57 lines
1.5 KiB
Python
57 lines
1.5 KiB
Python
"""
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View more, visit my tutorial page: https://mofanpy.com/tutorials/
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My Youtube Channel: https://www.youtube.com/user/MorvanZhou
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Dependencies:
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torch: 0.1.11
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"""
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import torch
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from torch.autograd import Variable
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# Variable in torch is to build a computational graph,
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# but this graph is dynamic compared with a static graph in Tensorflow or Theano.
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# So torch does not have placeholder, torch can just pass variable to the computational graph.
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tensor = torch.FloatTensor([[1,2],[3,4]]) # build a tensor
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variable = Variable(tensor, requires_grad=True) # build a variable, usually for compute gradients
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print(tensor) # [torch.FloatTensor of size 2x2]
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print(variable) # [torch.FloatTensor of size 2x2]
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# till now the tensor and variable seem the same.
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# However, the variable is a part of the graph, it's a part of the auto-gradient.
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t_out = torch.mean(tensor*tensor) # x^2
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v_out = torch.mean(variable*variable) # x^2
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print(t_out)
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print(v_out) # 7.5
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v_out.backward() # backpropagation from v_out
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# v_out = 1/4 * sum(variable*variable)
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# the gradients w.r.t the variable, d(v_out)/d(variable) = 1/4*2*variable = variable/2
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print(variable.grad)
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'''
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0.5000 1.0000
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1.5000 2.0000
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'''
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print(variable) # this is data in variable format
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"""
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Variable containing:
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1 2
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3 4
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[torch.FloatTensor of size 2x2]
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"""
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print(variable.data) # this is data in tensor format
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"""
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1 2
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3 4
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[torch.FloatTensor of size 2x2]
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"""
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print(variable.data.numpy()) # numpy format
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"""
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[[ 1. 2.]
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[ 3. 4.]]
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""" |