Keras:LSTM辍学和LSTM反复辍学之间的区别
来自Keras文档:
dropout:在0到1之间浮动. 输入的线性变换.
dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs.
recurrent_dropout:在0到1之间浮动. 递归状态的线性变换.
recurrent_dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state.
任何人都可以指向每个辍学下方图像上的什么位置吗?
Can anyone point to where on the image below each dropout happens?
我建议看看本文.定期在输入和/或输出上施加压降，这意味着从x_t到h_t的垂直箭头.在您的情况下，如果将其添加为图层的参数，它将掩盖输入.您可以在循环图层之后添加一个Dropout图层，以屏蔽输出.递归退出屏蔽(或丢弃")递归单元之间的连接；那就是图片中的水平箭头.
I suggest taking a look at (the first part of) this paper. Regular dropout is applied on the inputs and/or the outputs, meaning the vertical arrows from x_t and to h_t. In your case, if you add it as an argument to your layer, it will mask the inputs; you can add a Dropout layer after your recurrent layer to mask the outputs as well. Recurrent dropout masks (or "drops") the connections between the recurrent units; that would be the horizontal arrows in your picture.
这张照片是从上面的纸上拍摄的.在左侧，输入和输出的定期删除.在右侧，常规辍学加上经常性辍学:
This picture is taken from the paper above. On the left, regular dropout on inputs and outputs. On the right, regular dropout PLUS recurrent dropout:
(在这种情况下，请忽略箭头的颜色；在本文中，他们进一步强调了在每个时间步上都保留相同的退出蒙版)
(Ignore the colour of the arrows in this case; in the paper they are making a further point of keeping the same dropout masks at each timestep)