A lightweight deep learning library
Project description
tinynn
tinynn is a lightweight deep learning framework written in Python3 (with NumPy).
There are two branches (master
and mini
) in thie repo. The mini
branch contains the minimal components to run a neural network. The master
branch holds the latest stable code with more components and features. See Components down below.
Getting Started
Install
git clone https://github.com/borgwang/tinynn.git
cd tinynn
pip install -r requirements.txt
Examples
cd tinynn
# MNIST classification
python examples/mnist/run.py
# a toy regression task
python examples/nn_paint/run.py
# reinforcement learning demo (gym environment required)
python examples/rl/run.py
Components
- layers: Dense, Conv2D, ConvTranspose2D, MaxPool2D, Dropout, BatchNormalization
- activation: ReLU, LeakyReLU, Sigmoid, Tanh, Softplus
- losses: SoftmaxCrossEntropy, SigmoidCrossEntropy, MAE, MSE, Huber
- optimizer: RAdam, Adam, SGD, Momentum, RMSProp, Adagrad, Adadelta
Contribute
Please follow the Google Python Style Guide for Python coding style.
In addition, please sort the module import order alphabetically in each file. To do this, one can use tools like isort (be sure to use --force-single-line-imports
option to enforce the coding style).
License
MIT
Project details
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