Linear Regression giving poor results

Hi there, I implemented a simple linear regression and I’m getting some poor results. Just wondering if these results are normal or I’m making some silly mistake.

I tried different optimizers and learning rates, I always get bad/poor results

Here is my code:

import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt
from torch.autograd import Variable 

class LinearRegressionPytorch(nn.Module):
   def __init__(self, input_dim=1, output_dim=1):
       super(LinearRegressionPytorch, self).__init__()
       self.linear = nn.Linear(input_dim, output_dim)
   def forward(self,x):
       x = x.view(x.size(0),-1)
       y = self.linear(x)
       return y

output_dim = 1
if torch.cuda.is_available():
   model = LinearRegressionPytorch(input_dim, output_dim).cuda()
   model = LinearRegressionPytorch(input_dim, output_dim) 

criterium = nn.MSELoss()
l_rate =0.00001
optimizer = torch.optim.SGD(model.parameters(), lr=l_rate)
#optimizer = torch.optim.Adam(model.parameters(),lr=l_rate)
epochs = 100

#create data
x = np.random.uniform(0,10,size = 100) #np.linspace(0,10,100); 
y = 6*x+5
dy = 1 +5*np.random.random(y.shape)
noise = np.random.normal(0,dy)
y_noise = y+noise

plt.scatter(x,y,c='red', alpha = 0.25)
plt.legend(('Noisy data','clean data'))

#pass it to pytorch
x_data = torch.from_numpy(x).float()
y_data = torch.from_numpy(y_noise).float()
if torch.cuda.is_available():
   inputs = Variable(x_data).cuda()
   target = Variable(y_data).cuda()
   inputs = Variable(x_data)
   target = Variable(y_data)

for epoch in range(epochs):

   #predict data
   pred_y= model(inputs)

   #compute loss
   loss = criterium(pred_y, target)

   #zero grad and optimization
   #if epoch % 50 == 0:
   #   print(f'epoch = {epoch}, loss =  {loss.item()}')

model.eval() # evaluate mode
predict = model(inputs)
predict = predict.cpu()
predict =

plt.scatter(x_data.numpy(), y_data.numpy())
plt.plot(x_data.numpy(), predict,'r')
plt.legend(('Noisy data','Model prediction'))  

#print params
for name, param in model.named_parameters():
   if param.requires_grad:

Here are the poor results visually:

linear.weight tensor([[1.7374]], device='cuda:0')
linear.bias tensor([0.1815], device='cuda:0')

The results should be weight = 6 , bias = 5

Could you print the shapes of pred_y and target before passing them to your loss function?

thank you, that was the problem. I changed this line

loss = criterium(pred_y, target.view(-1,1))
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