Python实现多层感知机

在Python中实现多层感知机需要使用一些机器学习库，如NumPy、SciPy和TensorFlow等。下面是一个简单的多层感知机实现示例：

首先，我们需要导入所需的库和模块：

import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

接下来，我们加载数据集并进行预处理：

# 加载数据集
iris = load_iris()
X = iris.data
y = iris.target
# 将数据集划分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# 数据标准化处理
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

接下来，我们定义多层感知机模型：

class MLP:
    def __init__(self, n_hidden=10, learning_rate=0.01, n_iterations=1000):
        self.n_hidden = n_hidden
        self.learning_rate = learning_rate
        self.n_iterations = n_iterations
        self.weights1 = None
        self.bias1 = None
        self.weights2 = None
        self.bias2 = None
    def sigmoid(self, x):
        return 1 / (1 + np.exp(-x))
    def sigmoid_derivative(self, x):
        return x * (1 - x)