pygad.torchga Module

This section of the PyGAD’s library documentation discusses the pygad.torchga module.

The pygad.torchga module has helper a 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.

Steps Summary

The summary of the steps used to train a PyTorch model using PyGAD is 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.

Create PyTorch Model

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.

pygad.torchga.TorchGA Class

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.

__init__()

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.

Instance Attributes

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.

Methods in the TorchGA Class

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

create_population()

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.

Functions in the pygad.torchga Module

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

pygad.torchga.model_weights_as_vector()

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.

pygad.torch.model_weights_as_dict()

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.

pygad.torchga.predict()

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.

Examples

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

Example 1: Regression Example

The next code builds a simple PyTorch model for regression. The next subsections discuss each part in the code.

import torch
import torchga
import pygad

def fitness_func(solution, sol_idx):
    global data_inputs, data_outputs, torch_ga, model, loss_function

    predictions = pygad.torchga.predict(model=model,
                                        solution=solution,
                                        data=data_inputs)

    abs_error = loss_function(predictions, data_outputs).detach().numpy() + 0.00000001

    solution_fitness = 1.0 / abs_error

    return solution_fitness

def callback_generation(ga_instance):
    print("Generation = {generation}".format(generation=ga_instance.generations_completed))
    print("Fitness    = {fitness}".format(fitness=ga_instance.best_solution()[1]))

# Create the PyTorch model.
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)
# print(model)

# Create an instance of the pygad.torchga.TorchGA class to build the initial population.
torch_ga = torchga.TorchGA(model=model,
                           num_solutions=10)

loss_function = torch.nn.L1Loss()

# Data inputs
data_inputs = torch.tensor([[0.02, 0.1, 0.15],
                            [0.7, 0.6, 0.8],
                            [1.5, 1.2, 1.7],
                            [3.2, 2.9, 3.1]])

# Data outputs
data_outputs = torch.tensor([[0.1],
                             [0.6],
                             [1.3],
                             [2.5]])

# Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#pygad-ga-class
num_generations = 250 # Number of generations.
num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool.
initial_population = torch_ga.population_weights # Initial population of network weights

ga_instance = pygad.GA(num_generations=num_generations,
                       num_parents_mating=num_parents_mating,
                       initial_population=initial_population,
                       fitness_func=fitness_func,
                       on_generation=callback_generation)

ga_instance.run()

# After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations.
ga_instance.plot_fitness(title="PyGAD & PyTorch - Iteration vs. Fitness", linewidth=4)

# Returning the details of the best solution.
solution, solution_fitness, solution_idx = ga_instance.best_solution()
print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness))
print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx))

# Make predictions based on the best solution.
predictions = pygad.torchga.predict(model=model,
                                    solution=solution,
                                    data=data_inputs)
print("Predictions : \n", predictions.detach().numpy())

abs_error = loss_function(predictions, data_outputs)
print("Absolute Error : ", abs_error.detach().numpy())

Create a PyTorch model

According to the steps mentioned previously, the first step is to create a PyTorch model. Here is the code that builds the model using the Functional API.

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)

Create an Instance of the pygad.torchga.TorchGA Class

The second step is to create an instance of the pygad.torchga.TorchGA class. There are 10 solutions per population. Change this number according to your needs.

import pygad.torchga

torch_ga = torchga.TorchGA(model=model,
                           num_solutions=10)

Prepare the Training Data

The third step is to prepare the training data inputs and outputs. Here is an example where there are 4 samples. Each sample has 3 inputs and 1 output.

import numpy

# Data inputs
data_inputs = numpy.array([[0.02, 0.1, 0.15],
                           [0.7, 0.6, 0.8],
                           [1.5, 1.2, 1.7],
                           [3.2, 2.9, 3.1]])

# Data outputs
data_outputs = numpy.array([[0.1],
                            [0.6],
                            [1.3],
                            [2.5]])

Build the Fitness Function

The fourth step is to build the fitness function. This function must accept 2 parameters representing the solution and its index within the population.

