Saga - A small C++ framework for deep learning.

This project is still very much work in progress.

Features

Relies exclusively on NVIDIA's cuDNN and cuda libraries. Ie, this does not work without an NVIDIA GPU.
Fully flexible tensor layouts (ie both NCHW and NHWC tensor are fully supported)
FP32 and FP16 inference and training mode.
Adam optimizer with mixed precision training and dynamic gradient scaling.
Data augmentation using 2d affine transforms (scaling, rotation, translation)
Fully pipelined JPEG decoder using Nvidia's GPU accelerated decoding library
Supported layers:
- Activation
- Affine transformation
- Batchnorm
- Category Classifier
- Concat
- Convolution
- Transposed Convolution
- Dropout
- Elementwise math operations
- Fully conneceted
- JPEG decoder
- Pooling
- Softmax
- Sum
Node optimizations:
- Concat is transformed to strided tensors
- Element-wise sum is transformed to outputs with GEMM beta set to 1
Double buffered tensors at edge of graph Allows updating next mini-batch and reading out values from previous mini-batch while GPU is process current mini-batch. Ensuring 100% GPU utilization
Can load (some) ONNX models

I wanted to get a better understanding of how deep neural nets work.

The etymology of the name Sága is generally held to be connected to the Old Norse verb sjá, meaning "to see" (from Proto-Germanic *sehwan).