I built this framework as a small side project for educational purposes to allow people new to the field of Deep Learning to try their hands at building simple Neural Networks with minimal setup.
The only dependency for the framework is NumPy!
The framework while being extremely easy to use also allows a relatively high degree of customizability, from being able to configure every single Optimizer to generating your own classification training data right inside the framework allows for a ton of possibilities.
All implementations are fully documented to help understand the underlying logic for each component better.
- demo_Network_categorical.py
- demo_Network_binary_logistic_regression.py
- demo_Network_linear_regression.py
- Fully connected, Dropout Layers
- ReLU, Softmax, Sigmoid, and Linear activations
- Generate synthetic training data (Classification, and Regression data)
- Categorical Cross Entropy Loss, Binary Cross Entropy Loss, Mean Squared Error
- L1 & L2 regularization
- Backpropogation
- Optimizers
- SGD (with decay and momentum)
- AdaGrad
- RMSprop
- Adam
- Network Wrapper with TinyFlow backend
- The wrapper supports setting up a Neural Network and running a simple training loop using the above components, without having to write long error prone code.
- Make sure you have NumPy installed.
- Clone the repository to your system and place the TinyFlow folder in your project directory.
- Follow the steps in the demo.py to build your own network.
- Documentation