#!/usr/bin/python3

import torch

m = torch.tensor([[2., 1.], [-1., 2.]])


def fun(x):
    return m @ x


def loss(y):
    return torch.sum((y - torch.tensor([3., 2.]))**2)


x = torch.rand(2, requires_grad=True)

learning_rate = torch.tensor(0.01)

for _ in range(100):
    y = fun(x)
    cost = loss(y)
    print(x, " => ", y, " ", cost)
    cost.backward()

    with torch.no_grad():
        x = x - learning_rate * x.grad

    x.requires_grad_(True)