Neural Networks Weight matrices explained
Let's say :

X input vector, (size 80*1)
H hidden layer vector, (size 40*1)
Y output vector, (size 1*1)

You have :

H = W1 * X
Y = W2 * H

So :

W1 has size (40*80)
W2 has size (1*40)

Note : size (m*n) means m rows, n columns
Hi guys was wondering if anyone could help me with what I would actually answer for this question on a upcoming exam I have on AI in Python. The question confuses me as I thought I would usually need more info to answer but it is not provided. the question asked is

A Python class is used to represent a neural network and the feed forward operation is called as indicated below:
' y_hat = NN.forward(X) '
where y_hat is the output and X is the input matrix.
The neural network has an input size of 80, one hidden layer of size 40 and an output layer of size 1.
What size will be the W1 and W2 matrices?

If anyone could help me with this as my lecturer is not replying to the classes emails.
Many Thanks!