mathdatech/Python
1
0
Fork 0
mirror of https://github.com/TheAlgorithms/Python.git synced 2024-11-24 13:31:07 +00:00
Code Issues Packages Projects Releases Wiki Activity

Merge pull request #115 from RiptideBo/stephen_branch

Browse Source 
add neuralnetwork_bp3.py
This commit is contained in:
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
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

44
Neural_Network/neuralnetwork_bp3.py
View File

@ -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
@ -49,9 +49,9 @@ class Bpnw():
''' '''
data_train = np.asarray(data_train) data_train = np.asarray(data_train)
data_teach = np.asarray(data_teach) data_teach = np.asarray(data_teach)
print('-------------------Start Training-------------------------') # print('-------------------Start Training-------------------------')
print(' - - Shape: Train_Data ',np.shape(data_train)) # print(' - - Shape: Train_Data ',np.shape(data_train))
print(' - - Shape: Teach_Data ',np.shape(data_teach)) # print(' - - Shape: Teach_Data ',np.shape(data_teach))
rp = 0 rp = 0
all_mse = [] all_mse = []
mse = 10000 mse = 10000
@ -95,9 +95,9 @@ class Bpnw():
plt.ylabel('All_mse') plt.ylabel('All_mse')
plt.grid(True,alpha = 0.7) plt.grid(True,alpha = 0.7)
plt.show() plt.show()
print('------------------Training Complished---------------------') # print('------------------Training Complished---------------------')
print(' - - Training epoch: ', rp, ' - - Mse: %.6f'%mse) # print(' - - Training epoch: ', rp, ' - - Mse: %.6f'%mse)
print(' - - Last Output: ', final_out3) # print(' - - Last Output: ', final_out3)
if draw_e: if draw_e:
draw_error() draw_error()
@ -108,9 +108,9 @@ class Bpnw():
''' '''
data_test = np.asarray(data_test) data_test = np.asarray(data_test)
produce_out = [] produce_out = []
print('-------------------Start Testing-------------------------') # print('-------------------Start Testing-------------------------')
print(' - - Shape: Test_Data ',np.shape(data_test)) # print(' - - Shape: Test_Data ',np.shape(data_test))
print(np.shape(data_test)) # print(np.shape(data_test))
for g in range(np.shape(data_test)[0]): for g in range(np.shape(data_test)[0]):
net_i = data_test[g] net_i = data_test[g]
@ -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()