A model to generate sanskrit text and learn reprenstations of sankrit aksharas(unit of graphemic symbols in the Indian writing system).
Sanskrit is a finely structured language. Minor variations in aksharas change the meaning of the words. Composition and translation of Sanskrit texts is still a challenging task due to the semantic perplexity in its beautiful and rich verses. Refer to the following examples-
| विभक्ति – Cases | एकवचन – Singular | द्विवचन – Dual | बहुवचन – Plular |
|---|---|---|---|
| 1. | रामः | रामौ | रामाः |
| 2. | रामं | रामौ | रामान् |
| 3. | रामेण | रामाभ्यां | रामैः |
Here look at rama – अत्र रामं पश्य Here look at two ramas – अत्र रामौ पश्य in a rama - रामे
सः पश्यति → स पश्यति < saḥ paśyati → sa paśyati > He sees.
Residual connection[1] in the model will aid in learning simple aksharas mappings, whereas LSTM[2] cells will learn complex relationships.
Note: This model was trained on Nvidia GeForce GT 730 using Chapter-7 of Mahabharata ~ 677972 characters. If you've more computation resources, you can use bigger training corpus.
- Thenao
- Keras
python reslstm.py
- Remove layers weight intializations from layers.
- Uncomment line 139 in reslstm.py and add your weights.
- Fine-tuning: load given weights and add parameter trainable = False for layers which doesn't require fine-tuning.
####Citations: [1]He, K., Zhang, X., Ren, S., & Sun, J. (2016). Identity mappings in deep residual networks. arXiv preprint arXiv:1603.05027.
[2]Graves, A., & Schmidhuber, J. (2005). Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Networks, 18(5), 602-610.