Answers.Body: <p>The layer contains multiple parallel LSTM units (hidden states in tensorflow and dimensionality of the output space); they are identical and in parallel learn different things.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-09-01T19:15:16.657Z
Answers.LastActivityDate: 2021-09-01T19:15:16.657Z
Answers.UserDisplayName: Damir Tenishev
Body: <p>I have a basic understanding how a cell and a layer of an LSTM works. However, I get confused by what &quot;number of units&quot; (as termed in tensorflow) exactly means. A unit is, as far as I understand one &quot;instance&quot; of a LSTM, consisting of <span class="math-container">$t$</span> cells, for a sequence of length <span class="math-container">$t$</span>. When I have more than one unit, do these work in parallel (i.e. 10 units not interacting with each other) or sequentially (ouput of unit 1 is the input of unit 2 and so on).</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-09-02T15:06:39.130Z
Comments.Text: IIRC a unit is a neuron.
Comments.UserDisplayName: Criticize SE actions means ban
Tags: <tensorflow><long-short-term-memory>
Title: Do LSTM in tensorflow work sequentially or in parallel
UserDisplayName: Georg B
ThreadText: # Do LSTM in tensorflow work sequentially or in parallel
  
  (Asked by: username_0 on 2021-09-01)
  
  I have a basic understanding how a cell and a layer of an LSTM works. However, I get confused by what "number of units" (as termed in tensorflow) exactly means. A unit is, as far as I understand one "instance" of a LSTM, consisting of $t$ cells, for a sequence of length $t$. When I have more than one unit, do these work in parallel (i.e. 10 units not interacting with each other) or sequentially (ouput of unit 1 is the input of unit 2 and so on).
  
  **Tags:** tensorflow, long-short-term-memory
  > **Comments:**
  > **username_1**: IIRC a unit is a neuron.  
  
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  ## 1 answers
  
  ### Answer by username_2 on 2021-09-01. Score: 0 | Accepted answer
  
  The layer contains multiple parallel LSTM units (hidden states in tensorflow and dimensionality of the output space); they are identical and in parallel learn different things.
  
  
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