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.

CodePudding user response：

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

CodePudding user response：

It looks like the following is what is needed:

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

where we append in a specific layer to the models layers attribute.