# Matrix multiplication doesn't work - no output

I am having problem in the Matrix multiplication of my Python neural network. Being still a High schooler, I know next to nothing about MM except a couple of tutorials. My Neural network was working fine when my inputs were like ([0,1,1]...) but when I changed them to doubles, my MM is going wrong(No Output)

# The initialization
import numpy as np
iterations = int(0)

#TO THE CODING=>

def sigmoid(x):
return 1/(1 + np.exp(-x))

#The function- 2

def Sigmoid_Derivative(x):
return sigmoid(x) * (1-sigmoid(x))

Training_inputs = np.array([[0,0],
[1,1],
[1,0],
[0,1]])          # 4 rows * 2 columns

Training_outputs = np.array([[1, 1,
0, 0]]).T               # 2 rows * 2 columns

Correct_Outputs = np.array([[,,
,]])

np.random.seed(1)

synaptic_weights = np.random.random((2, 1)) - 1

print ("Random starting synaptic weight:")
print (synaptic_weights)

for iteration in range(20000):
Input_Layer = Training_inputs

Outputs = sigmoid(np.dot(Input_Layer, synaptic_weights))
erorr = Training_outputs - Outputs

Rounder = np.around([np.array(Outputs)],decimals = 2,)
Final_Outputs = Rounder.astype(int)
iterations += 1

if np.array_equal(Final_Outputs, Correct_Outputs):
print ("Synaptic weights after trainig:")
print (synaptic_weights)

print ("Outputs after training: ")
print (Outputs)

print ("NO. of iterations required: ",iterations)
break
else:
continue


P.S The if-else iteration checker was my own coding. Maybe it might have a potential implication.