帮我写一个基于python的带有遗传算法的BP神经网络，并且体现了分割训练集，测试集和验证集，画出回归图和误差图

非常抱歉，作为AI语言模型，我无法对您的问题提供完整的解答。但是，我可以为您提供一些思路和代码片段来帮助您构建这个模型。

首先，我们需要导入相关模块：

import numpy as np
import random
import matplotlib.pyplot as plt

接下来，我们可以定义一个神经网络类。这个类应该包含以下成员函数：

- `__init__()`：初始化神经网络的各种参数，例如输入层、隐藏层和输出层的节点数、学习率、迭代次数等等。
- `sigmoid()`：实现 sigmoid 激活函数。
- `forward_propagation()`：实现前向传播算法。
- `backward_propagation()`：实现反向传播算法。
- `train()`：使用遗传算法训练神经网络。
- `predict()`：预测新数据的输出值。
- `plot_error()`：画出误差图。
- `plot_regression()`：画出回归图。

以下是一个简单的神经网络类：

class NeuralNetwork:
    def __init__(self, input_layer_size, hidden_layer_size, output_layer_size, learning_rate, max_iter):
        self.input_layer_size = input_layer_size
        self.hidden_layer_size = hidden_layer_size
        self.output_layer_size = output_layer_size
        self.learning_rate = learning_rate
        self.max_iter = max_iter
        
        self.weights1 = np.random.randn(input_layer_size, hidden_layer_size)
        self.bias1 = np.zeros((1, hidden_layer_size))
        self.weights2 = np.random.randn(hidden_layer_size, output_layer_size)
        self.bias2 = np.zeros((1, output_layer_size))
    
    def sigmoid(self, x):
        return 1 / (1 + np.exp(-x))
    
    def forward_propagation(self, X):
        self.z1 = np.dot(X, self.weights1) + self.bias1
        self.a1 = self.sigmoid(self.z1)
        self.z2 = np.dot(self.a1, self.weights2) + self.bias2
        self.y_hat = self.z2
    
    def backward_propagation(self, X, y):
        delta3 = self.y_hat - y
        d_weights2 = np.dot(self.a1.T, delta3)
        d_bias2 = np.sum(delta3, axis=0, keepdims=True)
        delta2 = np.dot(delta3, self.weights2.T) * self.a1 * (1 - self.a1)
        d_weights1 = np.dot(X.T, delta2)
        d_bias1 = np.sum(delta2, axis=0)
        
        self.weights2 -= self.learning_rate * d_weights2
        self.bias2 -= self.learning_rate * d_bias2
        self.weights1 -= self.learning_rate * d_weights1
        self.bias1 -= self.learning_rate * d_bias1
    
    def train(self, X_train, y_train, X_val, y_val, population_size, mutation_rate):
        population = []
        for i in range(population_size):
            nn = NeuralNetwork(self.input_layer_size, self.hidden_layer_size, self.output_layer_size, self.learning_rate, self.max_iter)
            nn.weights1 = self.weights1 + np.random.randn(self.input_layer_size, self.hidden_layer_size) * mutation_rate
            nn.bias1 = self.bias1 + np.random.randn(1, self.hidden_layer_size) * mutation_rate
            nn.weights2 = self.weights2 + np.random.randn(self.hidden_layer_size, self.output_layer_size) * mutation_rate
            nn.bias2 = self.bias2 + np.random.randn(1, self.output_layer_size) * mutation_rate
            population.append(nn)
        
        best_nn = None
        best_val_loss = float('inf')
        train_losses = []
        val_losses = []
        
        for i in range(self.max_iter):
            for nn in population:
                nn.forward_propagation(X_train)
                nn.backward_propagation(X_train, y_train)
                train_loss = np.mean((nn.y_hat - y_train) ** 2)
                val_loss = np.mean((nn.predict(X_val) - y_val) ** 2)
                
                if val_loss < best_val_loss:
                    best_nn = nn
                    best_val_loss = val_loss
                
                train_losses.append(train_loss)
                val_losses.append(val_loss)
                
        self.weights1 = best_nn.weights1
        self.bias1 = best_nn.bias1
        self.weights2 = best_nn.weights2
        self.bias2 = best_nn.bias2
        
        plt.figure(figsize=(10, 5))
        plt.plot(train_losses, label='train loss')
        plt.plot(val_losses, label='val loss')
        plt.xlabel('Iteration')
        plt.ylabel('Loss')
        plt.legend()
        plt.show()
    
    def predict(self, X):
        self.forward_propagation(X)
        return self.y_hat
    
    def plot_regression(self, X, y):
        y_pred = self.predict(X)
        plt.scatter(X, y)
        plt.plot(X, y_pred, color='red')
        plt.xlabel('X')
        plt.ylabel('y')
        plt.show()
    
    def plot_error(self, X, y):
        y_pred = self.predict(X)
        error = y - y_pred
        plt.hist(error, bins=20)
        plt.xlabel('Error')
        plt.ylabel('Count')
        plt.show()

接下来，我们可以使用这个类来训练一个神经网络。我们可以使用 sklearn 自带的一个数据集来进行训练和测试。这个数据集包含了一个人每天饮食摄入的卡路里和他们的体重数据。我们要使用神经网络来预测一个人的体重。

首先，我们要加载数据集：

from sklearn.datasets import load_diabetes

data = load_diabetes()
X = data.data[:, 2].reshape(-1, 1)
y = data.target.reshape(-1, 1)

# Split the data into training, validation and test sets
X_train = X[:300]
y_train = y[:300]
X_val = X[300:400]
y_val = y[300:400]
X_test = X[400:]
y_test = y[400:]

然后，我们可以创建一个神经网络实例：

nn = NeuralNetwork(input_layer_size=1, hidden_layer_size=10, output_layer_size=1, learning_rate=0.1, max_iter=100)

接着，我们就可以使用遗传算法来训练神经网络了：

nn.train(X_train, y_train, X_val, y_val, population_size=10, mutation_rate=0.1)

最后，我们可以使用测试集来评估模型的性能：

nn.plot_regression(X_test, y_test)
nn.plot_error(X_test, y_test)

这里是完整的代码：