"""Test pygad.kerasga with pygad.GA."""

# XOR data

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

data_outputs = numpy.array([[1, 0], [0, 1], [0, 1], [1, 0]]) # One-hot encoded

input_layer = tensorflow.keras.layers.Input(shape=(2,))

dense_layer = tensorflow.keras.layers.Dense(4, activation="relu")(input_layer)

output_layer = tensorflow.keras.layers.Dense(2, activation="softmax")(dense_layer)

model = tensorflow.keras.Model(inputs=input_layer, outputs=output_layer)

keras_ga = pygad.kerasga.KerasGA(model=model, num_solutions=10)

def fitness_func(ga_instance, solution, solution_idx):

model_weights_matrix = pygad.kerasga.model_weights_as_matrix(model=model,

weights_vector=solution)

model.set_weights(weights=model_weights_matrix)

predictions = model.predict(data_inputs, verbose=0)

cce = tensorflow.keras.losses.CategoricalCrossentropy()

loss = cce(data_outputs, predictions).numpy()

fitness = 1.0 / (loss + 0.00000001)

return fitness

ga_instance = pygad.GA(num_generations=2,

num_parents_mating=5,

initial_population=keras_ga.population_weights,

fitness_func=fitness_func,

suppress_warnings=True)

ga_instance.run()

assert ga_instance.run_completed

assert ga_instance.generations_completed == 2