The next fitness function calculates the mean absolute error (MAE) of the PyTorch model based on the parameters in the solution. The reciprocal of the MAE is used as the fitness value. Feel free to use any other loss function to calculate the fitness value.

loss_function = torch.nn.L1Loss()

def fitness_func(solution, sol_idx):
    global data_inputs, data_outputs, torch_ga, model, loss_function

    predictions = pygad.torchga.predict(model=model,
                                        solution=solution,
                                        data=data_inputs)

    abs_error = loss_function(predictions, data_outputs).detach().numpy() + 0.00000001

    solution_fitness = 1.0 / abs_error

    return solution_fitness

Create an Instance of the pygad.GA Class

The fifth step is to instantiate the pygad.GA class. Note how the initial_population parameter is assigned to the initial weights of the PyTorch models.

For more information, please check the parameters this class accepts.

# Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#pygad-ga-class
num_generations = 250 # Number of generations.
num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool.
initial_population = torch_ga.population_weights # Initial population of network weights

ga_instance = pygad.GA(num_generations=num_generations,
                       num_parents_mating=num_parents_mating,
                       initial_population=initial_population,
                       fitness_func=fitness_func,
                       on_generation=callback_generation)

Run the Genetic Algorithm

The sixth and last step is to run the genetic algorithm by calling the run() method.

ga_instance.run()

After the PyGAD completes its execution, then there is a figure that shows how the fitness value changes by generation. Call the plot_fitness() method to show the figure.

ga_instance.plot_fitness(title="PyGAD & PyTorch - Iteration vs. Fitness", linewidth=4)

Here is the figure.

To get information about the best solution found by PyGAD, use the best_solution() method.

# Returning the details of the best solution.
solution, solution_fitness, solution_idx = ga_instance.best_solution()
print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness))
print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx))
Fitness value of the best solution = 145.42425295191546
Index of the best solution : 0

The next code restores the trained model weights using the model_weights_as_dict() function. The restored weights are used to calculate the predicted values.

predictions = pygad.torchga.predict(model=model,
                                    solution=solution,
                                    data=data_inputs)
print("Predictions : \n", predictions.detach().numpy())
Predictions :
[[0.08401088]
 [0.60939324]
 [1.3010881 ]
 [2.5010352 ]]

The next code measures the trained model error.

abs_error = loss_function(predictions, data_outputs)
print("Absolute Error : ", abs_error.detach().numpy())
Absolute Error :  0.006876422

Example 2: XOR Binary Classification

The next code creates a PyTorch model to build the XOR binary classification problem. Let’s highlight the changes compared to the previous example.

import torch
import torchga
import pygad

def fitness_func(solution, sol_idx):
    global data_inputs, data_outputs, torch_ga, model, loss_function

    predictions = pygad.torchga.predict(model=model,
                                        solution=solution,
                                        data=data_inputs)

    solution_fitness = 1.0 / (loss_function(predictions, data_outputs).detach().numpy() + 0.00000001)

    return solution_fitness

def callback_generation(ga_instance):
    print("Generation = {generation}".format(generation=ga_instance.generations_completed))
    print("Fitness    = {fitness}".format(fitness=ga_instance.best_solution()[1]))

# Create the PyTorch model.
input_layer  = torch.nn.Linear(2, 4)
relu_layer = torch.nn.ReLU()
dense_layer = torch.nn.Linear(4, 2)
output_layer = torch.nn.Softmax(1)

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

# Create an instance of the pygad.torchga.TorchGA class to build the initial population.
torch_ga = torchga.TorchGA(model=model,
                           num_solutions=10)

loss_function = torch.nn.BCELoss()

# XOR problem inputs
data_inputs = torch.tensor([[0.0, 0.0],
                            [0.0, 1.0],
                            [1.0, 0.0],
                            [1.0, 1.0]])

# XOR problem outputs
data_outputs = torch.tensor([[1.0, 0.0],
                             [0.0, 1.0],
                             [0.0, 1.0],
                             [1.0, 0.0]])

# Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#pygad-ga-class
num_generations = 250 # Number of generations.
num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool.
initial_population = torch_ga.population_weights # Initial population of network weights.

