Flint

A toy deep learning framework implemented in Numpy from scratch with a PyTorch-like API. I’m trying to make it as clean as possible.

Flint is not as powerful as torch, but it is still able to start a fire.

Installation

git clone https://github.com/Renovamen/flint.git
cd flint
python setup.py install

or

pip install git+https://github.com/Renovamen/flint.git --upgrade

API Documentation

• flint
• flint.tensor
• flint.nn
• flint.nn.functional
• flint.nn.init
• flint.optim
• flint.utils.data

Tutorials

• Simple Example

Features

Core

Support autograding on the following operations:

• Add

• Substract

• Negative

• Muliply

• Divide

• Matmul

• Power

• Natural Logarithm

• Exponential

• Sum

• Max

• Softmax

• Log Softmax

• View

• Transpose

• Permute

• Squeeze

• Unsqueeze

• Padding

Layers

• Linear

• Convolution (1D / 2D)

• MaxPooling (1D / 2D)

• Unfold

• Flatten

• Dropout

• Sequential

• Identity

Optimizers

• SGD

• Momentum

• Adagrad

• RMSprop

• Adadelta

• Adam

Loss Functions

• Cross Entropy

• Negative Log Likelihood

• Mean Squared Error

• Binary Cross Entropy

Activation Functions

• ReLU

• Sigmoid

• Tanh

• Leaky ReLU

• GELU

Initializers

• Fill with zeros / ones / other given constants

• Uniform / Normal

• Xavier (Glorot) uniform / normal (Understanding the Difficulty of Training Deep Feedforward Neural Networks. Xavier Glorot and Yoshua Bengio. AISTATS 2010.)

• Kaiming (He) uniform / normal (Delving Deep into Rectifiers: Surpassing Human-level Performance on ImageNet Classification. Kaiming He, et al. ICCV 2015.)

• LeCun uniform / normal (Efficient Backprop. Yann LeCun, et al. 1998.)

Others

• Dataloaders

License

MIT

Acknowledgements

Flint is inspired by the following projects:

• PyTorch

• karpathy/micrograd

• teddykoker/tinyloader