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