def train(data_in, outputs, Ws, Bs, iters, alpha):
    c_his = []
    i_his = []
    for i in range(iters):
        (c, Bu, Wu) = costs(data_in, outputs, Ws, Bs)
        for j in range(len(Ws)):
            Bs[j] = np.add(Bs[j], np.multiply((- alpha), Bu[j]))
            Ws[j] = np.add(Ws[j], np.multiply((- alpha), Wu[j]))
        c_his.append(c)
        i_his.append(i)
    plt.plot(i_his, c_his)
    plt.show()
def train(data_in, outputs, Ws, Bs, iters, alpha):
    c_his = []
    i_his = []
    for i in range(iters):
        (c, Bu, Wu) = costs(data_in, outputs, Ws, Bs)
        for j in range(len(Ws)):
            Bs[j] = np.add(Bs[j], np.multiply((- alpha), Bu[j]))
            Ws[j] = np.add(Ws[j], np.multiply((- alpha), Wu[j]))
        c_his.append(c)
        i_his.append(i)
    plt.plot(i_his, c_his)
    plt.show()