#!/usr/bin/env python3

import torch                     # PyTorch needs to be installed

dim = 2
eps = 0.1
x = torch.ones(dim, requires_grad=True)  # leaf of computational graph
print("x           : ",x)
print("x.data      : ",x.data)
print("x[0]        : ",x[0])
print("x[0].item() : ",x[0].item())
print()

y = x + 2
out = torch.dot(y,y)             # scalar product
print("y      : ",y)
print("out    : ",out)
print()

out.backward()                   # backward pass --> gradients
print("x.grad : ",x.grad)

with torch.no_grad():            # detach from computational graph
  x -= eps*x.grad                # updating parameter tensor
  x.grad = None                  # flush

print("x      : ",x.data)

print("\n#---")
print("#--- .backward() adds new gradient to old gradient")
print("#---             convenient for batch updating")
print("#---\n")

y = torch.zeros(dim, requires_grad=True)  
torch.dot(y+1,y+1).backward()
print("y.grad : ",y.grad)
torch.dot(y+1,y+1).backward()
print("y.grad : ",y.grad)
torch.dot(y+1,y+1).backward()
print("y.grad : ",y.grad)
torch.dot(y+1,y+1).backward()