Deep learning library for research experiments
[!] This code is under development and mainly for my personal use. This project is for fast prototyping of deep learning and machine learning model with minimal code. Some parts of the code may not be well-commented or lack of citation.
dlex is an open source framework for machine learning scientific experiment.
- [ ] Configuration-based experiment setup. Less code for more efficiency and reproducibility
- [ ] Pytorch or Tensorflow 2.0 or scikit-learn as backend with similar training flow
- [ ] Convenient "environment" for training similar models or tuning hyperparameter
To install the current release
pip install dlex
Try your first dlex program
from dlex import yaml_configs, Configs from dlex.torch import PytorchBackend @yaml_configs("""backend: pytorch model: name: dlex.torch.models.DNN layers: [200, 100] dataset: name: dlex.datasets.MNIST num_train: 100 num_test: 10 num_classes: 5 train: num_epochs: 10 batch_size: 128 optimizer: name: adam lr: 0.01 test: metrics: [acc]""") def train(configs: Configs): params = configs.get_default_params() report = PytorchBackend(params).run_train() print(report.results) if __name__ == "__main__": train()
- Getting Started
- Various model implementations
- Implementations of machine learning algorithms for graph
Contributions are more than welcome! Please get in touch if you would like to help out.
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