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import torch
import math
import numpy as np
device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float
# a = torch.randn((), device=device, dtype=dtype)
# b = torch.randn((), device=device, dtype=dtype)
# c = torch.randn((), device=device, dtype=dtype)
# d = torch.randn((), device=device, dtype=dtype)
# params = [a, b, c, d]
lr = 1e-6
def train(X, y):
a = torch.randn((), device=device, dtype=dtype)
b = torch.randn((), device=device, dtype=dtype)
c = torch.randn((), device=device, dtype=dtype)
d = torch.randn((), device=device, dtype=dtype)
for i in range(2000):
y_pred = a + b*X + c*X**2 + d*X**3
loss = (y_pred - y).pow(2).sum().item()
if i % 50 == 0:
print(i, loss)
loss_grad = 2*(y_pred - y)
a_grad = loss_grad.sum()
b_grad = (loss_grad * X).sum()
c_grad = (loss_grad * X**2).sum()
d_grad = (loss_grad * X**3).sum()
# if i % 50 == 0:
# print(a_grad, b_grad, c_grad, d_grad)
a -= lr * a_grad
b -= lr * b_grad
c -= lr * c_grad
d -= lr * d_grad
print('a = {}, b = {}, c = {}, d = {}'.format(a.item(), b.item(), c.item(), d.item()))
if __name__ == '__main__':
X = torch.linspace(-math.pi, math.pi, 2000, device=device, dtype=torch.float)
y = torch.sin(X)
train(X, y)
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