This section of the documentation discusses the pygad.torchga module.

The pygad.torchga module has a helper class and 2 functions to train PyTorch models using the genetic algorithm (PyGAD).

The contents of this module are:

1. TorchGA: A class for creating an initial population of all parameters in the PyTorch model.
2. model_weights_as_vector(): A function to reshape the PyTorch model weights to a single vector.
3. model_weights_as_dict(): A function to restore the PyTorch model weights from a vector.
4. predict(): A function to make predictions based on the PyTorch model and a solution.

More details are given in the next sections.

The steps used to train a PyTorch model using PyGAD are summarized as follows:

1. Create a PyTorch model.
2. Create an instance of the pygad.torchga.TorchGA class.
3. Prepare the training data.
4. Build the fitness function.
5. Create an instance of the pygad.GA class.
6. Run the genetic algorithm.

Before discussing training a PyTorch model using PyGAD, the first thing to do is to create the PyTorch model. To get started, please check the PyTorch library documentation.

Here is an example of a PyTorch model.

import torch

input_layer = torch.nn.Linear(3, 5)
relu_layer = torch.nn.ReLU()
output_layer = torch.nn.Linear(5, 1)

model = torch.nn.Sequential(input_layer,
                            relu_layer,
                            output_layer)

Feel free to add the layers of your choice.

The pygad.torchga module has a class named TorchGA for creating an initial population for the genetic algorithm based on a PyTorch model. The constructor, methods, and attributes within the class are discussed in this section.

The pygad.torchga.TorchGA class constructor accepts the following parameters:

• model: An instance of the PyTorch model.
• num_solutions: Number of solutions in the population. Each solution has different parameters of the model.

All parameters in the pygad.torchga.TorchGA class constructor are used as instance attributes in addition to adding a new attribute called population_weights.

Here is a list of all instance attributes:

• model
• num_solutions
• population_weights: A nested list holding the weights of all solutions in the population.

This section discusses the methods available for instances of the pygad.torchga.TorchGA class.

The create_population() method creates the initial population of the genetic algorithm as a list of solutions where each solution represents different model parameters. The list of networks is assigned to the population_weights attribute of the instance.

This section discusses the functions in the pygad.torchga module.

The model_weights_as_vector() function accepts a single parameter named model representing the PyTorch model. It returns a vector holding all model weights. The reason for representing the model weights as a vector is that the genetic algorithm expects all parameters of any solution to be in a 1D vector form.

The function accepts the following parameters:

• model: The PyTorch model.

It returns a 1D vector holding the model weights.

The model_weights_as_dict() function accepts the following parameters:

1. model: The PyTorch model.
2. weights_vector: The model parameters as a vector.

It returns the restored model weights in the same form used by the state_dict() method. The returned dictionary is ready to be passed to the load_state_dict() method for setting the PyTorch model's parameters.

The predict() function makes a prediction based on a solution. It accepts the following parameters:

1. model: The PyTorch model.
2. solution: The solution evolved.
3. data: The test data inputs.

It returns the predictions for the data samples.

This section gives the complete code of some examples that build and train a PyTorch model using PyGAD. Each subsection builds a different network.

::::{grid} 1 2 2 2 :gutter: 3

:::{grid-item-card} Example 1: Regression Example :link: torchga_regression :link-type: doc :::

:::{grid-item-card} Example 2: XOR Binary Classification :link: torchga_xor :link-type: doc :::

:::{grid-item-card} Example 3: Image Multi-Class Classification (Dense Layers) :link: torchga_image_dense :link-type: doc :::

:::{grid-item-card} Example 4: Image Multi-Class Classification (Conv Layers) :link: torchga_image_conv :link-type: doc :::

::::

:::{toctree} :hidden:

torchga_regression torchga_xor torchga_image_dense torchga_image_conv :::