神经网络插值计算代码

神经网络插值计算的代码如下：

import numpy as np

def sigmoid(x):
    return 1 / (1 + np.exp(-x))

def neural_network_interpolation(x_train, y_train, x_test, num_hidden_units):
    num_input_units = len(x_train)
    num_output_units = len(x_test)
    
    # 初始化权重
    w1 = np.random.randn(num_hidden_units, num_input_units)
    w2 = np.random.randn(num_output_units, num_hidden_units)
    
    # 前向传播
    hidden_units = sigmoid(np.dot(w1, x_train))
    output_units = np.dot(w2, hidden_units)
    
    # 计算误差
    error = output_units - y_train
    
    # 反向传播更新权重
    d_w2 = np.dot(error, hidden_units.T)
    d_w1 = np.dot(np.dot(w2.T, error) * hidden_units * (1 - hidden_units), x_train.T)
    
    # 更新权重
    learning_rate = 0.01
    w2 -= learning_rate * d_w2
    w1 -= learning_rate * d_w1
    
    # 根据训练好的神经网络进行插值计算
    hidden_units = sigmoid(np.dot(w1, x_test))
    output_units = np.dot(w2, hidden_units)
    
    return output_units

x_train = np.array([1, 2, 3, 4, 5])
y_train = np.array([2, 4, 6, 8, 10])
x_test = np.array([2.5, 3.5])

num_hidden_units = 3

output_units = neural_network_interpolation(x_train, y_train, x_test, num_hidden_units)
print(output_units)