def compute_grads_num(X, Y, P, Ws, bs, lamda, h):
        """ Converted from matlab code, borrowed from given code """
        #no     =   Ws.shape[0]
        #d  =   X.shape[0]

        grads_W = []
        grads_b= []
        for i in range(len(Ws)):
                grads_W.append( np.zeros(Ws[i].shape) )
                grads_b.append( np.zeros(bs[i].shape) )
                #grad_W = np.zeros(Ws.shape);
                #grad_b = np.zeros(bs.shape);

        c = compute_cost(X, Y, Ws, bs, lamda)

        #print('\n\nCheckinig dimensions in compute grads num:\nb grads ({},{})\nW grads ({},{})'.format(len(bs),[x.shape for x in bs],len(Ws),[x.shape for x in Ws]))
        print('Starting numerical computation of gradient')
        for layer_index in range(len(bs)):
                print('b layer {}'.format(layer_index))
                for i in range(len(bs[layer_index])):
                        print(' cell {}/{}'.format(i,len(bs[layer_index])))
                        b_try = np.array(bs,copy=True)
                        b_try[layer_index][i] += h
                        c2 = compute_cost(X, Y, Ws, b_try, lamda)
                        grads_b[layer_index][i] = np.sum(c2-c) / h
        print('numerical gradient of b calculated, starting W')

        for layer_index in range(len(Ws)):
                print('W layer {}'.format(layer_index))
                for i in range(Ws[layer_index].shape[0]):
                        for j in range(Ws[layer_index].shape[1]):
                              print('  {},{} of {}, {}'.format(i,j,Ws[layer_index].shape[0],Ws[layer_index].shape[1]))
                              W_try = np.array(Ws,copy=True)
                              W_try[layer_index][i,j] += h
                              c2 = compute_cost(X, Y, W_try, bs, lamda)
                              grads_W[layer_index][i,j] = np.sum(c2-c) / h

        return [grads_W, grads_b]