240727 深度神经网络

红色是实际数据，绿色是预测的点

误差图

# -*- coding: utf-8 -*-
 import neurolab as nl
 import numpy as np
 import matplotlib.pyplot as plt# 生成数据
 min_value = -12
 max_value = 12
 num_datapoints = 90
 x = np.linspace(min_value, max_value, num_datapoints)
 y = 2 * np.square(x) + 7
 y /= np.linalg.norm(y)
 # 数组变形
 data = x.reshape(num_datapoints, 1)
 labels = y.reshape(num_datapoints, 1)# 画出原始数据图
 plt.figure()
 plt.scatter(data, labels)
 plt.xlabel('X-axis')
 plt.ylabel('Y-axis')
 plt.title('Input data')# 定义深度神经网络 每个隐藏层有10个神经元
 multilayer_net = nl.net.newff([[min_value, max_value]], [10, 10, 1])# 设置训练算法为梯度下降
 multilayer_net.trainf = nl.train.train_gd# 训练网络
 error = multilayer_net.train(data, labels, epochs=800, show=100, goal=0.01)# 预测结果
 predicted_output = multilayer_net.sim(data)# 画出误差
 plt.figure()
 plt.plot(error)
 plt.xlabel('Number of epochs')
 plt.ylabel('Error')
 plt.title('Training error progress')# 画出预测结果
 x2 = np.linspace(min_value, max_value, num_datapoints * 2)
 y2 = multilayer_net.sim(x2.reshape(x2.size,1)).reshape(x2.size)
 y3 = predicted_output.reshape(num_datapoints)plt.figure()
 plt.plot(x2, y2, '-', x, y, '.', x, y3, 'p')
 plt.title('Ground truth vs predicted output')plt.show()