- Clone this repository :
git clone https://github.com/Wizaron/deep-forecast-pytorch.git - Download and install Anaconda or Miniconda
- Go to the "reseg-pytorch/code/pytorch" :
cd reseg-pytorch/code/pytorch - Create environment :
conda env create -f conda_environment.yml - Activate environment :
source activate deep-forecast-pytorch
- "data" : Stores data and scripts to prepare dataset for training.
- "lib" : Stores miscellaneous scripts for training and testing.
- "arch.py" : Defines network architecture
- "model.py" : Defines model (Minibatching mechanism, optimization, criterion, fit, predict, etc.)
- "prediction.py" : Metrics and plots to evaluate the performance of the trained model
- "data.py" : Creates training, validation and testings datasets
- "loader.py" : Creates Dataset loader for PyTorch
- "train.py" : Main training script.
- "test.py" : Main testing script.
- "settings.py" : Defines hyper-parameters of the model.
- Data is downloaded from IEM
- Download data and save it under "data/raw"
- To prepare dataset, run the scripts in "data/scripts"
- Train :
python train.py --data [PATH OF PREPARED DATASET] - Test :
python test.py --data [PATH OF PREPARED DATASET] --model [PATH OF THE SAVED MODEL] - For more info :
python train.py --help,python test.py --help
- It saves models and logs under "models"
- At the end of the training, it saves predictions under "outputs"
- It saves predictions under the directory of the model.
