title: Meaning of the hidden state in Keras LSTM
body: <p>Since I am new to deep learning this question may be funny to you. but I couldn't visualize it in the mind. That's why I am asking about it. </p>
  
  <p>I am giving a sentence as the vector to the LSTM, Think I have a sentence contains 10 words. Then I change those sentences to the vectors and giving it to the LSTM.</p>
  
  <p>The length of the LSTM cells should be 10. But in most of the tutorials, I have seen they have added 128 hidden states. I couldn't understand and visualize it. What's that the word means by LSTM layer with <code>"128-dimensional hidden state"</code> </p>
  
  <p>for example:</p>
  
  <pre><code>X = LSTM(128, return_sequences=True)(embeddings)
  </code></pre>
  
  <p>The summery of this looks</p>
  
  <pre><code>lstm_1 (LSTM)                (None, 10, 128)           91648    
  </code></pre>
  
  <p>Here It looks like 10 LSTM cells are added but why are that 128 hidden states there? Hope you may understand what I am expecting. </p>