为什么不管怎样，我的MLP总是输出-1

Question

我需要取从单位正方形均匀生成的二维输入数据，然后根据两个假设函数h1(x) = w1^T*x和h2(x) = w2^T*x的异或将数据标记为1或-1，其中w1 = [0, 1, -1]和w2 = [0, 1, 1]。从那里，我应该使用θ的符号函数通过一个三层多层感知器来运行数据。出于某种原因，无论怎样，我的MLP对所有点都输出-1。我的错误在哪里？这是我的代码：

import numpy as np
import matplotlib.pyplot as plt
x = np.random.uniform(-.5, .5, (100,3))
x[:,0] = 1
y = np.ones(100)
w1 = np.array([0, 1, -1]).T
w2 = np.array([0, 1, 1]).T
h1 = np.sign(w1[1]*x[:,1] + w1[2]*x[:,2])
h2 = np.sign(w2[1]*x[:,1] + w2[2]*x[:,2])
for i in range(np.size(x,0)):
if (h1[i]<0 and h2[i]>0) or (h1[i]>0 and h2[i]<0):
y[i] = 1
else:
y[i] = -1
print("h1:")
print(h1)
print("h2:")
print(h2)
#nodes 21 and 31 are just 1 with weights -1.5 and 1.5, respectively
node22 = np.ones((100,1))
node23 = np.ones((100,1))
node32 = np.ones((100,1))
node33 = np.ones((100,1))
out = np.ones((100,1))
for j in range(np.size(x,0)):
node22[j] = np.matmul(w1,x[j,:])
node23[j] = np.matmul(w2,x[j,:])
node32[j] = np.sign(-1.5 + node22[j] - node23[j])
node33[j] = np.sign(-1.5 - node22[j] + node23[j])
out[j] = np.sign(1.5 + node32[j] + node33[j])
print("Layer 2, Node 2:")
print(node22)
print("Layer 2, Node 3:")
print(node23)
print("Layer 3, Node 2:")
print(node32)
print("Layer 3, Node 3:")
print(node33)
print("f:")
print(out)
error = 0
for k in range(np.size(x,0)):
if y[k] != out[k]:
error +=1
error = error/np.size(x,0)
print(error)

Answer 1

我想明白了。对于节点22和23，计算需要在符号函数内，因此循环的j应该如下所示：

for j in range(np.size(x,0)):
node22[j] = np.sign(np.matmul(w1,x[j,:]))
node23[j] = np.sign(np.matmul(w2,x[j,:]))
node32[j] = np.sign(-1.5 + node22[j] - node23[j])
node33[j] = np.sign(-1.5 - node22[j] + node23[j])
out[j] = np.sign(1.5 + node32[j] + node33[j])
'''