[机器学习]机器学习笔记整理11-神经网络算法简单实现

原理

[机器学习]机器学习笔记整理10- 神经网络算法

1. 关于非线性转化方程(non-linear transformation function)

sigmoid函数(S 曲线)用来作为activation function:

 1.1 双曲函数(tanh)
 1.2  逻辑函数(logistic function)

2. 实现一个简单的神经网络算法



#!/usr/bin/python # -*- coding:utf-8 -*- # 每个图片8x8 识别数字:0,1,2,3,4,5,6,7,8,9 import numpy as np from sklearn.datasets import load_digits from sklearn.metrics import confusion_matrix, classification_report from sklearn.preprocessing import LabelBinarizer from NeuralNetwork import NeuralNetwork from sklearn.cross_validation import train_test_split digits = load_digits() X = digits.data y = digits.target X -= X.min() # normalize the values to bring them into the range 0-1 X /= X.max() nn = NeuralNetwork([64, 100, 10], 'logistic') X_train, X_test, y_train, y_test = train_test_split(X, y) labels_train = LabelBinarizer().fit_transform(y_train) labels_test = LabelBinarizer().fit_transform(y_test) print "start fitting" nn.fit(X_train, labels_train, epochs=3000) predictions = [] for i in range(X_test.shape[0]): o = nn.predict(X_test[i]) predictions.append(np.argmax(o)) print confusion_matrix(y_test, predictions) print classification_report(y_test, predictions) 

运行结果

《[机器学习]机器学习笔记整理11-神经网络算法简单实现》

    原文作者:神经网络算法
    原文地址: https://blog.csdn.net/baihuaxiu123/article/details/69831409
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