How to get a output of a hidden layer of a single-layer LSTM
You'll definitely want to name the layer you want to observe first (otherwise you'll be doing guesswork with the sequentially generated layer names):

model &lt;- keras_model_sequential() %&gt;% 
  layer_lstm(units = 64, 
             dropout = 0.2, 
             input_shape = c(seqlength, length(chars)),
             name = "lstm") %&gt;% 
  ...


The rest is pretty straightforward:

lstm_layer_model &lt;- keras_model(
  inputs = model$input, 
  outputs = get_layer(model, "lstm")$output
) 
lstm_output &lt;- predict(lstm_layer_model, new_data)


So if you want to know the LSTM neuron values for a given case, define that case's features as new_data and run the last line.