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python如何实现多层感知器MLP-创新互联
这篇文章主要介绍了python如何实现多层感知器MLP，具有一定借鉴价值，感兴趣的朋友可以参考下，希望大家阅读完这篇文章之后大有收获，下面让小编带着大家一起了解一下。
本文实例为大家分享了python实现多层感知器MLP的具体代码，供大家参考，具体内容如下
1、加载必要的库，生成数据集
import math import random import matplotlib.pyplot as plt import numpy as np class moon_data_class(object): def __init__(self,N,d,r,w): self.N=N self.w=w self.d=d self.r=r def sgn(self,x): if(x>0): return 1; else: return -1; def sig(self,x): return 1.0/(1+np.exp(x)) def dbmoon(self): N1 = 10*self.N N = self.N r = self.r w2 = self.w/2 d = self.d done = True data = np.empty(0) while done: #generate Rectangular data tmp_x = 2*(r+w2)*(np.random.random([N1, 1])-0.5) tmp_y = (r+w2)*np.random.random([N1, 1]) tmp = np.concatenate((tmp_x, tmp_y), axis=1) tmp_ds = np.sqrt(tmp_x*tmp_x + tmp_y*tmp_y) #generate double moon data ---upper idx = np.logical_and(tmp_ds > (r-w2), tmp_ds < (r+w2)) idx = (idx.nonzero())[0] if data.shape[0] == 0: data = tmp.take(idx, axis=0) else: data = np.concatenate((data, tmp.take(idx, axis=0)), axis=0) if data.shape[0] >= N: done = False #print (data) db_moon = data[0:N, :] #print (db_moon) #generate double moon data ----down data_t = np.empty([N, 2]) data_t[:, 0] = data[0:N, 0] + r data_t[:, 1] = -data[0:N, 1] - d db_moon = np.concatenate((db_moon, data_t), axis=0) return db_moon
2、定义激活函数
def rand(a,b): return (b-a)* random.random()+a def sigmoid(x): #return np.tanh(-2.0*x) return 1.0/(1.0+math.exp(-x)) def sigmoid_derivate(x): #return -2.0*(1.0-np.tanh(-2.0*x)*np.tanh(-2.0*x)) return x*(1-x) #sigmoid函数的导数
3、定义神经网络
class BP_NET(object):
def __init__(self):
self.input_n = 0
self.hidden_n = 0
self.output_n = 0
self.input_cells = []
self.bias_input_n = []
self.bias_output = []
self.hidden_cells = []
self.output_cells = []
self.input_weights = []
self.output_weights = []
self.input_correction = []
self.output_correction = []
def setup(self, ni,nh,no):
self.input_n = ni+1#输入层+偏置项
self.hidden_n = nh
self.output_n = no
self.input_cells = [1.0]*self.input_n
self.hidden_cells = [1.0]*self.hidden_n
self.output_cells = [1.0]*self.output_n
self.input_weights = make_matrix(self.input_n,self.hidden_n)
self.output_weights = make_matrix(self.hidden_n,self.output_n)
for i in range(self.input_n):
for h in range(self.hidden_n):
self.input_weights[i][h] = rand(-0.2,0.2)
for h in range(self.hidden_n):
for o in range(self.output_n):
self.output_weights[h][o] = rand(-2.0,2.0)
self.input_correction = make_matrix(self.input_n , self.hidden_n)
self.output_correction = make_matrix(self.hidden_n,self.output_n)
def predict(self,inputs):
for i in range(self.input_n-1):
self.input_cells[i] = inputs[i]
for j in range(self.hidden_n):
total = 0.0
for i in range(self.input_n):
total += self.input_cells[i] * self.input_weights[i][j]
self.hidden_cells[j] = sigmoid(total)
for k in range(self.output_n):
total = 0.0
for j in range(self.hidden_n):
total+= self.hidden_cells[j]*self.output_weights[j][k]# + self.bias_output[k]
self.output_cells[k] = sigmoid(total)
return self.output_cells[:]
def back_propagate(self, case,label,learn,correct):
#计算得到输出output_cells
self.predict(case)
output_deltas = [0.0]*self.output_n
error = 0.0
#计算误差 = 期望输出-实际输出
for o in range(self.output_n):
error = label[o] - self.output_cells[o] #正确结果和预测结果的误差：0,1，-1
output_deltas[o]= sigmoid_derivate(self.output_cells[o])*error#误差稳定在0~1内
hidden_deltas = [0.0] * self.hidden_n
for j in range(self.hidden_n):
error = 0.0
for k in range(self.output_n):
error+= output_deltas[k]*self.output_weights[j][k]
hidden_deltas[j] = sigmoid_derivate(self.hidden_cells[j])*error
for h in range(self.hidden_n):
for o in range(self.output_n):
change = output_deltas[o]*self.hidden_cells[h]
#调整权重：上一层每个节点的权重学习*变化+矫正率
self.output_weights[h][o] += learn*change
#更新输入->隐藏层的权重
for i in range(self.input_n):
for h in range(self.hidden_n):
change = hidden_deltas[h]*self.input_cells[i]
self.input_weights[i][h] += learn*change
error = 0
for o in range(len(label)):
for k in range(self.output_n):
error+= 0.5*(label[o] - self.output_cells[k])**2
return error
def train(self,cases,labels, limit, learn,correct=0.1):
for i in range(limit):
error = 0.0
# learn = le.arn_speed_start /float(i+1)
for j in range(len(cases)):
case = cases[j]
label = labels[j]
error+= self.back_propagate(case, label, learn,correct)
if((i+1)%500==0):
print("error:",error)
def test(self): #学习异或
N = 200
d = -4
r = 10
width = 6
data_source = moon_data_class(N, d, r, width)
data = data_source.dbmoon()
# x0 = [1 for x in range(1,401)]
input_cells = np.array([np.reshape(data[0:2*N, 0], len(data)), np.reshape(data[0:2*N, 1], len(data))]).transpose()
labels_pre = [[1.0] for y in range(1, 201)]
labels_pos = [[0.0] for y in range(1, 201)]
labels=labels_pre+labels_pos
self.setup(2,5,1) #初始化神经网络：输入层，隐藏层，输出层元素个数
self.train(input_cells,labels,2000,0.05,0.1) #可以更改
test_x = []
test_y = []
test_p = []
y_p_old = 0
for x in np.arange(-15.,25.,0.1):
for y in np.arange(-10.,10.,0.1):
y_p =self.predict(np.array([x, y]))
if(y_p_old <0.5 and y_p[0] > 0.5):
test_x.append(x)
test_y.append(y)
test_p.append([y_p_old,y_p[0]])
y_p_old = y_p[0]
#画决策边界
plt.plot( test_x, test_y, 'g--')
plt.plot(data[0:N, 0], data[0:N, 1], 'r*', data[N:2*N, 0], data[N:2*N, 1], 'b*')
plt.show()
if __name__ == '__main__':
nn = BP_NET()
nn.test()4、运行结果
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