Single model that predicts 2 (x_pole, y_pole) time series at once. Recurrent model is trained using dropout that
can be specified via dropout and recurrent_dropout parameters of the script. Number of units in recurrent layers
can be specified via ncells.
First train a network (parameters can be specified):
python model_dense_GRU3.py --dropout=0.2 --recurrent_dropout=0.2 --ncells=128
Set environment variable CUDA_VISIBLE_DEVICES to specify GPU device to use or choose another model:
env CUDA_VISIBLE_DEVICES=0 python model_dense_GRU3.py
Two models can run on the same GPU if they both fit into GPU memory.
A new model can be created in a distinct file, import train_model and parse_args utils from train_model.py to
unify your runs. New model should specify
Each run creates a separate directory in checkpoints_single_model named with a timestamp to store best checkpoints and
corresponding parameters.
Training process of all runs can be tracked using Tensorboard in a separate terminal window:
tensorboard --logdir log
In order to make predictions using some checkpoint, run make_predictions.py script:
python make_predictions.py --checkpoint checkpoints_single_model/GRU/3layers/256cells/2019-01-30_20-39-32/model-004.h5
TODO: compare different RNN models.
Use compare_forecasts.py script.
For now paths to different RNN predictions will be hardcoded.
Compare RNN forecasts with:
- Bulletin A forecasts;
- Pulkovo observatory forecasts;
- SSA forecasts.