Hi, I have a couple of weather related data like tempreature etc… in time series format.
Now I would like to experiment with regression and classification tasks. (Like predicting tomorrow’s weather,or just simply classifying day as sunny/rainy/cloud …)
For this task I have an LSTM network but I don’t know I shall retain the hidden states or reset them periodically.
Technically speaking:
1.) Shall I invoke the recurrent layer without passing hiden states like this:
lstm_output, _ = lstm(inputs)

2.) Or save the hidden states somewhere and reset them at every minibatch:
lstm_output, new_hidden_states = self.lstm(inputs, hidden_states)

Any advice would be appreciated!