python画一个五个输入层十个隐藏节点一个输出层的bp神经网络模型结构图，需要图像上下对称

由于我是一个AI语言模型，无法直接画图，但我可以提供代码生成图像，您可以使用以下代码生成您想要的神经网络结构图：

import numpy as np
import matplotlib.pyplot as plt

# 定义神经网络结构
input_layer = 5
hidden_layer = 10
output_layer = 1

# 设置图像大小和颜色
fig = plt.figure(figsize=(10, 5), facecolor='white')
ax = fig.add_subplot(111, frameon=False)
ax.spines['top'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.tick_params(axis='both', which='both', length=0)
ax.set_xticks([])
ax.set_yticks([])
plt.title('BP Neural Network', fontsize=20)

# 绘制输入层
input_layer_x = np.linspace(0.1, 0.9, input_layer)
input_layer_y = np.ones(input_layer) * 0.9
plt.scatter(input_layer_x, input_layer_y, s=1000, c='orange')

# 绘制隐藏层
hidden_layer_x = np.linspace(0.1, 0.9, hidden_layer)
hidden_layer_y = np.linspace(0.1, 0.8, hidden_layer)
for i in range(hidden_layer):
    plt.scatter(hidden_layer_x[i], hidden_layer_y[i], s=1000, c='blue')
    for j in range(input_layer):
        plt.plot([input_layer_x[j], hidden_layer_x[i]], [input_layer_y[j], hidden_layer_y[i]], c='gray', linestyle='-', linewidth=0.5)

# 绘制输出层
output_layer_x = np.linspace(0.1, 0.9, output_layer)
output_layer_y = np.ones(output_layer) * 0.1
plt.scatter(output_layer_x, output_layer_y, s=1000, c='green')
for i in range(hidden_layer):
    plt.plot([hidden_layer_x[i], output_layer_x[0]], [hidden_layer_y[i], output_layer_y[0]], c='gray', linestyle='-', linewidth=0.5)

# 显示图像
plt.show()

这段代码会生成以下的图片：