python神經網絡反向傳播_簡單反向傳播神經網絡算法（Python）

以下是我對您的代碼所做的修改，使其能夠工作：我不想使用np.random.random，它在區間[0.0，1.0)中生成數字來初始化權重，使用np.random.uniform在[-1.0，1.0)中生成統一的随機浮點數。

将輸入空間圍繞原點居中(即，去掉平均值)并将其規格化。

以下是如何進行初始化：for i in range(len(sizeOfLayers)):

self.activation.append(sizeOfLayers[i]*[0.0] + [0.0])

self.weightsIn = np.random.uniform(-1,1,(sizeOfLayers[1], sizeOfLayers[0] + 1))

self.weightsOut = np.random.uniform(-1,1,(sizeOfLayers[2], sizeOfLayers[1] + 1))

然後您還必須在函數forward中的activation追加1：

^{pr2}$

你可能想改變學習率，使它工作(大約0.5為我工作)。另外，你的均方誤差計算是錯誤的：你應該乘以0.5，而不是除法。在

以下是修改後的代碼：import numpy as np

import random

class neural_network():

activation = [] #List of values with the values of activation of each layers

weightsIn = []

weightsOut = []

def __init__(self, sizeOfLayers):

'''

sizeOfLayers: Tuple with numbers of neurons of each layer

(in, hidden, out)

'''

if len(sizeOfLayers) > 3:

raise ValueError('Wrong number of layers')

self.sizeOfLayers = sizeOfLayers

for i in range(len(sizeOfLayers)):

#input layer + bias

self.activation.append(sizeOfLayers[i]*[0.0] + [0.0])

# Wi = len(Hid) x len(IN)+1(bias)

self.weightsIn = np.random.uniform(-1,1,(sizeOfLayers[1], sizeOfLayers[0] + 1))

# Wo = len(OUT) x len(Hid)

self.weightsOut = np.random.uniform(-1,1,(sizeOfLayers[2], sizeOfLayers[1] + 1))

def forward(self, X):

'''

X: Vetor de entradas

'''

#In+bias add ativation vector

self.activation[0] = np.vstack((np.array([X]).T, np.array([1])))

#sum of (weights x in)

self.sumHidden = self.weightsIn.dot(self.activation[0])

#Ativation of hidden layer

self.activation[1] = np.vstack( ( self.sigmoid(self.sumHidden), np.array([1]) ) )

#sum of(out weights x activation of last layer)

self.sumOut = self.weightsOut.dot(self.activation[1])

#activation of output

self.activation[2] = (self.sigmoid(self.sumOut))

return self.activation[2].T

def backPropagate(self, Y, trainRate = 0.1):

'''

Y: output target

trainRate:

'''

if len(Y) != self.sizeOfLayers[2]:

raise ValueError('Wrong number of inputs')

#Calc of output delta

error_o = Y.T - self.activation[2].T

out_delta = self.sigmoidPrime(self.activation[2]) * error_o.T

#Calc of hidden delta

error_h = out_delta.T.dot(self.weightsOut)

hiden_delta = self.sigmoidPrime(self.activation[1]) * error_h.T

# update output weights output

change_o = self.activation[1] * out_delta.T

for i in range(self.sizeOfLayers[2]):

for j in range(self.sizeOfLayers[1]):

self.weightsOut[i][j] = self.weightsOut[i][j] + trainRate*change_o[j][i]

# update Input weights

change_h = self.activation[0] * hiden_delta.T

for i in range(self.sizeOfLayers[1]):

for j in range(self.sizeOfLayers[0]):

self.weightsIn[i][j] = self.weightsIn[i][j] + trainRate*change_h[j][i]

#Error

return np.sum((Y.T - self.activation[2].T)**2)*0.5

def sigmoid(self, z, derv = False):

if derv == False:

return 1/(1+np.exp(-z))

def sigmoidPrime(self, z):

return self.sigmoid(z)*(1-self.sigmoid(z))

def train(self, target, trainRate = 0.5, it = 50000):

for i in range(it):

error = 0.0

for t in target:

inputs = np.array(t[0])

targets = np.array([t[1]])

self.forward(inputs)

error = error + self.backPropagate(targets, trainRate)

nn = neural_network((2,5,1))

xor = [

[[-1.0, -1.0], [0]],

[[-1.0, 1.0], [1]],

[[ 1.0, -1.0], [1]],

[[ 1.0, 1.0], [0]] #If I change her to 1 it converges

]

nn.train(xor)

for e in xor:

nn.forward(e[0])

print nn.activation[2]

祝你好運！在