Trojan horses with custom layers
In addition to lambda layers, custom layers in ML provide a mechanism for encapsulating custom computation or operations within a well-defined structure, allowing for seamless integration and backpropagation within neural network models.
In Keras and the Keras API for TensorFlow, custom layers can be created by subclassing tf.keras.layers.Layer and implementing the necessary methods, such as build and call. This allows for a high degree of flexibility and the ability to include arbitrary operations as part of the model.
Custom layers have an advantage over lambda layers for several reasons. They are available across ML frameworks, address the lambda serialization issues that we discussed in the previous section, and can be hidden in different files and called via innocuous imports.
For more information on custom layers in various ML frameworks, see the following links:
