02_Variable_Autograd

In [3]:

    

import torch
from torch.autograd import Variable


    

In [37]:

    

tensor = torch.FloatTensor([[1,2],[3,4]])            # build a tensor
x = Variable(tensor, requires_grad=True)      # build a variable, usually for compute gradients

print(tensor)       # [torch.FloatTensor of size 2x2]
print(x)     # [torch.FloatTensor of size 2x2]


    

    




 1  2
 3  4
[torch.FloatTensor of size 2x2]

Variable containing:
 1  2
 3  4
[torch.FloatTensor of size 2x2]

In [42]:

    

x.sum(), x.sqrt(), x**2


    

    
Out[42]:





(Variable containing:
  10
 [torch.FloatTensor of size 1], Variable containing:
  1.0000  1.4142
  1.7321  2.0000
 [torch.FloatTensor of size 2x2], Variable containing:
   1   4
   9  16
 [torch.FloatTensor of size 2x2])

In [32]:

    

x.data


    

    
Out[32]:





 1  2
 3  4
[torch.FloatTensor of size 2x2]

In [34]:

    

x.requires_grad, x.grad, x.grad_fn, x.volatile


    

    
Out[34]:





(True, None, None, False)

In [123]:

    

# create graph

x = Variable(torch.FloatTensor([3]),requires_grad=True)
y = 2*x +3

x.requires_grad, y.requires_grad


    

    
Out[123]:





(True, True)

In [77]:

    

y.grad_fn.next_functions[0][0]


    

    
Out[77]:





<torch.autograd.function.MulConstantBackward at 0x7f97386e28b8>

In [124]:

    

y.backward()  # calculate dy / dx   == y.backward(torch.Tensor([1.0]))


    

In [125]:

    

x.grad # dy / dx is stored and accmulated


    

    
Out[125]:





Variable containing:
 2
[torch.FloatTensor of size 1]

In [126]:

    

x.grad.data.zero_() # empty gradient storage


    

    
Out[126]:





 0
[torch.FloatTensor of size 1]

In [127]:

    

y.backward(torch.Tensor([3.0])) # calculate dy / dx


    

In [128]:

    

x.grad


    

    
Out[128]:





Variable containing:
 6
[torch.FloatTensor of size 1]

In [167]:

    

x1 = Variable(torch.ones(2), requires_grad=True)
x2 = Variable(torch.ones(2))


    

In [168]:

    

y = x1*2 +  x2
y


    

    
Out[168]:





Variable containing:
 3
 3
[torch.FloatTensor of size 2]

In [169]:

    

y.backward(torch.Tensor([1,1]))


    

In [170]:

    

x1.grad, x2.grad


    

    
Out[170]:





(Variable containing:
  2
  2
 [torch.FloatTensor of size 2], None)

In [171]:

    

x1.grad.data.numpy()


    

    
Out[171]:





array([ 2.,  2.], dtype=float32)

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