tinynn is a lightweight deep learning framework written in Python3 (with NumPy).
pip install tinynnExamples
62D6git clone https://github.com/borgwang/tinynn.git
cd tinynn/examples
# MNIST classification
python mnist/run.py
# a toy regression task
python nn_paint/run.py
# reinforcement learning demo (gym environment required)
python rl/run.py- layers: Dense, Conv2D, ConvTranspose2D, RNN, MaxPool2D, Dropout, BatchNormalization
- activation: ReLU, LeakyReLU, Sigmoid, Tanh, Softplus
- losses: SoftmaxCrossEntropy, SigmoidCrossEntropy, MAE, MSE, Huber
- optimizer: RAdam, Adam, SGD, Momentum, RMSProp, Adagrad, Adadelta
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).
MIT