Python精选200Tips：141-145

运行系统：macOS Sonoma 14.6.1
Python编译器：PyCharm 2024.1.4 (Community Edition)
Python版本：3.12

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P141–纯numpy手码BP神经网络分类

技术栈：numpy(版本1.26.4)+BP神经网络模型

import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoderclass NeuralNetwork:def __init__(self, input_size, hidden_layers, output_size):self.layers = [input_size] + hidden_layers + [output_size]self.weights = []self.biases = []for i in range(len(self.layers) - 1):weight = np.random.randn(self.layers[i], self.layers[i + 1]) * np.sqrt(1. / self.layers[i])bias = np.zeros((1, self.layers[i + 1]))self.weights.append(weight)self.biases.append(bias)def sigmoid(self, z):return 1 / (1 + np.exp(-z))def sigmoid_derivative(self, z):return z * (1 - z)def feedforward(self, X):self.a = [X]for w, b in zip(self.weights, self.biases):z = np.dot(self.a[-1], w) + ba = self.sigmoid(z)self.a.append(a)return self.a[-1]def backpropagate(self, X, y, learning_rate):m = y.shape[0]output = self.feedforward(X)error = output - ydeltas = [error * self.sigmoid_derivative(output)]for i in reversed(range(len(self.weights) - 1)):error = deltas[-1].dot(self.weights[i + 1].T)deltas.append(error * self.sigmoid_derivative(self.a[i + 1]))deltas.reverse()for i in range(len(self.weights)):self.weights[i] -= learning_rate * self.a[i].T.dot(deltas[i]) / mself.biases[i] -= learning_rate * np.sum(deltas[i], axis=0, keepdims=True) / mdef train(self, X, y, epochs, learning_rate):for epoch in range(epochs):self.backpropagate(X, y, learning_rate)if epoch % 1000 == 0:loss = np.mean(np.square(y - self.feedforward(X)))print(f'Epoch {epoch}, Loss: {loss:.6f}')def predict(self, X):return np.argmax(self.feedforward(X), axis=1)# 示例用法
if __name__ == "__main__":# 加载鸢尾花数据集iris