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    110
  • Rank 316,770 (Top 7 %)
  • Language
    Python
  • License
    Apache License 2.0
  • Created over 7 years ago
  • Updated over 6 years ago

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Repository Details

The light deep learning framework for study and for fun. Join us!

Version Version Version Version

lightnn

The light(`light` means not many codes here) deep learning framework for study and for fun. Join us!

How to install

pip install

pip install lightnn

python install

python setup.py install

Modual structure

models

  • Sequential
  • Model

activations

  • identity(None)
  • sigmoid
  • relu
  • softmax
  • tanh
  • leaky relu
  • elu
  • selu
  • thresholded relu
  • softplus
  • softsign
  • hard sigmoid

losses

  • MeanSquareLoss
  • BinaryCategoryLoss
  • LogLikelihoodLoss
  • FocalLoss

initializers

  • zeros
  • ones
  • xavier uniform initializer(glorot uniform initializer)
  • default weight initializer
  • large weight initializer
  • orthogonal initializer

optimizers

  • SGD
  • Momentum(Nestrov included)
  • RMSProp
  • Adam
  • Adagrad
  • Adadelta

layers

  • FullyConnected(Dense)
  • Conv2d
  • MaxPooling
  • AvgPooling
  • Softmax
  • Dropout
  • Flatten
  • Activation
  • RNN
  • LSTM
  • GRU

utils

  • label smoothing
  • sparse to dense

gradient check

  • Dense
  • CNN and Pooling
  • RNN, LSTM and GRU

examples

  • MLP MNIST Classification
  • CNN MNIST Classification
  • RNN Language Model
  • LSTM Language Model
  • GRU Language Model

Document instructions

  • English for classes and functions
  • Chinese for annotation

References

  1. Keras: a polular deep learning framework based on tensorflow and theano.
  2. NumpyDL: a simple deep learning framework with manual-grad, totally written with python and numpy.([Warning] Some errors in backward part of this project)
  3. paradox: a simple deep learning framework with symbol calculation system. Lightweight for learning and for fun. It's totally written with python and numpy.
  4. Bingtao Han's blogs: easy way to go for deep learning([Warning] Some calculation errors in RNN part).