如何使用tf.keras.型号.summary查看父模型中的子模型的层？

Question

我有一个子类模型tf.keras.型号，代码如下

import tensorflow as tf


class 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 x

inputs = 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：

^{pr2}$

但我想看到mobilenet的所有层，然后我试图提取mobilenet的所有层并将其放入模型中：

import tensorflow as tf


class 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 x

inputs = 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()

结果没有改变。在

    _________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
mobilenet_1.00_224 (Model)   (None, 1000)              4253864   
_________________________________________________________________
dense (Dense)                multiple                  61061     
=================================================================
Total params: 4,314,925
Trainable params: 4,293,037
Non-trainable params: 21,888
_________________________________________________________________

然后我试图提取一个层插入到模型中：

import tensorflow as tf


class 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.layers[1](inputs)
        x = self.classify_layer(x)
        return x

inputs = 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()

它也没有改变。我很困惑。在

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
mobilenet_1.00_224 (Model)   (None, 1000)              4253864   
_________________________________________________________________
dense (Dense)                multiple                  244       
=================================================================
Total params: 4,254,108
Trainable params: 4,232,220
Non-trainable params: 21,888
_________________________________________________________________

但是我发现致密层的参数发生了变化，我不知道发生了什么。在

Answer 1

为了能够查看主干层，您必须使用backbone.input和backbone.output构造新模型

from tensorflow.keras.models import Model
def  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 model

input_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()