How to create a dynamic number of layers in Tensorflow?

Question

In Keras, I would do the following to dynamically create a model's layers:

for i in range(number_dense_layers):
        model.add(layers.Dense(units=units, input_dim=input_dim,
                  kernel_initializer='normal', activation='relu'))

however, in the case of Tensorflow, I have the following:

class generic_vns_function(tf.keras.Model):
    def __init__(self, num_layers, num_class=10): 
        super().__init__() 
        # Convolutional layers and MaxPools

        self.conv1 = tf.keras.layers.Conv2D(64, 3, activation="relu") 
        self.conv2 = tf.keras.layers.Conv2D(64, 3, activation="relu") 

where I would want to do something like:

for i in range(num_layers):
            self.add(tf.keras.layers.Conv2D(64, 3, activation="relu"))

but I am unsure how to dynamically create this layer since the add function does not work in this context as it did in Keras.

Answer 1

You can append first and stack them later.

Here is a rough example:

import tensorflow as tf

class generic_vns_function(tf.keras.Model):
    def __init__(self, num_layers, num_class=10): 
        super().__init__() 
        self.convolutions = []
        ...
        for i in range(num_layers):
          self.convolutions.append(tf.keras.layers.Conv2D(64, 3, activation="relu"))

    def call(self, inputs):
      ...
      for layer in self.convolutions:
        x = layer(x)
      ...
      return x