, 2))(conv4)

#bottlneck
conv05 = layers.Conv2D(512, 4, activation = 'relu', 
                       strides = nstrides, padding="same")(pool4)
conv5 = layers.Conv2D(512, 4, activation = 'relu', 
                       strides = nstrides, padding="same")(conv05)
upconv5 = layers.Conv2DTranspose(256, kernel_size=(2, 2), 
                       strides = (2,2))(conv5)

#upblock 1
conc6 = layers.concatenate([upconv5, conv4])
conv06 = layers.Conv2D(256, 4, activation = 'relu', 
                       strides = nstrides, padding="same")(conc6)
conv6 = layers.Conv2D(256, 4, activation = 'relu', 
                       strides = nstrides, padding="same")(conv06)
up7 = layers.Conv2DTranspose(126, kernel_size=(2, 2), 
 strides = (2,2))(conv7)
.
.
.

#combine the model together
model = Model(inputs, outputs)

Answer 1

首先，为了找到所需的层（即新的输出张量），您可以首先执行

for i, layer in enumerate(model.layers):
    print(i, layer.name)
...
...
12 max_pooling2d_15
13 conv2d_65
14 conv2d_66
15 conv2d_transpose_12
16 concatenate_12
17 conv2d_67
...
...

此处，从 13 到 15 的层索引来自于瓶颈层你的模型。如果你想从这个瓶颈层获取输出张量，你可以这样做：

new_model = Model(model.input, 
                model.get_layer(index=15).output)
# or, 
new_model = Model(model.input,
                model.get_layer(name='conv2d_transpose_12').output)

两者都是相同的，第一个是按index，第二个是按层name 。