Hello. I need help with the forward propagation function as the code is not compiling correctly in the nn_model as the cost printed out is the same on each line. The error message says there is an issue with the following line:

Z = W @ X + b

I understand that Z must be an array with shape 1 x m (in the first case 30 and second 1,460). In the first case, W is shape 1 x 1 as n_y = 1 and X is shape 1 x 30. After matrix multiplication and the addition of vector b, Z should result as an array of m columns. However, I am getting an error message saying there is an issue with the sizes when completing the n. Can someone please help me out?

Error Message: ValueError: matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 2 is different from 1)

I will np.dot(), thank you.
Why should I use transpose? I was thinking to use transpose for W but it already has shape
n_y x n_x, or 1x1 and 1x2 in the two cases.

ValueError Traceback (most recent call last)
in
----> 1 w3_unittest.test_forward_propagation(forward_propagation)

~/work/w3_unittest.py in test_forward_propagation(target_forward_propagation)
286
287 for test_case in test_cases:
→ 288 result = target_forward_propagation(test_case[“input”][“X”], test_case[“input”][“parameters”])
289
290 try:

in forward_propagation(X, Y)
18 # Implement Forward Propagation to calculate Z.
19 ### START CODE HERE ### (~ 2 lines of code)
—> 20 Z = np.dot(W.transpose(),X) + b
21 Y_hat = Z
22 ### END CODE HERE ###

<array_function internals> in dot(*args, **kwargs)

ValueError: shapes (1,1) and (2,5) not aligned: 1 (dim 1) != 2 (dim 0)

This is the error message I am receiving for using transpose.

An example off the top of my head is just looking off of the second example. The shape of W in the second part of this assignment is 1 x 2. That is, n_y = 1 and n_x = 2. The shape of X is 2 x 1460. So W x X is valid but X x W is not valid.

A significant issue here is that w is a scalar (size 1,1), but X is size (2,5).
W and X must always have one common size - that is the number of features.

Where do your X and W come from? I’m not familiar with this assignment (Not a mentor for this course).

This assignment is the SIngle Perceptron Neural Networks for Linear Regression in the Linear Algebra course for Machine Learning.

The W is an array of scalars, where w is the weight. The assignment refers to W as a matrix that needs to be multiplied by matrix X which represents the input nodes. X has size 1 * m in the first example. I am stuck on this multiplication in the step of defining the method, forward propagation.

This is the task: Implement Forward Propagation. Compute Z multiplying arrays w, X and adding vector b. Set the prediction array 𝐴 equal to 𝑍.

I think A is unnecessary here as it is never mentioned in the outline of the method. I am supposed to fill in the code for the line: Z = ?
I have written Z = np.dot(W, X) + b.

In general, that’s the correct way to compute the predictions in a linear regression. In detail, you may need to transpose either W or X depending on how the data is stored (i.e. with the examples as the rows, or the examples as columns, and whether W is a matrix or a vector).

Then if it’s a layer in a neural network, then you typically use an activation function in each layer, such as sigmoid. In that case, you would have A = sigmoid(Z).