/NN-TimeLTSM

Primary LanguagePython

NN-TimeLSTM

Introduction

Development History

  • TimeLSTMLayer.py and LSTM_AlexGraves.py are 1st version of codes. However, it is obsolete because it ignores the information exchange among different neurons.
  • LSTM_v1.py is a self-written LSTM to test our ability and to prove that weight initialisation is important.
  • TimeLSTM_v1.py is our replication of Alex Graves LSTM
  • TimeLSTM_v2.py uses less parameters
  • TimeLSTM_v3.py uses 3 types of time gates
  • TimeLSTM_v4.py introduces the prediction end time as an additive output

Device Management

  • In our model nn.Parameter is kept on the CPU while only computation is performed on the GPU in a differentiable manner. That way, it will automatically accumulate back onto the CPU parameters. See the explanation by albanD
  • Also, our model supports multiple GPUs as in the stackoverflow explanation

hidden states -> predicted RSSI

  • 1st solution is to use a DNN to exact the predicted RSSI for the next available beacon interval Hidden states features at n features 
    unknown_feature
...
unknown_feature
rssi_last,
left_available_time,
    We make another time hidden states at n features. It is created by another DNN from the difference between the estimated time period and the last available time point (two difference).
  • 2nd solution is to take the difference between the estimated time period and the last available time point (two difference),

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