Understanding backpropagation with softmax

I am sorry if this is a stupid question, but I just can't wrap my head around this.I am trying to create my first neural network, which takes MNIST data (28x28) on which are hand-drawn numbers 0-9 and output which digit neural networks thinks it is. In the last layer, I need to do a softmax function, which can output all the probabilities of those numbers, which then sum up to 1.

def softmax(z):    exps = np.exp(z - z.max())    return exps/np.sum(exps), z

To this point, everything should be fine.But now we get to the backpropagation part => I have found out on the internet this softmax function for backpropagation.

def softmax_backward(dA, Z):    x, _ =softmax(dA)    s=x.reshape(-1,1)    return (np.diagflat(s) - np.dot(s, s.T))

Question 1: Is this softmax derivative function suitable for my NN?

If it is suitable, then I have error somewhere else.This is my error:

--------------------------------------------------------------------------- ValueError                                Traceback (most recent calllast) <ipython-input-21-b0721d5dd18f> in <module>---> 26 parameters = model(x_testone, y_testone, layer_dims)<ipython-input-20-0653ba2ad7e6> in model(X, y, layer_dims,learning_rate, epochs, print_cots, activation)     10         zCache = zCaches[l+1]     11 ---> 12         grads = L_model_backward(Al, y, linCaches, zCaches, activation)     13      14         parameters = update_parameters(parameters, grads, learning_rate)<ipython-input-18-8b76528a319a> in L_model_backward(Al, y, linCaches,zCaches, activation)---> 11     grads["dA"+ str(L-1)], grads["dW"+ str(L)], grads["db"+ str(L)] = liner_activ_backward(dAl, zCaches[L-1], linCaches[L-1],"softmax")     12 <ipython-input-17-6e9b3ba31571> in liner_activ_backward(dA, zCache,linCache, activation)     20         dZ = softmax_backward(dA, Z)---> 21         dA_prev, dW, db = linear_backward(dZ, linCache)     22         return dA_prev, dW, db     23 <ipython-input-16-4b15b65d0522> in linear_backward(dZ, linCache)----> 7     dW = (1/m) * np.dot(dZ, A_prev.T)      8     db = (1/m) * np.sum(dZ, axis=1, keepdims=True)      9     dA_prev = np.dot(W.T, dZ)ValueError: shapes (10000,10000) and (20,1000) not aligned: 10000 (dim1) != 20 (dim 0) ```

Now I think that my error is in liner_backward method, because it's not compatible with softmax. Am I right with this, or totally wrong?

Question 2: What method should I use instead of linear_backward method?

Many thanks for any help!