"""Test pygad.gacnn with pygad.GA."""

# Small dummy data

data_inputs = numpy.random.uniform(0, 1, (4, 10, 10, 3))

data_outputs = numpy.array([0, 1, 1, 0])

input_layer = pygad.cnn.Input2D(input_shape=(10, 10, 3))

conv_layer = pygad.cnn.Conv2D(num_filters=2,

kernel_size=3,

previous_layer=input_layer,

activation_function="relu")

flatten_layer = pygad.cnn.Flatten(previous_layer=conv_layer)

dense_layer = pygad.cnn.Dense(num_neurons=2,

previous_layer=flatten_layer,

activation_function="softmax")

model = pygad.cnn.Model(last_layer=dense_layer,

epochs=1,

learning_rate=0.01)

gacnn_instance = pygad.gacnn.GACNN(model=model,

num_solutions=4)

def fitness_func(ga_instance, solution, sol_idx):

predictions = gacnn_instance.population_networks[sol_idx].predict(data_inputs=data_inputs)

correct_predictions = numpy.where(predictions == data_outputs)[0].size

solution_fitness = (correct_predictions/data_outputs.size)*100

return solution_fitness

def callback_generation(ga_instance):

population_matrices = pygad.gacnn.population_as_matrices(population_networks=gacnn_instance.population_networks,

population_vectors=ga_instance.population)

gacnn_instance.update_population_trained_weights(population_trained_weights=population_matrices)

initial_population = pygad.gacnn.population_as_vectors(population_networks=gacnn_instance.population_networks)

ga_instance = pygad.GA(num_generations=2,

num_parents_mating=2,

initial_population=initial_population,

fitness_func=fitness_func,

on_generation=callback_generation,

suppress_warnings=True)

ga_instance.run()

assert ga_instance.run_completed

assert ga_instance.generations_completed == 2