Use of global variables

What are global variables and why are they useful?

Throughout the implementation of ML models, we constantly face the need of making changes in the architecture of our model. For instance, we might need to change the activation functions or the number of units of our layers. To make these changes, we have two options. First of all, we can simply go back to the model_description.yaml file, and manually change all the entries (for instance of the activation function) for their new value. This is however error-prone and can be a very frustrating task.

To solve this problem, we incorporate a second possibility (optional) which is much easier and faster than the previous, as it is based on the use of global variables. Global variables consist of, instead of hard-coding the specific values of a parameter of our model (e.g., activation function, number of units…), using a reference name for the actual value of such parameter. With this, we manage to potentially keep a separate file with all the specific values of the parameters that our GNN uses. This has several important advantages. First of all, this allows us to have a full picture of the most relevant parameters of our model, which can help debug purposes. Second of all, a single change in this file has an immediate effect on potentially many parts of the GNN architecture. For instance, by simply changing the activation function in this file (one single time), we can change all the activation functions of our model. Finally, this approach opens the doors to the use of automatic grid search -this being the automatic search of the optimal values of the different parameters-, which can have a major impact on the overall performance of our model.

How to adapt my model

To adapt our model, we need only to make minor changes on the model_description.yaml file. Below we will show a mock example of how to adapt several parts of the architecture, and finally, we will show how the Shortest-path example presented in quick tutorial can be adapted to use such a powerful tool.

Basic working principle

The idea consists of replacing any hard-coded value of a parameter (e.g., in the case of activation functions, they could be ReLU, selu…) by a reference name of our choice. Then, we simply need to add an entry in the global_variables.yaml file with its corresponding value. Let us see this with a very simple example of a definition of a Neural Network:

neural_networks:
- nn_name: readout_model
  nn_architecture:
  - type_layer: Dense
    units: my_number_of_units
    kernel_regularizer: my_regularization
    activation: my_activation
  - type_layer: Dense
    units: output_units
    kernel_regularizer: my_regularization
    activation: my_activation

In this example we can observe that the number of units, the kernel_regularizer, and the activation are all parametrized by a reference name (instead of defining its actual value). Then, we must simply define the following lines in the global_variables.yaml file:

my_number_of_units: 32
my_regularization: 0.1
my_activation: selu

This idea can be extended to any other part of the architecture of our GNN from the model_description.yaml file. Note, hence, that by changing any value of the global_variables.yaml file, I would be changing the architecture of all the parts of the GNN that used such reference.

Adaptation of the Shortest-Path example

To further explain how we can use global variables, click here.

In this file, we present the updated version of the model explained in quick start tutorial using the global variables that we defined in the file global_variables.yaml.