目标是为这个神经元找到最佳权重集，从而产生正确的结果。通过使用几个不同的训练样例训练神经元来做到这一点。在每一步计算神经元输出中的误差，然后反向传播梯度。计算神经元输出的步骤称为前向传播，而梯度计算称为反向传播。

以下是实施：

# Python program to implement a

# single neuron neural network

# import all necessery libraries

from numpy import exp, array, random, dot, tanh

# Class to create a neural

# network with single neuron

class NeuralNetwork():

def __init__(self):

# Using seed to make sure it'll

# generate same weights in every run

random.seed(1)

# 3x1 Weight matrix

self.weight_matrix = 2 * random.random((3, 1)) - 1

# tanh as activation fucntion

def tanh(self, x):

return tanh(x)