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Created 3 years ago

```
# Gradient descent(hard way). linear regretion
import torch
w = torch.tensor(-10.0, requires_grad = True) # Since requires_grad= True, python knows it is a variable
# Create values from -3 to 3 with increments of 0.1. The view command is used to add an additional dimension
X = torch.arange(-3,3,0.1).view(-1,1)
f = -3*x
import matplotlib.pyplot as plt
# The method numpy() convert to a numpy array (this allows to use matplotlib)
plt.plot(x.numpy(),f.numpy())
plt.show()
Y = f+0.1*torch.randn(x.size())
# Add some random noise
plt.plot(x.numpy(),y.numpy(),'ro')
# Define yhat= x*w
def forward(x):
return w*x
# Define the cost function
def criterion(yhat,y):
return torch.mean((yhat-y)**2)
Ir = 0.1 #Learning rate
# 4 iterations are performed
COST = [] # Create a list
for epoch in range(4):
Yhat = forward(X)
loss = criterion(Yhat,Y)
loss.backward() # This compute the derivative with respect all the variables in the cost function
w.data = w.data-Ir*w.grad.data
# .data atribute gives access to the date held in the variable; and .grad gives the derivative with respect to w
w.grad.data.zero_() # set the gradient to zero since pytorch calculate the gradient in iterative manner
w.data
COST.append(loss.item()) # item() is used to obtain the loss as a python number
```

```
import jovian
jovian.commit()
```

```
[jovian] Attempting to save notebook..
```

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