我有一个tf.keras.Model的子类模型，代码如下：
import tensorflow as tfclass Mymodel(tf.keras.Model): def __init__(self, classes, backbone_model, *args, **kwargs): super(Mymodel, self).__init__(self, args, kwargs) self.backbone = backbone_model self.classify_layer = tf.keras.layers.Dense(classes,activation='sigmoid') def call(self, inputs): x = self.backbone(inputs) x = self.classify_layer(x) return xinputs = tf.keras.Input(shape=(224, 224, 3))model = Mymodel(inputs=inputs, classes=61, backbone_model=tf.keras.applications.MobileNet())model.build(input_shape=(20, 224, 224, 3))model.summary()结果是：
_________________________________________________________________Layer (type) Output Shape Param # =================================================================mobilenet_1.00_224 (Model) (None, 1000) 4253864 _________________________________________________________________dense (Dense) multiple 61061 =================================================================Total params: 4,314,925Trainable params: 4,293,037Non-trainable params: 21,888_________________________________________________________________但我想查看mobilenet的所有层，于是我尝试提取mobilenet的所有层并放入模型中：
import tensorflow as tfclass Mymodel(tf.keras.Model): def __init__(self, classes, backbone_model, *args, **kwargs): super(Mymodel, self).__init__(self, args, kwargs) self.backbone = backbone_model self.classify_layer = tf.keras.layers.Dense(classes,activation='sigmoid') def my_process_layers(self,inputs): layers = self.backbone.layers tmp_x = inputs for i in range(1,len(layers)): tmp_x = layers[i](tmp_x) return tmp_x def call(self, inputs): x = self.my_process_layers(inputs) x = self.classify_layer(x) return xinputs = tf.keras.Input(shape=(224, 224, 3))model = Mymodel(inputs=inputs, classes=61, backbone_model=tf.keras.applications.MobileNet())model.build(input_shape=(20, 224, 224, 3))model.summary()结果没有变化。
_________________________________________________________________Layer (type) Output Shape Param # =================================================================mobilenet_1.00_224 (Model) (None, 1000) 4253864 _________________________________________________________________dense (Dense) multiple 61061 =================================================================Total params: 4,314,925Trainable params: 4,293,037Non-trainable params: 21,888_________________________________________________________________然后我尝试提取一层插入到模型中：
结果也没有变化。我很困惑。
_________________________________________________________________Layer (type) Output Shape Param # =================================================================mobilenet_1.00_224 (Model) (None, 1000) 4253864 _________________________________________________________________dense (Dense) multiple 244 =================================================================Total params: 4,254,108Trainable params: 4,232,220Non-trainable params: 21,888_________________________________________________________________但我发现Dense层的参数变了，我不知道发生了什么。
回答：
为了能够查看backbone的层，你需要使用backbone.input和backbone.output来构建你的新模型
from tensorflow.keras.models import Modeldef Mymodel(backbone_model, classes): backbone = backbone_model x = backbone.output x = tf.keras.layers.Dense(classes,activation='sigmoid')(x) model = Model(inputs=backbone.input, outputs=x) return modelinput_shape = (224, 224, 3)model = Mymodel(backbone_model=tf.keras.applications.MobileNet(input_shape=input_shape, include_top=False, pooling='avg'), classes=61)model.summary()