Merge pull request #115 from RiptideBo/stephen_branch

add neuralnetwork_bp3.py

Author: AnupKumarPanwar

Neural_Network/neuralnetwork_bp3.py

@@ -9,7 +9,7 @@
 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 @@ def sig_plain(self,x):
     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 @@ def trian(self,patterns,data_train, data_teach, n_repeat, error_accuracy,draw_e
         '''
         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 @@ def draw_error():
             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 @@ def predict(self,data_test):
         '''
         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 @@ def predict(self,data_test):
 
 
 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()