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Deep learning library for research experiments

Project description

[!] 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.


  • <input type="checkbox" disabled="" /> Configuration-based experiment setup. Less code for more efficiency and reproducibility
  • <input type="checkbox" disabled="" /> Pytorch or Tensorflow 2.0 or scikit-learn as backend with similar training flow
  • <input type="checkbox" disabled="" /> 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
    name: dlex.torch.models.DNN
    layers: [200, 100]
    name: dlex.datasets.MNIST
    num_train: 100
    num_test: 10
    num_classes: 5
    num_epochs: 10
    batch_size: 128
        name: adam
        lr: 0.01
    metrics: [acc]""")
def train(configs: Configs):
    params = configs.get_default_params()
    report = PytorchBackend(params).run_train()

if __name__ == "__main__":




Contributions are more than welcome! Please get in touch if you would like to help out.

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