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Merge pull request #115 from RiptideBo/stephen_branch

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add neuralnetwork_bp3.py
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Anup Kumar Panwar 2017-09-22 11:49:31 +05:30 committed by GitHub
commit a38e684a73
1 changed files with 31 additions and 13 deletions
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44
Neural_Network/neuralnetwork_bp3.py
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@ -9,7 +9,7 @@ BP neural network with three layers
import numpy as np
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):
'''
@ -38,7 +38,7 @@ class Bpnw():
def do_round(self,x):
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 data_train: training data x; numpy.ndarray
@ -49,9 +49,9 @@ class Bpnw():
'''
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))
# 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
@ -95,9 +95,9 @@ class Bpnw():
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)
# print('------------------Training Complished---------------------')
# print(' - - Training epoch: ', rp, ' - - Mse: %.6f'%mse)
# print(' - - Last Output: ', final_out3)
if draw_e:
draw_error()
@ -108,9 +108,9 @@ class Bpnw():
'''
data_test = np.asarray(data_test)
produce_out = []
print('-------------------Start Testing-------------------------')
print(' - - Shape: Test_Data ',np.shape(data_test))
print(np.shape(data_test))
# 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]
@ -127,8 +127,26 @@ class Bpnw():
def main():
#I will fish the mian function later
pass
#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()