一、使用背景
在使用 keras 进行 finetune 有时需要冻结一些网络层加速训练
keras中提供冻结单个层的方法：layer.trainable = False
二、冻结 model 所有网络层
base_model = DenseNet121(include_top=False, weights="imagenet", input_shape=(224, 224, 3))
for layer in base_model.layers:
layer.trainable = False
三、冻结 model 某些网络层
在 keras 中除了从 model.layers 取得 layer，我们还可以通过 model.get_layer(layer_name) 获取。
base_model = VGG19(weights='imagenet')
base_model.get_layer('block4_pool').trainable = False
如何知道 layer_name？
答案是通过 model.summary() 输出一下
如下所示，最左面一列就是 layer_name（注意是括号外面的）
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) (None, 224, 224, 3) 0
__________________________________________________________________________________________________
NASNet (Model) (None, 7, 7, 1056) 4269716 input_1[0][0]
__________________________________________________________________________________________________
resnet50 (Model) (None, 7, 7, 2048) 23587712 input_1[0][0]
__________________________________________________________________________________________________
densenet121 (Model) (None, 7, 7, 1024) 7037504 input_1[0][0]
__________________________________________________________________________________________________
global_average_pooling2d_1 (Glo (None, 1056) 0 NASNet[1][0]
__________________________________________________________________________________________________
global_average_pooling2d_2 (Glo (None, 2048) 0 resnet50[1][0]
__________________________________________________________________________________________________
global_average_pooling2d_3 (Glo (None, 1024) 0 densenet121[1][0]
__________________________________________________________________________________________________
concatenate_5 (Concatenate) (None, 4128) 0 global_average_pooling2d_1[0][0]
global_average_pooling2d_2[0][0]
global_average_pooling2d_3[0][0]
__________________________________________________________________________________________________
dropout_1 (Dropout) (None, 4128) 0 concatenate_5[0][0]
__________________________________________________________________________________________________
classifier (Dense) (None, 200) 825800 dropout_1[0][0]
==================================================================================================
Total params: 35,720,732
Trainable params: 825,800
Non-trainable params: 34,894,932
__________________________________________________________________________________________________
None