nn.Dropout随机丢神经元的用法

• 人工智能

前言：

pytorch与tensorflow中均有nn.dropout,两者之间的使用方法，下面将介绍。

一、torch.nn.dropout

说明文档：

r"""During training, randomly zeroes some of the elements of the input
tensor with probability :attr:`p` using samples from a Bernoulli
distribution. Each channel will be zeroed out independently on every forward
call.

This has proven to be an effective technique for regularization and
preventing the co-adaptation of neurons as described in the paper
`Improving neural networks by preventing co-adaptation of feature
detectors`_ .

Furthermore, the outputs are scaled by a factor of :math:`\frac{1}{1-p}` during
training. This means that during evaluation the module simply computes an
identity function.

大致的翻译：

在训练期间，随机地将输入的一些元素归零，以概率为`p`，使用伯努利分布的样本。每个通道将在每次前向调用时被独立清零。

这已被证明是一种有效的正则化技术，可以 防止神经元的共同适应，如论文中所述 &#

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