BILSTM

Description

This API calls the BILSTM model, which can be used for anomaly detection, classification, time series prediction, and forecasting.

BILSTM Model Architecture

Image Source: Modelling Radiological Language with Bidirectional Long Short-Term Memory Networks, Cornegruta et al

Extends the functionality of the traditional LSTM (Long Short-Term Memory) network by adding an additional LSTM layer that processes the sequence in reverse order.

A typical BILSTM layer consists of:

  • A forward LSTM layer.
    • Process the sequence from start to end.
  • A backward LSTM layer.
    • Process the sequence from end to start.

Finally, the outputs of the two layers are combined (e.g., concatenated or averaged) to generate the final representation.

Key Features of BILSTM:

  • Context Awareness
    • Unlike standard LSTMs, which only consider past information, BILSTM uses both past and future context, making it more effective for tasks where the entire sequence context is important.
  • Sequence Dependency Handling
    • It excels in tasks where dependencies between distant time steps are critical.
  • Flexibility
    • Works well for time series data, text data, and any sequence data.

API module path

from api.v2.model.BILSTM import BiLSTM

Parameters

Input_dim

  • Specifies the input feature size.
  • Example
    • If data's shape (x,x,1).
    • input_dim = 1

Hidden_dim

  • Specifies the hidden dim size.
  • This value sets the number of nodes in the intermediate layer.
  • Example
    • This value is typically set as a power of 2.
    • Hidden_dim = 64

Num_layer

  • Specifies the number of LSTM layers.
  • Example
    • num_layers = 2

Output_dim

  • Specifies the output dimension.
  • The value varies depending on the objective of the model.

Dropout

  • Specifies the dropout rate of LSTM layers.
  • Default : 0.2
  • Example
    • dropout = 0.2

Use_revin

  • Specifies whether to use RevIN normalization.
  • default : False
  • Example
    • Use_revin = False

Example Sample Code (Python)

Results

Check the entire module code.

datahub/api/v2/model/BILSTM.py at main · machbase/datahub
All Industrial IoT DataHub with data visualization and AI source - machbase/datahub

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