# Create an instance of the pygad.GA class
ga_instance = pygad.GA(num_generations=num_generations,
                       num_parents_mating=num_parents_mating,
                       initial_population=initial_population,
                       fitness_func=fitness_func,
                       on_generation=callback_generation)

# Start the genetic algorithm evolution.
ga_instance.run()

# After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations.
ga_instance.plot_fitness(title="PyGAD & PyTorch - Iteration vs. Fitness", linewidth=4)

# Returning the details of the best solution.
solution, solution_fitness, solution_idx = ga_instance.best_solution()
print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness))
print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx))

# Make predictions based on the best solution.
predictions = pygad.torchga.predict(model=model,
                                    solution=solution,
                                    data=data_inputs)
print("Predictions : \n", predictions.detach().numpy())

# Calculate the binary crossentropy for the trained model.
print("Binary Crossentropy : ", loss_function(predictions, data_outputs).detach().numpy())

# Calculate the classification accuracy of the trained model.
a = torch.max(predictions, axis=1)
b = torch.max(data_outputs, axis=1)
accuracy = torch.sum(a.indices == b.indices) / len(data_outputs)
print("Accuracy : ", accuracy.detach().numpy())

Compared to the previous regression example, here are the changes:

  • The PyTorch model is changed according to the nature of the problem. Now, it has 2 inputs and 2 outputs with an in-between hidden layer of 4 neurons.
input_layer  = torch.nn.Linear(2, 4)
relu_layer = torch.nn.ReLU()
dense_layer = torch.nn.Linear(4, 2)
output_layer = torch.nn.Softmax(1)

model = torch.nn.Sequential(input_layer,
                            relu_layer,
                            dense_layer,
                            output_layer)
  • The train data is changed. Note that the output of each sample is a 1D vector of 2 values, 1 for each class.
# XOR problem inputs
data_inputs = torch.tensor([[0.0, 0.0],
                            [0.0, 1.0],
                            [1.0, 0.0],
                            [1.0, 1.0]])

# XOR problem outputs
data_outputs = torch.tensor([[1.0, 0.0],
                             [0.0, 1.0],
                             [0.0, 1.0],
                             [1.0, 0.0]])
  • The fitness value is calculated based on the binary cross entropy.
loss_function = torch.nn.BCELoss()

After the previous code completes, the next figure shows how the fitness value change by generation.

Here is some information about the trained model. Its fitness value is 100000000.0, loss is 0.0 and accuracy is 100%.

Fitness value of the best solution = 100000000.0

Index of the best solution : 0

Predictions :
[[1.0000000e+00 1.3627675e-10]
 [3.8521746e-09 1.0000000e+00]
 [4.2789325e-10 1.0000000e+00]
 [1.0000000e+00 3.3668417e-09]]

Binary Crossentropy :  0.0

Accuracy :  1.0

Example 3: Image Multi-Class Classification (Dense Layers)

Here is the code.

import torch
import torchga
import pygad
import numpy

def fitness_func(solution, sol_idx):
    global data_inputs, data_outputs, torch_ga, model, loss_function

    predictions = pygad.torchga.predict(model=model,
                                        solution=solution,
                                        data=data_inputs)

    solution_fitness = 1.0 / (loss_function(predictions, data_outputs).detach().numpy() + 0.00000001)

    return solution_fitness

def callback_generation(ga_instance):
    print("Generation = {generation}".format(generation=ga_instance.generations_completed))
    print("Fitness    = {fitness}".format(fitness=ga_instance.best_solution()[1]))

# Build the PyTorch model using the functional API.
input_layer = torch.nn.Linear(360, 50)
relu_layer = torch.nn.ReLU()
dense_layer = torch.nn.Linear(50, 4)
output_layer = torch.nn.Softmax(1)

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

# Create an instance of the pygad.torchga.TorchGA class to build the initial population.
torch_ga = torchga.TorchGA(model=model,
                           num_solutions=10)

loss_function = torch.nn.CrossEntropyLoss()

# Data inputs
data_inputs = torch.from_numpy(numpy.load("dataset_features.npy")).float()

# Data outputs
data_outputs = torch.from_numpy(numpy.load("outputs.npy")).long()
# The next 2 lines are equivelant to this Keras function to perform 1-hot encoding: tensorflow.keras.utils.to_categorical(data_outputs)
# temp_outs = numpy.zeros((data_outputs.shape[0], numpy.unique(data_outputs).size), dtype=numpy.uint8)
# temp_outs[numpy.arange(data_outputs.shape[0]), numpy.uint8(data_outputs)] = 1

# Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#pygad-ga-class
num_generations = 200 # Number of generations.
num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool.
initial_population = torch_ga.population_weights # Initial population of network weights.

