[Pytorch] Linear Regression Prediction

간단한 Linear Regression모델 선언

import torch.nn as nn

class LR(nn.Module):
	def __init__(self, in_size, output_size):
    	super(LR, self).__init__()
        self.linear = nn.Linear(in_size, output_size)
        
    def forward(self, x):
    	out = self.linear(x)
        return out
        
# Linear Regression model 생성
model = LR(1, 1)

# Linear Regression bias, weight 변수 확인
model.state_dict()
"""
OrderedDict([('linear.weight', tensor([[-0.3027]])),
             ('linear.bias', tensor([0.4232]))])
"""

model.parameters()
"""
[Parameter containing:
 tensor([[-0.3027]], requires_grad=True), Parameter containing:
 tensor([0.4232], requires_grad=True)]
"""

# weight, bias 변수를 고치고싶다면
model.state_dict()['linear.weight'].data[0] = torch.tensor([0.5133])
model.state_dict()['linear.bias'].data[0] = torch.tensor([-0.4414])

x = torch.tensor([1.0])
yhat = model(x)
yhat # tensor([[0.0739]])

x= torch.tensor([[1.0], [2.0], [3.0]])
yhat = model(x)
yhat
"""
tensor([[-0.8548],
        [-1.4548],
        [-2.0548]], grad_fn=<AddmmBackward>)
"""

nn.Linear(in_size, output_size)

in_size, output_size는 bias, weight의 shape를 조정하는 수치