pytorch之 Variable

 import torch
 from torch.autograd import Variable

 # Variable in torch is to build a computational graph,
 # but this graph is dynamic compared with a static graph in Tensorflow or Theano.
 # So torch does not have placeholder, torch can just pass variable to the computational graph.

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

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

 # till now the tensor and variable seem the same.
 # However, the variable is a part of the graph, it's a part of the auto-gradient.

 t_out = torch.mean(tensor*tensor)       # x^2
 v_out = torch.mean(variable*variable)   # x^2
 print(t_out)
 print(v_out)    # 7.5

 v_out.backward()    # backpropagation from v_out
 # v_out = 1/4 * sum(variable*variable)
 # the gradients w.r.t the variable, d(v_out)/d(variable) = 1/4*2*variable = variable/2
 print(variable.grad)
 '''
  0.5000  1.0000
  1.5000  2.0000
 '''

 print(variable)     # this is data in variable format
 """
 Variable containing:
  1  2
  3  4
 [torch.FloatTensor of size 2x2]
 """

 print(variable.data)    # this is data in tensor format
 """
  1  2
  3  4
 [torch.FloatTensor of size 2x2]
 """

 print(variable.data.numpy())    # numpy format
 """
 [[ 1.  2.]
  [ 3.  4.]]
 """