python神经网络模型_python实现BP神经网络回归预测模型

神经网络模型一般用来做分类，回归预测模型不常见，本文基于一个用来分类的BP神经网络，对它进行修改，实现了一个回归模型，用来做室内定位。模型主要变化是去掉了第三层的非线性转换，或者说把非线性激活函数Sigmoid换成f(x)=x函数。这样做的主要原因是Sigmoid函数的输出范围太小，在0-1之间，而回归模型的输出范围较大。模型修改如下：

代码如下：

#coding: utf8

''''

author: Huangyuliang

'''

import json

import random

import sys

import numpy as np

#### Define the quadratic and cross-entropy cost functions

class CrossEntropyCost(object):

@staticmethod

def fn(a, y):

return np.sum(np.nan_to_num(-y*np.log(a)-(1-y)*np.log(1-a)))

@staticmethod

def delta(z, a, y):

return (a-y)

#### Main Network class

class Network(object):

def __init__(self, sizes, cost=CrossEntropyCost):

self.num_layers = len(sizes)

self.sizes = sizes

self.default_weight_initializer()

self.cost=cost

def default_weight_initializer(self):

self.biases = [np.random.randn(y, 1) for y in self.sizes[1:]]

self.weights = [np.random.randn(y, x)/np.sqrt(x)

for x, y in zip(self.sizes[:-1], self.sizes[1:])]

def large_weight_initializer(self):

self.biases = [np.random.randn(y, 1) for y in self.sizes[1:]]

self.weights = [np.random.randn(y, x)

for x, y in zip(self.sizes[:-1], self.sizes[1:])]

def feedforward(self, a):</