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When we fit an LSTM model, each LSTM has a cell state which contains the information we want. I am wondering what's the dimension in the cell state (i.e. cell state itself should be a vector, then how large is the vector?)?

enter image description here (from http://colah.github.io/posts/2015-08-Understanding-LSTMs/ )

i.e. In the above architecture, how do we determine the dimension of the matrix Wc?

If I am using Keras package, how do I find the Cell state dimensions of each LSTM unit? Is it something that one can adjust?

Thank you!

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2 Answers 2

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In Keras, the first argument in LSTM gives the dimensionality of the cell state.

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    $\begingroup$ Feel free to correct me but the first argument is "units", which is the number of LSTM neurons in the layer. Source: keras.io/layers/recurrent/#lstm $\endgroup$
    – pcko1
    Jun 3, 2018 at 20:50
  • $\begingroup$ In this case, neurons = dimensionality. $\endgroup$
    – sssam
    Jun 5, 2018 at 4:17
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Notice that the next hidden state $h_t$ is produced by applying pointwise scalar operations to the next cell state $C_t$. (The precise formula $h_t = o_t * \tanh(C_t)$ can be found in the blog post you link to.) Therefore, the cell state and the hidden state have the same dimensionality.

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