告诉Keras合并和训练不从输入下降的层？

Question

考虑一下

import tensorflow as tf
units=11

entrada=tf.keras.Input(name="entrada", shape=(units,))
unidad= tf.Variable([[1.0]]) # + 0.0* entrada[:,:1]
denseSoftmax=tf.keras.layers.Dense(units,name="denseSoftmax",activation="softmax")
softMaxOutput=denseSoftmax(unidad)
finalproduct=tf.keras.layers.Multiply()([entrada,softMaxOutput])
modelo=tf.keras.Model(entrada,finalproduct)
modelo.summary()

此示例生成一个没有可训练参数的模型，因为denseSoftMax层在输入中不起作用。如果我通过取消注释+ 0.0 * entrada[:,:1]来伪造它，那么它将生成预期的图形。

 Layer (type)                   Output Shape         Param #     Connected to                     
==================================================================================================
 entrada (InputLayer)           [(None, 11)]         0           []                               
 tf.__operators__.getitem (Slic  (None, 1)           0           ['entrada[0][0]']                
 ingOpLambda)                                                                                     
 tf.math.multiply (TFOpLambda)  (None, 1)            0           ['tf.__operators__.getitem[0][0]'
 tf.__operators__.add (TFOpLamb  (None, 1)           0           ['tf.math.multiply[0][0]']       
 denseSoftmax (Dense)           (None, 11)           22          ['tf.__operators__.add[0][0]']   
 multiply (Multiply)            (None, 11)           0           ['entrada[0][0]',                
                                                                  'denseSoftmax[0][0]']        

但是，伪造输入的零值链接就像在输入层集合中添加一个常量分支一样糟糕。

是否有一种方法可以向keras宣布，它应该跟随一系列将与结果输出合并的层的子图，但不依赖于输入？

Answer 1

下面这个是你想要的吗？

class CustomModel(tf.keras.Model):
    def __init__(self,units) -> None:
        super().__init__()
        self.entrada = tf.keras.layers.InputLayer(input_shape=(units,))
        self.unidad= tf.Variable([[1.0]])
        self.denseSoftmax = tf.keras.layers.Dense(units,name="denseSoftmax",activation="softmax")
        self.finalproduct = tf.keras.layers.Multiply()
    def call(self,inputs):
        x = self.entrada(inputs)
        softMaxOutput = self.denseSoftmax(self.unidad)
        y = self.finalproduct([x,softMaxOutput])
        return y 
units = 11
modelo = CustomModel(units=units)
modelo.build(input_shape=(None,units))
modelo.summary()

Model: "custom_model"
_________________________________________________________________
 Layer (type)                Output Shape              Param #
=================================================================
 input_1 (InputLayer)        [(None, 11)]              0

 denseSoftmax (Dense)        multiple                  22

 multiply (Multiply)         multiple                  0

=================================================================
Total params: 23
Trainable params: 23
Non-trainable params: 0
_________________________________________________________________