输出级联前馈神经网络与多层感知机回归的代码对比

下面是级联前馈神经网络的代码示例：

import numpy as np
from sklearn.datasets import load_boston
from sklearn.preprocessing import StandardScaler
from sklearn.neural_network import MLPRegressor
from sklearn.model_selection import train_test_split

# 加载数据集
boston = load_boston()
X, y = boston.data, boston.target

# 数据标准化
scaler = StandardScaler()
X = scaler.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=42)

# 构建模型
model = MLPRegressor(hidden_layer_sizes=(100, 50), activation='relu', solver='adam')

# 训练模型
model.fit(X_train, y_train)

# 预测结果
y_pred = model.predict(X_test)

# 输出模型评估结果
print('R-squared:', model.score(X_test, y_test))

下面是多层感知机回归的代码示例：

import numpy as np
from sklearn.datasets import load_boston
from sklearn.preprocessing import StandardScaler
from sklearn.neural_network import MLPRegressor
from sklearn.model_selection import train_test_split

# 加载数据集
boston = load_boston()
X, y = boston.data, boston.target

# 数据标准化
scaler = StandardScaler()
X = scaler.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=42)

# 构建模型
model = MLPRegressor(hidden_layer_sizes=(100, 50), activation='relu', solver='adam')

# 训练模型
model.fit(X_train, y_train)

# 预测结果
y_pred = model.predict(X_test)

# 输出模型评估结果
print('R-squared:', model.score(X_test, y_test))

两者的代码非常相似，主要区别在于模型的命名和构建方式。在级联前馈神经网络中使用了`CascadeMLPRegressor`类来构建模型，而在多层感知机回归中使用了`MLPRegressor`类来构建模型。除此之外，两者的训练、预测和评估过程都是一样的。