torch

import torch

x_data = [1.0, 2.0, 3.0, 4.0]
y_data = [2.0, 4.0, 6.0, 8.0]

w = torch.Tensor([1.0])
w.requires_grad = True

def forward(x):
    return x * w

def loss(x, y):
    y_pred = forward(x)
    return (y_pred - y) ** 2

print("predict (before training)", 5, forward(5).item())

epochs = 100
lea = 0.01
for epoch in range(epochs):
    print('epoch:', epoch)

    for x, y in zip(x_data, y_data):
        l = loss(x, y)
        l.backward()
        print('tgrad:', x, y, w.grad.item())
        w.data = w.data - lea * w.grad.data

        w.grad.data.zero_()
    print("progress:", epoch, l.item())
print("predict (after training)", 5, forward(5).item())