You can find all the accompanying code in this Github repo

This is Part 2 of the PyTorch Primer Series.

**Linear Regression **is linear approach for modeling the relationship between inputs and the predictions

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 dependent variable.

**Creating Models in PyTorch**

- Create a Class
- Declare your Forward Pass
- Tune the HyperParameters

```
class LinearRegressionModel(nn.Module):
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 = 1
output_dim = 1
```

### Steps

- Create instance of model
- Select Loss Criterion
- Choose Hyper Parameters

```
model = LinearRegressionModel(input_dim,output_dim)
criterion = nn.MSELoss()# Mean Squared Loss
l_rate = 0.01
optimiser = torch.optim.SGD(model.parameters(), lr = l_rate) #Stochastic Gradient Descent
epochs = 2000
```

**Training The Model**

```
for epoch in range(epochs):
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.

You can find me on twitter @bhutanisanyam1

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