hw04.py

import torch

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

p=torch.Tensor([1.0])
p.requires_grad = True
q=torch.Tensor([1.0])
q.requires_grad = True
b=torch.Tensor([1.0])
b.requires_grad = True


def forward(x):
    return p*x*x + q*x + b

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

print('Predict(before training):', 4, forward(4).item())
for epoch in range(100):
    for x,y in zip(x_data, y_data):
        l = loss(x, y)
        l.backward()
        print('\tgrad', x, y, p.grad.item(), q.grad.item(), b.grad.item())
        p.data = p.data - 0.01 * p.grad.data
        q.data = q.data - 0.01 * q.grad.data
        b.data = b.data - 0.01 * b.grad.data
        p.grad.data.zero_()
        q.grad.data.zero_()
        b.grad.data.zero_()
    print('Progress:' ,epoch, l.item())
print('Predict(after training):', 4, forward(4).item())