Simple neural network gives wrong output after training

问题

I've been working on a simple neural network.

It takes in a data set with 3 columns, if the first column's value is a 1, then the output should be a 1. I've provided comments so it is easier to follow.

Code is as follows:

import numpy as np
import random

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

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

def think(weights, inputs):
    sum = (weights[0] * inputs[0]) + (weights[1] * inputs[1]) + (weights[2] * inputs[2])
    return sigmoid(sum)

if __name__ == "__main__":

    # Assign random weights
    weights = [-0.165, 0.440, -0.867]

    # Training data for the network.
    training_data = [
        [0, 0, 1],
        [1, 1, 1],
        [1, 0, 1],
        [0, 1, 1]
    ]

    # The answers correspond to the training_data by place,
    # so first element of training_answers is the answer to the first element of training_data
    # NOTE: The pattern is if there's a 1 in the first place, the result should be a one
    training_answers = [0, 1, 1, 0]

    # Train the neural network
    for iteration in range(50000):
        # Pick a random piece of training_data
        selected = random.randint(0, 3)

        training_output = think(weights, training_data[selected])

        # Calculate the error
        error = training_output - training_answers[selected]

        # Calculate the adjustments that need to be applied to the weights
        adjustments = np.dot(training_data[selected], error * sigmoid_derivative(training_output))

        # Apply adjustments, maybe something wrong is going here?
        weights += adjustments

    print("The Neural Network has been trained!")

    # Result of print below should be close to 1
    print(think(weights, [1, 0, 0]))

The result of the last print should be close to 1, however it is not?

I have a feeling that I'm not adjusting the weights correctly.

来源：https://stackoverflow.com/questions/61239698/simple-neural-network-gives-wrong-output-after-training