My one neuron neural network does not work with my dataset

Question

I can't find the problem I tried normalizing the dataset but It didn't work too. When I work with other datasets such as

[[0,0,1],[1,1,1],[1,0,1],[0,1,1]]] as the input and [[0,1,1,0]] as the output

it works.

Also I don't want to use any other libraries than numpy and pandas.

import numpy as np
import pandas as pd

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

def sigmoid_derivative(x):
    return x*(1-x)

training_inputs = np.array([
                    [1, 0, 0],
                    [0, 1, 0],
                    [0, 0, 1],
                    [1, 1, 0],
                    [0, 1, 1],
                    [1, 1, 1],
                    [2, 0, 0]
                ])

training_outputs = np.array([[1,2,0,1,0,2,3]]).T

np.random.seed(1)

synaptic_weights = 2 * np.random.random((3,1))-1
print('Random starting synaptic weights: ')
print(synaptic_weights)

for iteration in range(2000):

    input_layer = training_inputs

    outputs = sigmoid(np.dot(input_layer, synaptic_weights))

    error = training_outputs - outputs

    adjustments = error * sigmoid_derivative(outputs)

    synaptic_weights += np.dot(input_layer.T,adjustments)

print('Synaptic Weights After Training: ')
print(synaptic_weights)

print('Outputs after training: ')
print(outputs)

The predictions are

[0.99869975]
 [0.98680038]
 [0.00212543]
 [0.99998259]
 [0.13736242]
 [0.99189007]
 [0.9999983 ]

but the real outputs are

[[1,2,0,1,0,2,3]] 

1 is smaller than 2 so 0.99<0.98 in the predictions should not be occured.