我使用pyGAD Python库提供的遗传算法实现训练了一组神经网络。我编写的代码如下所示：
import numpy as npimport pandas as pdimport matplotlib.pyplot as pltimport pygad.gannimport timeimport pickleret = -1n_sect = 174population_size = 500num_parents_mating = 4 num_generations = 1000mutation_percent = 5parent_selection_type = "rank"crossover_type = "two_points"mutation_type = "random"keep_parents = 1init_range_low = -2init_range_high = 5n_div = 15data = pd.read_csv("delta_results/sub_delta_{}.csv".format(n_sect), index_col=0)data.index = pd.to_datetime(data.index)data = list(data["Delta"])function_inputs = np.array([data[i:i+n_div][:ret] for i in range(0, len(data), n_div)])required_outputs = np.array([[data[i:i+n_div][ret]] for i in range(0, len(data), n_div)])input_layer_size = function_inputs.shape[1]n_hidden_layers = 2hidden_layer_1_size = input_layer_size - 2hidden_layer_2_size = input_layer_size - 4output_layer_size = 1population = pygad.gann.GANN( num_solutions=population_size, num_neurons_input=input_layer_size, num_neurons_output=output_layer_size, num_neurons_hidden_layers=[hidden_layer_1_size, hidden_layer_2_size], # 2 Hidden Layers hidden_activations=["relu", "relu"], output_activation="None")population_vectors = pygad.gann.population_as_vectors(population_networks=population.population_networks)initial_population = population_vectors.copy()def normalize(x): return x/np.linalg.norm(x, ord=2, axis=0, keepdims=True)def fitness(solution, solution_index): prediction = pygad.nn.predict(last_layer=population.population_networks[solution_index], data_inputs=function_inputs, problem_type="regression") prediction = np.array(prediction) error = (prediction+0.0001)-required_outputs fitness = np.nan_to_num((np.abs(error)**(-2))).astype(np.float64) solution_fitness = np.sum(normalize(fitness)) return solution_fitnessdef on_generation(population_instance): global population population_matrices = pygad.gann.population_as_matrices(population_networks=population.population_networks, population_vectors=population_instance.population) population.update_population_trained_weights(population_trained_weights=population_matrices)population_instance = pygad.GA( num_generations=num_generations, num_parents_mating=num_parents_mating, initial_population=initial_population, fitness_func=fitness, mutation_percent_genes=mutation_percent, init_range_low=init_range_low, init_range_high=init_range_high, parent_selection_type=parent_selection_type, crossover_type=crossover_type, mutation_type=mutation_type, keep_parents=keep_parents, on_generation=on_generation)saved_population = pygad.load(filename=".../population_data_v2")best_solution = saved_population.best_solution()print("Population Best Solution Info:\n| Attributes:\n{}\n| Fitness: {}\n| Solution Index: {}".format(best_solution[0], best_solution[1], best_solution[2]))saved_population.plot_result()运行遗传算法后，我将种群数据保存到一个名为population_data_v2.pkl的文件中（上述代码未显示） – 文件成功创建并保存。
然而，一旦我打开文件，我不知道如何从种群中找到最佳神经网络的信息。
我得到的是一个nd.numpy.array类型的解决方案(best_solution[0])，我不知道如何查询它，或者如何传入函数输入并查看最佳解决方案的预测结果。
任何帮助将不胜感激！
回答：
感谢使用PyGAD。
我看到您正确构建了示例。您可以使用以下三个简单步骤轻松使用最佳解决方案进行预测。
请注意，每一代之后，population属性都会更新为最新的种群。这意味着当PyGAD完成所有代数后，最后的种群将保存在population属性中。
步骤1
在您使用pygad.load()函数加载保存的模型后，就像您在适应度函数中所做的那样，您可以使用population属性恢复网络的权重，如下所示：
population_matrices = pygad.gann.population_as_matrices(population_networks=population.population_networks, population_vectors=saved_population.population)population.update_population_trained_weights(population_trained_weights=population_matrices)步骤2
best_solution()方法返回三个输出，其中第三个表示最佳解决方案的索引。您可以使用它进行预测，如下所示：
best_solution = saved_population.best_solution()prediction = pygad.nn.predict(last_layer=population.population_networks[best_solution[2]], data_inputs=function_inputs, problem_type="regression")步骤3
最后，您可以打印预测值：
prediction = np.array(prediction)print("Prediction of the best solution: {pred}".format(pred=prediction))完整代码
根据上述讨论，以下是基于最佳解决方案进行预测的完整代码：
population_matrices = pygad.gann.population_as_matrices(population_networks=population.population_networks, population_vectors=saved_population.population)population.update_population_trained_weights(population_trained_weights=population_matrices)best_solution = saved_population.best_solution()prediction = pygad.nn.predict(last_layer=population.population_networks[best_solution[2]], data_inputs=function_inputs, problem_type="regression")prediction = np.array(prediction)print("Prediction of the best solution: {pred}".format(pred=prediction))如果有任何问题，请告诉我。
再次感谢您使用PyGAD。
