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Stephen Lee 2017-09-22 10:30:19 +08:00
parent e1befed976
commit 53b6fe15c9
1 changed files with 22 additions and 4 deletions
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26
Neural_Network/neuralnetwork_bp3.py
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@ -9,7 +9,7 @@ BP neural network with three layers
import numpy as np import numpy as np
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
class Bpnw(): class Bpnn():
def __init__(self,n_layer1,n_layer2,n_layer3,rate_w=0.3,rate_t=0.3): def __init__(self,n_layer1,n_layer2,n_layer3,rate_w=0.3,rate_t=0.3):
''' '''
@ -38,7 +38,7 @@ class Bpnw():
def do_round(self,x): def do_round(self,x):
return round(x, 3) return round(x, 3)
def trian(self,patterns,data_train, data_teach, n_repeat, error_accuracy,draw_e = bool): def trian(self,patterns,data_train, data_teach, n_repeat, error_accuracy, draw_e=False):
''' '''
:param patterns: the number of patterns :param patterns: the number of patterns
:param data_train: training data x; numpy.ndarray :param data_train: training data x; numpy.ndarray
@ -127,8 +127,26 @@ class Bpnw():
def main(): def main():
#I will fish the mian function later #example data
pass 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__': if __name__ == '__main__':
main() main()