In TensorFlow 2.2.0, a peculiar issue arises when dealing with custom layers where the layer weights do not change as expected during training. This problem particularly occurs with the weighted average when trying to implement custom behavior. This article explores this phenomenon, delves into potential causes, and provides examples and solutions.

Understanding TensorFlow Layers and Weights

TensorFlow’s architecture relies heavily on layers, which are foundational blocks for building neural networks. Each layer in TensorFlow has weights and biases, which are essentially trainable parameters updated during the training process using gradient descent or other optimization techniques.

Custom Layers in TensorFlow:

Custom layers provide the flexibility to define operations that are not available in the standard library. In creating a custom layer, you subclass tf.keras.layers.Layer and define your own logic for the forward pass. The build() method is commonly used for defining and initializing the weights.

Weighted Average and Its Application

Weighted averages are often used in neural networks to combine different outputs or channels with varying levels of importance.

Example Implementation:

• If the weights are not initialized properly, TensorFlow may not recognize them as trainable.
• If there's an issue in the calculation graph such that the gradients do not backpropagate through the custom layer, the weights will remain unchanged.
• Ensure that all custom-defined weights are acknowledged by TensorFlow through the _trainable_weights or using the add_weight() method.