def Single_OC_SVM_Train(real_x, y, x, answer_list, CK_T):
temp = net.Sequential()
temp.add(net.DenseLayer(hid_nodes, out_nodes * 10, lr=0.3))
temp.add(net.ActivationLayer(net.Sigmoid))
temp.add(net.DenseLayer(out_nodes * 10, out_nodes, lr=0.2))
temp_x = []
temp_y = []
while True:
z = random.randint(0, len(answer_list) - 1)
if answer_list[z] <= 0:
temp_x.append(x[z])
temp_y.append(1)
break
while True: #data extraction, for partial training
z = random.randint(0, len(answer_list) - 1)
if answer_list[z] > 0:
temp_x.append(x[z])
temp_y.append(-1)
break
temp_y = np.asarray(temp_y).reshape(len(temp_y), 1)
temp.fit(temp_x, temp_y, 50)
print("")
Improve_train.Improve_train(x, temp, 100, CK_T)
prediction = temp.predict(x)
print(len(prediction))
#Following is used to supervise the unsupervised network --> how it is doing now --> and have no interference with the code
if len(prediction) > 0:
current_label = Score_Metrics.Get_Sample(prediction, real_x, y)
Current_Score = Score_Metrics.Score(prediction, current_label, y)
print("")
print("Current defined answer",
Score_Metrics.Answer_defined(prediction))
print("")
'''
print("The following is current score after pretrained") #It seems that it has some problem here.
print(Current_Score, len(y))
'''
print("_____________________________________________")
#print(prediction)
return temp
else:
print("Error: ", temp.predict(x))
def Core_train(in_nodes, hid_nodes, out_nodes, x, y, epochs):
Core = net.Sequential()
Core.add(net.DenseLayer(in_nodes, hid_nodes, lr=0.2))
Core.add(net.ActivationLayer(net.Sigmoid))
Core.add(net.DenseLayer(hid_nodes, hid_nodes * 2, lr=0.2))
Core.add(net.ActivationLayer(net.Sigmoid))
#Core.add(net.DenseLayer(hid_nodes*2,hid_nodes*2,lr = 0.2))
#Core.add(net.ActivationLayer(net.Sigmoid))
Core.add(net.DenseLayer(hid_nodes * 2, hid_nodes, lr=0.2))
Core.add(net.ActivationLayer(net.Sigmoid))
#Below is the same structure/ similar structure an ocsvm used
Core.add(net.DenseLayer(hid_nodes, out_nodes * 10, lr=0.3))
Core.add(net.ActivationLayer(net.Sigmoid))
Core.add(net.DenseLayer(out_nodes * 10, out_nodes, lr=0.2))
#To 'pretrain', the specific random data class is extracted for training
for kll in range(
10): #Maximum 20 in this dataset, in fact can set as len(x)
z = 300
temp_x = []
temp_y = []
label_chosen = y[
0] #input("Input the label you want to choose to initiate: ")
#print("Label chosen: ",label_chosen)
temp_x, temp_y = Get_Certain_label(x, y, label_chosen,
int(z * (kll + 1)))
#################
Core.fit(temp_x, temp_y, epochs)
prediction = Core.predict(x)
#print('',y[0])
Current_Score = Score_Metrics.Score(prediction, label_chosen, y)
print("")
print("The following is current score")
print(Current_Score)
print("_____________________________________________")
pass
return Core
pass
