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tutorial-contents/202_variable.py

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+"""
+Know more, visit 莫烦Python: https://morvanzhou.github.io/tutorials/
+My Youtube Channel: https://www.youtube.com/user/MorvanZhou
+
+Dependencies:
+torch: 0.1.11
+"""
+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.]]
+"""