LSTM 模型中的时间步长到底是什么? [英] What exactly is timestep in an LSTM Model?

问题描述

作为一个整体，我是 LSTM 和 RNN 的新手，我一直在绞尽脑汁想了解什么是时间步长.我真的很感激对此的直观解释

解决方案

让我们从 Chris Olah 的博客(高度

在循环神经网络中，您会多次重复同一单元格.推理的方式是 - 您获取一些输入 (x₀)，将其通过单元格以获得一些输出₁(用图片右侧的黑色箭头表示)，然后将 output₁ 作为输入(可能在图像上添加更多输入组件 - x₁)传递到同一单元格，产生新的输出 output₂，再次将其作为输入传递给同一个单元格(同样可能还有额外的输入组件 x₂)，产生输出₃，依此类推.

时间步长是单元格的单次出现 - 例如在第一个时间步产生输出₁，h₀，在第二个时间步产生输出₂，依此类推.

I am a newbie to LSTM and RNN as a whole, I've been racking my brain to understand what exactly is a timestep. I would really appreciate an intuitive explanation to this

解决方案

Let's start with a great image from Chris Olah's blog (a highly recommended read btw):

In a recurrent neural network you have multiple repetitions of the same cell. The way inference goes is - you take some input (x₀), pass it through the cell to get some output₁(depicted with black arrow to the right on the picture), then pass output₁ as input(possibly adding some more input components - x₁ on the image) to the same cell, producing new output output₂, pass that again as input to the same cell(again with possibly additional input component x₂), producing output₃ and so on.

A time step is a single occurrence of the cell - e.g. on the first time step you produce output₁, h₀, on the second time step you produce output₂ and so on.

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