我正在尝试使用反向传播实现多层感知器,但仍然无法教他XOR,我也经常会得到数学范围错误。我看了书和谷歌学习规则和错误反向传播方法,但我仍然不知道我的错误在哪里
def logsig(net):
return 1/(1+math.exp(-net))
def perceptron(coef = 0.5, iterations = 10000):
inputs = [[0,0],[0,1],[1,0],[1,1]]
desiredOuts = [0,1,1,0]
bias = -1
[input.append(bias) for input in inputs]
weights_h1 = [random.random() for e in range(len(inputs[0]))]
weights_h2 = [random.random() for e in range(len(inputs[0]))]
weights_out = [random.random() for e in range(3)]
for itteration in range(iterations):
out = []
for input, desiredOut in zip(inputs, desiredOuts):
#1st hiden neuron
net_h1 = sum(x * w for x, w in zip(input, weights_h1))
aktivation_h1 = logsig(net_h1)
#2st hiden neuron
net_h2 = sum(x * w for x, w in zip(input, weights_h2))
aktivation_h2 = logsig(net_h2)
#output neuron
input_out = [aktivation_h1, aktivation_h2, bias]
net_out = sum(x * w for x, w in zip(input_out, weights_out))
aktivation_out = logsig(net_out)
#error propagation
error_out = (desiredOut - aktivation_out) * aktivation_out * (1- aktivation_out)
error_h1 = aktivation_h1 * (1-aktivation_h1) * weights_out[0] * error_out
error_h2 = aktivation_h2 * (1-aktivation_h2) * weights_out[1] * error_out
#learning
weights_out = [w + x * coef * error_out for w, x in zip(weights_out, input_out)]
weights_h1 = [w + x * coef * error_out for w, x in zip(weights_h1, input)]
weights_h2 = [w + x * coef * error_out for w, x in zip(weights_h2, input)]
out.append(aktivation_out)
formatedOutput = ["%.2f" % e for e in out]
return formatedOutput
答案 0 :(得分:2)
我唯一注意到的是,您使用weights_h1
而不是weights_h2
和error_out
更新error_h1
和error_h2
。换句话说:
weights_h1 = [w + x * coef * error_h1 for w, x in zip(weights_h1, input)]
weights_h2 = [w + x * coef * error_h2 for w, x in zip(weights_h2, input)]
答案 1 :(得分:0)
数学范围错误可能来自math.exp(-net)计算,因为净负数很大。