# Create an instance of the pygad.GA class
ga_instance = pygad.GA(num_generations=num_generations,
                       num_parents_mating=num_parents_mating,
                       initial_population=initial_population,
                       fitness_func=fitness_func,
                       on_generation=callback_generation)

# Start the genetic algorithm evolution.
ga_instance.run()

# After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations.
ga_instance.plot_fitness(title="PyGAD & PyTorch - Iteration vs. Fitness", linewidth=4)

# Returning the details of the best solution.
solution, solution_fitness, solution_idx = ga_instance.best_solution()
print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness))
print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx))

# Fetch the parameters of the best solution.
best_solution_weights = torchga.model_weights_as_dict(model=model,
                                                        weights_vector=solution)
model.load_state_dict(best_solution_weights)
predictions = model(data_inputs)
# print("Predictions : \n", predictions)

# Calculate the crossentropy loss of the trained model.
print("Crossentropy : ", loss_function(predictions, data_outputs).detach().numpy())

# Calculate the classification accuracy for the trained model.
accuracy = torch.sum(torch.max(predictions, axis=1).indices == data_outputs) / len(data_outputs)
print("Accuracy : ", accuracy.detach().numpy())

Compared to the previous binary classification example, this example has multiple classes (4) and thus the loss is measured using cross entropy.

loss_function = torch.nn.CrossEntropyLoss()

Prepare the Training Data

Before building and training neural networks, the training data (input and output) needs to be prepared. The inputs and the outputs of the training data are NumPy arrays.

The data used in this example is available as 2 files:

  1. dataset_features.npy: Data inputs. https://github.com/ahmedfgad/NumPyANN/blob/master/dataset_features.npy
  2. outputs.npy: Class labels. https://github.com/ahmedfgad/NumPyANN/blob/master/outputs.npy

The data consists of 4 classes of images. The image shape is (100, 100, 3). The number of training samples is 1962. The feature vector extracted from each image has a length 360.

import numpy

data_inputs = numpy.load("dataset_features.npy")

data_outputs = numpy.load("outputs.npy")

The next figure shows how the fitness value changes.

Here are some statistics about the trained model.

Fitness value of the best solution = 1.3446997034434534
Index of the best solution : 0
Crossentropy :  0.74366045
Accuracy :  1.0

Example 4: Image Multi-Class Classification (Conv Layers)

Compared to the previous example that uses only dense layers, this example uses convolutional layers to classify the same dataset.

Here is the complete code.

import torch
import torchga
import pygad
import numpy

def fitness_func(solution, sol_idx):
    global data_inputs, data_outputs, torch_ga, model, loss_function

    predictions = pygad.torchga.predict(model=model,
                                        solution=solution,
                                        data=data_inputs)

    solution_fitness = 1.0 / (loss_function(predictions, data_outputs).detach().numpy() + 0.00000001)

    return solution_fitness

def callback_generation(ga_instance):
    print("Generation = {generation}".format(generation=ga_instance.generations_completed))
    print("Fitness    = {fitness}".format(fitness=ga_instance.best_solution()[1]))

# Build the PyTorch model.
input_layer = torch.nn.Conv2d(in_channels=3, out_channels=5, kernel_size=7)
relu_layer1 = torch.nn.ReLU()
max_pool1 = torch.nn.MaxPool2d(kernel_size=5, stride=5)

conv_layer2 = torch.nn.Conv2d(in_channels=5, out_channels=3, kernel_size=3)
relu_layer2 = torch.nn.ReLU()

flatten_layer1 = torch.nn.Flatten()
# The value 768 is pre-computed by tracing the sizes of the layers' outputs.
dense_layer1 = torch.nn.Linear(in_features=768, out_features=15)
relu_layer3 = torch.nn.ReLU()

dense_layer2 = torch.nn.Linear(in_features=15, out_features=4)
output_layer = torch.nn.Softmax(1)

model = torch.nn.Sequential(input_layer,
                            relu_layer1,
                            max_pool1,
                            conv_layer2,
                            relu_layer2,
                            flatten_layer1,
                            dense_layer1,
                            relu_layer3,
                            dense_layer2,
                            output_layer)

