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 15:16:59 浏览: 43
好的,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:
```python
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neural_network import MLPRegressor
from sklearn.model_selection import GridSearchCV
# 加载数据集
dataset = pd.read_csv('data.csv')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, -1].values
# 数据预处理
sc = StandardScaler()
X = sc.fit_transform(X)
# 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=0)
# 构建BP神经网络模型
mlp = MLPRegressor(hidden_layer_sizes=(100,), activation='relu', solver='adam', alpha=0.01, max_iter=1000)
# 设置参数范围并进行参数优化
param_grid = {
'hidden_layer_sizes': [(50,), (100,), (150,)],
'activation': ['relu', 'tanh', 'logistic'],
'solver': ['adam', 'sgd', 'lbfgs'],
'alpha': [0.001, 0.01, 0.1],
'max_iter': [500, 1000, 1500]
}
grid_search = GridSearchCV(mlp, param_grid, cv=5, n_jobs=-1)
grid_search.fit(X_train, y_train)
# 输出最佳参数组合
print("Best parameters: ", grid_search.best_params_)
# 使用最优参数进行模型训练和预测
mlp = MLPRegressor(**grid_search.best_params_)
mlp.fit(X_train, y_train)
y_pred = mlp.predict(X_test)
# 输出模型评估指标
from sklearn.metrics import r2_score, mean_squared_error
print("R2 score: ", r2_score(y_test, y_pred))
print("MSE: ", mean_squared_error(y_test, y_pred))
```
在这个例子中,我们首先加载数据集,使用StandardScaler进行标准化处理,然后划分训练集和测试集。接下来,我们构建了一个包含一个隐藏层(100个神经元)的BP神经网络模型,并使用GridSearchCV进行参数优化。最后,我们使用最优参数进行模型训练和预测,并输出了模型评估指标R2 score和MSE。