L_model_forward.py

def L_model_forward(X, parameters):

    caches = []
    A = X
    L = len(parameters) // 2    # number of layers in the neural network
    
    for l in range(1, L):
        A_prev = A 
        A, cache = linear_activation_forward(A_prev, 
                                             parameters['W' + str(l)], 
                                             parameters['b' + str(l)], 
                                             activation='relu')
        caches.append(cache)
    
    AL, cache = linear_activation_forward(A, 
                                          parameters['W' + str(L)], 
                                          parameters['b' + str(L)], 
                                          activation='sigmoid')
    caches.append(cache)
    
    assert(AL.shape == (1, X.shape[1]))
            
    return AL, caches