# Create an instance of the pygad.torchga.TorchGA class to build the initial population.
torch_ga = torchga.TorchGA(model=model,
                           num_solutions=10)

loss_function = torch.nn.CrossEntropyLoss()

# Data inputs
data_inputs = torch.from_numpy(numpy.load("dataset_inputs.npy")).float()
data_inputs = data_inputs.reshape((data_inputs.shape[0], data_inputs.shape[3], data_inputs.shape[1], data_inputs.shape[2]))

# Data outputs
data_outputs = torch.from_numpy(numpy.load("dataset_outputs.npy")).long()

# Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#pygad-ga-class
num_generations = 200 # Number of generations.
num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool.
initial_population = torch_ga.population_weights # Initial population of network weights.

# Create an instance of the pygad.GA class
ga_instance = pygad.GA(num_generations=num_generations,
                       num_parents_mating=num_parents_mating,
                       initial_population=initial_population,
                       fitness_func=fitness_func,
                       on_generation=callback_generation)

# Start the genetic algorithm evolution.
ga_instance.run()

# After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations.
ga_instance.plot_fitness(title="PyGAD & PyTorch - Iteration vs. Fitness", linewidth=4)

# Returning the details of the best solution.
solution, solution_fitness, solution_idx = ga_instance.best_solution()
print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness))
print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx))

# Make predictions based on the best solution.
predictions = pygad.torchga.predict(model=model,
                                    solution=solution,
                                    data=data_inputs)
# print("Predictions : \n", predictions)

# Calculate the crossentropy for the trained model.
print("Crossentropy : ", loss_function(predictions, data_outputs).detach().numpy())

# Calculate the classification accuracy for the trained model.
accuracy = torch.sum(torch.max(predictions, axis=1).indices == data_outputs) / len(data_outputs)
print("Accuracy : ", accuracy.detach().numpy())

Compared to the previous example, the only change is that the architecture uses convolutional and max-pooling layers. The shape of each input sample is 100x100x3.

input_layer = torch.nn.Conv2d(in_channels=3, out_channels=5, kernel_size=7)
relu_layer1 = torch.nn.ReLU()
max_pool1 = torch.nn.MaxPool2d(kernel_size=5, stride=5)

conv_layer2 = torch.nn.Conv2d(in_channels=5, out_channels=3, kernel_size=3)
relu_layer2 = torch.nn.ReLU()

flatten_layer1 = torch.nn.Flatten()
# The value 768 is pre-computed by tracing the sizes of the layers' outputs.
dense_layer1 = torch.nn.Linear(in_features=768, out_features=15)
relu_layer3 = torch.nn.ReLU()

dense_layer2 = torch.nn.Linear(in_features=15, out_features=4)
output_layer = torch.nn.Softmax(1)

model = torch.nn.Sequential(input_layer,
                            relu_layer1,
                            max_pool1,
                            conv_layer2,
                            relu_layer2,
                            flatten_layer1,
                            dense_layer1,
                            relu_layer3,
                            dense_layer2,
                            output_layer)

Prepare the Training Data

The data used in this example is available as 2 files:

  1. dataset_inputs.npy: Data inputs. https://github.com/ahmedfgad/NumPyCNN/blob/master/dataset_inputs.npy
  2. dataset_outputs.npy: Class labels. https://github.com/ahmedfgad/NumPyCNN/blob/master/dataset_outputs.npy

The data consists of 4 classes of images. The image shape is (100, 100, 3) and there are 20 images per class for a total of 80 training samples. For more information about the dataset, check the Reading the Data section of the pygad.cnn module.

Simply download these 2 files and read them according to the next code.

import numpy

data_inputs = numpy.load("dataset_inputs.npy")

data_outputs = numpy.load("dataset_outputs.npy")

The next figure shows how the fitness value changes.

Here are some statistics about the trained model. The model accuracy is 97.5% after the 200 generations. Note that just running the code again may give different results.

Fitness value of the best solution = 1.3009520689219258
Index of the best solution : 0
Crossentropy :  0.7686678
Accuracy :  0.975