利用python实现简单的神经网络算法回归分析
2023年亚太杯数学建模C题可以使用这个代码进行分析

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Dense# 读取 Excel 文件数据data = pd.read_excel('这里输入你的excel路径')# 提取输入特征和输出标签X = data[['这里输入你的特征标签']].values
y = data[['这里输入你的输出标签']].values# 数据标准化scaler_x = StandardScaler()
scaler_y = StandardScaler()
X = scaler_x.fit_transform(X)
y = scaler_y.fit_transform(y)# 划分训练集和测试集X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)# 构建神经网络模型input_layer = Input(shape=(X.shape[1],))
dense1 = Dense(64, activation='relu')(input_layer)
dense2 = Dense(32, activation='relu')(dense1)
output_layer = Dense(3)(dense2)  # 输出层有3个节点，对应 a1, h1, J1 三个输出model = Model(inputs=input_layer, outputs=output_layer)# 编译模型model.compile(optimizer='adam', loss='mean_squared_error')# 训练模型model.fit(X_train, y_train, epochs=50, batch_size=32, validation_data=(X_test, y_test))# 打印模型的输入对输出分别的影响程度weights = model.get_weights()
input_weights = weights[0]
output_weights = weights[-1]input_importance = np.abs(input_weights).sum(axis=1) / np.abs(input_weights).sum()
output_importance = np.abs(output_weights).sum(axis=0) / np.abs(output_weights).sum()# 计算特征与输出之间的相关系数correlation_matrix = np.corrcoef(X.T, y.T)
feature_output_correlation = correlation_matrix[:X.shape[1], X.shape[1]:]print('Input Importance:', input_importance)
print('Output Importance:', output_importance)
print('Feature-Output Correlation:')
print(feature_output_correlation)