I’ve implemented a seq2seq generative character model in pure Numpy on top of CS231n’s assignment code. It’s nothing fancy but I think being able to see things in matrix multiplication level gives good intuitions about concepts in machine learning. Thus this repository contains reference implementations for

• CharRNN (Sequence to Sequence RNN to produce characters with temperature)
• Unit tests with numerical gradients to check implementations
• Character Embedding
• Recurrent Neural Networks
• Temporal Affine Layer
• Temporal Softmax
• Adam optimizer
• Solver (Run training loop, update weights, save model, record logs, etc.)
• Data loading, character mapping, masking non-frequent characters

Hope it will be useful for others, as well!

Repo

https://github.com/kirbiyik/generate-any-text