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import torch
import math
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
dtype = torch.float
lr = 1e-3
class Poly3(torch.nn.Module):
def __init__(self):
super(Poly3, self).__init__()
self.a = torch.nn.Parameter(torch.randn(()))
self.b = torch.nn.Parameter(torch.randn(()))
self.c = torch.nn.Parameter(torch.randn(()))
self.d = torch.nn.Parameter(torch.randn(()))
def forward(self, x):
return self.a + self.b * x + self.c * x**2 + self.d * x**3
def __repr__(self):
return f'y = {self.a.item()} + {self.b.item()} x + {self.c.item()} x^2 + {self.d.item()} x^3'
def train(X, y):
for i in range(2000):
y_pred = model(X)
loss = loss_fn(y_pred, y)
if i % 100 == 0:
print('{}/{}: {}'.format(i, 2000, loss.item()))
# model.zero_grad()
opt.zero_grad()
loss.backward()
# with torch.no_grad():
# for param in model.parameters():
# param -= lr * param.grad
opt.step()
if __name__ == '__main__':
X = torch.linspace(-math.pi, math.pi, 2000, device=device, dtype=dtype)
y = torch.sin(X)
# p = torch.Tensor([1, 2, 3])
# X = X.unsqueeze(-1).pow(p)
# model = torch.nn.Sequential(
# torch.nn.Linear(3, 1),
# torch.nn.Flatten(0, 1)
# )
model = Poly3()
loss_fn = torch.nn.MSELoss(reduction='sum')
opt = torch.optim.RMSprop(model.parameters(), lr=lr)
train(X, y)
# weight_layer = model[0]
#
# print('y = {} + {}x + {}x^2 + {}x^3'.format(weight_layer.bias.item(),
# weight_layer.weight[0, 0].item(),
# weight_layer.weight[0, 1].item(),
# weight_layer.weight[0, 2].item()))
print(model)
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