[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：0,1,2,3,4,5,6,7,8,9import numpy as npfrom sklearn.datasets import load_digitsfrom sklearn.metrics import confusion_matrix, classification_reportfrom sklearn.preprocessing import LabelBinarizerfrom NeuralNetwork import NeuralNetworkfrom sklearn.cross_validation import train_test_splitdigits = load_digits()X = digits.datay = digits.targetX -= X.min()  # normalize the values to bring them into the range 0-1X /= 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