Tefla is a deep learning mini-framework that sits on top of Tensorflow. Tefla's primary goal is to enable simple, stable, end-to-end deep learning. This means that Tefla supports:

• Data setup
• Batch preprocessing and data layout.
• Training
• A model definition DSL.
• A training config DSL.
• Data loading with data-augmentation and rebalancing.
• Training with support for visualization, logging, custom metrics, and most importantly - resumption of training from an earlier epoch with a new learning rate.
• Pluggable learning rate decay policies.
• Stability and solidity - which translates to days and weeks of training without memory blowup and epoch time degradations.
• Tensorboard visualization of epoch metrics, augmented images, model graphs, and layer activations, weights and gradients.
• Prediction (with ensembling via mean score or voting).
• Metrics on prediction outputs.
• First class support for transfer learning and fine-tuning based on vgg16, resnet50, resnet101, and resnet152.
• Serving of models via a REST API (coming soon).

Tefla contains command line scripts to do batch preprocessing, training, prediction, and metrics, thus supporting a simple yet powerful deep learning workflow.

Documentation is coming soon. For now, the mnist example(s) can help you to get started.

Tefla is very much a work in progress. Contributions are welcome!

An interesting fork of tefla is available here: www.github.com/n3011/tefla. Both projects are evolving independently, with a cross-pollination of ideas.