mirror of
https://github.com/TheAlgorithms/Python.git
synced 2024-11-24 21:41:08 +00:00
153 lines
5.0 KiB
Python
153 lines
5.0 KiB
Python
#-*- coding:utf-8 -*-
|
|
'''
|
|
Author: Stephen Lee
|
|
Date: 2017.9.21
|
|
|
|
BP neural network with three layers
|
|
'''
|
|
|
|
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
|
|
class Bpnn():
|
|
|
|
def __init__(self,n_layer1,n_layer2,n_layer3,rate_w=0.3,rate_t=0.3):
|
|
'''
|
|
:param n_layer1: number of input layer
|
|
:param n_layer2: number of hiden layer
|
|
:param n_layer3: number of output layer
|
|
:param rate_w: rate of weight learning
|
|
:param rate_t: rate of threshold learning
|
|
'''
|
|
self.num1 = n_layer1
|
|
self.num2 = n_layer2
|
|
self.num3 = n_layer3
|
|
self.rate_weight = rate_w
|
|
self.rate_thre = rate_t
|
|
self.thre2 = -2*np.random.rand(self.num2)+1
|
|
self.thre3 = -2*np.random.rand(self.num3)+1
|
|
self.vji = np.mat(-2*np.random.rand(self.num2, self.num1)+1)
|
|
self.wkj = np.mat(-2*np.random.rand(self.num3, self.num2)+1)
|
|
|
|
def sig(self,x):
|
|
return 1 / (1 + np.exp(-1*x))
|
|
|
|
def sig_plain(self,x):
|
|
return 1 / (1 + np.exp(-1*x))
|
|
|
|
def do_round(self,x):
|
|
return round(x, 3)
|
|
|
|
def trian(self,patterns,data_train, data_teach, n_repeat, error_accuracy, draw_e=False):
|
|
'''
|
|
:param patterns: the number of patterns
|
|
:param data_train: training data x; numpy.ndarray
|
|
:param data_teach: training data y; numpy.ndarray
|
|
:param n_repeat: echoes
|
|
:param error_accuracy: error accuracy
|
|
:return: None
|
|
'''
|
|
data_train = np.asarray(data_train)
|
|
data_teach = np.asarray(data_teach)
|
|
# print('-------------------Start Training-------------------------')
|
|
# print(' - - Shape: Train_Data ',np.shape(data_train))
|
|
# print(' - - Shape: Teach_Data ',np.shape(data_teach))
|
|
rp = 0
|
|
all_mse = []
|
|
mse = 10000
|
|
while rp < n_repeat and mse >= error_accuracy:
|
|
alle = 0
|
|
final_out = []
|
|
for g in range(np.shape(data_train)[0]):
|
|
net_i = data_train[g]
|
|
out1 = net_i
|
|
|
|
net_j = out1 * self.vji.T - self.thre2
|
|
out2=self.sig(net_j)
|
|
|
|
net_k = out2 * self.wkj.T - self.thre3
|
|
out3 = self.sig(net_k)
|
|
|
|
# learning process
|
|
pd_k_all = np.multiply(np.multiply(out3,(1 - out3)),(data_teach[g]-out3))
|
|
pd_j_all = np.multiply(pd_k_all * self.wkj,np.multiply(out2,1-out2))
|
|
#upgrade weight
|
|
self.wkj = self.wkj + pd_k_all.T * out2 *self.rate_weight
|
|
self.vji = self.vji + pd_j_all.T * out1 * self.rate_weight
|
|
#upgrade threshold
|
|
self.thre3 = self.thre3 - pd_k_all * self.rate_thre
|
|
self.thre2 = self.thre2 - pd_j_all * self.rate_thre
|
|
#calculate sum of error
|
|
errors = np.sum(abs((data_teach[g] - out3)))
|
|
|
|
alle = alle + errors
|
|
final_out.extend(out3.getA().tolist())
|
|
final_out3 = [list(map(self.do_round,each)) for each in final_out]
|
|
|
|
rp = rp + 1
|
|
mse = alle/patterns
|
|
all_mse.append(mse)
|
|
def draw_error():
|
|
yplot = [error_accuracy for i in range(int(n_repeat * 1.2))]
|
|
plt.plot(all_mse, '+-')
|
|
plt.plot(yplot, 'r--')
|
|
plt.xlabel('Learning Times')
|
|
plt.ylabel('All_mse')
|
|
plt.grid(True,alpha = 0.7)
|
|
plt.show()
|
|
# print('------------------Training Complished---------------------')
|
|
# print(' - - Training epoch: ', rp, ' - - Mse: %.6f'%mse)
|
|
# print(' - - Last Output: ', final_out3)
|
|
if draw_e:
|
|
draw_error()
|
|
|
|
def predict(self,data_test):
|
|
'''
|
|
:param data_test: data test, numpy.ndarray
|
|
:return: predict output data
|
|
'''
|
|
data_test = np.asarray(data_test)
|
|
produce_out = []
|
|
# print('-------------------Start Testing-------------------------')
|
|
# print(' - - Shape: Test_Data ',np.shape(data_test))
|
|
# print(np.shape(data_test))
|
|
for g in range(np.shape(data_test)[0]):
|
|
|
|
net_i = data_test[g]
|
|
out1 = net_i
|
|
|
|
net_j = out1 * self.vji.T - self.thre2
|
|
out2 = self.sig(net_j)
|
|
|
|
net_k = out2 * self.wkj.T - self.thre3
|
|
out3 = self.sig(net_k)
|
|
produce_out.extend(out3.getA().tolist())
|
|
res = [list(map(self.do_round,each)) for each in produce_out]
|
|
return np.asarray(res)
|
|
|
|
|
|
def main():
|
|
#example data
|
|
data_x = [[1,2,3,4],
|
|
[5,6,7,8],
|
|
[2,2,3,4],
|
|
[7,7,8,8]]
|
|
data_y = [[1,0,0,0],
|
|
[0,1,0,0],
|
|
[0,0,1,0],
|
|
[0,0,0,1]]
|
|
|
|
test_x = [[1,2,3,4],
|
|
[3,2,3,4]]
|
|
|
|
#building network model
|
|
model = Bpnn(4,10,4)
|
|
#training the model
|
|
model.trian(patterns=4,data_train=data_x,data_teach=data_y,
|
|
n_repeat=100,error_accuracy=0.1,draw_e=True)
|
|
#predicting data
|
|
model.predict(test_x)
|
|
|
|
if __name__ == '__main__':
|
|
main()
|