You can find all the accompanying code in this Github repo This is Part 2 of the PyTorch . Primer Series is linear approach for modeling the relationship between inputs and the predictions Linear Regression Source: Wikipedia We find a ‘Linear fit’ to the data. Fit: We are trying to predict a variable y, by fitting a curve (line here) to the data. The curve in linear regression follows a linear relationship between the scalar (x) and variable. dependent Creating Models in PyTorch Create a Class Declare your Forward Pass Tune the HyperParameters (nn.Module): class LinearRegressionModel **def** \_\_init\_\_(self, input\_dim, output\_dim):  

    super(LinearRegressionModel, self).\_\_init\_\_()   
    _\# Calling Super Class's constructor_  
    self.linear = nn.Linear(input\_dim, output\_dim)  
    _\# nn.linear is defined in nn.Module_  

**def** forward(self, x):  
    _\# Here the forward pass is simply a linear function_  

    out = self.linear(x)  
    **return** out input_dim = 1output_dim = 1 Steps Create instance of model Select Loss Criterion Choose Hyper Parameters model = LinearRegressionModel(input_dim,output_dim) criterion = nn.MSELoss() l_rate = 0.01optimiser = torch.optim.SGD(model.parameters(), lr = l_rate) # Mean Squared Loss #Stochastic Gradient Descent epochs = 2000 Training The Model epoch range(epochs): for in epoch +=1  
#increase the number of epochs by 1 every time

inputs = Variable(torch.from\_numpy(x\_train))  
labels = Variable(torch.from\_numpy(y\_correct))  

_#clear grads as discussed in prev post_

optimiser.zero\_grad()

_#forward to get predicted values_

outputs = model.forward(inputs)  
loss = criterion(outputs, labels)  
loss.backward()_\# back props_  
optimiser.step()_\# update the parameters_  
print('epoch **{}**, loss **{}**'.format(epoch,loss.data\[0\])) Finally, Print the Predicted Values predicted =model.forward(Variable(torch.from_numpy(x_train))).data.numpy() plt.plot(x_train, y_correct, 'go', label = 'from data', alpha = .5)plt.plot(x_train, predicted, label = 'prediction', alpha = 0.5)plt.legend()plt.show()print(model.state_dict()) If you want to read about Week 2 in my Self Driving Journey, here is the blog post The Next Part in the Series will discuss about . Linear Regression , connect with me on You can find me on Twitter @bhutanisanyam1 Linkedin here Subscribe to my Newsletter for a weekly curated list of Deep Learning and Computer Vision Reads

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Linear Regression in 2 Minutes (using PyTorch)

Linear Regression in 2 Minutes (using PyTorch)

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