keras：如何阻止卷積層權重

Question

在這里，我有一個針對Keras的GoogleNet模型。 是否有任何可能的方式來阻止網絡各個層的更改？ 我想阻止預訓練模型的前兩層進行更改。

Answer 1

通過“阻止更改各個圖層”，我假設您不想訓練那些圖層，也就是說，您不想修改加載的權重（可能是在以前的訓練中學到的）。

如果是這樣，您可以將trainable=False傳遞給該層，並且該參數將不會用於訓練更新規則。

例：

from keras.models import Sequential
from keras.layers import Dense, Activation

model = Sequential([
    Dense(32, input_dim=100),
    Dense(output_dim=10),
    Activation('sigmoid'),
])

model.summary()

model2 = Sequential([
    Dense(32, input_dim=100,trainable=False),
    Dense(output_dim=10),
    Activation('sigmoid'),
])

model2.summary()

您可以在第二個模型的模型摘要中看到參數被視為不可訓練的參數。

____________________________________________________________________________________________________
Layer (type)                     Output Shape          Param #     Connected to                     
====================================================================================================
dense_1 (Dense)                  (None, 32)            3232        dense_input_1[0][0]              
____________________________________________________________________________________________________
dense_2 (Dense)                  (None, 10)            330         dense_1[0][0]                    
____________________________________________________________________________________________________
activation_1 (Activation)        (None, 10)            0           dense_2[0][0]                    
====================================================================================================
Total params: 3,562
Trainable params: 3,562
Non-trainable params: 0
____________________________________________________________________________________________________
____________________________________________________________________________________________________
Layer (type)                     Output Shape          Param #     Connected to                     
====================================================================================================
dense_3 (Dense)                  (None, 32)            3232        dense_input_2[0][0]              
____________________________________________________________________________________________________
dense_4 (Dense)                  (None, 10)            330         dense_3[0][0]                    
____________________________________________________________________________________________________
activation_2 (Activation)        (None, 10)            0           dense_4[0][0]                    
====================================================================================================
Total params: 3,562
Trainable params: 330
Non-trainable params: 3,232