Question

import math



inp = 0.1
target = 0.3
weight = 0.04
learning_rate = 1
bias = 0


def sigmoid(x):
    return (1/1+(math.e**(-x)))


for count in range(10):
    net = (weight*inp)+(bias*1)
    out = sigmoid(net)
    error_total = 0.5*((target - out)**2)
    print('error',error_total,'|| output',out,'|| weight',weight)
    adjustment = (out - target)*(out)*(1 - out)*(inp)
    weight = weight - (learning_rate*(adjustment))

输出

error 1.4382215499593243 || output 1.9960079893439915 || weight 0.04
error 1.3827597601324302 || output 1.9629851232842885 || weight 0.3771731560625728
error 1.3336445885853887 || output 1.9331837548698485 || weight 0.6915314696982848
error 1.2897634204261337 || output 1.9060905456580794 || weight 0.9861603791287348
error 1.2502583453938265 || output 1.8813022136162503 || weight 1.2635467711722324
error 1.2144557693222424 || output 1.8584965635651831 || weight 1.5257260148874747
error 1.1818184468701014 || output 1.8374124019729394 || weight 1.7743861541249517
error 1.1519119478941984 || output 1.8178352663541577 || weight 2.0109434853536103
error 1.1243806861957226 || output 1.7995870672926748 || weight 2.2365985045783425
error 1.0989304444985601 || output 1.7825184278777517 || weight 2.4523780684160923

在我的神经网络中，我想预测单个输入上的单个输出我尝试将偏见和学习率设置为不同的值但没有用

并且权重继续增加且错误率降低但网络无法达到目标输出

Answer 1

你的sigmoid定义是错误的。应该是

def sigmoid(x):
    return 1/(1+(math.e**(-x)))

你应该除以（一个+指数）;相反，你逐个划分，并将指数加到除法的结果上（显然是1）。

Answer 2

将学习率更改为0.1或0.001。它不应该是<= 1

神经网络预测一个神经元坏

2 个答案: