tell Keras to merge and train layers that do not descend from input?

Question

Consider


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()

This example produces a model without trainable parameters, because the denseSoftMax layer does not act in the input. If I fake it by uncommenting + 0.0 * entrada[:,:1] then it produces the expected graph

 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]']

But faking a zero valued link to an input seems as bad as adding a constant branch in the set of input layers.

Is there a way to announce to keras that it should follow the subgraph for a series of layers that are going to be merged with the resulting output, but do not depend on the input?

score 0 · Accepted Answer · answered Jun 14 '22 at 14:32

Is the following your desired?

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
_________________________________________________________________

Amusing. They look the same. – arivero Jun 15 '22 at 12:54 — arivero, Jun 15 '22 at 12:54

tell Keras to merge and train layers that do not descend from input?

1 Answers1