Statefull or stateless architecture?

TensorFlow General Discussion
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1

I’m on working on classification problem

My model architecture looks:

model = Sequential()
model.add(Masking(mask_value=0.0, input_shape=(30, 2)))
model.add(LSTM(100, return_sequences=True))
model.add(LSTM(50, return_sequences=True))
model.add(LSTM(50, return_sequences=False)) 
model.add(Dense(10, activation='relu'))
model.add(Dense(3, activation='softmax'))

The input is represented like this:

  • 2 features- up to 30 samples (time series with different series length, between 10-30 samples).
  • 3 different classes
  • Each series (each input) may be taken from different times.

I have read several articles and posts about stateless and stateful LSTMs.

If I understand correctly, then for the problem I’m describing above - the correct architecture is stateless LSTM
(because these are sample lengths from different times (and also different lengths).

Am I right ?