Explore LSTM On Time Series Data (Without Any Code)
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The sole purpose for the development of this software was to make it easy for learners to understand how tuning several hyperparameters can effect the result of an LSTM (Long Short Term Memory) network on various types of time series data.
- Python 3.x
Running the App
REQUIREMENTS.txt, by running
pip install -r REQUIREMENTS.txt
- Set the hyperparameter values ( Dropout, Lag, Test Ratio, Max Epoch )
- Select a preloaded dataset
- To reset the console click
The application has been tested on Windows and Linux platforms. In case of any issue, feel free to raise an issue.
Increasing Sales dataset
Sinusoidal Curve Dataset
Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= unified_lstm (UnifiedLSTM) (None, LAG-1, 30) 3840 _________________________________________________________________ dropout (Dropout) (None, LAG-1, 30) 0 _________________________________________________________________ unified_lstm_1 (UnifiedLSTM) (None, 30) 7320 _________________________________________________________________ dense (Dense) (None, 1) 31 ================================================================= Total params: 11,191 Trainable params: 11,191 Non-trainable params: 0 _________________________________________________________________ None
Currently, the application supports 5 different datasets. We are going to add more datasets and probably improve the model in the next iteration of development. Contributions are welcomed.
- Sine Wave
- Cosine Wave
- Increasing Sales
- Decreasing Sales
- Random Data
Amitrajit Bose +