I am trying to implement a simple learning algorithm with error backpropagation. The logic is classic and simple. However, I get a weird error `operands could not be broadcast together with shapes (3,) (3,2) `

when subtracting two NumPy arrays with the same shape.

Here is what I tried

```
import numpy as np
from numpy import linalg as LA
import sys
learning_factor=0.3
entry=[1,2,3]
theoretical_output=[0.1,0.3,0.7]
boltzmann=lambda x: 1/(1+np.exp(-x))
normalize=np.vectorize(boltzmann)
def train(wr,wc,zr,zc,x,t):
# Transform X and T to numpy arrays
t = np.asarray(t)
x = np.asarray(x)
# Generate random weights matrix W and Z
W = np.random.rand(wr,wc)
Z=np.random.rand(zr,zc)
# Add Epsilon to make T different from O
o = t + sys.float_info.epsilon
while(LA.norm(t-o)>sys.float_info.epsilon):
b=np.matmul(W,x)
h = normalize(b)
a = np.matmul(Z,h)
o = normalize(a)
error = t-o
output_error=np.matmul(o,1-o,error)
Z=Z+learning_factor*output_error[:, np.newaxis] * (np.transpose(h))
hidden_error = np.matmul(h, 1 - h, (np.transpose(Z) * output_error))
W = W + learning_factor * hidden_error[:, np.newaxis] * (np.transpose(x))
print("--- W ---", "\n", W, "\n")
print("--- Z ---", "\n", Z, "\n")
train(2,3,3,2,entry,theoretical_output)
```

Thank you for your help!