这篇文章主要介绍了对pytroch中torch.autograd.backward的思考，文中通过示例代码介绍的非常详细，对大家的学习或者工作具有一定的参考学习价值，需要的朋友们下面随着小编来一起学习学习吧
反向传递法则是深度学习中最为重要的一部分，torch中的backward可以对计算图中的梯度进行计算和累积
这里通过一段程序来演示基本的backward操作以及需要注意的地方
>>> import torch >>> from torch.autograd import Variable >>> x = Variable(torch.ones(2,2), requires_grad=True) >>> y = x + 2 >>> y.grad_fn Out[6]: >>> y.grad >>> z = y*y*3 >>> z.grad_fn Out[9]: >>> z Out[10]: Variable containing: 27 27 27 27 [torch.FloatTensor of size 2x2] >>> out = z.mean() >>> out.grad_fn Out[12]: >>> out.backward() # 这里因为out为scalar标量，所以参数不需要填写 >>> x.grad Out[19]: Variable containing: 4.5000 4.5000 4.5000 4.5000 [torch.FloatTensor of size 2x2] >>> out # out为标量 Out[20]: Variable containing: 27 [torch.FloatTensor of size 1] >>> x = Variable(torch.Tensor([2,2,2]), requires_grad=True) >>> y = x*2 >>> y Out[52]: Variable containing: 4 4 4 [torch.FloatTensor of size 3] >>> y.backward() # 因为y输出为非标量，求向量间元素的梯度需要对所求的元素进行标注，用相同长度的序列进行标注 Traceback (most recent call last): File "C:\Users\dell\Anaconda3\envs\my-pytorch\lib\site-packages\IPython\core\interactiveshell.py", line 2862, in run_code exec(code_obj, self.user_global_ns, self.user_ns) File "", line 1, in y.backward() File "C:\Users\dell\Anaconda3\envs\my-pytorch\lib\site-packages\torch\autograd\variable.py", line 156, in backward torch.autograd.backward(self, gradient, retain_graph, create_graph, retain_variables) File "C:\Users\dell\Anaconda3\envs\my-pytorch\lib\site-packages\torch\autograd\__init__.py", line 86, in backward grad_variables, create_graph = _make_grads(variables, grad_variables, create_graph) File "C:\Users\dell\Anaconda3\envs\my-pytorch\lib\site-packages\torch\autograd\__init__.py", line 34,<i style="color:transparent">来源gaodai$ma#com搞$代*码*网</i> in _make_grads raise RuntimeError("grad can be implicitly created only for scalar outputs") RuntimeError: grad can be implicitly created only for scalar outputs >>> y.backward(torch.FloatTensor([0.1, 1, 10])) >>> x.grad #注意这里的0.1,1.10为梯度求值比例 Out[55]: Variable containing: 0.2000 2.0000 20.0000 [torch.FloatTensor of size 3] >>> y.backward(torch.FloatTensor([0.1, 1, 10])) >>> x.grad # 梯度累积 Out[57]: Variable containing: 0.4000 4.0000 40.0000 [torch.FloatTensor of size 3] >>> x.grad.data.zero_() # 梯度累积进行清零 Out[60]: 0 0 0 [torch.FloatTensor of size 3] >>> x.grad # 累积为空 Out[61]: Variable containing: 0 0 0 [torch.FloatTensor of size 3] >>> y.backward(torch.FloatTensor([0.1, 1, 10])) >>> x.grad Out[63]: Variable containing: 0.2000 2.0000 20.0000 [torch.FloatTensor of size 3]
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转载请注明原文链接：浅谈对pytroch中torch.autograd.backward的思考
