def forward_neuralnet(x):

    global weight, bias

    output = np.matmul(x, weight) + bias

    return output, x


def backprop_neuralnet(G_output, x):

    global weight, bias

    g_output_w = x.transpose()

    

    G_w = np.matmul(g_output_w, G_output)

    G_b = np.sum(G_output, axis=0)


    weight -= LEARNING_RATE * G_w

    bias -= LEARNING_RATE * G_b


신경망구조 수정해서 data accurancy 높이고 싶은데 경사하강법으로 높이려면 어떤 식으로 수정해야할지 모르겠음..