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.
Features
- 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
Install
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()
Resources
- Documentation
- Getting Started
- Various model implementations
- Implementations of machine learning algorithms for graph
License
Contributing
Contributions are more than welcome! Please get in touch if you would like to help out.
Project details
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