带有参数优化的BP神经网络回归预测的python实例
时间: 2023-10-19 11:16:58 浏览: 132
以下是一个带有参数优化的BP神经网络回归预测的Python实例:
```
import numpy as np
import pandas as pd
from sklearn.neural_network import MLPRegressor
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.preprocessing import StandardScaler
# 读取数据
data = pd.read_csv('data.csv')
X = data.iloc[:, :-1].values
y = data.iloc[:, -1].values
# 数据预处理
scaler = StandardScaler()
X = scaler.fit_transform(X)
# 分割训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
# 参数优化
param_grid = {'hidden_layer_sizes': [(10,), (20,), (30,)], 'activation': ['relu', 'tanh'], 'solver': ['adam', 'sgd'], 'max_iter': [1000, 2000]}
model = MLPRegressor()
grid_search = GridSearchCV(model, param_grid, cv=5)
grid_search.fit(X_train, y_train)
best_params = grid_search.best_params_
# 构建模型
model = MLPRegressor(hidden_layer_sizes=best_params['hidden_layer_sizes'], activation=best_params['activation'], solver=best_params['solver'], max_iter=best_params['max_iter'])
model.fit(X_train, y_train)
# 预测结果
y_pred = model.predict(X_test)
# 评估模型
from sklearn.metrics import r2_score
r2_score(y_test, y_pred)
```
在这个例子中,我们首先将数据进行标准化处理,然后将其分割为训练集和测试集。接着,我们使用GridSearchCV类来进行参数优化,通过交叉验证来选择最佳的超参数组合。最后,我们使用最佳的超参数组合来构建模型,使用fit方法训练模型,并使用predict方法来预测测试集结果。最后,我们使用r2_score方法来评估模型性能,这是回归问题中常用的评估指标之一。
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