How to apply Mean Square Error row-wise in Python using NumPy without looping

I'm building a primitive neural network to emulate AND gate. The loss-fucntion is MSE:

def mse(predicted, desired): 
    return np.square(np.subtract(predicted, desired)).mean()

In the following there are a prediction, and the desired outputs (a.k.a. labels):

predicted = np.array
    ([[0.5000, 0.5000],   # 0 AND 0
      [0.4721, 0.5279],   # 0 AND 1
      [0.3049, 0.6951],   # 1 AND 0
      [0.3345, 0.6655]])  # 1 AND 1

desired = np.array
    ([[1, 0],   # False
      [1, 0],   # False
      [1, 0],   # False
      [0, 1]])  # True

Each row (in both of the above matrices) indicates a single case. I want to keep all the cases to be held together like this, rather than splitting them into vectors. The catch is, I need to treat each row individually.

I'm trying to get the following result, but yet I couldn't:

returned output = 
    [0.2500,  # 1st CASE ERROR
     0.2786,  # 2nd CASE ERROR
     0.4831,  # 3rd CASE ERROR 
     0.1118]  # 4th CASE ERROR

I tried the following function...

np.apply_along_axis(mse, 1, predicted, desired)

but it didn't work because "desire" is being passed as the whole matrix, rather than a row at a time. So, is there any way to achieve that without changing "mse function" implementation or loops?

Because all your data is in nicely formed ndarrays you can make NumPy do all the heavy lifting. In this case you can convert your for loop into a reduction along one of the array dimensions.

np.square(np.subtract(predicted, desired)).mean(1)

or

((predicted-desired)**2).mean(1)

which is more readable IMO.