import optimizer pytorch

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Miguel
28 Mar 2020
1import torch
2import torch.nn as nn
3import torch.optim as optim
4from torch.autograd import Variable
5
6# Let's make some data for a linear regression.
7A = 3.1415926
8b = 2.7189351
9error = 0.1
10N = 100 # number of data points
11
12# Data
13X = Variable(torch.randn(N, 1))
14
15# (noisy) Target values that we want to learn.
16t = A * X + b + Variable(torch.randn(N, 1) * error)
17
18# Creating a model, making the optimizer, defining loss
19model = nn.Linear(1, 1)
20optimizer = optim.SGD(model.parameters(), lr=0.05)
21loss_fn = nn.MSELoss()
22
23# Run training
24niter = 50
25for _ in range(0, niter):
26	optimizer.zero_grad()
27	predictions = model(X)
28	loss = loss_fn(predictions, t)
29	loss.backward()
30	optimizer.step()
31
32	print("-" * 50)
33	print("error = {}".format(loss.data[0]))
34	print("learned A = {}".format(list(model.parameters())[0].data[0, 0]))
35	print("learned b = {}".format(list(model.parameters())[1].data[0]))
36
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