PyTorch基本用法(二)――Variable

本文主要是PyTorch中Variable变量的一些用法。

import torch from torch.autograd import Variable  tensor = torch.FloatTensor([[1, 2], [3, 4]])  # 定义Variable, requires_grad用来指定是否需要计算梯度 variable = Variable(tensor, requires_grad = True)  print tensor print variable

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

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# 计算x^2的均值 tensor_mean = torch.mean(tensor * tensor) variable_mean = torch.mean(variable * variable) print tensor_mean print variable_mean

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7.5 Variable containing:  7.5000 [torch.FloatTensor of size 1] 

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# variable进行反向传播 # 梯度计算如下： # variable_mean = 1/4 * sum(variable * variable) # d(variable_mean)/d(variable) = 1/4 * 2 * variable = 1/2 * variable variable_mean.backward()  # 输出variable中的梯度 print variable.grad

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Variable containing:  0.5000  1.0000  1.5000  2.0000 [torch.FloatTensor of size 2x2] 

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# *表示逐元素点乘,不是矩阵乘法 print tensor * tensor print variable * variable

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  1   4   9  16 [torch.FloatTensor of size 2x2]  Variable containing:   1   4   9  16 [torch.FloatTensor of size 2x2] 

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# 输出variable中的data, data是tensor print variable.data

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

文章来源: PyTorch基本用法(二)――Variable