رگرسیون خطی با استفاده از PyTorch

# We can run this Python code on a Jupyter notebook
# to automatically install the correct version of 
# PyTorch.

# http://pytorch.org / from os import path
from wheel.pep425tags import get_abbr_impl, get_impl_ver, get_abi_tag
platform = '{}{}-{}'.format(get_abbr_impl(), get_impl_ver(), get_abi_tag())

accelerator = 'cu80' if path.exists('/opt / bin / nvidia-smi') else 'cpu'

! pip install -q http://download.pytorch.org / whl/{accelerator}/torch-1.3.1.post4-{platform}-linux_x86_64.whl torchvision

import torch
from torch.autograd import Variable

x_data = Variable(torch.Tensor([[1.0], [2.0], [3.0]]))
y_data = Variable(torch.Tensor([[2.0], [4.0], [6.0]]))

class LinearRegressionModel(torch.nn.Module):

	def __init__(self):
		super(LinearRegressionModel, self).__init__()
		self.linear = torch.nn.Linear(1, 1) # One in and one out

	def forward(self, x):
		y_pred = self.linear(x)
		return y_pred

# our model
our_model = LinearRegressionModel()

criterion = torch.nn.MSELoss(size_average = False)
optimizer = torch.optim.SGD(our_model.parameters(), lr = 0.01)

for epoch in range(500):

	# Forward pass: Compute predicted y by passing 
	# x to the model
	pred_y = our_model(x_data)

	# Compute and print loss
	loss = criterion(pred_y, y_data)

	# Zero gradients, perform a backward pass, 
	# and update the weights.
	optimizer.zero_grad()
	loss.backward()
	optimizer.step()
	print('epoch {}, loss {}'.format(epoch, loss.item()))

new_var = Variable(torch.Tensor([[4.0]]))
pred_y = our_model(new_var)
print("predict (after training)", 4, our_model(new_var).item())

import torch
from torch.autograd import Variable

x_data = Variable(torch.Tensor([[1.0], [2.0], [3.0]]))
y_data = Variable(torch.Tensor([[2.0], [4.0], [6.0]]))


class LinearRegressionModel(torch.nn.Module):

	def __init__(self):
		super(LinearRegressionModel, self).__init__()
		self.linear = torch.nn.Linear(1, 1) # One in and one out

	def forward(self, x):
		y_pred = self.linear(x)
		return y_pred

# our model
our_model = LinearRegressionModel()

criterion = torch.nn.MSELoss(size_average = False)
optimizer = torch.optim.SGD(our_model.parameters(), lr = 0.01)

for epoch in range(500):

	# Forward pass: Compute predicted y by passing 
	# x to the model
	pred_y = our_model(x_data)

	# Compute and print loss
	loss = criterion(pred_y, y_data)

	# Zero gradients, perform a backward pass, 
	# and update the weights.
	optimizer.zero_grad()
	loss.backward()
	optimizer.step()
	print('epoch {}, loss {}'.format(epoch, loss.item()))

new_var = Variable(torch.Tensor([[4.0]]))
pred_y = our_model(new_var)
print("predict (after training)", 4, our_model(new_var).item())