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import math
import torch
dtype = torch.float
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
lr = 1e-6
def train(X, y):
a = torch.randn((), device=device, dtype=dtype, requires_grad=True)
b = torch.randn((), device=device, dtype=dtype, requires_grad=True)
c = torch.randn((), device=device, dtype=dtype, requires_grad=True)
d = torch.randn((), device=device, dtype=dtype, requires_grad=True)
for i in range(2000):
y_pred = a + b*X + c*X**2 + d*X**3
loss = (y_pred - y).pow(2).sum()
if i % 100 == 0:
print('{}/{}: {}'.format(i, 2000, loss.item()))
loss.backward()
with torch.no_grad():
a -= lr * a.grad
b -= lr * b.grad
c -= lr * c.grad
d -= lr * d.grad
a.grad = None
b.grad = None
c.grad = None
d.grad = None
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)
y = torch.sin(X)
train(X, y)
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