Simple Neural Network (Python)

neural_network.py

import numpy as np
from numpy import exp, array, random, dot

class NeuralNetwork():

    def __init__(self):
        self.synaptic_weights = 2 * random.random((3, 1)) - 1
    
    def __sigmoid(self, x):
        return 1 / (1 + exp(-x))
    
    def __sigmoid_derivative(self, x):
        return x * (1 - x)

    def train(self, training_set_inputs, training_set_outputs, number_of_training_iterations):
        for iteration in range(number_of_training_iterations):
            output = self.think(training_set_inputs)
            error = training_set_outputs - output
            adjustment = dot(training_set_inputs.T, error * self.__sigmoid_derivative(output))
            self.synaptic_weights += adjustment
    
    def think(self, inputs):
        return self.__sigmoid(dot(inputs, self.synaptic_weights))

neural_network = NeuralNetwork()
training_set_inputs  = array([[0, 0, 1], [1, 1, 1], [1, 0, 1], [0, 1, 1]])
training_set_outputs = array([[0, 1, 1, 0]]).T
neural_network.train(training_set_inputs, training_set_outputs, 10000)
print("Consider new situation [1, 0, 0] -> ?:")
print(neural_network.think(array([1, 0, 0])))
# Consider new situation [1, 0, 0] -> ?:
# [0.99993701]