A Machine Learning library written in pure Go designed to support relevant neural architectures in Natural Language Processing. Spago is self-contained, in that it uses its own lightweight computational graph both for training and inference, easy to understand from start to finish. The core module of Spago relies only on testify for unit testing. In other words, it has "zero dependencies", and we are committed to keeping it that way as much as possible. Spago uses a multi-module workspace to ensure that additional dependencies are downloaded only when specific features (e.g. persistent embeddings) are used. A good place to start is by looking at the implementation of built-in neural models, such as the LSTM. Except for a few linear algebra operations written in assembly for optimal performance (a bit of copying from Gonum), it's straightforward Go code, so you don't have to worry.
Features
- Automatic differentiation via dynamic define-by-run execution
- Gradient descent optimizers (Adam, RAdam, RMS-Prop, AdaGrad, SGD)
- Feed-forward layers (Linear, Highway, Convolution...)
- Recurrent layers (LSTM, GRU, BiLSTM...)
- Attention layers (Self-Attention, Multi-Head Attention...)
- Gob compatible neural models for serialization
Project Samples
